Citations¶

Publications that cited COBRA Toolbox¶

Rosina Nkuna, Nikwando Mohlomi, and Tonderayi S. Matambo. From omics to applications: how bioinformatics and multi-omics approaches are revolutionizing metal bioleaching. 16(1):56–56, 2026. [DOI].
M. Krishnamurthy, Harish P S, and Abhishek Subramanian. Naviflux: a visualization-centric platform for interactive analysis, refinement and design of genome-scale metabolic networks. ():, 2026. [DOI].
Melvin Li, Bradley Priem, Luke V. Loftus, M. Betenbaugh, Kenneth J. Pienta, and Sarah R. Amend. Drug-tolerant persister cells reallocate carbon sources to fuel antioxidant metabolism for survival. ():, 2026. [DOI].

Nick Quinn-Bohmann, Alex V. Carr, Christian Diener, and Sean M. Gibbons. Moving from genome-scale to community-scale metabolic models for the human gut microbiome. 10(5):1055–1066, 2025. [DOI].
Iqra Mariam, Ulrika Rova, Paul Christakopoulos, Λεωνίδας Μάτσακας, and Alok Patel. Data-driven synthetic microbes for sustainable future. 11(1):74–74, 2025. [DOI].
Zahra Negahban, Valerie C. A. Ward, Anne Richelle, Chris McCready, and Hector Budman. Hybrid dynamic flux balance modeling approach for bioprocesses: an e. coli case study. 48(5):841–856, 2025. [DOI].
Deepanwita Banerjee, Javier Menasalvas, Yan Chen, Jennifer Gin, Edward E. K. Baidoo, Christopher J. Petzold, Thomas Eng, and Aindrila Mukhopadhyay. Addressing genome scale design tradeoffs in pseudomonas putida for bioconversion of an aromatic carbon source. 11(1):8–8, 2025. [DOI].
Sílvia Àvila-Cabré, Joan Albiol, and Pau Ferrer. Metabolic engineering of komagataella phaffii for enhanced 3-hydroxypropionic acid (3-hp) production from methanol. 19(1):19–19, 2025. [DOI].
Érica Mangaravite, Christina Cleo Vinson, Eduardo Luís Menezes de Almeida, and Thomas Christopher Rhys Williams. Genome-scale metabolic models in plant stress physiology: implications for future climate resilience. 199(2):, 2025. [DOI].
Mariam Mohagheghi, Ali Navid, Thomas Mossington, Congwang Ye, Matthew A. Coleman, and Steven Hoang-Phou. Developing a media formulation to sustain ex vivo chloroplast function. 13():1560200–1560200, 2025. [DOI].
Daniel Alejandro Caballero Cerbon and Dirk Weuster‐Botz. Exchange of the l-cysteine exporter after in-vivo metabolic control analysis improved the l-cysteine production process with engineered escherichia coli. 24(1):95–95, 2025. [DOI].
Xavier Benedicto, Åsmund Flobak, Miguel Ponce-de-León, and Alfonso Valencia. Using constraint-based metabolic modeling to elucidate drug-induced metabolic changes in a cancer cell line. ():, 2025. [DOI].
Alexis L. Marsh, Myra B. Cohen, and Robert W. Cottingham. A tale from the trenches: applying metamorphic and differential testing to bioinformatics software. ():553–564, 2025. [DOI].
Delphine Nègre, Larhlimi Abdelhalim, and Samuel Bertrand. A required sensitivity analysis for predictive reliability of genome–scale metabolic networks: probing biomass, nutrient conditions, and specialised metabolism with penicillium rubens as a case study. ():, 2025. [DOI].
Albert Fina, Pierre Millard, Cécilia Bergès, Pau Ferrer, Joan Albiol, and Stéphanie Heux. High-throughput stationary 13c-based metabolic flux analysis of pichia pastoris strains. 2697():153–170, 2025. [DOI].
Xavier Benedicto, Åsmund Flobak, Miguel Ponce-de-León, and Alfonso Valencia. Constraint based modeling of drug induced metabolic changes in a cancer cell line. 11(1):111–111, 2025. [DOI].
Isamar-Maydeth Vidal-Silva, Antonio Loza, and Rosa-María Gutiérrez-Ríos. Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation. 41(10):384–384, 2025. [DOI].
Anna Procopio, Elvira Immacolata Parrotta, Stefania Scalise, Paolo Zaffino, R. Granata, Francesco Amato, Giovanni Cuda, and Carlo Cosentino. Hipscgem01: a genome-scale metabolic model for fibroblast-derived human ipscs. 12(10):1128–1128, 2025. [DOI].
Edda Klipp. Simulation and modeling of metabolic networks. ():, 2025. [DOI].
Lokesh Venkatesh Babu and Pramod P. Wangikar. Constraint‐based metabolic modeling of synechococcus elongatus pcc 11801 and pcc 11802 for photosynthetic biomanufacturing. ():, 2025. [DOI].
Artai R. Moimenta, Diego Troitiño-Jordedo, David Henriques, Alba Contreras-Ruíz, Romain Minebois, Miguel Morard, Eladio Barrio, Amparo Querol, and Eva Balsa‐Canto. An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of the saccharomyces genus. 10(2):e0161524–e0161524, 2025. [DOI].
W.S. Verwoerd and Longfei Mao. The fba solution space kernel: introduction and illustrative examples. 26(1):182–182, 2025. [DOI].
M. K. Weldon and Christian Euler. Physiology-informed use of cupriavidus necator in biomanufacturing: a review of advances and challenges. 24(1):30–30, 2025. [DOI].
Xianghua Wen, Xin Sun, and Xingyu Liu. Synergizing mechanistic and ai models for deeper insights into algal-bacterial systems in sustainable wastewater treatment. 76():108169–108169, 2025. [DOI].
Ying Zhang, Zhihao Liu, Jingmin Hu, Qing Yao, Hongfei Xu, Qing Zhang, Shouwen Chen, and Y.Y. Wang. Elucidating metabolic mechanisms underlying the influence of specific growth rate on alkaline protease synthesis in bacillus licheniformis through combined omics and computational modeling analysis. 434():132762–132762, 2025. [DOI].
Esraa Gabal, Marwah Azaizeh, and Priyanka Baloni. Investigating lipid and energy dyshomeostasis induced by per- and polyfluoroalkyl substances (pfas) congeners in mouse model using systems biology approaches. 15(8):499–499, 2025. [DOI].
Sébastien Moretti, Anne Niknejad, Marco Pagni, and Florence Mehl. Metanetx: a bridge between metabolic resources for enhanced curation and multi-omics data harmonization. ():, 2025. [DOI].
Mohammad Zim, Christian Euler, and Matthew P. Scott. Constraints on metabolic network analysis in bacterial physiology. 3(2):, 2025. [DOI].
Ruijian Shao, Zhe Huang, Su Sun, Hongbo Yu, Shangxian Xie, and Fuying Ma. Constructing metabolic pathway of lignin monomers and their derivatives based on metabolic recombination models and yield models. 434():132786–132786, 2025. [DOI].
Joseph Zavorskas, Penny Vlahos, Kristina Wagstrom, and Ranjan Srivastava. Dynamic flux balance analysis reveals climate‐driven shifts in arctic diatom succession and bloom dynamics. 31(7):e70339–e70339, 2025. [DOI].
Manish Kumar, Ballamoole Krishna Kumar, Veena Shetty, R. Shyama Prasad Rao, and Pavan Gollapalli. Perspective on integrated multi-omics approaches and constraint-based modeling to explore metabolic functionality on the evolution of bacterial antibiotic resistance. 208():107999–107999, 2025. [DOI].
Alvaro R. Lara, Marie B. Andersen, Anna Madsen, Kathrine Gravlund Fønss, Marta Irla, Hilal Taymaz‐Nikerel, Luz María Martínez, Max Daniel Dicke, Marcel Mann, Jørgen Barsett Magnus, and Guillermo Gosset. Biomanufacturing potential of streamlined cells. 122(12):3309–3318, 2025. [DOI].
P. M. Priyadarshan and Rodomiro Ortíz. Voyage of plant breeding to 2050. ():3–78, 2025. [DOI].
Sumit Tiwari, Lumbini Devi Darapu, and Prateek Gupta. Systems biology approach for plant breeding. ():637–657, 2025. [DOI].
Maria Masid, Ioanna A. Rota, David Barras, Flavia De Carlo, Pierpaolo Ginefra, Mathieu Desbuisson, Yaquelin Ortiz-Miranda, Dmitriy Zamarin, Sohrab P Shah, Nicola Vannini, George Coukos, Denarda Dangaj Laniti, Vassily Hatzimanikatis, and Matthieu Desbuisson. Computational inference of metabolic programs: a case study analyzing the effect of brca1 loss. ():, 2025. [DOI].
Lihao Lin, Francesco Lapi, Bruno Giovanni Galuzzi, Marco Vanoni, Lilia Alberghina, and Chiara Damiani. Mechanistically informed machine learning links non-canonical tca cycle activity to warburg metabolism and hallmarks of malignancy. 21(12):e1013384–e1013384, 2025. [DOI].
Qiannan Peng, Cheng Zhao, Xiaopeng Wang, Kai Cheng, Congcong Wang, Xihui Xu, and Lu Lin. Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium. 16(1):639–639, 2025. [DOI].
Xiaowei Li, Yanyan Wang, Xin Chen, Leon Eisentraut, Chunjun Zhan, Jens Nielsen, and Yun Chen. Modular deregulation of central carbon metabolism for efficient xylose utilization in saccharomyces cerevisiae. 16(1):4551–4551, 2025. [DOI].
Jun Feng, Qingke Wang, Xiaolong Guo, Jialei Hu, Geng Wang, Li Lü, Zhen Qin, Hongxin Fu, Jufang Wang, and Shang‐Tian Yang. Metabolic engineering of clostridium tyrobutyricum for high-yield n-butanol production by increasing intracellular reducing equivalent with nadph-dependent 3-hydroxybutyryl-coa dehydrogenase. 14(6):2341–2353, 2025. [DOI].
Daniel Fässler, Almut Heinken, and Johannes Hertel. Characterising functional redundancy in microbiome communities via relative entropy. 27():1482–1497, 2025. [DOI].
Pablo Di Giusto, Dong‐Hyuk Choi, Athanasios Antonakoudis, Vikash Gokul Duraikannan, Pierrick Craveur, Nicholas Luke Cowie, Tejaswini Ganapathy, K Ramesh, Santiago Benavides-López, Camila A. Orellana, Natalia E. Jiménez, Leo Alexander Dworkin, James Morrissey, Igor Marín de Mas, Benjamin Strain, Norma A. Valdez‐Cruz, Mauricio A. Trujillo‐Roldán, Jannis Marzluf, Verónica S. Martínez, Leopold Zehetner, Claudia Altamirano, Ana María Vega-Letter, Bradley Priem, Haoyu Chris Cao, Martin Hold, Juchen Ma, Yi Fan Hong, Saratram Gopalakrishnan, Blaise Manga Enuh, Chaimaa Tarzi, Kuin Tian Pang, Claudio Angione, Jürgen Zanghellini, Cleo Kontoravdi, Hooman Hefzi, Michael J. Betenbaugh, Lars K. Nielsen, Meiyappan Lakshmanan, Dong‐Yup Lee, Anne Richelle, and Nathan E. Lewis. A community-consensus reconstruction of chinese hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free cho cells. ():, 2025. [DOI].
Kashif R. Siddique, Debajyoti Bose, Riya Bhattacharya, Raul V. Rodriguez, and Aritra Ray. Artificial intelligence driven bioinformatics for sustainable bioremediation: integrating computational intelligence with ecological restoration. 4(12):1987–1997, 2025. [DOI].
Mohamed E. Hasan, Mohammed Osman, Alaa A. Hemeida, and Mahmoud ElHefnawi. Bioinformatics and artificial intelligence approaches in metabolic pathways. ():17–32, 2025. [DOI].
Sasha Yogiswara, Jonas Rombout, Giovanni Micharikopoulos, Sam De Craemer, Beatriz Herrera‐Malaver, Lotte van Landschoot, Sofie Mannaerts, Marcelo do Amaral, Karin Voordeckers, Stijn Spaepen, Jan Steensels, Quinten Deparis, Bart Ghesquière, and Kevin J. Verstrepen. Metabolic engineering of saccharomyces cerevisiae for co-production of ethanol and 3-methyl-1-butanol from sugarcane molasses. 18(1):86–86, 2025. [DOI].
Anthony L. Shiver, Jiawei Sun, Rebecca N. Culver, Arvie Violette, Coral Wynter, Marta Nieckarz, Samara Paula Mattiello, Prabhjot Kaur Sekhon, Francesca Bottacini, Lisa Friess, Hans K. Carlson, Daniel P.G.H. Wong, Steven K. Higginbottom, Meredith Weglarz, Weigao Wang, Benjamin D. Knapp, Emma R. Guiberson, Juan M. Sánchez, Po‐Hsun Huang, Paulo Alonso Gaona-García, Cullen Buie, Benjamin H. Good, Brian C. DeFelice, Felipe Cava, Joy Scaria, Justin L. Sonnenburg, Douwe van Sinderen, Adam M. Deutschbauer, and Kerwyn Casey Huang. Genome-scale resources in the infant gut symbiont bifidobacterium breve reveal genetic determinants of colonization and host-microbe interactions. 188(7):2003–2021.e19, 2025. [DOI].
Nora Scherer, Daniel Fässler, Oleg Borisov, Yurong Cheng, Pascal Schlosser, Matthias Wuttke, Stefan Haug, Yong Li, Fabian Telkämper, Suraj Patil, Heike Meiselbach, Christina S.F. Wong, Urs Berger, Peggy Sekula, Anselm Hoppmann, Ulla T. Schultheiß, Sahar V. Mozaffari, Yannan Xi, Robert Graham, Miriam Schmidts, Michael Köttgen, Peter J. Oefner, Felix Knauf, Kai‐Uwe Eckardt, Sarah C. Grünert, Karol Estrada, Ines Thiele, Johannes Hertel, and Anna Köttgen. Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. 57(1):193–205, 2025. [DOI].
Almut Heinken, Timothy Otto Hulshof, Bram Nap, Filippo Martinelli Boneschi, Arianna Basile, Amy O’Brolchain, Neil Francis O’Sullivan, Celine Gallagher, E.L. Magee, Francesca McDonagh, Ian Lalor, Maeve Bergin, Phoebe Evans, Rachel Daly, Ronan Farrell, Robert Delaney, Saoirse Hill, Saoirse Roisin McAuliffe, Trevor Kilgannon, Ronan M. T. Fleming, Cyrille C. Thinnes, and Ines Thiele. A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. 16(2):101196–101196, 2025. [DOI].
Biki Bapi Kundu, Jayanth Krishnan, Richard Szubin, Arjun Patel, Bernhard Ø. Palsson, Daniel C. Zielinski, and Caroline M. Ajo‐Franklin. Extracellular respiration is a latent energy metabolism in escherichia coli. 188(11):2907–2924.e23, 2025. [DOI].
Alise Žagare, Jānis Kurlovičs, Catarina Serra-Almeida, Daniele Ferrante, Daniela Frangenberg, Armelle Vitali, Gemma Gomez‐Giro, Christian Jäger, Paul Antony, Rashi Halder, Rejko Krueger, Enrico Glaab, Egils Stalidzāns, Giuseppe Arena, and Jens C. Schwamborn. Insulin resistance compromises midbrain organoid neuronal activity and metabolic efficiency predisposing to parkinson’s disease pathology. 16():20417314241295928–20417314241295928, 2025. [DOI].
Lijuan Liao, Mengjun Xie, Xiaoshan Zheng, Zhou Zhao, Zixin Deng, and Jiangtao Gao. Molecular insights fast-tracked: ai in biosynthetic pathway research. 42(5):911–936, 2025. [DOI].
Shamsur Rehman, Muhammad Ibtisam Nasar, Cristina Sousa Mesquita, Souhaila Al Khodor, Richard A. Notebaart, Sascha Ott, Sunil Mundra, Ramesh P Arasardanam, Khalid Muhammad, and Mohammad Tauqeer Alam. Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions. 26(4):, 2025. [DOI].
Sungwon Jung. Advances in functional analysis of the microbiome: integrating metabolic modeling, metabolite prediction, and pathway inference with next-generation sequencing data. 63(1):e:2411006–e:2411006, 2025. [DOI].
Diego Tec-Campos, Juan D. Tibocha‐Bonilla, Chunguo Jiang, Anurag Passi, Deepan Thiruppathy, Cristal Zúñiga, Camila Posadas, Alejandro Zepeda, and Karsten Zengler. A genome-scale metabolic model for the denitrifying bacterium thauera sp. mz1t accurately predicts degradation of pollutants and production of polymers. 21(1):e1012736–e1012736, 2025. [DOI].
Steffen Klamt, Jürgen Zanghellini, and Axel von Kamp. Minimal cut sets in metabolic networks: from conceptual foundations to applications in metabolic engineering and biomedicine. 26(2):, 2025. [DOI].
Lena Peters, Moritz Drechsler, Michael A. Herrera, Jing Liu, Barbara Pees, Johanna Jarstorff, Anna Czerwinski, Francesca Lubbock, Georgia Angelidou, Liesa Salzer, Karlis Arturs Moors, Nicole Paczia, Yi‐Ming Shi, Hinrich Schulenburg, Christoph Kaleta, Michael Witting, Manuel Liebeke, Dominic J. Campopiano, Helge B. Bode, and Katja Dierking. Polyketide synthase-derived sphingolipids mediate microbiota protection against a bacterial pathogen in c. elegans. 16(1):5151–5151, 2025. [DOI].
Simran Kaur Aulakh, Oliver Lemke, Łukasz Szyrwiel, Stephan Kamrad, Yu Chen, Johannes Hartl, Michael Mülleder, Jens Nielsen, and Markus Ralser. The molecular landscape of cellular metal ion biology. 16(7):101319–101319, 2025. [DOI].
Òscar Puiggené, Jaime Muñoz-Triviño, Laura Civil-Ferrer, Line Gille, Helena Schulz-Mirbach, Daniel Bergen, Tobias J. Erb, Birgitta E. Ebert, and Pablo I. Nikel. Systematic engineering of synthetic serine cycles in pseudomonas putida uncovers emergent topologies for methanol assimilation. 43(10):2539–2565, 2025. [DOI].
Drew Alessi, Chloe V. McCreery, and Ali R. Zomorrodi. In silico dietary interventions using whole-body metabolic models reveal sex-specific and differential dietary risk profiles for metabolic syndrome. 16():1586750–1586750, 2025. [DOI].
Gong‐Hua Li, Feifei Han, Efthymios Kalafatis, Qing‐Peng Kong, and Wenzhong Xiao. Systems modeling reveals shared metabolic dysregulation and potential treatments in me/cfs and long covid. 26(13):6082–6082, 2025. [DOI].
Tim Hensen and Ines Thiele. Metabolic modeling links gut microbiota to metabolic markers of parkinson’s disease. 17(1):2554195–2554195, 2025. [DOI].
Almut Heinken, John M. Asara, Gopalan Gnanaguru, and Charandeep Singh. Systemic regulation of retinal medium-chain fatty acid oxidation repletes tca cycle flux in oxygen-induced retinopathy. 8(1):25–25, 2025. [DOI].
Venkat R. Pannala, Archana Hari, Mohamed AbdulHameed, Michele R. Balik-Meisner, Deepak Mav, Dhiral Phadke, Elizabeth H. Scholl, Ruchir Shah, Scott S. Auerbach, and Anders Wallqvist. Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model. 204(2):154–168, 2025. [DOI].
Òscar Puiggené, Jaime Muñoz-Triviño, Laura Civil-Ferrer, Line Gille, Helena Schulz-Mirbach, Daniel Bergen, Tobias J. Erb, Birgitta E. Ebert, and Pablo I. Nikel. Systematic engineering of synthetic serine cycles in pseudomonas putida uncovers emergent topologies for methanol assimilation. ():, 2025. [DOI].
Chloe V. McCreery, Drew Alessi, Katarina Mollo, Alessio Fasano, and Ali R. Zomorrodi. Investigating intestinal epithelium metabolic dysfunction in celiac disease using personalized genome-scale models. 23(1):95–95, 2025. [DOI].
Vikash Pandey. Mineapy: enhancing enrichment network analysis in metabolic networks. 41(3):, 2025. [DOI].
Chunjun Zhan, Guangxu Lan, Qingyun Dan, Ning Qin, Allie Pearson, Peter Mellinger, Yuzhong Liu, Zilong Wang, Seokjung Cheong, Chang Dou, Chenyi Li, Robert W. Haushalter, and Jay D. Keasling. Hybrid biological-chemical strategy for converting polyethylene into a recyclable plastic monomer using engineered corynebacterium glutamicum. 90():106–116, 2025. [DOI].
Zhenqiang Zhao, Rongshuai Zhu, Xuanping Shi, Fengyu Yang, Meijuan Xu, Minglong Shao, Rongzhen Zhang, Youxi Zhao, Jiajia You, and Zhiming Rao. Combining biosensor and metabolic network optimization strategies for enhanced l-threonine production in escherichia coli. 18(1):37–37, 2025. [DOI].
Dilara Uzuner, Atılay İlgün, Fatma Betül Bozkurt, and Tunahan Çakır. A personalized metabolic modelling approach through integrated analysis of rna-seq-based genomic variants and gene expression levels in alzheimer’s disease. 8(1):502–502, 2025. [DOI].
Devlin C. Moyer, Justin Reimertz, Daniel Segrè, and Juan I. Fuxman Bass. Macaw: a method for semi-automatic detection of errors in genome-scale metabolic models. 26(1):79–79, 2025. [DOI].
Xiangyan Zhao, Hongwei Guo, Hai Huang, Ming Zheng, Xianping Zhang, Jing Li, Congcong Li, Bo Yuan, Chunmei Pan, and Zhoutong Sun. Contamination and biotransformation of deoxynivalenol (don) in common commercial foods: current status, challenges and future perspectives. ():, 2025. [DOI].
Srijith Sasikumar, S Pavan Kumar, Nirav Bhatt, and Himanshu Sinha. Genome-scale metabolic modelling identifies reactions mediated by snp-snp interactions associated with yeast sporulation. 11(1):50–50, 2025. [DOI].
Amal Saeed Alblooshi, Muhammad Ibtisam Nasar, Shamsur Rehman, and Mohammad Tauqeer Alam. Genomic and metabolic network properties in thermophiles and psychrophiles compared to mesophiles. 15(1):19757–19757, 2025. [DOI].
B.B. BAIMAKHANOVA, А.К. САДАНОВ, И.А. Ратникова, Gul Baimakhanova, Saltanat Orasymbet, Aigul Amitova, Gulzat Aitkaliyeva, and Ardak B. Kakimova. In silico modeling of metabolic pathways in probiotic microorganisms for functional food biotechnology. 11(8):458–458, 2025. [DOI].
Andreas Kremling. From flux analysis to self contained cellular models. 5():1546072–1546072, 2025. [DOI].
Jeffrey J. Czajka, Joonhoon Kim, Yinjie Tang, Kyle Pomraning, Aindrila Mukhopadhyay, and Héctor García Martín. Fluxretap: a reaction target prioritization genome-scale modeling technique for selecting genetic targets. 41(9):, 2025. [DOI].
Eduardo Luís Menezes de Almeida and Wendel Batista da Silveira. Insights into the response and tolerance mechanisms of papiliotrema laurentii to acetic acid stress by rna-seq and genome-scale metabolic modeling analysis. 215():109634–109634, 2025. [DOI].
John Metzcar, Rachael Guenter, Yafei Wang, Kimberly Mantzke Baker, and Kate E Lines. Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers. 5(1):e240025–e240025, 2025. [DOI].
Luke N. Yaeger, David Sychantha, Princeton Luong, Shahrokh Shekarriz, Océane Goncalves, Annamaria Dobrin, Michael R. M. Ranieri, Ryan P. Lamers, Hanjeong Harvey, George C. diCenzo, Michael G. Surette, Jean‐Philippe Côté, Jakob Magolan, and Lori L. Burrows. Perturbation of pseudomonas aeruginosa peptidoglycan recycling by anti-folates and design of a dual-action inhibitor. 16(3):e0298424–e0298424, 2025. [DOI].
Sonal Omer, Subasree Sridhar, Devarajan Karunagaran, and G. K. Suraishkumar. Mechanistic insights into hypoxia‐induced metabolic reprogramming in colorectal cancer through genome‐scale modeling. 41(3):e70002–e70002, 2025. [DOI].
M.E. Emetere. Progress, limitations, and advances of biohydrogen technologies: bringing the technology close to energy participants in developing countries. 16(8):912–928, 2025. [DOI].
Maurício Alexander de Moura Ferreira, Eduardo Luís Menezes de Almeida, Wendel Batista da Silveira, and Zoran Nikoloski. Protein-constrained models pinpoints the role of underground metabolism in robustness of metabolic phenotypes. 28(3):112126–112126, 2025. [DOI].
Jyoti Jyoti and Marc-Thorsten Hütt. Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns. 41(4):, 2025. [DOI].
Mustafa Sertbaş and Kutlu Ö. Ülgen. Exploring human brain metabolism via genome-scale metabolic modeling with highlights on multiple sclerosis. 16(7):1346–1360, 2025. [DOI].
Pengyang Liu, Yingwei Ai, Muzi Li, Jiacheng Shi, Ning Xiao, Xiaoyu Zhang, Hongbo Yu, Fuying Ma, Su Sun, and Shangxian Xie. Discovery of mannose as an alternative non-nutrient-deficient regulator of lipid accumulation in microalgae. 79():239–250, 2025. [DOI].
Ziwei Yang and Takeyuki Tamura. Dbgdel: database-enhanced gene deletion framework for growth-coupled production in genome-scale metabolic models. 22(4):1415–1427, 2025. [DOI].
Rami Balasubramanian, Debajit Saha, A. Arun, and P. K. Vinod. Hypometabolism in autism spectrum disorder: insights from brain and blood transcriptomics. 62(8):10765–10778, 2025. [DOI].
Ian Sofian Yunus, David Carruthers, Yan Chen, Jennife W Gin, Edward E. K. Baidoo, Christopher J. Petzold, Héctor García Martín, Paul D. Adams, Aindrila Mukhopadhyay, and Taek Soon Lee. Predictive genome-wide crispr-mediated gene downregulation for enhanced bioproduction. ():, 2025. [DOI].
Min Chen, Junfeng Jiang, Tingting Xie, Yingping Zhuang, and Jianye Xia. Allosteric effectors outcompete transcript levels and substrate concentration in regulating central carbon flux during the crabtree effect transition. 20(4):e70024–e70024, 2025. [DOI].
Merve Yarıcı, Furkan Cantürk, Serdar Dursun, Hatice Nur Aydın, and Muhammed Erkan Karabekmez. Rsea: a web server for pathway enrichment analysis of metabolic reaction sets. 122(8):2251–2258, 2025. [DOI].
Sultana Mohammed Al Zubaidi, Muhammad Ibtisam Nasar, Richard A. Notebaart, Markus Ralser, and Mohammad Tauqeer Alam. An enzyme activation network reveals extensive regulatory crosstalk between metabolic pathways. 21(7):870–888, 2025. [DOI].
Luisella Spiga, Ryan T. Fansler, Alexandra Grote, Madison Langford-Butler, Asia K. Miller, M J Neal, Owen F. Hale, Yifan Wu, Deepanshu Singla, M. Wade Calcutt, Angelika Rose, Madeline M. Bresson, Alexandra C. Schrimpe‐Rutledge, Brittany Berdy, Simona G. Codreanu, M. Kay Washington, Benjamin P. Bratton, Stacy D. Sherrod, John A. McLean, Karsten Zengler, Cynthia L. Sears, Megan G. Behringer, Andreas Gnirke, Jonathan Livny, Danyvid Olivares–Villagómez, Ashlee M. Earl, and Wenhan Zhu. An anaerobic pathogen rewires host metabolism to fuel oxidative growth in the inflamed gut. ():, 2025. [DOI].
Y. Zhang, Guannan Mao, Wei Hu, Zhineng Wu, Zhao Yang, Yanan Wang, Mark Bartlam, and Yingying Wang. Enhanced bde-47 biotransformation via cross-feeding in an aerobic-facultative anaerobic bacterial synthetic community. 282():122047–122047, 2025. [DOI].
Xi Luo, Diana C. El Assal, Yanjun Liu, Samira Ranjbar, and Ronan M. T. Fleming. Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of dopaminergic neurons in parkinson’s disease. 19():1594330–1594330, 2025. [DOI].
S Pavan Kumar and Nirav Bhatt. Thermoptcobra: thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions. 28(8):113005–113005, 2025. [DOI].
Seo‐Young Park, Yi Qing Lee, Dong-Seok Kim, Dong Kyung Sung, Kyoung Jin Kim, Jae‐Seung Lee, Joo‐Youn Cho, Won Sik Lee, Se In Sung, and Dong‐Yup Lee. Deciphering dynamic antibiotics-microbiome-metabolome interactions in preterm infants using systems biology. 28(8):113038–113038, 2025. [DOI].
Boyu Jiang, Nick Quinn-Bohmann, Christian Diener, Vignesh Bose Nathan, Yu Han-Hallett, Lavanya Reddivari, Sean M. Gibbons, and Priyanka Baloni. Understanding disease-associated metabolic changes in human colonic epithelial cells using the icolonepithelium metabolic reconstruction. 21(7):e1013253–e1013253, 2025. [DOI].
Anurag Passi, Diego Tec-Campos, Manish Kumar, Juan D. Tibocha‐Bonilla, Cristal Zúñiga, Beth B. Peacock, Amanda R. Hale, Rodrigo Santibáñez, James Borneman, and Karsten Zengler. Unveiling organ-specific metabolism of citrus clementina. 122(29):e2503406122–e2503406122, 2025. [DOI].
Míriam Tarrado‐Castellarnau, Carles Foguet, Josep Tarragó‐Celada, Marc Palobart, Claudia Hernández-Carro, Jordi Perarnau, Erika Zodda, Ibrahim H. Polat, Silvia Marín, Alejandro Suárez‐Bonnet, Juan José Lozano, Mariia Yuneva, Timothy M. Thomson, and Marta Cascante. Glutaminase as a metabolic target of choice to counter acquired resistance to palbociclib by colorectal cancer cells. 44(36):3386–3406, 2025. [DOI].
Massimo Bilancioni and Massimiliano Esposito. Energy transduction in complex networks with multiple resources: the chemistry paradigm. 163(4):, 2025. [DOI].
Walter Vieri, Veronica Ghini, Paola Turano, Lara Massai, Luigi Messori, and Marco Fondi. Modeling the metabolic response of a2780 ovarian cancer cells to gold-based cytotoxic drugs. 11(1):83–83, 2025. [DOI].
Wenbo Han, Luchi Xiao, Haocheng Sun, G. M. Xiang, Qiyue Jia, Haoyu Wang, Boyang Ji, Cheng Zhang, Eduard J. Kerkhoven, Jens Nielsen, and Hongzhong Lu. Alphagem enables precise genome-scale metabolic modelling by integrating protein structure alignment with deep-learning-based dark metabolism mining. ():, 2025. [DOI].
David Armitage, Alexandro Alonso-Sánchez, Samantha R Coy, Zhuli Cheng, Arno Hagenbeek, Karla P López-Martínez, Yong Heng Phua, and Alden R Sears. Adaptive pangenomic remodeling in the azolla cyanobiont amid a transient microbiome. 19(1):, 2025. [DOI].
Jintao Lu, Beining Wang, Xiqiang Liu, Jung‐Kul Lee, Vipin Chandra Kalia, and Chunjie Gong. Revolutionizing caffeic acid production: advanced microbial metabolic engineering and synthetic biology approaches. 20(8):e70091–e70091, 2025. [DOI].
Devlin C. Moyer, Justin Reimertz, Juan I. Fuxman Bass, and Daniel Segrè. Flux sampling and context-specific genome-scale metabolic models for biotechnological applications. ():, 2025. [DOI].
Almut Heinken, Hussein Awada, Vito Riccardo Tomaso Zanotelli, D. Sean Froese, Rosa‐Maria Guéant‐Rodriguez, and Jean‐Louis Guéant. Personalized genome‐scale modeling reveals metabolic perturbations in fibroblasts of methylmalonic aciduria patients. 48(5):e70077–e70077, 2025. [DOI].
Eva Balsa‐Canto, Núria Campo-Manzanares, Artai R. Moimenta, Gildas Roudaut, and Diego Troitin ̃o-Jordedo. Quantifying and managing uncertainty in systems biology: mechanistic and data-driven models. 42():100557–100557, 2025. [DOI].
Jonathan Josephs‐Spaulding, Hannah Clara Rettig, Johannes Zimmermann, Mariam Chkonia, Alexander Mischnik, Sören Franzenburg, Simon Graspeuntner, Jan Rupp, and Christoph Kaleta. Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection. 11(1):183–183, 2025. [DOI].
Francesca Casagli, Andrea Turolla, Damien J. Batstone, Gabriel Capson‐Tojo, Elena Ficara, Joan Garcı́a, Eva González-Flo, Julien Laurent, Tatjana Lorenz, Michaël Pierrelée, Benedek G. Plósz, Gustavo Henrique Ribeiro da Silva, Ángel Robles, Simone Rossi, Estel Rueda-Hernández, Lars Stegemüller, Jean‐Philippe Steyer, Olivier Bernard, and Borja Valverde‐Pérez. Modelling challenges to unlock the power of phototrophic systems for wastewater valorization. 85():108709–108709, 2025. [DOI].
Aline Ovalle, Estefanía López, Jimena Sierralta, Nuria Paricio, and Daniel Garrido. A rationally designed microbial consortium modulates neurodegeneration in a drosophila melanogaster model of parkinson’s disease. 11(1):185–185, 2025. [DOI].
Pınar Kocabaş. Modeling meets metabolic engineering: the ipichia consensus model as basis for metabolic studies in komagataella phaffii. 225():109940–109940, 2025. [DOI].
Jinhui Zhao, Yanyan Zhou, Han Bao, Xinjie Zhao, Xinxin Wang, Chunxia Zhao, Wangshu Qin, Xin Lu, and Guowang Xu. Moda: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms. 26(5):, 2025. [DOI].
Henry Kurniawan, Sarah Nickels, Alise Žagare, Elisa Zuccoli, Isabel Rosety, Gemma Gomez‐Giro, Enrico Glaab, and Jens C. Schwamborn. The parkinson’s disease-associated lrrk2-g2019s variant restricts serine metabolism, leading to microglial inflammation and dopaminergic neuron degeneration. 22(1):244–244, 2025. [DOI].
Ian Sofian Yunus, David N. Carruthers, Yan Chen, Jennifer Gin, Edward E. K. Baidoo, Christopher J. Petzold, Héctor García Martín, Paul D. Adams, Aindrila Mukhopadhyay, and Taek Soon Lee. Predictive crispr-mediated gene downregulation for enhanced production of sustainable aviation fuel precursor in pseudomonas putida. 94():67–76, 2025. [DOI].
Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Janneth González, Jason A. Papin, Andrés Felipe Aristizábal-Pachón, and Andrés Pinzón. Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data. 4():1500710–1500710, 2025. [DOI].
Shuvechha Chakraborty, Indumathi Palanikumar, Yash Gune, K. V. Venkatesh, Karthik Raman, and Susan Idicula‐Thomas. An integrated systems biology approach establishes arginine biosynthesis as a metabolic weakness incandida albicansduring host infection. ():, 2025. [DOI].
Rydberg Supo-Escalante, Germán Plata, Dinara R. Usmanova, and Dennis Vitkup. Percolation and lifestyle transition in microbial metabolism. ():, 2025. [DOI].
Joshua A. M. Kaste, Ran Ji, Patrick William Sydow, Ruairidh J. H. Sawers, and Megan L. Matthews. Metabolic modeling predicts synergistic growth benefits between arbuscular mycorrhizal fungi and theoretical n2-fixing rhizobia symbiosis in maize. ():, 2025. [DOI].
S. Srinivasan, Karthik Raman, and Smita Srivastava. Investigating the impact of carbamazepine on tomato plant metabolism using genome-scale metabolic modelling. ():, 2025. [DOI].
Thomas A. Pugsley, Guy T. Hanke, and Christopher D. P. Duffy. Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling. ():, 2025. [DOI].
Cyrille C. Thinnes, Renee Waschkowitz, Eoghan Courtney, Eoghan Culligan, Katie Fahy, Ruby A. M. Ferrazza, C. Ferris, Angeline Lagali, Rebecca Lane, Colm Maye, Olivia Murphy, David Noone, Scott Ryan, Mihaela Bet, Maripat Corr, H.T. Cummins, David P. Hackett, Eugene Healy, Nina Kulczycka, NP Lang, L. V. Madden, Louise McHugh, Ivana Pyne, C.D. Varley, Niamh Harkin, Roger Meade, Grace O’Donnell, Bram Nap, Filippo Martinelli Boneschi, Almut Heinken, and Ines Thiele. The micromap is a network visualisation resource for microbiome metabolism. ():, 2025. [DOI].
Shripad Mukund Upasani. Computational genomics: finding out mystery of whole genome. ():125–149, 2025. [DOI].
Yang Cheng, Wenwen Yu, Xinyu Bi, Yanfeng Liu, J Li, Guocheng Du, Jian Chen, Xueqin Lv, and Long Liu. Carveadorncurate: a versatile cloud-based platform for constructing multiscale metabolic models. 43(5):1234–1259, 2025. [DOI].
Mustafa Sertbaş and Kutlu Ö. Ülgen. Genome-scale metabolic modeling of human pancreas with focus on type 2 diabetes. 29(4):125–138, 2025. [DOI].
Riccardo Aucello, Simone Pernice, Dora Tortarolo, Raffaele Calogero, Celia Herrera-Rincon, Giulia Ronchi, Stefano Geuna, Francesca Cordero, Píetro Lió, and Marco Beccuti. Unifiedgreatmod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale. 41(3):, 2025. [DOI].
David Armitage, Alexandro Alonso-Sánchez, Samantha R Coy, Zhongjun Cheng, Anton Hagenbeek, Karla P López-Martínez, Yong Heng Phua, and Alden R Sears. Adaptive pangenomic remodeling in the azolla cyanobiont amid a transient microbiome. ():, 2025. [DOI].
Bastien Nihant, Job A.J. Verdonschot, Sonia Balăn, Eva Thielecke, Joost Luiken, Miranda Nabben, Stéphane Heymans, Marian Breuer, and Michiel Adriaens. Metabolic task analysis reveals distinct metabotypes in end-stage dilated cardiomyopathy. ():, 2025. [DOI].
Elena Matveishina, Bartosz J. Bartmanski, Sara Benito-Vaquerizo, and Maria Zimmermann‐Kogadeeva. Gemsembler: cross-tool structural comparison and ensemble modeling improve metabolic model performance. ():, 2025. [DOI].
Carlos Coello-Castillo, Freddy Castillo-Alfonso, and Roberto Olivares‐Hernández. Computational metabolic engineering using genome-scale metabolic models and constraint-based methods. ():89–110, 2025. [DOI].
Yadira Boada, Marcelo Flores, Martin T. Stiebritz, Marco Córdova, Francisco Flores, and Alejandro Vignoni. Synthetic biology design principles enable efficient bioproduction of heparosan with low molecular weight and low polydispersion index for the biomedical industry. 10(1):ysaf006–ysaf006, 2025. [DOI].
Paul Matthay, Thomas Schalck, Kenneth Simoens, Dorien Kerstens, Bert F. Sels, Natalie Verstraeten, Kristel Bernaerts, and Jan Michiels. In silico identification of gene targets to enhance c12 fatty acid production in escherichia coli. 109(1):116–116, 2025. [DOI].
Cyriel Huijer, Xiang Jiao, Yun Chen, and Rosemary Yu. Improved flux profiling in genome-scale modeling of human cell metabolism. ():, 2025. [DOI].
Pedro Fontanarrosa, Chania Clare, Alex J. H. Fedorec, and C. Barnes. Mimic: a python package for simulating, inferring, and predicting microbial community interactions and dynamics. 41(5):, 2025. [DOI].
Jia Bao Zhang, Wei Chen, Yongjun Yang, and Zhenzhen Liu. Discovery and green metabolic engineering of a self-sufficient genistein pathway in paenibacillus jilinensis. 27(25):7575–7585, 2025. [DOI].
Daniel J. Willard and Robert M. Kelly. Metabolic engineering in hot acid: strategies enabling chemolithotrophy in thermoacidophilic archaea. 91():366–378, 2025. [DOI].
W. A. Santos, Rafael David de Oliveira, José Gregório Cabrera Gomez, and G. Roux. Analysis of poly-3-hydroxybutyrate production with different microorganisms using the dynamic simulations for evaluation of economic potential approach. 10(26):27756–27774, 2025. [DOI].
María Teresita Castañeda, Sebastián Núñez, Martín Jamilis, and Hernán De Battista. Enhancement of lipid production in rhodosporidium toruloides: designing feeding strategies through dynamic flux balance analysis. 11(6):354–354, 2025. [DOI].
Emma M. Glass, Glynis L. Kolling, and Jason A. Papin. A systems biology approach to evaluate potential probiotic candidates for women’s vaginal health. ():, 2025. [DOI].
Xin Zhao, Haotong Li, Tao Zhang, Jiajing Qi, Yahui Cao, Ming Yang, and Weijie Liu. An efficient multi-objective shuffled frog leaping algorithm-based method for metabolic pathway design. ():1–4, 2025. [DOI].
Zhihao Liu, Minghao Chen, Jingmin Hu, Yonghong Wang, and Yu Chen. Minimization of proteome reallocation explains metabolic transition in hierarchical utilization of carbon sources. 10(7):e0069025–e0069025, 2025. [DOI].
V Vipina and Sanu Shameer. Modelling leaf metabolism using a charge- and proton-balanced diel flux balance analysis model. 7(2):, 2025. [DOI].
Danni Guo, Yuanyuan Chen, Yahong Wu, Jingmin Cheng, Wei Lai, Wentao Ma, Hang Yang, Lianyi Han, Lan Ma, Huaijie Jia, and Xiao Liu. Multi-omics characterization of the skin microbiota reveals the anti-aging roles of stenotrophomonas maltophilia. ():, 2025. [DOI].
Raúl J. Cano, Jaret C. Daniels, Martha Carlin, and Don M. Huber. Enhancing soil health and corn productivity with a co-fermented microbial inoculant (cfmi-8): a field-based evaluation. 13(7):1638–1638, 2025. [DOI].
Sarfraz Ahmad, Maksud Hasan Shah, Mohammad Ghani Raghib, Vikas Belwal, Shaukat Ali, and Rubina Khan. High-throughput metabolomics for plant phenotyping. ():236–251, 2025. [DOI].
Juan Pablo Molina Ortiz, Dale D. McClure, Andrew Holmes, Stuart A. Rice, Mark Read, and Erin R. Shanahan. Genome-scale metabolic modelling of human gut microbes to inform rational community design. 17(1):2534673–2534673, 2025. [DOI].
Lin Zhang, Bin Wang, Ruiqi Zhang, Zhen He, Mingzhi Zhang, Tong Hao, and Jinsheng Sun. Screening of potential drug targets based on the genome-scale metabolic network model of vibrio parahaemolyticus. 47(7):575–575, 2025. [DOI].
Quanxiu Gao, Luhui Zhang, Wei‐Ran Huang, Yue Zhang, Richard Hewage, Mengkai Zhou, Yang Xu, Yang Han, Junxiong Yu, Zejian Wang, Zhengwei Xie, Jing Han, and Hua Xiang. Improving poly(3-hydroxybutyrate-co-3-hydroxyvalerate) synthesis with the construction of a genome-scale metabolic model for haloferax mediterranei. 321(Pt 2):146335–146335, 2025. [DOI].
Rigoberto Rincón-Ballesteros, Francisco J. Alvarez‐Padilla, and German Preciat. Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning. ():, 2025. [DOI].
Jörg Schwender. Resolving cofactor imbalance in triacylglycerol biosynthesis in oilseeds through glycolytic shunts: a modeling study. ():, 2025. [DOI].
Kristin Klier, Ameneh Mehrjerd, Daniel Fässler, Maximilien Franck, Antoine Weihs, Kathrin Budde, Martin Bahls, Fabian Frost, Ann‐Kristin Henning, Almut Heinken, Henry Völzke, Marcus Dörr, Matthias Nauck, Hans J. Grabe, Nele Friedrich, and Johannes Hertel. Integrating population-based metabolomics with computational microbiome modelling identifies methanol as a urinary biomarker for protective diet–microbiome–host interactions. 16(18):7067–7081, 2025. [DOI].
Álvaro Altamirano, Ignacio Tapia, Vicente Acuña, Daniel Garrido, and Pedro A. Saa. Metacone: a scalable framework for exploring the conversion cone of metabolic networks. 120(Pt 1):108607–108607, 2025. [DOI].
Matthias Mattanovich, Holda A. Anagho, Lidia Argemi‐Muntadas, Michael L. Nielsen, Qian Gao, and Thomas Möritz. Multi-omics analysis of thermogenic lipolysis in brown adipocytes. 28(9):113382–113382, 2025. [DOI].
Pau Obregón-Gutierrez, Juan Nogales, Cintya González-Torres, Eva Huerta, Alejandro Rubio, Mariano Domingo, Joaquím Segalés, Karl Kochanowski, Antonio J. Pérez‐Pulido, Virginia Aragón, Florencia Correa‐Fiz, and Marina Sibila. Exploring the pangenome of mycoplasma hyorhinis in search of potential virulence markers. ():, 2025. [DOI].
Shouyong Jiang, Wenbo Shan, Xiongyan Yang, Chao Yang, and Jichun Li. Evolutionary multi-objective design of growth-coupled microbial cell factories. ():261–274, 2025. [DOI].
Djordje Bajić, Marco van Oort, Minke Gabriëls, and Uroš Gojković. Structuring complexity by mapping the possible in microbial ecosystems. 88():102658–102658, 2025. [DOI].
P. B. Manjunatha, Rupa Bhowmick, and Sriram Chandrasekaran. Systems biology methods to study the epigenome and metabolic-epigenetic interactions. ():617–662, 2025. [DOI].
Benjamín R. Elizondo and Pedro A. Saa. Complex kinetic models predict β-carotene production and reveal flux limitations in recombinant saccharomyces cerevisiae strains. 14(9):3457–3472, 2025. [DOI].
Jhang Ho Pak, H.H. Park, K.-H. Baek, Seung-Ho Seo, and Suryang Kwak. Yeast-based precision fermentation for the biosynthesis of terpenoids. 12():100752–100752, 2025. [DOI].
Elena Matveishina, Bartosz J. Bartmanski, Sara Benito-Vaquerizo, and Maria Zimmermann‐Kogadeeva. Gemsembler: consensus model assembly and structural comparison of genome-scale metabolic models across tools improve functional performance. 10(10):e0057425–e0057425, 2025. [DOI].
Huiyuan Guo, Wenli Zhang, Weihui Xu, Wenjing Chen, Yunlong Hu, and Zhigang Wang. Microbial consortium accelerates di(2-ethylhexyl) phthalate degradation through amino acid exchange-mediated inter-strain synergism. ():, 2025. [DOI].
Huijun Long, Jai W. Mehat, Huihai Wu, Arnoud H. M. van Vliet, and Roberto M. La Ragione. Use of genome-scale metabolic reconstructions of avian pathogenic escherichia coli (apec) phylogroups for the identification of lineage-specific metabolic pathways. 11(9):, 2025. [DOI].
Anonymous. Computational biology for cell-free systems. ():, 2025. [DOI].
Tunahan Çakır and Ecehan Abdi̇k. Personalized metabolite biomarker predictions reveal heterogeneous characteristics of parkinson’s disease. ():, 2025. [DOI].
Vítor Geniselli da Silva, Nick W. Smith, Jane Mullaney, Nicole C. Roy, Clare Wall, and Warren C. McNabb. Mathematical models of the colonic microbiota: an evaluation of accuracy using in vitro fecal fermentation data. 12():1623418–1623418, 2025. [DOI].
Qingqing Chen, Xin Qi, Sheng Dong, Xin Ge, Xiao Men, and Haibo Zhang. Recent advances and perspectives in biosynthesis of paclitaxel: key enzymes and intermediates. 331(Pt 1):148049–148049, 2025. [DOI].
Juan M. Olaguez-Gonzalez, Isaac Chaírez, Luz Bretón‐Deval, and Mariel Alfaro-Ponce. In-silico assessment of dynamic symbiotic microbial interactions in a reduced microbiota related to the autism spectrum disorder symptoms. 27():4078–4088, 2025. [DOI].
Zahrasadat Seyyedi, Hamed Haddad Kashani, Atefeh Parchebafi, Romina Ghayoumi, Mohammad Mehdi Haghighat Lari, and Elahe Seyed Hosseini. Integrating probiotics and microbiome-derived metabolites into cancer therapy: mechanistic insights, multi-omics strategies, and clinical potential. 10():100340–100340, 2025. [DOI].
Martina Du, Jeremy M. Chacón, Heejoon Park, Campbell Putnam, Tomáš Gedeon, William R. Harcombe, and Ross P. Carlson. Cellular economics of exchanged metabolites alter ratios of microbial trading partners in a predictable manner. 14(11):4373–4387, 2025. [DOI].
Qian Liu, Shuang Gao, Yanan Hou, Jianfeng Liu, Qianqian Yuan, Cong Huang, Nanqi Ren, and Cong Huang. Ai- and biotechnology-driven digital design of biohydrogen-producing microbiota. ():, 2025. [DOI].
Le Minh Thao Doan, Suraj Verma, Noushin Eftekhari, Claudio Angione, and Annalisa Occhipinti. From bulk to single-cell and spatial data: an ai framework to characterise breast cancer metabolic dysregulations across modalities. 198(Pt B):111195–111195, 2025. [DOI].
Reetesh Kumar, Rupak Nagraik, Sorabh Lakhanpal, Mosleh Mohammad Abomughaid, Niraj Kumar Jha, and Rohan Gupta. Artificial intelligence in gut microbiome research: toward predictive diagnostics for neurodegenerative disorders. 72(4):296–312, 2025. [DOI].
B. Maruthi Shankar, Sarayu Murali, Shagun Shagun, Shyam Kumar Masakapalli, Karthik Raman, and Smita Srivastava. Genome-scale metabolic model guided metabolic flux analysis in the endophyte alternaria burnsii ncim1409. ():, 2025. [DOI].
Mohammadreza Moghimi, Filippo Martinelli, Jonas Widder, Tim Hensen, Luciana Hannibal, Ute Spiekerkoetter, and Ines Thiele. Elucidating a potential role of the infant gut microbiome on the bioavailability of l-tyrosine in phenylketonuria. ():, 2025. [DOI].
Tim Hensen, Lora Khatib, Lucas Patel, Daniel McDonald, Antonio G. González, Siamak MahmoudianDehkordi, Colette Blach, Rob Knight, Rima Kaddurah‐Daouk, Ines Thiele, and Ines Thiele. Personalised whole-body modelling links gut microbiota to metabolic perturbations in alzheimer’s disease. ():, 2025. [DOI].
Nan Xu, Jiaqi Kang, Chengkun Zheng, Lin Zhou, Cong Gao, and Minliang Guo. Reconstruction and application of a genome-scale metabolic model for streptococcus suis. 26(1):997–997, 2025. [DOI].
Clémence Dupont Thibert, Sylvaine Roy, Sónia Carneiro, Bruno Pereira, Rafael Carreira, Paulo Vilaça, Séverine Collin, Eric Marechal, Elodie Billey, Gilles Curien, Maxime Durot, and Juliette Jouhet. A new genome-scale metabolic model of oleaginous microalgae with refined lipid metabolism clarifies microchloropsis gaditana mutant phenotypes. 441():133619–133619, 2025. [DOI].
Andrea Dell’Olio, Joy Iheoma van Geerestein, Josep Rubert, and William T. Scott. Genome-level constraint-based analysis of megasphaera sp. mjr8396c reveals dual metabolic roles in the human gut. ():, 2025. [DOI].
Linh Khanh Nong, Chandran Sathesh‐Prabu, Sung Kuk Lee, and Dong-Hyuk Kim. Redefining hexr regulatory landscape in pseudomonas putida kt2440 through integrative systems biology. 94():77–89, 2025. [DOI].
Jiangpeng Yu, Kelin Cheng, Shenyang Qu, Jie Wang, Xun Wang, Cheng Zhao, and Wei Li. Genome‐scale metabolic modeling guided escherichia coli engineering for de novo biosynthesis of chrysanthemic acid. ():e12736–e12736, 2025. [DOI].
Cyrille C. Thinnes, Renee Waschkowitz, Eoghan Courtney, Eoghan Culligan, Katie Fahy, Ruby A. M. Ferrazza, C. Ferris, Angeline Lagali, Rebecca Lane, Colm Maye, Olivia Murphy, David Noone, Scott Ryan, Mihaela Bet, Maripat Corr, H.T. Cummins, David P. Hackett, Eugene Healy, Nina Kulczycka, NP Lang, L. V. Madden, Louise McHugh, Ivana Pyne, C.D. Varley, Niamh Harkin, Roger Meade, Grace O’Donnell, Bram Nap, Filippo Martinelli, Almut Heinken, and Ines Thiele. Micromap: a network visualisation resource for human microbiome metabolism. 11(1):222–222, 2025. [DOI].
German Preciat, Agnieszka B. Wegrzyn, Xi Luo, Ines Thiele, Thomas Hankemeier, and Ronan M. T. Fleming. Xomicstomodel: omics data integration and generation of thermodynamically consistent metabolic models. ():, 2025. [DOI].
Shivraj Hariram Nile, Abhilasha Sharma, Aman Sharma, Navdeep Kaur, Amisha Rani, Sumandeep Kaur, and Kritika Kuksal. In silico metabolic profiling of an indian cohort refutes the "one-diet-fits-all" paradigm in colorectal cancer. ():, 2025. [DOI].
Xiaochen Wang and Guodong Wang. Plant terpenoid diversity: from pathway elucidation to ecological functions. ():, 2025. [DOI].
Anonymous. The synergy of foundation technologies and computer sciences advances microbial expression systems in biomedical genetic engineering. ():, 2025. [DOI].
Hasan Rahimi-Tamandegani, Sayed‐Amir Marashi, Ghazaleh Ghavami, Mahya Mehrmohamadi, and Soroush Sardari. Genome-scale metabolic modeling identifies synergistic metabolites that enhance 5-fluorouracil efficacy in colon cancer. 29(5):321–334, 2025. [DOI].
Bo Wang, Shouyong Jiang, Shibo Xu, and Jichun Li. Bilevel modelling of metabolic networks for computational strain design. 18(12):786–786, 2025. [DOI].
Maria Pires Pacheco, Déborah Gerard, Riley J. Mangan, Alec R. Chapman, Dennis Hecker, Manolis Kellis, Marcel H. Schulz, Lasse Sinkkonen, and Thomas Sauter. Epigenetic control of metabolic identity across cell types. ():, 2025. [DOI].
Arslan Sarwar, Linh Thanh Nguyen, and Eun Yeol Lee. Development of an ethanol-based microbial platform for 3-hydroxypropionic acid production using engineered pseudomonas putida kt2440. ():, 2025. [DOI].
S Pavan Kumar and Nirav Bhatt. Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data. 11(1):142–142, 2025. [DOI].
Lydia Rachbauer, Deepika Awashti, Sara Tejedor-Sanz, Justin Panich, Neha Bansal, Diep N. Pham, Emili Toppari, Eric Sundstrom, and Blake A. Simmons. Biomanufacturing from gaseous c1 feedstocks: a perspective on opportunities and challenges. 20():100601–100601, 2025. [DOI].
Gian Marco Messa, Peng Liu, Francesco Napolitano, Jesper Tegner, Xin Gao, and Valerio Orlando. Thermo-dynamic flux balance analysis, a novel approach to simulate circadian metabolism. ():, 2025. [DOI].
Lingyun Li, Xin Chen, Yijie Zhang, Ning Qin, Yu Chen, Xu Ji, Jens Nielsen, and Zihe Liu. Mechanistic and applied study of phosphofructokinases, the “gatekeeper” of the glycolytic pathway on the central carbon metabolism. 22():e00268–e00268, 2025. [DOI].
Zahra Nikuiyan, Fatemeh Tabandeh, Ehsan Motamedian, Saeed Salehi, Marjan Talebi, and Rouzbeh Almasi Ghale. Reconstruction of a genome-scale metabolic model for streptococcus zooepidemicus: comparison with corynebacterium glutamicum to study hyaluronic acid production. 20(12):e0335509–e0335509, 2025. [DOI].
P. Obregon-Gutierrez, J. Nogales, C. Gonzalez-Torres, E. Huerta, A. Rubio, M. Domingo, J. Segales, K. Kochanowski, A. J. Perez-Pulido, V. Aragon, F. Correa-Fiz, and M. Sibila. Exploring the pangenome of mycoplasma hyorhinis in search of potential virulence markers. ():, 2025. [DOI].
Zhepu Ruan, Jialin Tan, Qingyu Feng, Kai Yang, Danning Li, Yuanqing Chao, P. N. Wang, Zhuobiao Ni, Jingjing Chen, and Rongliang Qiu. Potentiators empower synthetic microbiomes as silent guardians against co-contamination. ():, 2025. [DOI].

Nayeon Kim, Junyeong Ma, Wonjong Kim, Jungyeon Kim, Peter Belenky, and Insuk Lee. Genome-resolved metagenomics: a game changer for microbiome medicine. 56(7):1501–1512, 2024. [DOI].
Chaimaa Tarzi, Guido Zampieri, Neil T. Sullivan, and Claudio Angione. Emerging methods for genome-scale metabolic modeling of microbial communities. 35(6):533–548, 2024. [DOI].
Maya Raman. Bioinformatics and computational tools. ():107–134, 2024. [DOI].
Shengbo Wu, Zheping Qu, Danlei Chen, Hao Wu, Qinggele Caiyin, and Jianjun Qiao. Deciphering and designing microbial communities by genome-scale metabolic modelling. 23():1990–2000, 2024. [DOI].
Kathirvel Iyappan and Neela Gayathri Ganesan. Computational strategies to enhance cell-free protein synthesis efficiency. 4(3):2022–2042, 2024. [DOI].
Eduardo Luís Menezes de Almeida, Eduard J. Kerkhoven, and Wendel Batista da Silveira. Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives. 29(1):35–67, 2024. [DOI].
Carolina Deantas-Jahn, Sebastián N. Mendoza, Cuauhtémoc Licona‐Cassani, Camila A. Orellana, and Pedro A. Saa. Metabolic modeling of halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation. 108(1):310–310, 2024. [DOI].
Márcia R. Couto, Joana L. Rodrigues, Adelaide Braga, Óscar Dias, and L. R. Rodrigues. Optimization of chondroitin production in e. coli using genome scale models. 9(6):597–611, 2024. [DOI].
Natchapon Srinak, Porntip Chiewchankaset, Saowalak Kalapanulak, Pornpan Panichnumsin, and Treenut Saithong. Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters. 20(10):e1012533–e1012533, 2024. [DOI].
Zixin Yang, Jinlin Zhu, Wenwei Lu, Fengwei Tian, Hao Zhang, and Wei Chen. Integrating artificial intelligence in exploring multiscale gut microbiota and diet relations for health promotion: a comprehensive review. 61():104541–104541, 2024. [DOI].
Wei-Ze Kong and Morihisa Fujita. Glycomaple: recent updates and applications in visualization and analysis of glycosylation pathways. 417(5):885–894, 2024. [DOI].
Jingyi Qian and Chao Ye. Development and applications of genome-scale metabolic network models. 126():1–26, 2024. [DOI].
Sandhya Mishra, Anju Patel, Pankaj Bhatt, Shaohua Chen, and Pankaj Kumar Srivastava. Perspective evaluation of synthetic biology approaches for effective mitigation of heavy metal pollution. 262(1):, 2024. [DOI].
Cristina Herencias, Virginia Rivero‐Buceta, Sergio Salgado, Natalia Hernández‐Herreros, Fernando Baquero, Rosa del Campo, Juan Nogales, and M. Auxiliadora Prieto. Bdellovibrio’s prey-independent lifestyle is fueled by amino acids as a carbon source. 108(1):422–422, 2024. [DOI].
Marina Maurizio, María Masid, Kerry Woods, Reto Caldelari, John G. Doench, Arunasalam Naguleswaran, Denis Alain Henri Lucien Joly, Martín González Fernández, Jonas Zemp, Mélanie Borteele, Vassily Hatzimanikatis, Volker T. Heussler, Sven Rottenberg, and Philipp Olias. Host cell crispr genomics and modelling reveal shared metabolic vulnerabilities in the intracellular development of plasmodium falciparum and related hemoparasites. 15(1):6145–6145, 2024. [DOI].
Jesse Bucksot, Katherine Ritchie, Matthew Biancalana, John A. Cole, and Daniel Cook. Pan-cancer, genome-scale metabolic network analysis of over 10,000 patients elucidates relationship between metabolism and survival. 16(13):2302–2302, 2024. [DOI].
Anna Kattel, Valter Aro, Petri‐Jaan Lahtvee, Jekaterina Kazantseva, Arvi Jõers, Ranno Nahku, and Isma Belouah. Exploring the resilience and stability of a defined human gut microbiota consortium: an isothermal microcalorimetric study. 13(4):, 2024. [DOI].
Sílvia Àvila-Cabré, Joan Albiol, and Pau Ferrer. Leveraging lactate transporters for superior 3-hydroxypropionic (3-hp) acid production from methanol in komagataella phaffii. ():, 2024. [DOI].
Bruno Veronez de Lima, Ricardo Luna, Daniel Martins Lima, Julio E. Normey‐Rico, and José Ricardo Pérez‐Correa. Advancing wine fermentation: extended kalman filter for early fault detection. ():, 2024. [DOI].
Isaac Klapper, Daniel B. Szyld, Xinli Yu, Karsten Zengler, Tianyu Zhang, and Cristal Zúñiga. A domain decomposition method for solution of a pde-constrained generalized nash equilibrium model of biofilm community metabolism. 84(1):97–113, 2024. [DOI].
Gholamreza Abdi, Nil Patil, Mukul Jain, and Mukul Machhindra Barwant. Integration of metabolomics and flux balance analysis: applications and challenges. ():199–237, 2024. [DOI].
Achintya Saha, Megha Jethwa, and Aditi Gangopadhyay. Application of computation in the study of biosynthesis of phytochemicals. ():321–355, 2024. [DOI].
Deepanwita Banerjee, Javier Menasalvas, Yan Chen, Jennifer Gin, Edward E. K. Baidoo, Christopher J. Petzold, Thomas Eng, and Aindrila Mukhopadhyay. Bottlenecks in the implementation of genome scale metabolic model based designs for bioproduction from aromatic carbon sources. ():, 2024. [DOI].
Jan Zrimec, Sandra M. Correa, Maja Zagorščak, Marko Petek, Carissa Bleker, Katja Stare, Christian Schuy, Sophia Sonnewald, Kristina Gruden, and Zoran Nikoloski. Evaluating plant growth-defence trade-offs by modelling the interaction between primary and secondary metabolism. ():, 2024. [DOI].
Sonia Alcalá, Lara Villarino, Laura Ruíz-Cañas, José R. Couceiro, Miguel Martínez‐Calvo, Adrián Palencia‐Campos, Diego Navarro, Pablo Cabezas‐Sainz, Iker Rodríguez-Arabaolaza, Alfonso Cordero‐Barreal, Lucía Trilla‐Fuertes, Juan A. Rubiolo, Sandra Batres-Ramos, Mireia Vallespinós, Cristina González-Paramos, Jéssica Rodríguez, Angelo Gámez‐Pozo, Juan Ángel Fresno Vara, Sara Fra‐Fernández, Amparo Benito Berlinches, Nicolás Moreno‐Mata, Ana María Torres Redondo, Alfredo Carrato, Patrick Hermann, Laura Sánchez, Susana Torrente, Miguel Ángel Fernández‐Moreno, José L. Mascareñas, and Bruno Sáinz. Targeting cancer stem cell oxphos with tailored ruthenium complexes as a new anti-cancer strategy. 43(1):33–33, 2024. [DOI].
Pritam Kundu, Satyajit Beura, Suman Mondal, Amit Kumar Das, and Amit Ghosh. Machine learning for the advancement of genome-scale metabolic modeling. 74():108400–108400, 2024. [DOI].
Lorenzo Pasotti, Ilaria Massaiu, Paolo Magni, and Cinzia Calvio. Metabolic engineering of bacillus subtilis for the production of poly-γ-glutamic acid from glycerol feedstock. 10(6):319–319, 2024. [DOI].
Yogesh K. Ahlawat, V.J. Srivastava, Maryam Samani, Sarahani Harun, Vinothienii Rajuloo, and Darshna Chaudhary. Computational strategies for maximizing biomass and metabolite yields for bioproduction. ():143–161, 2024. [DOI].
Alexander A. Aksenov, Alex Blacutt, Nichole Ginnan, Philippe E. Rolshausen, Alexey V. Melnik, Ali Lotfi, Emily C. Gentry, Manikandan Ramasamy, Cristal Zúñiga, Karsten Zengler, Kranthi K. Mandadi, Greg McCollum, Pieter C. Dorrestein, and M. Caroline Roper. Spatial chemistry of citrus reveals molecules bactericidal to candidatus liberibacter asiaticus. 14(1):20306–20306, 2024. [DOI].
Aparajitha Srinivasan, Kevin Chen-Xiao, Deepanwita Banerjee, Asun Oka, Venkataramana R. Pidatala, Aymerick Eudes, Blake A. Simmons, Thomas Eng, and Aindrila Mukhopadhyay. Sustainable production of 2,3,5,6-tetramethylpyrazine at high titer in engineered corynebacterium glutamicum. 51():, 2024. [DOI].
Bruno G. Galuzzi, Luca Milazzo, and Chiara Damiani. Adjusting for false discoveries in constraint-based differential metabolic flux analysis. 150():104597–104597, 2024. [DOI].
Karthik Raman, Miroslav Kratochvíl, Brett G. Olivier, Matthias König, Pratyay Sengupta, Dinesh Kumar Kuppa Baskaran, Tung V. N. Nguyen, Daniel Lobo, St. Elmo Wilken, Krishna Kumar Tiwari, Aswathy K. Raghu, Indumathi Palanikumar, Lavanya Raajaraam, Maziya Ibrahim, Sanjaay Balakrishnan, Shreyansh Umale, Frank Bergmann, Tanisha Malpani, Venkata Satagopam, Reinhard Schneider, Moritz E. Beber, Sarah Keating, Mihail Anton, Alina Renz, Meiyappan Lakshmanan, Dong‐Yup Lee, Lokanand Koduru, Reihaneh Mostolizadeh, Óscar Dias, Emanuel Cunha, Alexandre Oliveira, Yi Qing Lee, Karsten Zengler, Rodrigo Santibáñez-Palominos, Manish Kumar, Matteo Barberis, Bhanwar Lal Puniya, Tomáš Helikar, Hoang V. Dinh, Patrick F. Suthers, Costas D. Maranas, Isabella Casini, Seyed Babak Loghmani, Nadine Veith, Nantia Leonidou, Feiran Li, Yu Chen, Jens Nielsen, GaRyoung Lee, Sang Mi Lee, Gi Bae Kim, Pedro T. Monteiro, Miguel C. Teixeira, Hyun Uk Kim, Sang Yup Lee, Ulf W. Liebal, Lars M. Blank, Christian Lieven, Chaimaa Tarzi, Claudio Angione, Manga Enuh Blaise, Celik Pinar Aytar, Mikhail A. Kulyashov, llya Akberdin, Dohyeon Kim, Sung Ho Yoon, Zhaohui Xu, Jyotshana Gautam, William T. Scott, Peter J. Schaap, Jasper J. Koehorst, Cristal Zúñiga, Gabriela Canto-Encalada, Sara Benito-Vaquerizo, Ivette Parera Olm, María Suárez‐Diez, Qianqian Yuan, Hongwu Ma, Mohammad Mazharul Islam, Jason A. Papin, Francisco Zorrilla, Kiran Raosaheb Patil, Arianna Basile, Juan Nogales, Granado San Leon, Freddy Castillo-Alfonso, Roberto Olivares‐Hernández, Gabriel Vigueras-Ramírez, Henning Hermjakob, Andreas Dräger, Rahuman S. Malik‐Sheriff, and Rahuman S Malik-Sheriff. Frog analysis ensures the reproducibility of genome scale metabolic models. ():, 2024. [DOI].
Pablo Rodríguez-Mier, Martín Garrido‐Rodríguez, Attila Gábor, and Julio Sáez-Rodríguez. Unified knowledge-driven network inference from omics data. ():, 2024. [DOI].
Anita L. Ziegler, Lena Ullmann, Manuel Boßmann, Karla Stein, Ulf W. Liebal, Alexander Mitsos, and Lars M. Blank. Itaconic acid production by co‐feeding of ustilago maydis : a combined approach of experimental data, design of experiments, and metabolic modeling. 121(6):1846–1858, 2024. [DOI].
Jasmeet Kaur Khanijou, Yan Ting Hee, Clement P M Scipion, Xixian Chen, and Kumar Selvarajoo. Systems biology approach for enhancing limonene yield by re-engineering escherichia coli. 10(1):109–109, 2024. [DOI].
Josh A. Taylor, Alain Rapaport, and Denis Dochain. Convex representation of metabolic networks with michaelis–menten kinetics. 86(6):65–65, 2024. [DOI].
Thomaz Satuye Prieto-de Lima, Keilor Rojas-Jiménez, and Christopher Vaglio. Strategy for optimizing vitamin b12 production in pseudomonas putida kt2440 using metabolic modeling. 14(11):636–636, 2024. [DOI].
Carolina Shene, Allison Leyton, Liset Flores, Daniela Chavez, Juan A. Asenjo, and Yusuf Chisti. Genome‐scale metabolic modeling of thraustochytrium sp. rt2316‐16: effects of nutrients on metabolism. 121(6):1986–2001, 2024. [DOI].
Yang Xia, Huiying Wang, Dongqin Ding, Huan Fang, Huina Dong, and Dawei Zhang. A hybrid rna-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis. 9(3):513–521, 2024. [DOI].
Peng Su, Konstantine Tchourine, and Dennis Vitkup. Ecological scaling of temporal fluctuations with bacterial abundance in gut microbiota depends on functional properties of individual microbial species and bacterial communities. ():, 2024. [DOI].
Chenguang Liang, Sue Murray, Yang Li, Richard Lee, Audrey Low, Shruti Sasaki, Austin W.T. Chiang, Wen-Jen Lin, Joel Mathews, Will Barnes, and Nathan E. Lewis. Lipidsim: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, markov modeling framework. 82():110–122, 2024. [DOI].
Abhirami Thumsi, Diego Martínez, Srivatsan J. Swaminathan, Arezoo Esrafili, Abhirami P. Suresh, Madhan Mohan Chandrasekhar Jaggarapu, Kelly Lintecum, Michelle Halim, Shivani Mantri, Yasmine Sleiman, Nicole Appel, Haiwei Gu, Marion Curtis, Cristal Zúñiga, and Abhinav P. Acharya. Inverse‐vaccines for rheumatoid arthritis re‐establish metabolic and immunological homeostasis in joint tissues. 14(5):e2303995–e2303995, 2024. [DOI].
Mitzi de la Cruz, Flavio Kunert, Hilal Taymaz‐Nikerel, Juan‐Carlos Sigala, Guillermo Gosset, Jochen Büchs, and Alvaro R. Lara. Increasing the pentose phosphate pathway flux to improve plasmid dna production in engineered e. coli. 12(1):150–150, 2024. [DOI].
Alvaro R. Lara, Flavio Kunert, Vincent Vandenbroucke, Hilal Taymaz‐Nikerel, Luz María Martínez, Juan‐Carlos Sigala, Frank Delvigne, Guillermo Gosset, and Jochen Büchs. Transport‐controlled growth decoupling for self‐induced protein expression with a glycerol‐repressible genetic circuit. 121(6):1789–1802, 2024. [DOI].
Alexander A. Aksenov, Alex Blacutt, Nichole Ginnan, Philippe E. Rolshausen, Alexey V. Melnik, Ali Lotfi, Emily C. Gentry, Manikandan Ramasamy, Cristal Zúñiga, Karsten Zengler, Kranthi K. Mandadi, Pieter C. Dorrestein, and M. Caroline Roper. Spatial chemistry of citrus reveals molecules bactericidal to candidatus liberibacter asiaticus. ():, 2024. [DOI].
Zainab S. Attarbashi, Mohit Nigam, and Lalit Kumar Singh. Metabolic engineering of lipid biosynthesis pathway to enhance the oil content in microalgae. ():37–63, 2024. [DOI].
Priyanka Narad, Romasha Gupta, Sudeepti Kulshrestha, and Abhishek Sengupta. Systems biology in understanding the human gut microbiome and related diseases highlighting metabolic modeling and analysis. ():437–465, 2024. [DOI].
Harishankar Mahto and Dipa Mahato. In silico characterization of natural products in plants. ():161–185, 2024. [DOI].
Carlos Alberto García-Mogollón, Diego F. Mendoza, and Juan Carlos Quintero. Electrostatic ethanol fermentation: experimental study and kinetic-based metabolic modeling. 10(17):e36587–e36587, 2024. [DOI].
Rayna Nolen, Lene H. Petersen, Karl Kaiser, Antonietta Quigg, and David Hala. In silico biomarker analysis of the adverse effects of perfluorooctane sulfonate (pfos) exposure on the metabolic physiology of embryo-larval zebrafish. 4():1367562–1367562, 2024. [DOI].
Artai R. Moimenta, Diego Troitiño-Jordedo, David Henriques, Alba Contreras-Ruíz, Romain Minebois, Miguel Morard, Eladio Barrio, Amparo Querol, Eva Balsa‐Canto, and Eva Balsa–Canto. A continuous dynamic genome-scale model explains batch fermentations led by species of thesaccharomycesgenus. ():, 2024. [DOI].
Jean‐Marc Schwartz and Zita Soons. Metabolic models. ():510–518, 2024. [DOI].
Alexander A. Aksenov, Alex Blacutt, Nichole Ginnan, Philippe E. Rolshausen, Alexey V. Melnik, Ali Lotfi, Emily C. Gentry, Manikandan Ramasamy, Cristal Zúñiga, Karsten Zengler, Kranthi K. Mandadi, Pieter C. Dorrestein, and M. Caroline Roper. Spatial chemistry of citrus reveals molecules bactericidal tocandidatusliberibacter asiaticus. ():, 2024. [DOI].
Kumar Selvarajoo, Jasmeet Kaur Khanijou, Yan Ting Hee, Clement P M Scipion, and Xixian Chen. Systems biology approach for enhancing limonene yield by re-engineering escherichia coli. ():, 2024. [DOI].
W.S. Verwoerd and Longfei Mao. The fba solution space kernel – introduction and illustrative examples. ():, 2024. [DOI].
Jacob J. Valenzuela, Selva Rupa Christinal Immanuel, James G. Wilson, Serdar Turkarslan, Maryann Ruiz, Sean M. Gibbons, Kristopher A. Hunt, Nejc Stopnišek, Manfred Auer, Marcin Zemla, David A. Stahl, and Nitin S. Baliga. Origin of biogeographically distinct ecotypes during laboratory evolution. 15(1):7451–7451, 2024. [DOI].
M. Maton, Fernando N. Santos-Navarro, Jesús Picó, Philippe Bogaerts, and Alain Vande Wouwer. Towards constraint-based burden-aware models for metabolic engineering. 58(14):247–252, 2024. [DOI].
Tyler Hilsabeck and Shane L. Rea. Deep learning of cellular metabolic flux distributions predicts lifespan. ():, 2024. [DOI].
Lidan Bai, Jun Sun, Vasile Palade, Chao Li, Hengyang Lu, and Cong Gao. Optimizing metabolite production with neighborhood-based binary quantum-behaved particle swarm optimization and flux balance analysis. 32(1):64–88, 2024. [DOI].
Zhepu Ruan, Kai Chen, Weimiao Cao, Lei Meng, Bingang Yang, Mengjun Xu, Youwen Xing, Pengfa Li, Shiri Freilich, Chen Chen, Yanzheng Gao, Jiandong Jiang, and Xihui Xu. Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling. 15(1):4694–4694, 2024. [DOI].
Egeria Scoditti, Silvia Sabatini, Fabrizia Carli, and Amalia Gastaldelli. Hepatic glucose metabolism in the steatotic liver. 21(5):319–334, 2024. [DOI].
Yu Chen, Johan Gustafsson, Albert Tafur Rangel, Mihail Anton, Iván Domenzain, Cheewin Kittikunapong, Feiran Li, Le Yuan, Jens Nielsen, and Eduard J. Kerkhoven. Reconstruction, simulation and analysis of enzyme-constrained metabolic models using gecko toolbox 3.0. 19(3):629–667, 2024. [DOI].
Zachary M. Burcham, Aeriel D. Belk, Bridget B. McGivern, Amina Bouslimani, Parsa Ghadermazi, Cameron Martino, Liat Shenhav, Anru R. Zhang, Pixu Shi, Alexandra L. Emmons, Heather L. Deel, Zhenjiang Zech Xu, Victoria Nieciecki, Qiyun Zhu, Michael Shaffer, Morgan Panitchpakdi, Kelly C. Weldon, Kalen Cantrell, Asa Ben‐Hur, Sasha C. Reed, Greg C. Humphry, Gail Ackermann, Daniel McDonald, Siu Hung Joshua Chan, Melissa Connor, Derek A. Boyd, Jake Smith, Jenna M. S. Watson, Giovanna M. Vidoli, Dawnie Wolfe Steadman, Aaron M. Lynne, Sibyl R. Bucheli, Pieter C. Dorrestein, Kelly Wrighton, David Carter, Rob Knight, and Jessica L. Metcalf. A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables. 9(3):595–613, 2024. [DOI].
Ning Qin, Lingyun Li, Xiaozhen Wan, Xu Ji, Yu Chen, Chaokun Li, Ping Liu, Yijie Zhang, Weijie Yang, Junfeng Jiang, Jianye Xia, Shuobo Shi, Tianwei Tan, Jens Nielsen, Yun Chen, and Zihe Liu. Increased co2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast. 15(1):1591–1591, 2024. [DOI].
Zhijin Gong, Jiayao Chen, Xinyu Jiao, Hao Gong, Danzi Pan, Lingli Liu, Yang Zhang, and Tianwei Tan. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: current advances and future prospects. 72():108319–108319, 2024. [DOI].
Hongzhong Lu, Luchi Xiao, Wenbin Liao, Xuefeng Yan, and Jens Nielsen. Cell factory design with advanced metabolic modelling empowered by artificial intelligence. 85():61–72, 2024. [DOI].
Deepanwita Banerjee, Ian Sofian Yunus, Xi Wang, Jinho Kim, Aparajitha Srinivasan, Russel Menchavez, Yan Chen, Jennifer Gin, Christopher J. Petzold, Héctor García Martín, Jon Magnuson, Paul D. Adams, Blake A. Simmons, Aindrila Mukhopadhyay, Joonhoon Kim, Taek Soon Lee, Joonhoon Kim, and Taek Soon Lee. Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in pseudomonas putida. 82():157–170, 2024. [DOI].
Clémence Joseph, Haris Zafeiropoulos, Kristel Bernaerts, and Karoline Faust. Predicting microbial interactions with approaches based on flux balance analysis: an evaluation. 25(1):36–36, 2024. [DOI].
Pavel Dvořák, Barbora Burýšková, Barbora Popelářová, Birgitta E. Ebert, Tibor Botka, Dalimil Bujdoš, Alberto Sánchez-Pascuala, Hannah Schöttler, Heiko Hayen, Vı́ctor de Lorenzo, Lars M. Blank, and Martin Benešík. Synthetically-primed adaptation of pseudomonas putida to a non-native substrate d-xylose. 15(1):2666–2666, 2024. [DOI].
Guangjian Li, Hui Liang, Ruichen Gao, Ling Qin, Pei Xu, Mingtao Huang, Min‐Hua Zong, Yufei Cao, and Wen‐Yong Lou. Yeast metabolism adaptation for efficient terpenoids synthesis via isopentenol utilization. 15(1):9844–9844, 2024. [DOI].
Xi Luo, Yanjun Liu, Alexander Balck, Christine Klein, and Ronan M. T. Fleming. Identification of metabolites reproducibly associated with parkinson’s disease via meta-analysis and computational modelling. 10(1):126–126, 2024. [DOI].
Maria‐Tsampika Manoli, A. Gargantilla Becerra, Carlos del Cerro, Virginia Rivero‐Buceta, M. Auxiliadora Prieto, and Juan Nogales. A model-driven approach to upcycling recalcitrant feedstocks in pseudomonas putida by decoupling pha production from nutrient limitation. 43(4):113979–113979, 2024. [DOI].
Merve Atasoy, William T. Scott, Alberte Regueira, Miguel Mauricio‐Iglesias, Peter J. Schaap, and Hauke Smidt. Biobased short chain fatty acid production - exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches. 73():108363–108363, 2024. [DOI].
Cong Chen, Haiquan Yang, Kaizheng Zhang, Guangbin Ye, Huibo Luo, and Wei Zou. Revealing microbiota characteristics and predicting flavor-producing sub-communities in nongxiangxing baijiu pit mud through metagenomic analysis and metabolic modeling. 188():114507–114507, 2024. [DOI].
Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, and Ines Thiele. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. 36(8):1882–1897.e7, 2024. [DOI].
Sara Bertorello, Francesco Cei, Dorian Fink, Elena Niccolai, and Amedeo Amedei. The future exploring of gut microbiome-immunity interactions: from in vivo/vitro models to in silico innovations. 12(9):1828–1828, 2024. [DOI].
Magdalena Wutkowska, Vojtěch Tláskal, Sergio Bordel, Lisa Y. Stein, Justus Amuche Nweze, and Anne Daebeler. Leveraging genome-scale metabolic models to understand aerobic methanotrophs. 18(1):, 2024. [DOI].
Yonggang Gao and Cheng Zhao. Development and applications of metabolic models in plant multi-omics research. 15():1361183–1361183, 2024. [DOI].
Ye Li, Jingzhen Sun, Zhenhao Fu, Yubing He, Xiaorui Chen, Shijie Wang, Lele Zhang, Jiansheng Jian, Weihua Yang, Chunli Liu, Xiuxia Liu, Yankun Yang, and Zhonghu Bai. Engineering the l-tryptophan metabolism for efficient de novo biosynthesis of tryptophol in saccharomyces cerevisiae. 17(1):130–130, 2024. [DOI].
Ronan M. T. Fleming, Hulda S. Haraldsdóttir, German Preciat, Luojiao Huang, Ines Thiele, Amy C. Harms, and Thomas Hankemeier. Conserved moiety fluxomics. ():, 2024. [DOI].
Jorge E. Arellano-Villavicencio, Aarón Vázquez-Jiménez, Juan José Oropeza-Valdez, Cristian Padrón-Manrique, Heriberto Prado-Garcı́a, Armando R. Tovar, and Osbaldo Reséndis-Antonio. Intratumoral heterogeneity and metabolic cross-feeding in a three-dimensional breast cancer culture: an in silico perspective. 25(20):10894–10894, 2024. [DOI].
Klara Cerk, Pablo Ugalde‐Salas, Chabname Ghassemi Nedjad, Maxime Lecomte, Coralie Muller, David James Sherman, Falk Hildebrand, Simon Labarthe, and Clémence Frioux. Community‐scale models of microbiomes: articulating metabolic modelling and metagenome sequencing. 17(1):e14396–e14396, 2024. [DOI].
Mahnoor Zulfiqar, Vinay Singh, Christoph Steinbeck, and Maria Sorokina. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. 50(6):1053–1092, 2024. [DOI].
Manish Kumar, Juan D. Tibocha‐Bonilla, Zoltán Füssy, Chloe Lieng, Sarah M. Schwenck, Alice V. Lévesque, Mahmoud M. Al‐Bassam, Anurag Passi, M J Neal, Cristal Zúñiga, Farrah Kaiyom, Josh L. Espinoza, Hyun Gyu Lim, Shawn W. Polson, Lisa Zeigler Allen, and Karsten Zengler. Mixotrophic growth of a ubiquitous marine diatom. 10(29):eado2623–eado2623, 2024. [DOI].
Filippo Martinelli Boneschi, Almut Heinken, Ann‐Kristin Henning, Maria A. Ulmer, Tim Hensen, Antonio González, Matthias Arnold, Sanjay Asthana, Kathrin Budde, Corinne D. Engelman, Mehrbod Estaki, Hans J. Grabe, Margo B. Heston, Sterling C. Johnson, Gabi Kastenmüller, Cameron Martino, Daniel McDonald, Federico E. Rey, Ingo Kilimann, Olive Peters, Xiao Wang, Eike Jakob Spruth, Anja Schneider, Klaus Fließbach, Jens Wiltfang, Niels Hansen, Wenzel Glanz, Katharina Büerger, Daniel Janowitz, Christoph Laske, Matthias H. Munk, Annika Spottke, Nina Roy, Matthias Nauck, Stefan Teipel, Rob Knight, Rima Kaddurah‐Daouk, Barbara B. Bendlin, Johannes Hertel, and Ines Thiele. Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in alzheimer’s disease. 14(1):6095–6095, 2024. [DOI].
Wheaton L. Schroeder, Patrick F. Suthers, T. Willis, E. Mooney, and Costas D. Maranas. Current state, challenges, and opportunities in genome-scale resource allocation models: a mathematical perspective. 14(7):365–365, 2024. [DOI].
Cecilia Noecker and Peter J. Turnbaugh. Emerging tools and best practices for studying gut microbial community metabolism. 6(7):1225–1236, 2024. [DOI].
Xihui Xu, Can Li, Weimiao Cao, Lulu Yan, Lulu Cao, Qi Han, Minling Gao, Yahua Chen, Zhenguo Shen, Jiandong Jiang, and Chen Chen. Bacterial growth and environmental adaptation via thiamine biosynthesis and thiamine-mediated metabolic interactions. 18(1):, 2024. [DOI].
Arturo Vera-Ponce de León, Tim Hensen, Matthias Hoetzinger, Shashank Gupta, Bronson R. Weston, Sander M. Johnsen, Jacob Agerbo Rasmussen, Cecilie Grønlund Clausen, Louisa Pless, Ana Raquel Verissimo, Knut Rudi, Lars Snipen, Christian Karlsen, Morten T. Limborg, Stefan Bertilsson, Ines Thiele, Torgeir R. Hvidsten, Simen R. Sandve, Phillip B. Pope, and Sabina Leanti La Rosa. Genomic and functional characterization of the atlantic salmon gut microbiome in relation to nutrition and health. 9(11):3059–3074, 2024. [DOI].
Emanuel Lange, Lena Kranert, Jacob Krüger, Dirk Benndorf, and Robert Heyer. Microbiome modeling: a beginner's guide. 15():1368377–1368377, 2024. [DOI].
Boatema Ofori-Anyinam, Meagan Hamblin, Miranda L Coldren, Barry Li, Gautam Mereddy, Majid B. Shaikh, Avi Shah, Courtney Grady, Navpreet Ranu, Shou‐En Lu, Paul C. Blainey, Shuyi Ma, James J. Collins, and Jason H. Yang. Catalase activity deficiency sensitizes multidrug-resistant mycobacterium tuberculosis to the atp synthase inhibitor bedaquiline. 15(1):9792–9792, 2024. [DOI].
Silvia Sabatini, Partho Sen, Fabrizia Carli, Samantha Pezzica, Chiara Rosso, Erminia Lembo, Ornella Verrastro, Ann K. Daly, Olivier Govaere, Simon Cockell, Tuulia Hyötyläinen, Geltrude Mingrone, Elisabetta Bugianesi, Quentin M. Anstee, Matej Orešič, and Amalia Gastaldelli. Hepatic glucose production rises with the histological severity of metabolic dysfunction-associated steatohepatitis. 5(11):101820–101820, 2024. [DOI].
Seo‐Young Park, Jinsung Song, Dong‐Hyuk Choi, Uiseon Park, Hyeran Cho, Bee Hak Hong, Yaron Silberberg, and Dong‐Yup Lee. Exploring metabolic effects of dipeptide feed media on cho cell cultures by in silico model-guided flux analysis. 108(1):123–123, 2024. [DOI].
Meng Luo, Jinlin Zhu, Jiajia Jia, Hao Zhang, and Jianxin Zhao. Progress on network modeling and analysis of gut microecology: a review. 90(3):e0009224–e0009224, 2024. [DOI].
Jinlin Zhu, Jialin Yin, Jing Chen, Mingyi Hu, Wenwei Lu, Hongchao Wang, Hao Zhang, and Wei Chen. Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis. 16(1):2336877–2336877, 2024. [DOI].
Shengbo Wu, Yongsheng Zhou, Lei Dai, Aidong Yang, and Jianjun Qiao. Assembly of functional microbial ecosystems: from molecular circuits to communities. 48(6):, 2024. [DOI].
Abdulla Abu-Salah, Müberra Fatma Cesur, Aiesha Anchan, Muhammet Ay, Monica R. Langley, Ahmed Shah, Pablo Reina-Gonzalez, Rachel Strazdins, Tunahan Çakır, and Souvarish Sarkar. Comparative proteomics highlights that genx exposure leads to metabolic defects and inflammation in astrocytes. 58(46):20525–20539, 2024. [DOI].
Shivang Hina-Nilesh Joshi, Christopher A. Jenkins, David Ulaeto, and Thomas E. Gorochowski. Accelerating genetic sensor development, scale-up, and deployment using synthetic biology. 6():0037–0037, 2024. [DOI].
John R. Casey, Brian J. Bennion, Patrik D’haeseleer, Jeffrey A. Kimbrel, Gianna L. Marschmann, and Ali Navid. Transporter annotations are holding up progress in metabolic modeling. 4():1394084–1394084, 2024. [DOI].
Ross P. Carlson, Ashley E. Beck, Mauricio Garcia Benitez, William R. Harcombe, Radhakrishnan Mahadevan, and Tomáš Gedeon. Cell geometry and membrane protein crowding constrain growth rate, overflow metabolism, respiration, and maintenance energy. ():, 2024. [DOI].
Danlei Chen, Zhihua Qu, Shujuan Yang, Yujie Li, Shan Yu, Xin Yao Li, Yutong Lu, Aidong Yang, Chunjiang Liu, Shengbo Wu, and Jianjun Qiao. Quorum sensing regulating the productivity and stability of cross-feeding cocultivation. 502():158167–158167, 2024. [DOI].
Aswathy K. Raghu, Indumathi Palanikumar, and Karthik Raman. Designing function-specific minimal microbiomes from large microbial communities. 10(1):, 2024. [DOI].
Lvjing Wang, Xiaoyu Wang, Hao Wu, Haixia Wang, and Zhenmei Lü. Interspecies synergistic interactions mediated by cofactor exchange enhance stress tolerance by inducing biofilm formation. 9(9):e0088424–e0088424, 2024. [DOI].
Matthew Lima, Charandatta Muddana, Zhengyang Xiao, Anindita Bandyopadhyay, Pramod P. Wangikar, Himadri B. Pakrasi, and Yinjie Tang. The new chassis in the flask: advances in vibrio natriegens biotechnology research. 77():108464–108464, 2024. [DOI].
Xuangao Wu, Hee-Jong Yang, Myeong Seon Ryu, Su‐Jin Jung, Kyooseob Ha, Do-Yeon Jeong, and Sunmin Park. Association of mucin-degrading gut microbiota and dietary patterns with colonic transit time in constipation: a secondary analysis of a randomized clinical trial. 17(1):138–138, 2024. [DOI].
Olivia Gallup, Kirill Sechkar, Sebastian Towers, and Harrison Steel. Computational synthetic biology enabled through jax: a showcase. 13(9):3046–3050, 2024. [DOI].
M J Neal, William Brakewood, Michael J. Betenbaugh, and Karsten Zengler. Pan-genome-scale metabolic modeling of bacillus subtilis reveals functionally distinct groups. 9(11):e0092324–e0092324, 2024. [DOI].
Hatice Büşra Lüleci̇, Dilara Uzuner, Müberra Fatma Cesur, Atılay İlgün, Elif Düz, Ecehan Abdi̇k, Regan Odongo, and Tunahan Çakır. A benchmark of rna-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks. 10(1):124–124, 2024. [DOI].
Lizhan Zhang, Jianwen Ye, Gang Li, Helen Park, Hao Luo, Yina Lin, Shaowei Li, Weinan Yang, Yuying Guan, Fuqing Wu, Wuzhe Huang, Qiong Wu, Nigel S. Scrutton, Jens Nielsen, and Guo‐Qiang Chen. A long-term growth stable halomonas sp. deleted with multiple transposases guided by its metabolic network model halo-ecgem. 84():95–108, 2024. [DOI].
Farid Zare and Ronan M. T. Fleming. Integration of proteomic data with genome‐scale metabolic models: a methodological overview. 33(10):e5150–e5150, 2024. [DOI].
Henry Ehlers, Nicolas Brich, Michael Krone, Martin Nöllenburg, Jiacheng Yu, Hiroaki Natsukawa, Xiaoru Yuan, and Hsiang‐Yun Wu. An introduction to and survey of biological network visualization. 126():104115–104115, 2024. [DOI].
Rola Shaaban, Susheel Bhanu Busi, Paul Wilmes, Jean‐Louis Guéant, and Almut Heinken. Personalized modeling of gut microbiome metabolism throughout the first year of life. 4(1):281–281, 2024. [DOI].
Fernando Cruz, João Capela, Eugénio C. Ferreira, Miguel Rocha, and Óscar Dias. Bioiso: an objective-oriented application for assisting the curation of genome-scale metabolic models. 21(2):215–226, 2024. [DOI].
Loreto Román, Felipe Melis-Arcos, Tomás Pröschle, Pedro A. Saa, and Daniel Garrido. Genome-scale metabolic modeling of the human milk oligosaccharide utilization by bifidobacterium longum subsp. infantis. 9(3):e0071523–e0071523, 2024. [DOI].
Pedro A. Saa, Sebastián Zapararte, Christopher Drovandi, and Lars K. Nielsen. Looplessfluxsampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models. 25(1):3–3, 2024. [DOI].
Yu Chen, Johan Gustafsson, Jingyu Yang, Jens Nielsen, and Eduard J. Kerkhoven. Single-cell omics analysis with genome-scale metabolic modeling. 86():103078–103078, 2024. [DOI].
Telmo Blasco, Francesco Balzerani, Luis V. Valcárcel, Pedro Larrañaga, Concha Bielza, M. Pilar Francino, José Ángel Rufián‐Henares, Francisco J. Planes, and Sergio Pérez-Burillo. Bn-bacarena: bayesian network extension of bacarena for the dynamic simulation of microbial communities. 40(5):, 2024. [DOI].
Nicola Cortese, Anna Procopio, Alessio Merola, Paolo Zaffino, and Carlo Cosentino. Applications of genome-scale metabolic models to the study of human diseases: a systematic review. 256():108397–108397, 2024. [DOI].
Sol Han, Dohyeon Kim, Young-Shin Kim, and Sung Ho Yoon. Genome-scale metabolic network model and phenome of solvent-tolerant pseudomonas putida s12. 25(1):63–63, 2024. [DOI].
Leila Hassani, Mohammad R. Moosavi, Payam Setoodeh, and Habil Zare. Fastknock: an efficient next-generation approach to identify all knockout strategies for strain optimization. 23(1):37–37, 2024. [DOI].
Luis V. Valcárcel, Edurne San José‐Eneriz, Raquel Ordóñez, Iñigo Apaolaza, Danel Olaverri-Mendizabal, Naroa Barrena, Ana Valcárcel, Leire Gárate, Jesús F. San Miguel, Antonio Pineda‐Lucena, Xabier Agirre, Felipe Prósper, and Francisco J. Planes. An automated network-based tool to search for metabolic vulnerabilities in cancer. 15(1):8685–8685, 2024. [DOI].
Renee Oles, Marvic Carrillo Terrazas, Luke R. Loomis, M J Neal, Mousumi Paulchakrabarti, Simone Zuffa, Chia-Yun Hsu, Adriana Vasquez Ayala, Michael H. Lee, Caitlin Tribelhorn, Pedro Belda‐Ferre, MacKenzie Bryant, Jasmine Zemlin, Jocelyn Young, Parambir S. Dulai, William J. Sandborn, Mamata Sivagnanam, Manuela Raffatellu, David T. Pride, Pieter C. Dorrestein, Karsten Zengler, Biswa Choudhury, Rob Knight, and Hiutung Chu. Pathogenicbacteroides fragilisstrains can emerge from gut-resident commensals. ():, 2024. [DOI].
Zhepu Ruan, Mengjun Xu, Youwen Xing, Kaiqing Yang, Xihui Xu, Jiandong Jiang, and Rongliang Qiu. Enhanced growth of wheat in contaminated fields via synthetic microbiome as revealed by genome-scale metabolic modeling. 953():176047–176047, 2024. [DOI].
Somdutt Ravindran, Nima Hajinajaf, Pritam Kundu, Jackson Comes, David R. Nielsen, Arul M. Varman, and Amit Ghosh. Genome-scale metabolic model reconstruction and investigation into the fluxome of the fast-growing cyanobacterium synechococcus sp. pcc 11901. 13(10):3281–3294, 2024. [DOI].
Xavier Marbehan, Magali Roger, Frantz Fournier, Pascale Infossi, Emmanuel Guédon, Louis Delecourt, Régine Lebrun, Marie‐Thérèse Giudici‐Orticoni, and Stéphane Delaunay. Combining metabolic flux analysis with proteomics to shed light on the metabolic flexibility: the case of desulfovibrio vulgaris hildenborough. 15():1336360–1336360, 2024. [DOI].
Danel Olaverri-Mendizabal, Luis V. Valcárcel, Naroa Barrena, Carlos Rodriguez-Flores, and Francisco J. Planes. Review and meta-analysis of the genetic minimal cut set approach for gene essentiality prediction in cancer metabolism. 25(3):, 2024. [DOI].
Alexandra Manchel, Michelle Gee, and Rajanikanth Vadigepalli. From sampling to simulating: single-cell multiomics in systems pathophysiological modeling. 27(12):111322–111322, 2024. [DOI].
Tim Hensen, Shahzad Ahmad, Gabi Kastenmüller, Robert Kraaij, Mohsen Ghanbari, M. Arfan Ikram, Rima Kaddurah‐Daouk, and Ines Thiele. In silico metabolic modelling links microbiome-derived metabolites to risk factors of alzheimer’s disease. 2(1):, 2024. [DOI].
Freyr Jóhannsson, James T. Yurkovich, Steinn Guðmundsson, Ólafur E. Sigurjónsson, and Óttar Rolfsson. Temperature dependence of platelet metabolism. 14(2):91–91, 2024. [DOI].
Jose L. Cadavid, Nancy T. Li, and Alison P. McGuigan. Bridging systems biology and tissue engineering: unleashing the full potential of complex 3d in vitro tissue models of disease. 5(2):021301–021301, 2024. [DOI].
David M. Versluis, Ruud Schoemaker, Ellen Looijesteijn, Jan M.W. Geurts, and Roeland M. H. Merks. 2′-fucosyllactose helps butyrate producers outgrow competitors in infant gut microbiota simulations. 27(3):109085–109085, 2024. [DOI].
Panyawarin Soommat, Nachon Raethong, Ratchaprapa Ruengsang, Roypim Thananusak, Teeraphan Laomettachit, Kobkul Laoteng, Treenut Saithong, and Wanwipa Vongsangnak. Light-exposed metabolic responses of cordyceps militaris through transcriptome-integrated genome-scale modeling. 13(3):139–139, 2024. [DOI].
Mustafa Sertbaş and Kutlu Ö. Ülgen. Uncovering the effect of sars-cov-2 on liver metabolism via genome-scale metabolic modeling for reprogramming and therapeutic strategies. 9(13):15535–15546, 2024. [DOI].
Anqiang Ye, Jian-Ning Shen, Yong Li, Xiang Lian, Bin‐Guang Ma, and Feng‐Biao Guo. Reconstruction of the genome-scale metabolic network model of sinorhizobium fredii ccbau45436 for free-living and symbiotic states. 12():1377334–1377334, 2024. [DOI].
N. Suhas Jagannathan, Javier Yu Peng Koh, Younghwan Lee, Radoslaw M. Sobota, Aaron T. Irving, Lin‐Fa Wang, Yoko Itahana, Koji Itahana, and Lisa Tucker‐Kellogg. Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism. 13():, 2024. [DOI].
Huiying Zhao, Sarula Bai, Jian Tan, Ming Liu, Yuchao Zhao, and Linshu Jiang. Can meta-omics revolutionize our understanding of rumen methane emissions? 1():, 2024. [DOI].
Jianhang Zhou, Xiaogang Wu, Yiming Feng, Tangran Huo, Yun‐Peng Zhao, Juejun Pan, and Sitong Liu. Bacterial communication intelligently regulates their interactions in anammox consortia under decreasing temperatures. 954():176239–176239, 2024. [DOI].
Micaela Chacón, Guadalupe Alvarez-Gonzalez, Piya Gosalvitr, Adokiye Berepiki, Karl Fisher, Rosa M. Cuéllar-Franca, and Neil Dixon. Complex waste stream valorization through combined enzymatic hydrolysis and catabolic assimilation by pseudomonas putida. 43(3):647–672, 2024. [DOI].
Eliott Jacopin, Yuki Sakamoto, Kozo Nishida, Kazunari Kaizu, and Koichi Takahashi. An architecture for collaboration in systems biology at the age of the metaverse. 10(1):12–12, 2024. [DOI].
Roman G. Bielski and M. Ahsanul Islam. Pangenome-scale mathematical modelling of anammox bacteria metabolism. 2(1):70–84, 2024. [DOI].
Simran Kaur Aulakh, Oliver Lemke, Łukasz Szyrwiel, Stephan Kamrad, Yu Chen, Johannes Hartl, Michael Muelleder, Jens Nielsen, and Markus Ralser. The molecular landscape of cellular metal ion biology. ():, 2024. [DOI].
Soraya Mirzaei and Mojtaba Tefagh. Gem-based computational modeling for exploring metabolic interactions in a microbial community. 20(6):e1012233–e1012233, 2024. [DOI].
Lu Liu, Weihe Rong, Du Xiang, Qianqian Yuan, Zhaoyu Xu, Chang Ho Yu, Hongzhong Lu, Yanfei Wang, Yan Zhu, Zhijia Liu, and Guokun Wang. Integrating experimental and computational analyses of yeast protein profiles for optimizing the production of high-quality microbial proteins. 196(12):8741–8762, 2024. [DOI].
Ryan Schildcrout, Kirk Smith, Rupa Bhowmick, Suraj Menon, Minali Kapadia, Emily G. Kurtz, Anya Coffeen-Vandeven, Sriram Chandrasekaran, Srikar Nelakuditi, and Sriram Chandrasekaran. Recon8d: a metabolic regulome network from oct-omics and machine learning. ():, 2024. [DOI].
Helen Scott and Daniel Segrè. Metabolic flux modeling in marine ecosystems. 17(1):593–620, 2024. [DOI].
Sowmya Manojna Narasimha, Tanisha Malpani, Omkar S. Mohite, J. Saketha Nath, and Karthik Raman. Understanding flux switching in metabolic networks through an analysis of synthetic lethals. 10(1):104–104, 2024. [DOI].
Jungyeon Kim, Y S Kim, Juyoung Kim, Min‐Ju Seo, Soo‐Jin Yeom, and Bong Hyun Sung. Investigation of bottleneck enzyme through flux balance analysis to improve glycolic acid production in escherichia coli. 62(11):1023–1033, 2024. [DOI].
Siqing Fan, Hao Ren, Xueni Fu, Xiangyu Kong, Hao Wu, and Zhenmei Lv. Genome streamlining of pseudomonas putida b6-2 for bioremediation. 9(12):e0084524–e0084524, 2024. [DOI].
Gustav Sjöberg, Alīna Reķēna, Matilda Fornstad, Petri‐Jaan Lahtvee, and Antonius J. A. van Maris. Evaluation of enzyme-constrained genome-scale model through metabolic engineering of anaerobic co-production of 2,3-butanediol and glycerol by saccharomyces cerevisiae. 82():49–59, 2024. [DOI].
Tim Hulshof, Bram Nap, Filippo Martinelli Boneschi, and Ines Thiele. Microbial abundances retrieved from sequencing data—automated ncbi taxonomy (mars): a pipeline to create relative microbial abundance data for the microbiome modelling toolbox and utilizing homosynonyms for efficient mapping to resources. 4(1):vbae068–vbae068, 2024. [DOI].
Carlos J Rodriguez-Flores, Naroa Barrena, Danel Olaverri-Mendizabal, Idoia Ochoa, Luis V. Valcárcel, and Francisco J. Planes. Gmcspy: efficient and accurate computation of genetic minimal cut sets in python. 40(6):, 2024. [DOI].
Juan Pablo Molina Ortiz, Dale D. McClure, Andrew Holmes, Stuart A. Rice, Mark Read, and Erin R. Shanahan. Genome scale metabolic modelling of human gut microbes to inform rational community design. ():, 2024. [DOI].
Aruldoss Immanuel, Ragothaman M. Yennamalli, and Venkatasubramanian Ulaganathan. Targeting the bottlenecks in levan biosynthesis pathway in bacillus subtilis and strain optimization by computational modeling and omics integration. 28(2):49–58, 2024. [DOI].
Sun Jin Moon, Anush Chiappino-Pepe, Wentao Dong, Joanne K. Kelleher, Matthew G. Vander Heiden, Gregory Stephanopoulos, and Hadley D. Sikes. Nadph composite index analysis quantifies the relationship between compartmentalized nadph dynamics and growth rates in cancer cells. ():, 2024. [DOI].
Richard D. Paul, Johann F. Jadebeck, Anton Stratmann, Wolfgang Wiechert, and Katharina Nöh. Hopsy — a methods marketplace for convex polytope sampling in python. 40(7):, 2024. [DOI].
Jungyeon Kim, Gun-Hwi Yeon, Mi Jin Kim, Jung‐Hoon Bae, Jung‐Hoon Sohn, and Bong Hyun Sung. Systems metabolic engineering to elucidate and enhance intestinal metabolic activities of escherichia coli nissle 1917. 72(32):18234–18246, 2024. [DOI].
Muhammed Erkan Karabekmez. Insights into yeast response to chemotherapeutic agent through time series genome‐scale metabolic models. 121(10):3351–3359, 2024. [DOI].
Elena Lucy Carter, Nicholas R. Waterfield, Chrystala Constantinidou, and Mohammad Tauqeer Alam. A temperature-induced metabolic shift in the emerging human pathogen photorhabdus asymbiotica. 9(11):e0097023–e0097023, 2024. [DOI].
Sandra M. Correa, Asdrúbal Burgos, Álvaro Cuadros‐Inostroza, Ke Xu, Yariv Brotman, and Zoran Nikoloski. A data-integrative modeling approach accurately characterizes the effects of mutations on arabidopsis lipid metabolism. 197(2):, 2024. [DOI].
Zhihao Liu, Minghao Chen, Jingmin Hu, Yonghong Wang, and Yu Chen. Minimization of proteome reallocation explains metabolic transition in hierarchical utilization of carbon sources. ():, 2024. [DOI].
Chania Clare, Jack W. Rutter, Alex J. H. Fedorec, Stefanie Frank, and C. Barnes. Bacterial microcompartment utilisation in the human commensalescherichia colinissle 1917. ():, 2024. [DOI].
Renan Tosin, Igor Portis, Leandro Rodrigues, Igor Gonçalves, Catarina Barbosa, Jorge Teixeira, Rafael J. Mendes, Filipe Neves dos Santos, Conceição Santos, Rui C. Martins, and Mário Cunha. Integrating spectral sensing and systems biology for precision viticulture: effects of shade nets on grapevine leaves. 10(8):873–873, 2024. [DOI].
Yuchao Zhao, Jian Tan, Luoyun Fang, and Linshu Jiang. Harnessing meta-omics to unveil and mitigate methane emissions in ruminants: integrative approaches and future directions. 951():175732–175732, 2024. [DOI].
Nan Xu, Jiaojiao Zuo, Chenghao Li, Cong Gao, and Minliang Guo. Reconstruction and analysis of a genome-scale metabolic model of acinetobacter lwoffii. 25(17):9321–9321, 2024. [DOI].
Karel Olavarría and Diana Z. Sousa. Thermodynamic tools for more efficient biotechnological processes: an example in poly-(3-hydroxybutyrate) production from carbon monoxide. 90():103212–103212, 2024. [DOI].
Daniella M. Hernandez, Melissa M Marzouk, Madeline Cole, Marla C. Fortoul, S. Kethireddy, Rehan Contractor, Habibul Islam, Trent Moulder, Ariane R. Kalifa, Estefania Marin Meneses, Maximiliano Barbosa Mendoza, Ruth Thomas, S. Masud, Sheena Pubien, Patricia Milanes, Gabriela Diaz-Tang, Allison J. Lopatkin, and Robert P. Smith. Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics. 12(12):e0189524–e0189524, 2024. [DOI].
Miha Moškon and Tadeja Režen. Integration and analysis of omics data using genome-scale metabolic models. 14(11):595–595, 2024. [DOI].
Chania Clare, Jack W. Rutter, Alex J. H. Fedorec, Stefanie Frank, and C. Barnes. Bacterial microcompartment utilization in the human commensal escherichia coli nissle 1917. 206(12):e0026924–e0026924, 2024. [DOI].
Zijian Hu, Jingyi Qian, Yuzhou Wang, and Chao Ye. Current status and applications of genome‐scale metabolic models of oleaginous microorganisms. 3(4):492–511, 2024. [DOI].
Lena Peters, Moritz Drechsler, Barbara Pees, Georgia Angelidou, Liesa Salzer, Karlis Arturs Moors, Nicole Paczia, Hinrich Schulenburg, Yi‐Ming Shi, Christoph Kaleta, Michael Witting, Helge B. Bode, and Katja Dierking. Polyketide synthase-derived sphingolipids determine microbiota-mediated protection against pathogens inc. elegans. ():, 2024. [DOI].
Roland Sauter, Suraj Sharma, and Ines Heiland. Accounting for nad concentrations in genome-scale metabolic models captures important metabolic alterations in nad-depleted systems. 14(5):602–602, 2024. [DOI].
Maria Pires Pacheco, Déborah Gérard, Riley J. Mangan, Alec R. Chapman, Dennis Hecker, Manolis Kellis, Marcel H. Schulz, Lasse Sinkkonen, and Thomas Sauter. Epigenetic control of metabolic identity across cell types. ():, 2024. [DOI].
Hatice Büşra Lüleci̇, Attila Jones, and Tunahan Çakır. Multi‐omics analyses highlight molecular differences between clinical and neuropathological diagnoses in alzheimer's disease. 60(5):4922–4936, 2024. [DOI].
Luhui Zhang, Xinpei Sun, Jianwen Ye, Quan Yuan, Xin Zhang, Fei Sun, Yongpan An, Yutong Chen, Yong Qian, Daqian Yang, Qian Wang, Miaomiao Gao, Tao Chen, Hongwu Ma, Guo‐Qiang Chen, and Zhengwei Xie. Reconstruction and analyses of genome-scale halomonas metabolic network yield a highly efficient pha production. 19():e00251–e00251, 2024. [DOI].
Jiahang Li, Wolfram Weckwerth, and Steffen Waldherr. Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse jacobian. 10(1):137–137, 2024. [DOI].
Shaohua Guan, Zhichao Zhang, Zihan Zhang, and Hualin Shi. Universal scaling relation and criticality in metabolism and growth of escherichia coli. 6(1):, 2024. [DOI].
Naveed Ziari and Marc K. Hellerstein. Pymida: a graphical user interface for mass isotopomer distribution analysis. 96(6):2303–2308, 2024. [DOI].
Lena Peters, Moritz Drechsler, Barbara Pees, Georgia Angelidou, Liesa Salzer, Karlis Arturs Moors, Nicole Paczia, Hinrich Schulenburg, Yi‐Ming Shi, Christoph Kaleta, Michael Witting, Helge B. Bode, and Katja Dierking. Polyketide synthase-derived sphingolipids determine microbiota-mediated protection against pathogens in c. elegans. ():, 2024. [DOI].
Samvedna Singh, Himanshi Gupta, and Shakti Sahi. Computational methods for detection of host-pathogen interactions. ():103–130, 2024. [DOI].
Amandine Paulay, Ghjuvan Micaelu Grimaud, Raphaël Caballero, Béatrice Laroche, Marion Leclerc, Simon Labarthe, and Emmanuelle Maguin. Design of a proteolytic module for improved metabolic modeling of bacteroides caccae. 9(4):e0015324–e0015324, 2024. [DOI].
Tong Hao, Zhentao Song, Mingzhi Zhang, Lingrui Zhang, Jiarui Yang, Jinɡjinɡ Li, and Jinsheng Sun. Reconstruction of metabolic–protein interaction integrated network of eriocheir sinensis and analysis of ecdysone synthesis. 15(4):410–410, 2024. [DOI].
Jonathan Josephs‐Spaulding, Hannah Clara Rettig, Johannes Zimmermann, Mariam Chkonia, Alexander Mischnik, Sören Franzenburg, Simon Graspeuntner, Jan Rupp, and Christoph Kaleta. Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection. ():, 2024. [DOI].
Daniella M. Hernandez, Melissa M Marzouk, Madeline Cole, Marla C. Fortoul, S. Kethireddy, Rehan Contractor, Habibul Islam, Trent Moulder, Ariane R. Kalifa, Estefania Marin Meneses, Maximiliano Barbosa Mendoza, Ruth Thomas, S. Masud, Sheena Pubien, Patricia Milanes, Gabriela Diaz-Tang, Allison J. Lopatkin, and Robert P. Smith. Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics. ():, 2024. [DOI].
Ali Kishk, Maria Pires Pacheco, Tony Heurtaux, and Thomas Sauter. Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. 25(3):, 2024. [DOI].
Biki Bapi Kundu, Jayanth Krishnan, Richard Szubin, Arjun Patel, Bernhard Ø. Palsson, Daniel C. Zielinski, and Caroline M. Ajo‐Franklin. Extracellular respiration is a latent energy metabolism inescherichia coli. ():, 2024. [DOI].
Ehsan Motamedian, Matiss Ricards Baumanis, Jānis Daugavietis, Ieva Bērziņa, Karlis Shvirksts, Elīna Dāce, Jānis Liepiņš, and Egils Stalidzāns. A hybrid strategy for growth-associated production of sophorolipid using a reconstructed genome-scale metabolic model for the fructophilic yeast starmerella bombicola. 189():541–548, 2024. [DOI].
Carlos Focil, Gabriela Canto-Encalada, D. O. Campos, Cristal Zúñiga, and Alejandro Zepeda. Applying genome-scale metabolic modeling tools to understand microbial communities in wastewater treatment. ():297–332, 2024. [DOI].
Elías Vera-Sigüenza, Cristina Escribano-Gonzalez, Irene Serrano‐Gonzalo, Kattri‐Liis Eskla, Charlotte Speakman, Alejandro Huerta Uribe, Lisa A. Vettore, Himani Rana, Adam Boufersaoui, Hans Vellama, Ramin Nashebi, Ielyaas Cloete, Jennie Roberts, Supratik Basu, Mark T. Drayson, Christopher M. Bunce, Guy Pratt, Fabian Spill, Oliver D.K. Maddocks, and Daniel A. Tennant. Pyruvate from bone marrow mesenchymal stem cells supports myeloma redox homeostasis and anabolism. ():, 2024. [DOI].
Osbaldo Reséndis-Antonio, Jorge E. Arellano-Villavicencio, Aarón Vázquez-Jiménez, Juan José Oropeza-Valdez, Cristian Padrón-Manrique, Heriberto Prado-Garcı́a, and Armando R. Tovar. Intratumoral heterogeneity and metabolic cross-feeding in a three-dimensional model of breast cancer: an in silico perspective. ():, 2024. [DOI].
Carolina A. Contador, Ailin Liu, Ming‐Sin Ng, Yee‐Shan Ku, Siu Hung Joshua Chan, and Hon‐Ming Lam. Contextualized metabolic modelling revealed factors affecting isoflavone accumulation in soybean seeds. ():, 2024. [DOI].
Boyu Jiang, Nick Quinn-Bohmann, Christian Diener, Vignesh Bose Nathan, Yu Han-Hallett, Lavanya Reddivari, Sean M. Gibbons, and Priyanka Baloni. Understanding disease-associated metabolic changes in human colon epithelial cells usingicolonepithelium metabolic reconstruction. ():, 2024. [DOI].
Tunahan Çakır, Ecehan Abdi̇k, Dilara Uzuner, and Hatice Büşra Lüleci̇. Genome-scale metabolic modeling for diseases. ():258–265, 2024. [DOI].
Luojiao Huang, German Preciat, Jesus Alarcon-Gil, Edinson Lucumi Moreno, Agnieszka B. Wegrzyn, Ines Thiele, Emma Schymanski, Amy C. Harms, Ronan M. T. Fleming, and Thomas Hankemeier. Fluxtram: integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis. ():, 2024. [DOI].
Artai R. Moimenta, Diego Troitiño-Jordedo, and Eva Balsa‐Canto. Integrating kinetic models with genome-scale constraints to explain yeast-based batch fermentation. ():6311–6316, 2024. [DOI].
Muhammed Erkan Karabekmez. Insights into yeast response to chemotherapeutic agent through time series genome-scale metabolic models. ():, 2024. [DOI].
Emanuel Lange, Lena Kranert, Jacob Krüger, Dirk Benndorf, and Robert Heyer. Microbiome modeling: a beginner’s guide. ():, 2024. [DOI].
Chloe V. McCreery, Drew Alessi, Katarina Mollo, Alessio Fasano, and Ali R. Zomorrodi. Investigating intestinal epithelium metabolic dysfunction in celiac disease using personalized genome-scale models. ():, 2024. [DOI].
Fernando Silva-Lance, Isabel Montejano-Montelongo, Èric Bautista, Lars K. Nielsen, Pär I. Johansson, and Igor Marín de Mas. Integrating genome-scale metabolic models with patient plasma metabolome to study endothelial metabolism in situ. 25(10):5406–5406, 2024. [DOI].
Indumathi Palanikumar, Himanshu Sinha, and Karthik Raman. Panera: an innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models. 27(7):110358–110358, 2024. [DOI].
Miguel Angel Ramos-Valdovinos, Prisciluis Caheri Salas-Navarrete, Gerardo Ruíz Amores, Ana Lilia Hernández-Orihuela, and Agustino Martínez‐Antonio. Qualitative perturbation analysis and machine learning: elucidating bacterial optimization of tryptophan production. 17(7):282–282, 2024. [DOI].
Devlin C. Moyer, Justin Reimertz, Daniel Segrè, and Juan I. Fuxman Bass. Semi-automatic detection of errors in genome-scale metabolic models. ():, 2024. [DOI].
David Geller‐McGrath. Characterization of microbial primary and secondary metabolism in the marine realm. ():, 2024. [DOI].
Míriam Tarrado‐Castellarnau, Carles Foguet, Josep Tarragó‐Celada, Marc Palobart, Claudia Hernández-Carro, Jordi Perarnau, Erika Zodda, Ibrahim H. Polat, Silvia Marín, Alejandro Suárez‐Bonnet, Juan José Lozano, Mariia Yuneva, Timothy M. Thomson, and Marta Cascante. Glutaminase as a metabolic target of choice to counter acquired resistance to palbociclib by colorectal cancer cells. ():, 2024. [DOI].
Mr Goodluck Kam'bwemba - and Dr Johnson Okeniyi -. Information and communications technologies, to raise quality of deceased estate disbursement: administrator general's office, malawi. 6(1):, 2024. [DOI].
Carlos Rodriguez-Flores, Naroa Barrena, Danel Olaverri-Mendizabal, Idoia Ochoa, Luis V. Valcárcel, and Francisco J. Planes. Gmcspy: efficient and accurate computation of genetic minimal cut sets in python. ():, 2024. [DOI].
Aamir Faisal Ansari, Gayathri Sambamoorthy, Thrisha C Alexander, Yugandhar B. S. Reddy, Janhavi S. Raut, and Narendra M. Dixit. Disentangling competitive and cooperative components of the interactions between microbial species. ():, 2024. [DOI].
James Morrissey, Benjamin Strain, and Cleo Kontoravdi. Flux balance analysis of mammalian cell systems. 2774():119–134, 2024. [DOI].
Sowmya Manojna, Tanisha Malpani, Omkar S. Mohite, Saketha Nath, and Karthik Raman. Understanding flux switching in metabolic networks through an analysis of synthetic lethals. ():, 2024. [DOI].
Alexandre Maciel-Guerra, Kubra Babaarslan, Michelle Baker, Aura Rahman, Maqsud Muhammad Hossain, Abdus Sadique, Jahidul Alam, Salim Uzzaman, Mohammad Ferdous Rahman Sarker, Nasrin Sultana, Ashraful Islam Khan, Yasmin Ara Begum, Mokibul Hassan Afrad, Nicola Senin, Zakir Hossain Habib, Tahmina Shirin, Firdausi Qadri, and Tania Dottorini. Signature genomic traits of the core and accessory genome of vibrio cholerae o1 drive lineage transmission and disease severity. ():, 2024. [DOI].
Dilara Uzuner, Atılay İlgün, Fatma Betül Bozkurt, and Tunahan Çakır. A personalized metabolic modelling approach through integrated analysis of rna-seq-based genomic variants and gene expression levels in alzheimer’s disease. ():, 2024. [DOI].
Yadira Boada, Marcelo Flores Vázquez, Marco Córdova, Francisco Flores, and Alejandro Vignoni. Synthetic biology design principles enable efficient bioproduction of heparosan with low polydispersion index for the biomedical industry. ():, 2024. [DOI].
Nam Kyu Kang, Hyun Gi Koh, Donald R. Ort, and Yong‐Su Jin. Bioconversion of co2 into valuable bioproducts via a synthetic modular co-culture of engineered chlamydomonas reinhardtii and escherichia coli. ():, 2024. [DOI].
Holly R. Steach, Siddharth Viswanath, Yixuan He, Xitong Zhang, Natalia Ivanova, Matthew Hirn, Michael Perlmutter, and Smita Krishnaswamy. Inferring metabolic states from single cell transcriptomic data via geometric deep learning. ():235–252, 2024. [DOI].
Fatima Zulqarnain, Xueheng Zhao, Kenneth D.R. Setchell, Yash Sharma, Phillip Fernandes, Sanjana Srivastava, Aman Shrivastava, Lubaina Ehsan, Varun Jain, Shyam S. Raghavan, Christopher A. Moskaluk, Yael Haberman, Lee A. Denson, Khyati Mehta, Najeeha Talat Iqbal, Najeeb Rahman, Kamran Sadiq, Zubair Ahmad, Romana Idress, Junaid Iqbal, Sheraz Ahmed, Aneeta Hotwani, Fayyaz Umrani, Beatrice Amadi, Paul Kelly, Donald E. Brown, Sean R. Moore, Asad Ali, and Sana Syed. Machine-learning-based integrative –‘omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction. 27(6):110013–110013, 2024. [DOI].
Priyanka Choudhury, Sanjukta Dasgupta, Parthasarathi Bhattacharyya, Sushmita Roychowdhury, and Koel Chaudhury. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. 20(6):366–389, 2024. [DOI].
Telmo Blasco. Reconstruction and analysis of gut microbiota metabolic networks in the context of personalized nutrition. ():, 2024. [DOI].
Maurício Alexander de Moura Ferreira and Wendel Batista da Silveira. Multi-omics data and model integration reveal the main mechanisms associated with respiro-fermentative metabolism and ethanol stress responses in kluyveromyces marxianus. ():, 2024. [DOI].
Kayden KM Low and Maurice HT Ling. Cell modelling and simulation. ():445–455, 2024. [DOI].
Drew Alessi, Chloe V. McCreery, and Ali R. Zomorrodi. Unraveling the link between nutrition and metabolic syndrome risk through in silico dietary interventions. ():, 2024. [DOI].
Maurício Alexander de Moura Ferreira. Model-driven evaluation of microbial physiology: insights from protein allocation. ():, 2024. [DOI].
Eduardo Luís Menezes de Almeida, Maurício Alexander de Moura Ferreira, and Wendel Batista da Silveira. Lista-gem: the genome-scale metabolic reconstruction of lipomyces starkeyi. ():, 2024. [DOI].
Maurício Alexander de Moura Ferreira and Wendel Batista da Silveira. Multi-omics data and model integration reveal the main mechanisms associated with respiro-fermentative metabolism and ethanol stress responses in kluyveromyces marxianus. 211():109471–109471, 2024. [DOI].
Fatma Zehra Sarı and Tunahan Çakır. Deciphering antibiotic-targeted metabolic pathways in acinetobacter baumannii: insights from transcriptomics and genome-scale metabolic modeling. 14(9):1102–1102, 2024. [DOI].
Satyajit Beura, Amit Kumar Das, and Amit Ghosh. Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data. 5(3):103291–103291, 2024. [DOI].
Maurício Alexander de Moura Ferreira, Eduardo Luís Menezes de Almeida, Wendel Batista da Silveira, and Zoran Nikoloski. Integrating promiscuous enzyme activities in protein-constrained models pinpoints the role of underground metabolism in robustness of metabolic phenotypes. ():, 2024. [DOI].
Mi Ae Park, Sharifah Nora Ahmad Almunawar, Rachel Rui Xia Lim, Sumanto Haldar, Christiani Jeyakumar Henry, and Oleg V. Moskvin. Genomic reconstruction and dietary response assessment of three clostridium leptum -related bacteria isolated from fecal samples of singapore subjects. ():, 2024. [DOI].
Moohebat Pourmajidian and Sayed‐Amir Marashi. Investigation of cell metabolism dysfunction in autism spectrum disorders using genome-scale metabolic network analysis of neuronal development. ():, 2024. [DOI].
Yuchen Zhang, Wenkai Lai, Meiling Wang, Lai Shirong, Qing Liu, Qi Luo, and Yang Fenglong. Gut microbial metabolic disorder in depression: insights from computational modeling and mediation analysis. ():, 2024. [DOI].
Huili Yuan, Yang Bai, and Xiongfei Fu. A global resource constrained model to predict metabolic flux dynamics in fluctuating environments. ():, 2024. [DOI].
Francis Mairet. In silico identification of switching nodes in metabolic networks. 4():, 2024. [DOI].
Xin Bush, Erica J. Fratz‐Berilla, Casey Kohnhorst, Robert J. King, Cyrus Agarabi, David N. Powers, and Nicholas Trunfio. Defining golden batches in biomanufacturing processes from internal metabolic activity to detect process changes that may affect product quality. 122(2):298–305, 2024. [DOI].
Sergio Sánchez‐García, Adrián Povo‐Retana, Silvia Marín, Sergio Madurga, Marco Fariñas, Nuria Aleixandre, Antonio Castrillo, Juan Vladimir de la Rosa, Carlota Alvarez‐Lucena, Rodrigo Landauro‐Vera, Patricia Prieto, Marta Cascante, and Lisardo Boscá. Immunometabolic effect of nitric oxide on human macrophages challenged with the sars‐cov2‐induced cytokine storm. a fluxomic approach. 14(1):e2401688–e2401688, 2024. [DOI].
A. Hacibektascedilla ̃oghacek ̃lu, Ilija Dukovski, and Daniel Segrè. Dynamic metabolic modeling of atp allocation during viral infection. ():, 2024. [DOI].
Rami Balasubramanian, Debajit Saha, A. Arun, and P. K. Vinod. Hypometabolism in autism spectrum disorder: insights from brain and blood transcriptomics. ():, 2024. [DOI].
Elaine Zaunseder, Faiz Khan Mohammad, Ulrike Mütze, Stefan Kölker, Vincent Heuveline, and Ines Thiele. Dynamic whole-body models for infant metabolism. ():, 2024. [DOI].
Hadi Nazem‐Bokaee, Erik Hom, Sarah Mathews, and Cécile Gueidan. Analyzing sorbitol biosynthesis using a metabolic network flux model of a lichenized strain of the green microalga diplosphaera chodatii. 13(1):e0366023–e0366023, 2024. [DOI].
Qin-Tian Qiu, Caiyun Zhang, Zhipeng Gao, and Bin‐Guang Ma. Spatial chromosome organization and adaptation of the radiation-resistant extremophile deinococcus radiodurans. 301(1):108068–108068, 2024. [DOI].
Muhammed Erkan Karabekmez and Merve Yarıcı. Omics and community modeling applications in genome-scale metabolic models. ():544–555, 2024. [DOI].
Huijun Long, Jai W. Mehat, Huihai Wu, Arnoud H. M. van Vliet, and Roberto M. La Ragione. Use of genome scale metabolic reconstructions of avian pathogenic escherichia coli (apec) phylogroups for the identification of lineage-specific metabolic pathways. ():, 2024. [DOI].
Clémence Dupont Thibert, Souvik Roy, Gilles Curien, and Maxime Durot. Biomodtool: from biomass composition data to structured biomass objective functions for genome-scale metabolic models. 5(1):vbaf036–vbaf036, 2024. [DOI].
Alexandre Oliveira, Jorge Ferreira, Vítor Vieira, Bruno Sá, and Miguel Rocha. troppo: tissue-specific reconstruction and phenotype prediction using omics data. 5(1):vbaf113–vbaf113, 2024. [DOI].

Nicolás Gurdo, Daniel C. Volke, Douglas McCloskey, and Pablo I. Nikel. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. 74():1–15, 2023. [DOI].
Partho Sen and Matej Orešič. Integrating omics data in genome-scale metabolic modeling: a methodological perspective for precision medicine. 13(7):855–855, 2023. [DOI].
Signe Tang Karlsen, Martin Holm Rau, Benjamín J. Sánchez, Kristian Jensen, and Ahmad A. Zeidan. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. 47(4):, 2023. [DOI].
Sílvia Àvila-Cabré, Míriam Pérez‐Trujillo, Joan Albiol, and Pau Ferrer. Engineering the synthetic β-alanine pathway in komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid. 22(1):237–237, 2023. [DOI].
Peishun Li, Stefan Roos, Hao Luo, Boyang Ji, and Jens Nielsen. Metabolic engineering of human gut microbiome: recent developments and future perspectives. 79():1–13, 2023. [DOI].
Sizhe Qiu, Aidong Yang, and Hong Zeng. Flux balance analysis-based metabolic modeling of microbial secondary metabolism: current status and outlook. 19(8):e1011391–e1011391, 2023. [DOI].
Elena Lucy Carter, Chrystala Constantinidou, and Mohammad Tauqeer Alam. Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. 25(1):, 2023. [DOI].
Dae‐Hee Lee, Haseong Kim, Bong Hyun Sung, Byung‐Kwan Cho, and Seung‐Goo Lee. Biofoundries: bridging automation and biomanufacturing in synthetic biology. 28(6):892–904, 2023. [DOI].
Baljeet Singh Saharan, Twinkle Chaudhary, Balwan Singh Mandal, Dharmender Kumar, Ravinder Kumar, Pardeep Kumar Sadh, and Joginder Singh Duhan. Microbe-plant interactions targeting metal stress: new dimensions for bioremediation applications. 13(2):252–269, 2023. [DOI].
Zohreh Mirveis, Orla Howe, Paul A. Cahill, Nitin Patil, and Hugh J. Byrne. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. 19(8):67–67, 2023. [DOI].
M. Muthulakshmi, Aparajitha Srinivasan, and Smita Srivastava. Antioxidant green factories: toward sustainable production of vitamin e in plantin vitrocultures. 8(4):3586–3605, 2023. [DOI].
Zhongcai Li, Qian Liu, Jiahui Sun, Jianjian Sun, Mingjie Li, Yun Zhang, Aihua Deng, Shuwen Liu, and Tingyi Wen. Multivariate modular metabolic engineering for enhanced l-methionine biosynthesis in escherichia coli. 16(1):101–101, 2023. [DOI].
Miao Guo, Chunfei Wu, Stephen P. Chapman, Xi Yu, Tom Vinestock, Astley Hastings, Pete Smith, and Nilay Shah. Advances in biorenewables-resource-waste systems and modelling. 9():100142–100142, 2023. [DOI].
Enes Fahri Tezcan, Yigit Demirtas, Zeynep Petek Çakar, and Kutlu Ö. Ülgen. Comprehensive genome-scale metabolic model of the human pathogen cryptococcus neoformans: a platform for understanding pathogen metabolism and identifying new drug targets. 3():1121409–1121409, 2023. [DOI].
Won Jun Kim, Youngjoon Lee, Hyun Uk Kim, Jae Yong Ryu, Jung Eun Yang, and Sang Yup Lee. Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes. 14(11):990–1001.e5, 2023. [DOI].
Jingyi Qian, Yuzhou Wang, Xiner Liu, Zijian Hu, Nan Xu, Yuetong Wang, Tian‐Qiong Shi, Chao Ye, Tianqiong Shi, and Chao Ye. Improving acetoin production through construction of a genome-scale metabolic model. 158():106833–106833, 2023. [DOI].
Apostolos Chalkis, Vissarion Fisikopoulos, Marios Papachristou, and Elias Tsigaridas. Truncated log-concave sampling for convex bodies with reflective hamiltonian monte carlo. 49(2):1–25, 2023. [DOI].
Taghreed Elkasaby, Dao Duy Hanh, Hideo Kawaguchi, Masakazu Toyoshima, Akihiko Kondo, and Chiaki Ogino. Co-utilization of maltose and sodium acetate via engineered corynebacterium glutamicum for improved itaconic acid production. 28(5):790–803, 2023. [DOI].
Ehsan Motamedian, Kristaps Berzins, Reinis Muiznieks, and Egils Stalidzāns. Optenvelope: a target point guided method for growth-coupled production using knockouts. ():, 2023. [DOI].
Ehsan Motamedian, Kristaps Berzins, Reinis Muiznieks, and Egils Stalidzāns. Optenvelope: a target point guided method for growth-coupled production using knockouts. 18(11):e0294313–e0294313, 2023. [DOI].
Marc Griesemer and Ali Navid. Uses of multi-objective flux analysis for optimization of microbial production of secondary metabolites. 11(9):2149–2149, 2023. [DOI].
Michiel Busschaert, Florence H. Vermeire, and Steffen Waldherr. Modeling cell size distribution with heterogeneous flux balance analysis. 7():1903–1908, 2023. [DOI].
Carmen Bedia, Núria Dalmau, Lars K. Nielsen, Romá Tauler, and Igor Marín de Mas. A multi-level systems biology analysis of aldrin’s metabolic effects on prostate cancer cells. 11(2):11–11, 2023. [DOI].
Márta Balázs, Hunor Bartos, Szabolcs Lányi, Zsolt Bodor, and Ildikó Miklóssy. Substrate type and co2 addition significantly influence succinic acid production of basfia succiniciproducens. 45(9):1133–1145, 2023. [DOI].
Livnat Jerby, Lior Wolf, Carsten Denkert, Gideon Y. Stein, Mika Hilvo, Matej Orešič, Tamar Geiger, and Eytan Ruppin. Supplementary material and methods and figure legends from metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. ():, 2023. [DOI].
Livnat Jerby, Lior Wolf, Carsten Denkert, Gideon Y. Stein, Mika Hilvo, Matej Orešič, Tamar Geiger, and Eytan Ruppin. Supplementary material and methods and figure legends from metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. ():, 2023. [DOI].
Sílvia Àvila-Cabré, Míriam Pérez‐Trujillo, Joan Albiol, and Pau Ferrer. Engineering the synthetic β-alanine pathway in pichia pastoris for conversion of methanol into 3-hydroxypropionic acid. ():, 2023. [DOI].
Marina Maurizio, María Masid, Kerry Woods, Reto Caldelari, John G. Doench, Arunasalam Naguleswaran, Denis Alain Henri Lucien Joly, Martín González Fernández, Jonas Zemp, Mélanie Borteele, Vassily Hatzimanikatis, Volker T. Heussler, Sven Rottenberg, and Philipp Olias. Host metabolic pathways essential for malaria and related hemoparasites in the infection of nucleated cells. ():, 2023. [DOI].
Cristina Herencias, Virginia Rivero‐Buceta, Sergio Salgado, Fernando Baquero, Rosa del Campo, Juan Nogales, and M. Auxiliadora Prieto. bdellovibrio’sprey-independent growth is fuelled by amino acids as a carbon source. ():, 2023. [DOI].
Márcia R. Couto, Joana L. Rodrigues, Adelaide Braga, Óscar Dias, and L. R. Rodrigues. Optimization of chondroitin production ine. coliusing genome scale models. ():, 2023. [DOI].
Zhitao Mao, Qianqian Yuan, Haoran Li, Yue Zhang, Yuanyuan Huang, Chunhe Yang, Ruoyu Wang, Yongfu Yang, Yalun Wu, Shihui Yang, Xiaoping Liao, and Hongwu Ma. Cave: a cloud-based platform for analysis and visualization of metabolic pathways. 51(W1):W70–W77, 2023. [DOI].
Bethany L Holland, Megan L. Matthews, Pedro Bota, Lee Sweetlove, Stephen P. Long, and George C. diCenzo. A genome‐scale metabolic reconstruction of soybean and bradyrhizobium diazoefficiens reveals the cost–benefit of nitrogen fixation. 240(2):744–756, 2023. [DOI].
María Santos‐Merino, A. Gargantilla Becerra, Fernando Cruz, and Juan Nogales. Highlighting the potential of synechococcus elongatus pcc 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling. 14():1126030–1126030, 2023. [DOI].
Behnaz Nowrouzi, Pablo Torres-Montero, Eduard J. Kerkhoven, José L. Martínez, and Leonardo Rios‐Solis. Rewiring saccharomyces cerevisiae metabolism for optimised taxol® precursors production. 18():e00229–e00229, 2023. [DOI].
Charlotte Ramon and Jörg Stelling. Functional comparison of metabolic networks across species. 14(1):1699–1699, 2023. [DOI].
Müberra Fatma Cesur, Arianna Basile, Kiran Raosaheb Patil, and Tunahan Çakır. A new metabolic model ofdrosophila melanogasterand the integrative analysis of parkinson’s disease. 6(8):e202201695–e202201695, 2023. [DOI].
Maximilian Luginsland, Cleo Kontoravdi, Andrew J. Racher, Colin Jaques, and Alexandros Kiparissides. Elucidating lactate metabolism in industrial cho cultures through the combined use of flux balance and principal component analyses. 202():109184–109184, 2023. [DOI].
Sandra Correa Córdoba, Hao Tong, Asdrúbal Burgos, Feng Zhu, Saleh Alseekh, Alisdair R. Fernie, and Zoran Nikoloski. Identification of gene function based on models capturing natural variability of arabidopsis thaliana lipid metabolism. 14(1):4897–4897, 2023. [DOI].
Emanuel Cunha, Davide Lagoa, José P. Faria, Filipe Liu, Christopher S. Henry, and Óscar Dias. transyt, an innovative framework for identifying transport systems. 39(8):, 2023. [DOI].
Su-Kyung Kim, Minouk Lee, Yi Qing Lee, Hyun Jun Lee, Mina Rho, Yunkwan Kim, Jung Yeon Seo, Sung Hun Youn, Seung Jin Hwang, Nae Gyu Kang, Choong-Hwan Lee, Seo‐Young Park, and Dong‐Yup Lee. Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen cutibacterium acnes. 13():1099314–1099314, 2023. [DOI].
Yoon-Mi Choi, Dong‐Hyuk Choi, Yi Qing Lee, Lokanand Koduru, Nathan E. Lewis, Meiyappan Lakshmanan, and Dong‐Yup Lee. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. 21():3736–3745, 2023. [DOI].
Albert Fina, Pierre Millard, Joan Albiol, Pau Ferrer, and Stéphanie Heux. High throughput 13c-metabolic flux analysis of 3-hydroxypropionic acid producing pichia pastoris reveals limited availability of acetyl-coa and atp due to tight control of the glycolytic flux. 22(1):117–117, 2023. [DOI].
Anna T. Germann, Andreas Nakielski, Maximilian Dietsch, Tim Petzel, Daniel Moser, Sebastian Triesch, Philipp Westhoff, and Ilka M. Axmann. A systematic overexpression approach reveals native targets to increase squalene production in synechocystis sp. pcc 6803. 14():1024981–1024981, 2023. [DOI].
Andrea Angarita-Rodríguez, Yeimy González‐Giraldo, Juan J. Rubio-Mesa, Andrés Felipe Aristizábal-Pachón, Andrés Pinzón, and Janneth González. Control theory and systems biology: potential applications in neurodegeneration and search for therapeutic targets. 25(1):365–365, 2023. [DOI].
Kenta Miyoshi, Ryutaro Kawai, Teppei Niide, Yoshihiro Toya, and Hiroshi Shimizu. Functional evaluation of non-oxidative glycolysis in escherichia coli in the stationary phase under microaerobic conditions. 135(4):291–297, 2023. [DOI].
Diego Tec-Campos, Camila Posadas, Juan D. Tibocha‐Bonilla, Deepan Thiruppathy, Nathan Glonek, Cristal Zúñiga, Alejandro Zepeda, and Karsten Zengler. The genome-scale metabolic model for the purple non-sulfur bacterium rhodopseudomonas palustris bis a53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions. 19(8):e1011371–e1011371, 2023. [DOI].
Neema Jamshidi, Kabir Nigam, and Sanjay K. Nigám. Loss of the kidney urate transporter, urat1, leads to disrupted redox homeostasis in mice. 12(3):780–780, 2023. [DOI].
Amirhossein Shahbazinia, Saber Salehkaleybar, and Matin Hashemi. Paralingam: parallel causal structure learning for linear non-gaussian acyclic models. 176():114–127, 2023. [DOI].
Sarayu Murali, Maziya Ibrahim, Hemalatha Rajendran, Shagun Shagun, Shyam Kumar Masakapalli, Karthik Raman, and Smita Srivastava. Genome-scale metabolic model led engineering of nothapodytes nimmoniana plant cells for high camptothecin production. 14():1207218–1207218, 2023. [DOI].
Bruno G. Galuzzi, Luca Milazzo, and Chiara Damiani. Best practices in flux sampling of constrained-based models. ():234–248, 2023. [DOI].
Behnaz Nowrouzi, Pablo Torres-Montero, Eduard J. Kerkhoven, José L. Martínez, and Leonardo Rios‐Solis. Rewiringsaccharomyces cerevisiaemetabolism for optimised taxol® precursors production. ():, 2023. [DOI].
Mirco Plante. Epistemology of synthetic biology: a new theoretical framework based on its potential objects and objectives. 11():1266298–1266298, 2023. [DOI].
Bruno G. Galuzzi and Chiara Damiani. An efficient implementation of flux variability analysis for metabolic networks. ():58–69, 2023. [DOI].
Elías Vera-Sigüenza, Cristina Escribano-Gonzalez, Irene Serrano‐Gonzalo, Kattri‐Liis Eskla, Fabian Spill, and Daniel A. Tennant. Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. 19(9):e1011374–e1011374, 2023. [DOI].
Mohammadreza Yasemi and Mario Jolicœur. A genome-scale dynamic constraint-based modelling (gdcbm) framework predicts growth dynamics, medium composition and intracellular flux distributions in cho clonal variations. 78():209–222, 2023. [DOI].
Nela Pragathi Sneha, S. Akila Parvathy Dharshini, Y‐h. Taguchi, and M. Michael Gromiha. Investigating neuron degeneration in huntington’s disease using rna-seq based transcriptome study. 14(9):1801–1801, 2023. [DOI].
Jean‐Pierre Mazat. The metabolic control theory: its development and its application to mitochondrial oxidative phosphorylation. 234():105038–105038, 2023. [DOI].
Qianqian Yuan, Fan Wei, Xiaogui Deng, Aonan Li, Zhenkun Shi, Zhitao Mao, Feiran Li, and Hongwu Ma. Reconstruction and metabolic profiling of the genome-scale metabolic network model of pseudomonas stutzeri a1501. 8(4):688–696, 2023. [DOI].
Hananeh Ahmadpanah, Ehsan Motamedian, and Mohammad Mahdi Mardanpour. Metabolic regulation boosts bioelectricity generation in zymomonas mobilis microbial fuel cell, surpassing ethanol production. 13(1):20673–20673, 2023. [DOI].
Clémence Frioux, Sylvie Huet, Simon Labarthe, Julien Martinelli, Thibault Malou, David H. Sherman, Marie‐Luce Taupin, and Pablo Ugalde‐Salas. Accelerating metabolic models evaluation with statistical metamodels: application to salmonella infection models. 73():187–217, 2023. [DOI].
Josh A. Taylor, Alain Rapaport, and Denis Dochain. Convex representation of metabolic networks with michaelis-menten kinetics. ():, 2023. [DOI].
Albert Fina, Pierre Millard, Joan Albiol, Pau Ferrer, and Stéphanie Heux. High throughput 13c-metabolic flux analysis of 3-hydroxypropionic acid producing pichia pastoris reveals limited availability of acetyl-coa and atp due to tight control of the glycolytic flux. ():, 2023. [DOI].
Jacob J. Valenzuela, Selva Rupa Christinal Immanuel, James G. Wilson, Serdar Turkarslan, Maryann Ruiz, Sean M. Gibbons, Kristopher A. Hunt, Manfred Auer, Marcin Zemla, David A. Stahl, and Nitin S. Baliga. Origin of biogeographically distinct ecotypes during laboratory evolution. ():, 2023. [DOI].
Chenguang Liang, Sue Murray, Yang Li, Richard Lee, Audrey Low, Shruti Sasaki, Austin W.T. Chiang, Wen-Jen Lin, Joel Mathews, Will Barnes, and Nathan E. Lewis. Lipidsim: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic markov modeling framework. ():, 2023. [DOI].
Clémence FRIOUX and Anne SIEGEL. Combinatorial optimization problems for studying metabolism. ():45–88, 2023. [DOI].
Fatma Ece Altınışık Kaya and Fatma Gizem Avcı. Modeling the microbial cells for biotechnological applications. ():121–151, 2023. [DOI].
Anita L. Ziegler, L. Ullmann, Manuel Boßmann, Karla Stein, Ulf W. Liebal, Alexander Mitsos, and Lars M. Blank. Itaconic acid production by co-feeding ofustilago maydis: a combined approach of experimental data, design of experiments and metabolic modeling. ():, 2023. [DOI].
Aparajitha Srinivasan, Kevin Chen-Xiao, Deepanwita Banerjee, Asun Oka, Venkataramana R. Pidatala, Aymerick Eudes, Blake A. Simmons, Thomas Eng, and Aindrila Mukhopadhyay. Sustainable production of 2,3,5,6-tetramethylpyrazine at high titer in engineeredcorynebacterium glutamicumusing statistical design of experiments. ():, 2023. [DOI].
Guilherme de Oliveira Mendes Ohira. Desenvolvimento de ferramentas e algoritmos em computational strain optmization models. ():, 2023. [DOI].
Almut Heinken, Johannes Hertel, Geeta Acharya, Dmitry A. Ravcheev, Malgorzata Nyga, Onyedika Emmanuel Okpala, Marcus Hogan, Stefanía Magnúsdóttir, Filippo Martinelli Boneschi, Bram Nap, German Preciat, Janaka N. Edirisinghe, Christopher S. Henry, Ronan M. T. Fleming, and Ines Thiele. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. 41(9):1320–1331, 2023. [DOI].
Martin Schäfer, Alan R. Pacheco, Rahel Künzler, Miriam Bortfeld‐Miller, Christopher M. Field, Evangelia Vayena, Vassily Hatzimanikatis, and Julia A. Vorholt. Metabolic interaction models recapitulate leaf microbiota ecology. 381(6653):eadf5121–eadf5121, 2023. [DOI].
Roland Hellinger, Arnar Sigurdsson, Wenxin Wu, Elena V. Romanova, Lingjun Li, Jonathan V. Sweedler, Roderich D. Süßmuth, and Christian W. Gruber. Peptidomics. 3(1):, 2023. [DOI].
Emil Karlsen, Marianne Gylseth, Christian Schulz, and Eivind Almaas. A study of a diauxic growth experiment using an expanded dynamic flux balance framework. 18(1):e0280077–e0280077, 2023. [DOI].
Xia Wang, Ying Teng, Xiaomi Wang, Yongfeng Xu, Ran Li, Yi Sun, Shixiang Dai, Wenbo Hu, Hongzhe Wang, Yanning Li, Yan Fang, and Yongming Luo. Nitrogen transfer and cross-feeding between azotobacter chroococcum and paracoccus aminovorans promotes pyrene degradation. 17(12):2169–2181, 2023. [DOI].
William F. Kindschuh, Federico Baldini, Martin C. Liu, Jingqiu Liao, Yoli Meydan, Harry H. Lee, Almut Heinken, Ines Thiele, Christoph A. Thaiss, Maayan Levy, and Tal Korem. Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. 8(2):246–259, 2023. [DOI].
Francesc Puig‐Castellví, Romina Pacheco‐Tapia, Maxime Deslande, Manyi Jia, Petros Andrikopoulos, Kanta Chechi, Amélie Bonnefond, Philippe Froguel, and Marc‐Emmanuel Dumas. Advances in the integration of metabolomics and metagenomics for human gut microbiome and their clinical applications. 167():117248–117248, 2023. [DOI].
Ning Qin, Lingyun Li, Xu Ji, Rui Pereira, Yu Chen, Shile Yin, Chaokun Li, Xiaozhen Wan, Danye Qiu, Junfeng Jiang, Hao Luo, Yueping Zhang, Genlai Dong, Yiming Zhang, Shuobo Shi, Henning J. Jessen, Jianye Xia, Yun Chen, Christer Larsson, Tianwei Tan, Zihe Liu, and Jens Nielsen. Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates in yeast. 186(4):748–763.e15, 2023. [DOI].
Clara Correia‐Melo, Stephan Kamrad, Roland Tengölics, Christoph B. Messner, Pauline Trébulle, StJohn Townsend, Sreejith J. Varma, Anja Freiwald, Benjamin M. Heineike, Kate Campbell, Lucía Herrera-Domínguez, Simran Kaur Aulakh, Łukasz Szyrwiel, Jason Yu, Aleksej Zelezniak, Vadim Demichev, Michael Mülleder, Balázs Papp, Mohammad Tauqeer Alam, and Markus Ralser. Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan. 186(1):63–79.e21, 2023. [DOI].
Miguel Ponce-de-León, Arnau Montagud, Vincent Noël, Annika Meert, Gerard Pradas, Emmanuel Barillot, Laurence Calzone, and Alfonso Valencia. Physiboss 2.0: a sustainable integration of stochastic boolean and agent-based modelling frameworks. 9(1):54–54, 2023. [DOI].
William T. Scott, Sara Benito-Vaquerizo, Johannes Zimmermann, Djordje Bajić, Almut Heinken, María Suárez‐Diez, and Peter J. Schaap. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. 19(8):e1011363–e1011363, 2023. [DOI].
Yiming Zhang, Mo Su, Yu Chen, Zheng Wang, Jens Nielsen, and Zihe Liu. Engineering yeast mitochondrial metabolism for 3-hydroxypropionate production. 16(1):64–64, 2023. [DOI].
Bouchra Ezzamouri, Dorinês Rosário, Gholamreza Bidkhori, Sunjae Lee, Mathias Uhlén, and Saeed Shoaie. Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment. 9(1):2–2, 2023. [DOI].
Yun‐Peng Zhao, Yiming Feng, Jianhang Zhou, Kuo Zhang, Jingqi Sun, Lina Wang, and Sitong Liu. Potential bacterial isolation by dosing metabolites in cross-feedings. 231():119589–119589, 2023. [DOI].
Cecilia Noecker, Juan M. Sánchez, Jordan E. Bisanz, Veronica Escalante, Margaret Alexander, Kai Trepka, Almut Heinken, Yuanyuan Liu, Dylan Dodd, Ines Thiele, Brian C. DeFelice, and Peter J. Turnbaugh. Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe eggerthella lenta. 21(5):e3002125–e3002125, 2023. [DOI].
Sandra Gomez Romero and Nanette Boyle. Systems biology and metabolic modeling for cultivated meat: a promising approach for cell culture media optimization and cost reduction. 22(4):3422–3443, 2023. [DOI].
Thomas Eng, Deepanwita Banerjee, Javier Menasalvas, Yan Chen, Jennifer Gin, Hemant Choudhary, Edward E. K. Baidoo, Jian-Hua Chen, Axel Ekman, Ramu Kakumanu, Yuzhong Liu, Alex Codik, Carolyn A. Larabell, John M. Gladden, Blake A. Simmons, Jay D. Keasling, Christopher J. Petzold, and Aindrila Mukhopadhyay. Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering. 42(9):113087–113087, 2023. [DOI].
Arianna Basile, Almut Heinken, Johannes Hertel, Larry Smarr, Weizhong Li, Laura Treu, Giorgio Valle, Stefano Campanaro, and Ines Thiele. Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics. 15(1):2226921–2226921, 2023. [DOI].
Laura Judith Marcos-Zambrano, Víctor Manuel López-Molina, Burcu Bakır-Güngör, Marcus Frohme, Kanita Karađuzović-Hadžiabdić, Thomas Klammsteiner, Eliana Ibrahimi, Leo Lahti, Tatjana Lončar-Turukalo, Xhilda Dhamo, Andrea Simeon, Alina Nechyporenko, Gianvito Pio, Piotr Przymus, Alexia Sampri, Vladimir Trajkovik, Blanca Lacruz-Pleguezuelos, Oliver Aasmets, Ricardo Araújo, Ioannis Anagnostopoulos, Önder Aydemir, Magali Berland, M. Luz Calle, Michelangelo Ceci, Hatice Duman, Aycan Gündoğdu, Aki S. Havulinna, Kardokh Hama Najib Kaka Bra, Eglantina Kalluçi, Sercan Karav, Daniel Lode, Marta B. Lopes, Patrick May, Bram Nap, Miroslava Nedyalkova, Inês Paciência, Lejla Pašić, Meritxell Pujolassos, Rajesh Shigdel, Antonio Susín, Ines Thiele, Ciprian‐Octavian Truică, Paul Wilmes, Ercüment Yılmaz, Malik Yousef, Marcus J. Claesson, Jaak Truu, and Enrique Carrillo de Santa Pau. A toolbox of machine learning software to support microbiome analysis. 14():1250806–1250806, 2023. [DOI].
Le Gao, Jiao Meng, Wuling Dai, Zhaokun Zhang, Haofan Dong, Qianqian Yuan, Wuyuan Zhang, Shuguang Liu, and Xin Wu. Deciphering cell wall sensors enabling the construction of robust p. pastoris for single-cell protein production. 16(1):178–178, 2023. [DOI].
Mona Synnøve Bjune, Laurence Lawrence‐Archer, Johnny Laupsa‐Borge, Cathrine Horn Sommersten, Adrian McCann, Robert C. Glastad, Iain G. Johnston, Matthias Kern, Matthias Blüher, Gunnar Mellgren, and Simon N. Dankel. Metabolic role of the hepatic valine/3-hydroxyisobutyrate (3-hib) pathway in fatty liver disease. 91():104569–104569, 2023. [DOI].
Koray Malcı, Rodrigo Santibáñez, Nestor Jonguitud‐Borrego, Jorge H. Santoyo‐Garcia, Eduard J. Kerkhoven, and Leonardo Rios‐Solis. Improved production of taxol® precursors in s. cerevisiae using combinatorial in silico design and metabolic engineering. 22(1):243–243, 2023. [DOI].
William T. Scott, David Henriques, Eddy J. Smid, Richard A. Notebaart, and Eva Balsa‐Canto. Dynamic genome‐scale modeling of saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation. 120(7):1998–2012, 2023. [DOI].
Guido Zampieri, Georgios Efthimiou, and Claudio Angione. Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions. 76():120–132, 2023. [DOI].
Ibrahim Fakih, Jeanne Got, Carlos Eduardo Robles-Rodríguez, Anne Siegel, Évelyne Forano, and Rafael Muñoz‐Tamayo. dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium fibrobacter succinogenes s85 . 8(3):e0102722–e0102722, 2023. [DOI].
William T. Scott, Sara Benito-Vaquerizo, Johannes Zimmerman, Djordje Bajić, Almut Heinken, María Suárez‐Diez, and Peter J. Schaap. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. ():, 2023. [DOI].
Huan Du, Meng Li, and Yang Liu. Towards applications of genome‐scale metabolic model‐based approaches in designing synthetic microbial communities. 11(1):15–30, 2023. [DOI].
Weiren Liu, Huqiang Wang, Qianfu Zhao, Chenyang Tao, Wei‐Feng Qu, Yushan Hou, Run Huang, Zimei Sun, Gui‐Qi Zhu, Xi‐Fei Jiang, Yuan Fang, Jun Gao, Xiaoling Wu, Zhixiang Yang, Rongyu Ping, Jiafeng Chen, Rui Yang, Tianhao Chu, Jian Zhou, Jia Fan, Zheng Tang, Dong Yang, and Ying‐Hong Shi. Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (dgka) as a potential therapeutic target for intrahepatic cholangiocarcinoma. 44(2):226–250, 2023. [DOI].
Kyoshiro Nonaka, Tatsuya Osamura, and Fumikazu Takahashi. A 4-hydroxybenzoate 3-hydroxylase mutant enables 4-amino-3-hydroxybenzoic acid production from glucose in corynebacterium glutamicum. 22(1):168–168, 2023. [DOI].
James D. Brunner, Laverne Gallegos-Graves, and Marie Kroeger. Inferring microbial interactions with their environment from genomic and metagenomic data. 19(11):e1011661–e1011661, 2023. [DOI].
María Teresita Castañeda, Sebastián Núñez, Martín Jamilis, and Hernán De Battista. Computational assessment of lipid production in rhodosporidium toruloides in two‐stage and one‐stage batch bioprocesses. 121(1):238–249, 2023. [DOI].
Isabel Rosety, Alise Žagare, Cláudia Saraiva, Sarah Nickels, Paul Antony, Catarina Serra-Almeida, Enrico Glaab, Rashi Halder, Sergiy Velychko, Thomas Rauen, Hans R. Schöler, Silvia Bolognin, Thomas Sauter, Javier Jarazo, Rejko Krüger, and Jens C. Schwamborn. Impaired neuron differentiation in gba-associated parkinson’s disease is linked to cell cycle defects in organoids. 9(1):166–166, 2023. [DOI].
Philipp Wendering, Marius Arend, Zahra Razaghi‐Moghadam, and Zoran Nikoloski. Data integration across conditions improves turnover number estimates and metabolic predictions. 14(1):1485–1485, 2023. [DOI].
Sourav Chowdhury, Daniel C. Zielinski, Christopher Dalldorf, João V. Rodrigues, Bernhard Ø. Palsson, and Eugene I. Shakhnovich. Empowering drug off-target discovery with metabolic and structural analysis. 14(1):3390–3390, 2023. [DOI].
Alise Žagare, German Preciat, Sarah Nickels, Xi Luo, Anna S. Monzel, Gemma Gomez‐Giro, Graham Robertson, Christian Jaeger, Jafar Sharif, Haruhiko Koseki, Nico J. Diederich, Enrico Glaab, Ronan M. T. Fleming, and Jens C. Schwamborn. Omics data integration suggests a potential idiopathic parkinson’s disease signature. 6(1):1179–1179, 2023. [DOI].
Miha Moškon and Tadeja Režen. Context-specific genome-scale metabolic modelling and its application to the analysis of covid-19 metabolic signatures. 13(1):126–126, 2023. [DOI].
Marius Arend, David Zimmer, Rudan Xu, Frederik Sommer, Timo Mühlhaus, and Zoran Nikoloski. Proteomics and constraint-based modelling reveal enzyme kinetic properties of chlamydomonas reinhardtii on a genome scale. 14(1):4781–4781, 2023. [DOI].
Blas Blázquez, David San León, Antonia Rojas, Marta Tortajada, and Juan Nogales. New insights on metabolic features of bacillus subtilis based on multistrain genome-scale metabolic modeling. 24(8):7091–7091, 2023. [DOI].
Philip Fernandes, Yash Sharma, Fatima Zulqarnain, Brooklyn McGrew, Aman Shrivastava, Lubaina Ehsan, Dawson Payne, Lillian Dillard, Deborah Powers, Isabelle Aldridge, Jason D. Matthews, Subra Kugathasan, Facundo M. Fernández, David A. Gaul, Jason A. Papin, and Sana Syed. Identifying metabolic shifts in crohn's disease using 'omics-driven contextualized computational metabolic network models. 13(1):203–203, 2023. [DOI].
Kengo Watanabe, Tomasz Wilmanski, Priyanka Baloni, Max Robinson, Gonzalo G. Garcia, Michael R. Hoopmann, Mukul K. Midha, David Baxter, Michal Maes, Seamus R. Morrone, Kelly M. Crebs, Charu Kapil, Ulrike Kusebauch, Jack Wiedrick, Jodi Lapidus, Lance Pflieger, Christopher Lausted, Jared C. Roach, Gustavo Glusman, Steven R. Cummings, Nicholas J. Schork, Nathan D. Price, Leroy Hood, Richard A. Miller, Robert L. Moritz, and Noa Rappaport. Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers. 6(1):768–768, 2023. [DOI].
Zeqian Li, Ahmed Selim, and Seppe Kuehn. Statistical prediction of microbial metabolic traits from genomes. 19(12):e1011705–e1011705, 2023. [DOI].
Benjamin Strain, James Morrissey, Athanasios Antonakoudis, and Cleo Kontoravdi. How reliable are chinese hamster ovary (cho) cell genome‐scale metabolic models? 120(9):2460–2478, 2023. [DOI].
Jingwei Cai, Alexis Auster, Sungjoon Cho, and Zijuan Lai. Dissecting the human gut microbiome to better decipher drug liability: a once-forgotten organ takes center stage. 52():171–201, 2023. [DOI].
Christopher Riccardi, Marzia Calvanese, Veronica Ghini, Tania Alonso-Vásquez, Elena Perrin, Paola Turano, Giorgio Giurato, Alessandro Weisz, Ermenegilda Parrilli, Maria Luisa Tutino, and Marco Fondi. Metabolic robustness to growth temperature of a cold- adapted marine bacterium. 8(2):e0112422–e0112422, 2023. [DOI].
Andrew Walakira, Cene Skubic, Nejc Nadižar, Damjana Rozman, Tadeja Režen, Miha Mraz, and Miha Moškon. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. 159():106957–106957, 2023. [DOI].
Elif Esvap and Kutlu Ö. Ülgen. Neuroinflammation, energy and sphingolipid metabolism biomarkers are revealed by metabolic modeling of autistic brains. 11(2):583–583, 2023. [DOI].
Gonçalo dos Santos Correia, Julian R. Marchesi, and David A. MacIntyre. Moving beyond dna: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. 73():102292–102292, 2023. [DOI].
Nantia Leonidou, Alina Renz, Reihaneh Mostolizadeh, and Andreas Dräger. New workflow predicts drug targets against sars-cov-2 via metabolic changes in infected cells. 19(3):e1010903–e1010903, 2023. [DOI].
Joshua A. M. Kaste and Yair Shachar‐Hill. Model validation and selection in metabolic flux analysis and flux balance analysis. 40(1):e3413–e3413, 2023. [DOI].
Bo Wang, Cristal Zúñiga, Michael T. Guarnieri, Karsten Zengler, Michael J. Betenbaugh, and Jamey D. Young. Metabolic engineering of synechococcus elongatus 7942 for enhanced sucrose biosynthesis. 80():12–24, 2023. [DOI].
Qusheng Jin. Building microbial kinetic models for environmental application: a theoretical perspective. 158():105782–105782, 2023. [DOI].
Johannes Hertel, Almut Heinken, Daniel Fässler, and Ines Thiele. Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses. 3(10):100615–100615, 2023. [DOI].
Fidias D. González Camargo, Mary Santamaría-Torres, Mónica P. Cala, Marcela Guevara‐Suarez, Silvia Restrepo, Andrea del Pilar Sánchez‐Camargo, Miguel Fernández‐Niño, María Corujo, Ada Carolina Gallo Molina, Javier Cifuentes, Julian A. Serna, Juan C. Cruz, Carolina Muñoz-Camargo, and Andrés Fernando González Barrios. Genome-scale metabolic reconstruction, non-targeted lc-qtof-ms based metabolomics data, and evaluation of anticancer activity of cannabis sativa leaf extracts. 13(7):788–788, 2023. [DOI].
Patrick E. Gelbach and Stacey D. Finley. Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. 26(9):107569–107569, 2023. [DOI].
Adrián Povo‐Retana, Marco Fariñas, Rodrigo Landauro‐Vera, Marina Mojena, Carlota Alvarez‐Lucena, Miguel Ángel Fernández‐Moreno, Antonio Castrillo, Juan Vladimir de la Rosa, Sergio Sánchez‐García, Carles Foguet, Francesc Mas, Silvia Marín, Marta Cascante, and Lisardo Boscá. Immunometabolic actions of trabectedin and lurbinectedin on human macrophages: relevance for their anti-tumor activity. 14():1211068–1211068, 2023. [DOI].
Aliya Lakhani, Da Hyun Kang, Yea Eun Kang, and Junyoung O. Park. Toward systems-level metabolic analysis in endocrine disorders and cancer. 38(6):619–630, 2023. [DOI].
Matti Gralka. Searching for principles of microbial ecology across levels of biological organization. 63(6):1520–1531, 2023. [DOI].
Quinn S. Solfisburg, Federico Baldini, Brittany Baldwin-Hunter, George I. Austin, Harry H. Lee, Heekuk Park, Daniel E. Freedberg, Charles J. Lightdale, Tal Korem, and Julian A. Abrams. The salivary microbiome and predicted metabolite production are associated with barrett's esophagus and high-grade dysplasia or adenocarcinoma. 33(3):371–380, 2023. [DOI].
Xueting Wang, Ali Mohsin, Yifei Sun, Chao Li, Yingping Zhuang, and Guan Wang. From spatial-temporal multiscale modeling to application: bridging the valley of death in industrial biotechnology. 10(6):744–744, 2023. [DOI].
Hao Tong, Roosa A. E. Laitinen, and Zoran Nikoloski. Predicting plasticity of rosette growth and metabolic fluxes in arabidopsis thaliana. 240(1):426–438, 2023. [DOI].
Almut Heinken, Timothy Otto Hulshof, Bram Nap, Filippo Martinelli Boneschi, Arianna Basile, Amy O’Brolchain, Neil Francis O’Sullivan, Celine Gallagher, E.L. Magee, Francesca McDonagh, Ian Lalor, Maeve Bergin, Phoebe Evans, Rachel Daly, Ronan Farrell, Rose Marie Delaney, Saoirse Hill, Saoirse Roisin McAuliffe, Trevor Kilgannon, Ronan M. T. Fleming, Cyrille C. Thinnes, and Ines Thiele. Apollo: a genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. ():, 2023. [DOI].
Yawen Li, Jingyi Qian, Jiacong Huang, Dong‐Sheng Guo, Zhi-Kui Nie, Chao Ye, and Tian‐Qiong Shi. Improving gibberellin ga3 production with the construction of a genome-scale metabolic model of fusarium fujikuroi. 71(48):18890–18897, 2023. [DOI].
Haoyang Yao, Sanjeev Dahal, and Laurence Yang. Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by chlamydomonas reinhardtii. 22(1):13–13, 2023. [DOI].
Yanhua Liu and Hans V. Westerhoff. Competitive, multi‐objective, and compartmented flux balance analysis for addressing tissue‐specific inborn errors of metabolism. 46(4):573–585, 2023. [DOI].
Bronson R. Weston and Ines Thiele. A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models. 76():167–178, 2023. [DOI].
GaRyoung Lee, Sang Mi Lee, and Hyun Uk Kim. A contribution of metabolic engineering to addressing medical problems: metabolic flux analysis. 77():283–293, 2023. [DOI].
Nattharat Punyasu, Saowalak Kalapanulak, and Treenut Saithong. Co2 recycling by phosphoenolpyruvate carboxylase enables cassava leaf metabolism to tolerate low water availability. 14():1159247–1159247, 2023. [DOI].
Naroa Barrena, Luis V. Valcárcel, Danel Olaverri-Mendizabal, Iñigo Apaolaza, and Francisco J. Planes. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. 9(1):32–32, 2023. [DOI].
Agris Pentjuss, Emīls Bolmanis, Anastasija Šuleiko, Elina Didrihsone, Artūrs Šuleiko, Konstantīns Dubencovs, Jānis Liepiņš, Andris Kazāks, and Juris Vanags. Pichia pastoris growth—coupled heme biosynthesis analysis using metabolic modelling. 13(1):15816–15816, 2023. [DOI].
Nunthaphan Vikromvarasiri, Shuhei Noda, Tomokazu Shirai, and Akihiko Kondo. Investigation of two metabolic engineering approaches for (r,r)-2,3-butanediol production from glycerol in bacillus subtilis. 17(1):3–3, 2023. [DOI].
Frederick Clasen, Patrícia M. Nunes, Gholamreza Bidkhori, Nourdine Bah, Stefan Boeing, Saeed Shoaie, and Dimitrios Anastasiou. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. 26(2):106040–106040, 2023. [DOI].
Eric Libby, Christopher P. Kempes, and Jordan G. Okie. Metabolic compatibility and the rarity of prokaryote endosymbioses. 120(17):e2206527120–e2206527120, 2023. [DOI].
Darwin Carranza‐Saavedra, Jesús Torres‐Bacete, Blas Blázquez, Claudia Patricia Sánchez Henao, José E. Zapata, and Juan Nogales. System metabolic engineering of escherichia coli w for the production of 2-ketoisovalerate using unconventional feedstock. 11():1176445–1176445, 2023. [DOI].
Lvjing Wang, Xiaoyu Wang, Hao Wu, Haixia Wang, Yihan Wang, and Zhenmei Lü. Metabolic modeling of synthetic microbial communities for bioremediation. 53(24):2092–2111, 2023. [DOI].
Anna Stikane, Matiss Ricards Baumanis, Reinis Muiznieks, and Egils Stalidzāns. Impact of waste as a substrate on biomass formation, and optimization of spent microbial biomass re-use by sustainable metabolic engineering. 9(6):531–531, 2023. [DOI].
Rafael Muñoz‐Tamayo, Mohsen Davoudkhani, Ibrahim Fakih, Carlos Eduardo Robles-Rodríguez, Francesco Rubino, Christopher J. Creevey, and Évelyne Forano. Review: towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies. 17():100984–100984, 2023. [DOI].
M J Neal, Deepan Thiruppathy, and Karsten Zengler. Genome-scale metabolic modeling of the human gut bacterium bacteroides fragilis strain 638r. 19(10):e1011594–e1011594, 2023. [DOI].
Seo‐Young Park, Dong‐Hyuk Choi, Jinsung Song, Uiseon Park, Hyeran Cho, Bee Hak Hong, Yaron Silberberg, and Dong‐Yup Lee. Debottlenecking and reformulating feed media for improved cho cell growth and titer by data‐driven and model‐guided analyses. 18(12):e2300126–e2300126, 2023. [DOI].
Isabella Casini, Tim McCubbin, Sofia Esquivel‐Elizondo, Guillermo G. Luque, Daria Evseeva, Christian Fink, Sebastian Beblawy, Nicholas D. Youngblut, Ludmilla Aristilde, Daniel H. Huson, Andreas Dräger, Ruth E. Ley, Esteban Marcellin, Largus T. Angenent, and Bastian Molitor. An integrated systems biology approach reveals differences in formate metabolism in the genus methanothermobacter. 26(10):108016–108016, 2023. [DOI].
Takeyuki Tamura, Ai Muto-Fujita, Yukako Tohsato, and Tomoyuki Kosaka. Gene deletion algorithms for minimum reaction network design by mixed-integer linear programming for metabolite production in constraint-based models: gdel_minrn. 30(5):553–568, 2023. [DOI].
Patrick E. Gelbach and Stacey D. Finley. Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. ():, 2023. [DOI].
Hiroki Suyama, Laurence Don Wai Luu, Ling Zhong, Mark J. Raftery, and Ruiting Lan. Integrating proteomic data with metabolic modeling provides insight into key pathways of bordetella pertussis biofilms. 14():1169870–1169870, 2023. [DOI].
Ronan M. T. Fleming, Hulda S. Haraldsdóttir, Hoai Minh Le, Phan Tu Vuong, Thomas Hankemeier, and Ines Thiele. Cardinality optimization in constraint-based modelling: application to human metabolism. 39(9):, 2023. [DOI].
Peter Gwynne, Kee-Lee K. Stocks, Elysse S. Karozichian, Aarya Pandit, and Linden T. Hu. Metabolic modeling predicts unique drug targets in borrelia burgdorferi. 8(6):e0083523–e0083523, 2023. [DOI].
Ömer F. Bay, Kelly S. Hayes, Jean‐Marc Schwartz, Richard K. Grencis, and Ian S. Roberts. A genome-scale metabolic model of parasitic whipworm. 14(1):6937–6937, 2023. [DOI].
Mikhail A. Kulyashov, Semyon Kolmykov, Т. М. Хлебодарова, and Ilya R. Akberdin. State-of the-art constraint-based modeling of microbial metabolism: from basics to context-specific models with a focus on methanotrophs. 11(12):2987–2987, 2023. [DOI].
Zhidong Zhang, Qi Guo, Jingyi Qian, Chao Ye, and He Huang. Construction and application of the genome-scale metabolic model of streptomyces radiopugnans. 11():1108412–1108412, 2023. [DOI].
Garhima Arora, Mallar Banerjee, Jimpi Langthasa, Ramray Bhat, and Samrat Chatterjee. Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer. 26(11):108081–108081, 2023. [DOI].
Thummarat Paklao, Apichat Suratanee, and Kitiporn Plaimas. Icon-gems: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology. 24(1):492–492, 2023. [DOI].
Alec Eames and Sriram Chandrasekaran. Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth. 3(1):pgae013–pgae013, 2023. [DOI].
Heewon Jung, Hyun‐Seob Song, and Christof Meile. Complab v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics. 16(6):1683–1696, 2023. [DOI].
Joshua A. M. Kaste and Yair Shachar‐Hill. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling. 39(5):, 2023. [DOI].
Benedict Borer and Stefanía Magnúsdóttir. The media composition as a crucial element in high-throughput metabolic network reconstruction. 13(2):20220070–20220070, 2023. [DOI].
Igor Marín de Mas, Helena Herand, Jorge Carrasco Muriel, Lars K. Nielsen, and Pär I. Johansson. A protocol for the automatic construction of highly curated genome-scale models of human metabolism. 10(5):576–576, 2023. [DOI].
Håvard Molversmyr, Ove Øyås, Filip Rotnes, and Jon Olav Vik. Extracting functionally accurate context-specific models of atlantic salmon metabolism. 9(1):19–19, 2023. [DOI].
Xiaoxiang Gao, Jianxin Zhao, Wei Chen, and Qixiao Zhai. Food and drug design for gut microbiota-directed regulation: current experimental landscape and future innovation. 194():106867–106867, 2023. [DOI].
Johann F. Jadebeck, Wolfgang Wiechert, and Katharina Nöh. Practical sampling of constraint-based models: optimized thinning boosts chrr performance. 19(8):e1011378–e1011378, 2023. [DOI].
Delphine Nègre, Abdelhalim Larhlimi, and Samuel Bertrand. Reconciliation and evolution of penicillium rubens genome-scale metabolic networks–what about specialised metabolism? 18(8):e0289757–e0289757, 2023. [DOI].
Romeu Viana, Tiago Carreiro, Diogo Couceiro, Óscar Dias, Isabel Rocha, and Miguel C. Teixeira. Metabolic reconstruction of the human pathogen candida auris: using a cross-species approach for drug target prediction. 23():, 2023. [DOI].
Maurício Alexander de Moura Ferreira, Wendel Batista da Silveira, and Zoran Nikoloski. Parrot: prediction of enzyme abundances using protein-constrained metabolic models. 19(10):e1011549–e1011549, 2023. [DOI].
Brandt Bessell, Josh Loecker, Zhongyuan Zhao, Sara Sadat Aghamiri, Sabyasachi Mohanty, Rada Amin, Tomáš Helikar, and Bhanwar Lal Puniya. Como: a pipeline for multi-omics data integration in metabolic modeling and drug discovery. 24(6):, 2023. [DOI].
David Henriques, Romain Minebois, David dos Santos, Eladio Barrio, Amparo Querol, and Eva Balsa‐Canto. A dynamic genome-scale model identifies metabolic pathways associated with cold tolerance in saccharomyces kudriavzevii. 11(3):e0351922–e0351922, 2023. [DOI].
Shweta Shinde, Muhammed Hamdan, Prerna Bhalla, and Aravind Kumar Chandiran. Biocompatible cs2ptx6 (x = cl, br, i) vacancy ordered perovskites and shewanella oneidensis mr-1 bacteria hybrid for potential photocatalytic solar fuel production. 4(2):224–230, 2023. [DOI].
Debiao Wu, Feng Xu, Yaying Xu, Mingzhi Huang, Zhimin Li, and Ju Chu. Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design. 9(1):33–42, 2023. [DOI].
Reinis Muiznieks, Elīna Dāce, and Egils Stalidzāns. Integrated sustainability score implementation as an objective function in sustainable metabolic engineering. 9(6):548–548, 2023. [DOI].
Maryam Khoshnejat, Ali Mohammad Banaei‐Moghaddam, Ali Akbar Moosavi‐Movahedi, and Kaveh Kavousi. A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients. 18(6):e0287325–e0287325, 2023. [DOI].
Takeyuki Tamura. Metnetcomp: database for minimal and maximal gene-deletion strategies for growth-coupled production of genome-scale metabolic networks. 20(6):3748–3758, 2023. [DOI].
Chengjie Hou, Xin Song, Zhiqiang Xiong, Guangqiang Wang, Yongjun Xia, and Lianzhong Ai. Genome‐scale reconstruction of the metabolic network in <scp>streptococcus thermophilus</scp> s‐3 and assess urea metabolism. 104(3):1458–1469, 2023. [DOI].
Chi Hung Vo, Nishu Goyal, Markus Kraft, and Iftekhar A. Karimi. Carbon conversion by methanococcus maripaludis s2 under diazotrophy and a revised genome-scale metabolic model. 278():118910–118910, 2023. [DOI].
Bruno G. Galuzzi, Stefano Izzo, Fabio Giampaolo, Salvatore Cuomo, Marco Vanoni, Lilia Alberghina, Chiara Damiani, and Francesco Piccialli. Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity. ():185–192, 2023. [DOI].
Yanhua Liu and Hans V. Westerhoff. ‘social’ versus ‘asocial’ cells—dynamic competition flux balance analysis. 9(1):53–53, 2023. [DOI].
Daniel E. Schäffer, Wenrui Li, Abdurrahman Elbasir, Dario C. Altieri, Qi Long, and Noam Auslander. Microbial gene expression analysis of healthy and cancerous esophagus uncovers bacterial biomarkers of clinical outcomes. 3(1):128–128, 2023. [DOI].
Fayaz Soleymani Babadi, Zahra Razaghi‐Moghadam, Fatemeh Zare‐Mirakabad, and Zoran Nikoloski. Prediction of metabolite–protein interactions based on integration of machine learning and constraint-based modeling. 3(1):vbad098–vbad098, 2023. [DOI].
Thomas Sevrin, Lisa Strasser, Camille Ternet, Philipp Junk, Miriam Caffarini, Stella Prins, Cian D’Arcy, Simona Catozzi, Giorgio Oliviero, Kieran Wynne, Christina Kiel, and Philip J. Luthert. Whole-cell energy modeling reveals quantitative changes of predicted energy flows in ras mutant cancer cell lines. 26(2):105931–105931, 2023. [DOI].
Shen Qiu, Hua Yang, Qing‐Peng Kong, Gong‐Hua Li, and Li Li. Metabolic modeling identifies a novel molecular type of glioblastoma associated with good prognosis. 13(2):172–172, 2023. [DOI].
Guadalupe Alvarez-Gonzalez, Micaela Chacόn, Adokiye Berepiki, Karl Fisher, Piya Gosalvitr, Rosa M. Cuéllar-Franca, and Neil Dixon. Complex waste stream valorisation through combined enzymatic hydrolysis and catabolic assimilation bypseudomonas putida. ():, 2023. [DOI].
Jonathan Lo, Chao Wu, Jonathan R. Humphreys, Bin Yang, Zhenxiong Jiang, Xin Wang, Pin‐Ching Maness, Nicolas Tsesmetzis, and Wei Xiong. Thermodynamic and kinetic modeling directs pathway optimization for isopropanol production in a gas-fermenting bacterium. 8(2):e0127422–e0127422, 2023. [DOI].
Quinn S. Solfisburg, Federico Baldini, Brittany Baldwin-Hunter, Harry H. Lee, Heekuk Park, Daniel E. Freedberg, Charles J. Lightdale, Tal Korem, and Julian A. Abrams. The salivary microbiome and predicted metabolite production are associated with progression from barrett’s esophagus to esophageal adenocarcinoma. ():, 2023. [DOI].
Yang Zhang, Menglei Yang, Yang‐Yang Bao, Weihua Tao, Jinyou Tuo, Boya Liu, Luxi Gan, Shuilin Fu, and Hongbin Gong. A genome-scale metabolic model of the effect of dissolved oxygen on 1,3-propanediol fermentation by klebsiella pneumoniae. 46(9):1319–1330, 2023. [DOI].
Tamara Bintener, Maria Pires Pacheco, Demetra Philippidou, Christiane Margue, Ali Kishk, Greta Del Mistro, Luca Di Leo, María Moscardó Garcia, Rashi Halder, Lasse Sinkkonen, Daniela De Zio, Stephanie Kreis, Dagmar Kulms, and Thomas Sauter. Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma. 14(7):468–468, 2023. [DOI].
Pedro de Atauri, Carles Foguet, and Marta Cascante. Control analysis in the identification of key enzymes driving metabolic adaptations: towards drug target discovery. 231():104984–104984, 2023. [DOI].
Subasree Sridhar, Prerna Bhalla, Justin Kullu, Sriya Veerapaneni, Swagatika Sahoo, Nirav Bhatt, and G. K. Suraishkumar. A reactive species reactions module for integration into genome-scale metabolic models for improved insights: application to cancer. 80():78–93, 2023. [DOI].
Kelin Chen and Feng‐Sheng Wang. Cell‐specific genome‐scale metabolic modeling of <scp>sars‐cov</scp> ‐2‐infected lung to identify antiviral enzymes. 13(12):2172–2186, 2023. [DOI].
Ehsan Zangene, Sayed‐Amir Marashi, and Hesam Montazeri. Sl-scan identifies synthetic lethal interactions in cancer using metabolic networks. 13(1):15763–15763, 2023. [DOI].
Jorge Carrasco Muriel, Christopher P. Long, and Nikolaus Sonnenschein. Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated python implementation of gecko. 11(6):e0170523–e0170523, 2023. [DOI].
Nora Scherer, Daniel Fässler, Oleg Borisov, Yurong Cheng, Pascal Schlosser, Matthias Wuttke, Suraj Patil, Heike Meiselbach, Fabian Telkämper, Urs Berger, Sarah C. Grünert, Peggy Sekula, Ulla T. Schultheiß, Yong Li, Michael Köttgen, Peter J. Oefner, Felix Knauf, Kai‐Uwe Eckardt, Ines Thiele, Miriam Schmidts, Johannes Hertel, and Anna Köttgen. Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases. ():, 2023. [DOI].
Arturo Vera‐Ponce de León, Matthias Hoetzinger, Tim Hensen, Shashank Gupta, Bronson R. Weston, Sander M. Johnsen, Jacob Agerbo Rasmussen, Cecilie Grønlund Clausen, Louisa Pless, Ana Raquel Verissimo, Knut Rudi, Lars Snipen, Christian Karlsen, Morten T. Limborg, Stefan Bertilsson, Ines Thiele, Torgeir R. Hvidsten, Simen R. Sandve, Phillip B. Pope, and Sabina Leanti La Rosa. The salmon microbial genome atlas enables novel insights into bacteria-host interactions via functional mapping. ():, 2023. [DOI].
Nantia Leonidou, Alina Renz, Reihaneh Mostolizadeh, and Andreas Dräger. New workflow predicts drug targets against sars-cov-2 via metabolic changes in infected cells. ():, 2023. [DOI].
Jorge Carrasco Muriel, Christopher P. Long, and Nikolaus Sonnenschein. Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated python implementation of gecko. ():, 2023. [DOI].
Deepanwita Banerjee, Ian Sofian Yunus, Xi Wang, Jinho Kim, Aparajitha Srinivasan, Russel Menchavez, Yan Chen, Jennifer Gin, Christopher J. Petzold, Héctor García Martín, Paul D. Adams, Aindrila Mukhopadhyay, Joonhoon Kim, Taek Soon Lee, Joonhoon Kim, and Taek Soon Lee. Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production inpseudomonas putida. ():, 2023. [DOI].
Reinis Muiznieks, Elīna Dāce, and Egils Stalidzāns. Sustainable metabolic engineering design development employing a complex objective function. ():, 2023. [DOI].
Xingcun Fan, Lingfeng Cao, and Xuefeng Yan. Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models. 120(8):2301–2313, 2023. [DOI].
Jiahang Li, Wolfram Weckwerth, and Steffen Waldherr. Enzyme fluctuations data improve inference of metabolic interaction networks with an inverse differential jacobian approach. ():, 2023. [DOI].
Garhima Arora, Jimpi Langthasa, Mallar Banerjee, Ramray Bhat, and Samrat Chatterjee. Targeting metabolic fluxes reverts metastatic transitions in ovarian cancer. ():, 2023. [DOI].
Dipanka Tanu Sarmah, Abhijit Paul, Umang Berry, Milan Surjit, Nandadulal Bairagi, and Samrat Chatterjee. Bag6 is a novel player in controlling nonalcoholic steatohepatitis: result from a comprehensivein-silicostudy. ():, 2023. [DOI].
Zeqian Li, Ahmed Selim, and Seppe Kuehn. Statistical prediction of microbial metabolic traits from genomes. ():, 2023. [DOI].
Himel Nahreen Khaleque, Hadi Nazem‐Bokaee, Yosephine Gumulya, Ross P. Carlson, and Anna H. Kaksonen. Simulating compatible solute biosynthesis using a metabolic flux model of the biomining acidophile, acidithiobacillus ferrooxidans atcc 23270. 175(1-2):104115–104115, 2023. [DOI].
David Healy, Shuo Wang, Ghjuvan Micaelu Grimaud, Alicja K. Warda, R. Paul Ross, Catherine Stanton, and Eugene Dempsey. Longitudinal observational study protocol – preterm infants: microbiome establishment, neuro-crosstalk and origins (pimento). 13(9):e075060–e075060, 2023. [DOI].
Holly R. Steach, Siddharth Viswanath, Yixuan He, Xitong Zhang, Natalia Ivanova, Matthew Hirn, Michael Perlmutter, and Smita Krishnaswamy. Inferring metabolic states from single cell transcriptomic data via geometric deep learning. ():, 2023. [DOI].
Isabel Valenbreder, Sonia Balăn, Marian Breuer, and Michiel Adriaens. Mapping out the gut microbiota-dependent trimethylamine n-oxide super pathway for systems biology applications. 3():1074749–1074749, 2023. [DOI].
Dário Neves, Daniel Meinen, Tobias B. Alter, Lars M. Blank, and Birgitta E. Ebert. Expanding pseudomonas taiwanensis vlb120's acyl‐coa portfolio: propionate production in mineral salt medium. 17(1):e14309–e14309, 2023. [DOI].
Eliott Jacopin, Yuki Sakamoto, Kozo Nishida, Kazunari Kaizu, and Koichi Takahashi. A novel architecture for collaboration in systems biology at the age of the metaverse. ():, 2023. [DOI].
Juan Ricardo Velasco-Álvarez, Nimbe Torres, Isaac Chaírez, and José Luis Castrejón-Flores. Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions. 18(8):e0290082–e0290082, 2023. [DOI].
Jing Yu, Xiaowei Wang, Qianqian Yuan, Jiaxin Shi, Jingyi Cai, Zhichao Li, and Hongwu Ma. Elucidating the impact of in vitro cultivation on nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis. 14():1281348–1281348, 2023. [DOI].
Clémence Dupont-Thibert, Sylvaine Roy, Sónia Carneiro, Bruno Pereira, Rafael Careira, Paulo Vilaça, Séverine Collin, Éric Maréchal, Élodie Billey, Gilles Curien, Maxime Durot, and Juliette Jouhet. A new genome-scale metabolic model of oleaginous microalgae with refined lipid metabolism elucidates microchloropsis gaditana mutant phenotypes. ():, 2023. [DOI].
Egils Stalidzāns, Reinis Muiznieks, Konstantīns Dubencovs, Elīna Sīle, Kristaps Berzins, Artūrs Šuleiko, and Juris Vanags. A fermentation state marker rule design task in metabolic engineering. 10(12):1427–1427, 2023. [DOI].
J Schönborn, Irfan Akhtar, Andrea Droste, Stephanie Wesemann, and Mathias Beller. Physiological constraints limiting the growth ofdrosophilalarvae. ():, 2023. [DOI].
Lamiaa H.S. Hassan, Imran Ahmad, Mostafa El Sheekh, and Norhayati Abdullah. In silico models on algal cultivation and processing. ():989–1016, 2023. [DOI].
Ronan M. T. Fleming, Xi Luo, Yanjun Liu, Alexander Balck, and Christine Klein. Identification of metabolites reproducibly associated with parkinson’s disease via meta-analysis and computational modelling. ():, 2023. [DOI].
N Ofori-Anyinam, Michael R. Hamblin, ML Coldren, Bin Li, Gautam Mereddy, Majid B. Shaikh, Aditi Shah, Navpreet Ranu, Shou‐En Lu, PC Blainey, Si Ma, JJ Collins, and Jason H. Yang. Katg catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistantmycobacterium tuberculosis. ():, 2023. [DOI].
Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, and Ines Thiele. Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. ():, 2023. [DOI].
Gabriela Canto-Encalada, Jenna L. Armstrong, Carlos Focil-Espinosa, Ademikanra Adekunle-Fiyin, Ila Peeler, William Gebbie, J. Nuñez‐Garcia, Alexis Saldivar, Diego Martínez, and Cristal Zúñiga. Ten quick tips for accurate reconstruction of prokaryotic and eukaryotic genome-scale metabolic models. ():, 2023. [DOI].
Seo‐Young Park, Dong‐Hyuk Choi, Jinsung Song, Uiseon Park, Hyeran Cho, Bee Hak Hong, Yaron Silberberg, and Dong‐Yup Lee. Debottlenecking and reformulating feed media for improved cho cell growth and titer by data-driven and model-guided analyses. ():, 2023. [DOI].
Maurício Alexander de Moura Ferreira, Wendel Batista da Silveira, and Zoran Nikoloski. Parrot: prediction of enzyme abundances using protein-constrained metabolic models. ():, 2023. [DOI].
Agris Pentjuss, Emīls Bolmanis, Anastasija Šuleiko, Elina Didrihsone, Artūrs Šuleiko, Konstantīns Dubencovs, Jānis Liepiņš, Andris Kazāks, and Juris Vanags. pichia pastorisgrowth - coupled heme biosynthesis analysis using metabolic modelling. ():, 2023. [DOI].
Francis Mairet. in silico identification of switching nodes in metabolic networks. ():, 2023. [DOI].
Kristina Grausa, Shahida Anusha Siddiqui, Norbert Lameyer, Karin Wiesotzki, Sergiy Smetana, and Agris Pentjuss. Metabolic modeling of hermetia illucens larvae resource allocation for high-value fatty acid production. 13(6):724–724, 2023. [DOI].
Dipanka Tanu Sarmah, Abhijit Paul, Shivam Kumar, Nandadulal Bairagi, and Samrat Chatterjee. A data-driven multilayer approach for the identification of potential therapeutic targets in non-alcoholic steatohepatitis. 624():128955–128955, 2023. [DOI].
Yanhua Liu and Hans V. Westerhoff. ‘social’ versus ‘asocial’ cells— dynamic competition flux balance analysis. ():, 2023. [DOI].
Fangzhou Xiao, Jing Shuang Li, and John C. Doyle. Flux exponent control predicts metabolic dynamics from network structure. ():1189–1194, 2023. [DOI].
Jingning Chen, Huang Yi-ran, and Cheng Zhong. Minimizing enzyme mass to decompose flux distribution for identifying biologically relevant elementary flux modes. 231():104981–104981, 2023. [DOI].
Zihe Liu, Ning Qin, Lingyun Li, Xiaozhen Wan, Xu Ji, Yu Chen, Chaokun Li, Ping Liu, Yijie Zhang, Weijie Yang, Junfeng Jang, Jianye Xia, Shuobo Shi, Tianwei Tan, Jens Nielsen, and Yun Chen. Increased co2 fixation enables high carbon-yield production of the acrylic acid precursor 3-hydroxypropionic acid in yeast. ():, 2023. [DOI].
Karel Olavarría, Marco V. Becker, Diana Z. Sousa, Mark C.M. van Loosdrecht, and A. Wahl. Design and thermodynamic analysis of a pathway enabling anaerobic production of poly-3-hydroxybutyrate in escherichia coli. 8(4):629–639, 2023. [DOI].
Lingyun Li, Ning Qin, Xin Chen, Yu Chen, Xu Ji, Yijie Zhang, Jens Nielsen, and Zihe Li. Mechanistic understanding of the "gatekeeper" phosphofructokinase of the glycolytic pathway on the central carbon metabolism. ():, 2023. [DOI].
Famke Bäuerle, Gwendolyn O. Döbel, Laura Camus, Simon Heilbronner, and Andreas Dräger. Genome-scale metabolic models consistently predict in vitro characteristics of corynebacterium striatum. 3():1214074–1214074, 2023. [DOI].
Richard C. Law, Kanchan Pande, Samantha O’Keeffe, Glenn Nurwono, Rachel Ki, Pin‐Kuang Lai, and Junyoung O. Park. Accurate and rapid determination of metabolic flux by deep learning of isotope patterns. ():, 2023. [DOI].
Juan D. Tibocha‐Bonilla, Manish Kumar, Karsten Zengler, and Cristal Zúñiga. Integrating metabolic modeling and high‐throughput data to characterize diatoms metabolism. ():165–191, 2023. [DOI].
Sebastián Nelson Mendoza Farías. Metabolic modeling of microorganisms and dynamics of microbial communities in the food industry. ():, 2023. [DOI].
Christof Meile. Comment on egusphere-2022-1016. ():, 2023. [DOI].
Pınar Kocabaş. Comparative flux balance analyses of serine alkaline protease overproduction in bacillus subtilis at genome and small scale. 12(1):8–18, 2023. [DOI].
Naroa Barrena, Luis V. Valcárcel, Danel Olaverri-Mendizabal, Iñigo Apaolaza, and Francisco J. Planes. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. ():, 2023. [DOI].
Pranas Grigaitis. Constrain and conquer. ():, 2023. [DOI].
Lisardo Boscá, Adrián Povo‐Retana, Marco Fariñas, Rodrigo Landauro‐Vera, Marina Mojena, Carlota Alvarez‐Lucena, Miguel Ángel Fernández‐Moreno, Antonio Castrillo, Juan de la Rosa, Sergio Sánchez‐García, Carles Foguet, Francesc Mas, Silvia Marín, and Marta Cascante. Immunometabolic actions of trabectedin and lurbinectedin on human macrophages: relevance for their antitumor activity. ():, 2023. [DOI].
David M. Versluis, Ruud Schoemaker, Ellen Looijesteijn, Jan M.W. Geurts, and Roeland M. H. Merks. 2’-fucosyllactose helps butyrate producers outgrow competitors in infant gut microbiota simulations. ():, 2023. [DOI].
Thomas Eng, Deepanwita Banerjee, Javier Menasalvas, Yan Chen, Jennifer Gin, Hemant Choudhary, Edward E. K. Baidoo, Jian-Hua Chen, Axel Ekman, Ramu Kakumanu, Yuzhong Liu, Alex Codik, Carolyn A. Larabell, John M. Gladden, Blake A. Simmons, Jay D. Keasling, Christopher J. Petzold, and Aindrila Mukhopadhyay. Ensemble and iterative engineering for maximized bioconversion to the blue pigment, indigoidine from non-canonical sustainable carbon sources. ():, 2023. [DOI].
Fangzhou Xiao, Jing Shuang Li, and John C. Doyle. Flux exponent control predicts metabolic dynamics from network structure. ():, 2023. [DOI].
Haoran Zheng, Yupeng Qi, and Yanlong Zhao. A target prediction method to inhibit the replication process of sars-cov-2 via metabolic differential analysis. ():, 2023. [DOI].
Famke Bäuerle, Gwendolyn O. Gusak, Laura Camus, Simon Heilbronner, and Andreas Dräger. Genome-scale metabolic models consistently predictin vitrocharacteristics ofcorynebacterium striatum. ():, 2023. [DOI].
N. Suhas Jagannathan, Javier Yu Peng Koh, Younghwan Lee, Radoslaw M. Sobota, Aaron T. Irving, Lin‐Fa Wang, Yoko Itahana, Koji Itahana, and Lisa Tucker‐Kellogg. Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism. ():, 2023. [DOI].
Pavel Dvořák, Barbora Burýšková, Barbora Popelářová, Birgitta E. Ebert, Tibor Botka, Dalimil Bujdoš, Alberto Sánchez-Pascuala, Hannah Schöttler, Heiko Hayen, Vı́ctor de Lorenzo, Lars M. Blank, and Martin Benešík. Genomic and metabolic plasticity drive alternative scenarios for adaptingpseudomonas putidato non-native substrate d-xylose. ():, 2023. [DOI].
Aswathy K. Raghu, Indumathi Palanikumar, and Karthik Raman. Designing function-specific minimal microbiomes from large microbial communities. ():, 2023. [DOI].
Sara Benito-Vaquerizo. Model-driven design of synthetic microbial communities for the upcycling of one-carbon feedstocks. ():, 2023. [DOI].
Joe Jongpyo Lim, Julia Yue Cui, and Yuliang Wang. Genome-scale metabolic model predicts mechanistic differences in metabolic gradient of spatially resolved small intestine villi. ():, 2023. [DOI].
Leila Hassani, Mohammad R. Moosavi, Payam Setoodeh, and Habil Zare. Fastknock: an efficient next-generation approach to identify all knockout strategies for strain optimization. ():, 2023. [DOI].
Karel Olavarría, Marco V. Becker, Diana Z. Sousa, and A. Wahl. Design and thermodynamic analysis of a pathway enabling anaerobic production of poly-3-hydroxybutyrate in escherichia coli. ():, 2023. [DOI].
Francesco Balzerani. Enzyme promiscuity approaches to predict metabolic pathways in the human gut microbiota. ():, 2023. [DOI].
Gong‐Hua Li, Feifei Han, Rong‐Hua Luo, Peng Li, Chia-Jung Chang, Weihong Xu, Xin‐Yan Long, Jing‐Fei Huang, Yong‐Tang Zheng, Qing‐Peng Kong, and Wenzhong Xiao. Predictive systems biology modeling: unraveling host metabolic disruptions and potential drug targets in acute viral infections. ():, 2023. [DOI].
Shaohua Guan, Zhichao Zhang, Zihan Zhang, and Hualin Shi. Universal scaling relation and criticality in metabolism and growth of escherichia coli. ():, 2023. [DOI].
Anukriti Verma, Bhawna Rathi, Shivani Sharda, and Pallavi Somvanshi. Machine learning and systems level insights into the host–pathogen interactions of post-infectious reactive arthritis. ():729–744, 2023. [DOI].
Silvia Sabatini, Partho Sen, Fabrizia Carli, Samantha Pezzica, Chiara Rosso, Erminia Lembo, Ornella Verrastro, Ann K. Daly, Olivier Govaere, Simon Cockel, Tuulia Hyötyläinen, Geltrude Mingrone, Elisabetta Bugianesi, Quentin M. Anstee, Matej Orešič, and Amalia Gastaldelli. Higher hepatic glucose production and gluconeogenesis are features of severe metabolic dysfunction-associated steatohepatitis even in absence of t2d. ():, 2023. [DOI].
Eduardo Luís Menezes de Almeida, Maurício Alexander de Moura Ferreira, and Wendel Batista da Silveira. lista-gem: the genome-scale metabolic reconstruction oflipomyces starkeyi. ():, 2023. [DOI].
Mike Sportiello, Alexis Poindexter, Emma C. Reilly, Adam Geber, Kris Lambert Emo, Taylor N. Jones, and David J. Topham. Mouse memory cd8 t cell subsets defined by tissue-resident memory integrin expression exhibit distinct metabolic profiles. 7(10):652–669, 2023. [DOI].
Muhammed Erkan Karabekmez. Fe-flux: feasible flux boundary modification framework for constraint based metabolic models. ():, 2023. [DOI].
Shilin Ouyang, Zihao Li, Jiamin Hu, Miyuan Cao, Xiting Wang, Hongxia Sun, Yu Feng, and Longfei Mao. Sysrecon: a systematic data-driven tool for genome-scale metabolic reconstructions. ():, 2023. [DOI].
Anonymous. (4(43)):, 2023. [DOI].
Арман Исимов, Gulnur Bolatovna Admanova, Саржигитова Асылай Саржигитова, Назерке Кемалова, and Б. БАҚЫТЖАНҚЫЗЫ. Python как инструмент для реконструкции и анализа метаболической модели в масштабе генома. (4(43)):32–62, 2023. [DOI].
Richard D. Paul, Johann F. Jadebeck, Anton Stratmann, Wolfgang Wiechert, and Katharina Nöh. Hopsy - a methods marketplace for convex polytope sampling in python. ():, 2023. [DOI].
Kyoshiro Nonaka, Tatsuya Osamura, and Fumikazu Takahashi. Additional file 3 of a 4-hydroxybenzoate 3-hydroxylase mutant enables 4-amino-3-hydroxybenzoic acid production from glucose in corynebacterium glutamicum. ():, 2023. [DOI].

Adam Amara, Clément Frainay, Fabien Jourdan, Thomas Naake, Steffen Neumann, Elva María Novoa‐del‐Toro, Reza M. Salek, Liesa Salzer, Sarah Scharfenberg, and Michael Witting. Networks and graphs discovery in metabolomics data analysis and interpretation. 9():841373–841373, 2022. [DOI].
Xiaoping Liao, Hongwu Ma, and Yinjie Tang. Artificial intelligence: a solution to involution of design–build–test–learn cycle. 75():102712–102712, 2022. [DOI].
J. Ashwini John, Melvin S. Samuel, Muthusamy Govarthanan, and Selvarajan Ethiraj. A comprehensive review on strategic study of cellulase producing marine actinobacteria for biofuel applications. 214(Pt 3):114018–114018, 2022. [DOI].
Andreas Wagner. Competition for nutrients increases invasion resistance during assembly of microbial communities. 31(15):4188–4203, 2022. [DOI].
Mohammad Mazharul Islam, Andrea Goertzen, Pankaj K. Singh, and Rajib Saha. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. 25(6):104483–104483, 2022. [DOI].
Sang Mi Lee, GaRyoung Lee, and Hyun Uk Kim. Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models. 20():3041–3052, 2022. [DOI].
Gong‐Hua Li, Feifei Han, Fu‐Hui Xiao, Kang‐Su‐Yun Gu, Shen Qiu, Weihong Xu, Wenxing Li, Yanli Wang, Bin Liang, Jing‐Fei Huang, Wenzhong Xiao, and Qing‐Peng Kong. System‐level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity. 21(4):e13595–e13595, 2022. [DOI].
Kirk Smith, Fangzhou Shen, Ho‐Joon Lee, and Sriram Chandrasekaran. Metabolic signatures of regulation by phosphorylation and acetylation. 25(1):103730–103730, 2022. [DOI].
Prabir Kumar Das, Ansuman Sahoo, and Veeranki Venkata Dasu. Current status, and the developments of hosts and expression systems for the production of recombinant human cytokines. 59():107969–107969, 2022. [DOI].
Elif Gencturk and Kutlu Ö. Ülgen. Understanding hmf inhibition on yeast growth coupled with ethanol production for the improvement of bio-based industrial processes. 121():425–438, 2022. [DOI].
Kalaivani Paramasivan, Aneesha Abdulla, Nabarupa Gupta, and Sarma Mutturi. in silico target-based strain engineering of saccharomyces cerevisiae for terpene precursor improvement. 14(2):25–36, 2022. [DOI].
Tomokazu Shirai and Akihiko Kondo. In silico design strategies for the production of target chemical compounds using iterative single-level linear programming problems. 12(5):620–620, 2022. [DOI].
Carles Ciurans, Josep M. Guerrero, Ivan Martínez-Mongue, Claude‐Gilles Dussap, Igor Marín de Mas, and Francesc Gòdia. Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling. 13():970410–970410, 2022. [DOI].
Manish Kumar, Cristal Zúñiga, Juan D. Tibocha‐Bonilla, Sarah R. Smith, Joanna Coker, Andrew E. Allen, and Karsten Zengler. Constraint-based modeling of diatoms metabolism and quantitative biology approaches. ():775–808, 2022. [DOI].
Kusum Dhakar, Hanan Eizenberg, Zeev Ronen, Raphy Zarecki, and Shiri Freilich. Microbial degradation of herbicides in contaminated soils by following computational approaches. ():399–417, 2022. [DOI].
Mohamad Faiz Dzulkalnine, Mohd Saberi Mohamad, Yee Wen Choon, Muhammad Akmal Remli, and Hany Alashwal. Optimizing ethanol production in escherichia coli using a hybrid of particle swarm optimization and artificial bee colony. ():140–146, 2022. [DOI].
Daniela Calvetti and Erkki Somersalo. Brain energy metabolism. ():540–558, 2022. [DOI].
Eliza Atkinson, Alice Boo, Huadong Peng, Guy‐Bart Stan, and Rodrigo Ledesma‐Amaro. Principles, tools, and applications of synthetic consortia toward microbiome engineering. ():195–218, 2022. [DOI].
Naomi van Wijk and Michal Linial. Computational methods for the study of peroxisomes in health and disease. ():, 2022. [DOI].
Carmen Bedia, Núria Dalmau, Lars K. Nielsen, Romá Tauler, and Igor Marín de Mas. A multi-level approach to study the metabolic effects of chronic exposure to aldrin in prostate cancer cells. ():, 2022. [DOI].
Zehra Esra Ilhan and Melissa M. Herbst‐Kralovetz. Microbial drug interactions and human health. ():120–149, 2022. [DOI].
Shrameeta Shinde, Zhenxiong Jiang, and Xin Wang. Exploring the metabolic versatility of cyanobacteria for an emerging carbon-neutral bioeconomy. ():165–187, 2022. [DOI].
Kang Ning. Microbiome and big-data mining. ():197–222, 2022. [DOI].
Albert Fina, Pau Ferrer, and Joan Albiol. Supplementary materials of annex i. high throughput stationary 13c-based metabolic flux analysis of pichia pastoris strains. phd thesis albert fina romero. ():, 2022. [DOI].
Catherine Bjerre Collin, Tom Gebhardt, Martin Golebiewski, Tugce Karaderi, Maximilian Hillemanns, Faiz M. Khan, Ali Salehzadeh‐Yazdi, Marc W. Kirschner, Sylvia Krobitsch, Lars Kuepfer, and Lars Kuepfer. Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation. 12(2):166–166, 2022. [DOI].
Subham Choudhury, Michaël Moret, Pierre Salvy, Daniel Weilandt, Vassily Hatzimanikatis, and Ljubiša Mišković. Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks. 4(8):710–719, 2022. [DOI].
Nasser Delangiz, Sajad Aliyar, Neda Pashapoor, Khatereh Nobaharan, Behnam Asgari Lajayer, and Susana Rodríguez‐Couto. Can polymer-degrading microorganisms solve the bottleneck of plastics’ environmental challenges? 294():133709–133709, 2022. [DOI].
Jeffry C. Granados, Kian Falah, Imhoi Koo, Ethan W. Morgan, Gary H. Perdew, Andrew D. Patterson, Neema Jamshidi, and Sanjay K. Nigám. Ahr is a master regulator of diverse pathways in endogenous metabolism. 12(1):16625–16625, 2022. [DOI].
Chao Ye, Xinyu Wei, Tian‐Qiong Shi, Xiao‐Man Sun, Nan Xu, Cong Gao, and Wei Zou. Genome-scale metabolic network models: from first-generation to next-generation. 106(13-16):4907–4920, 2022. [DOI].
Ceri Proffitt, Gholamreza Bidkhori, Sunjae Lee, Abdellah Tebani, Adil Mardinoğlu, Mathias Uhlén, David L. Moyes, and Saeed Shoaie. Genome-scale metabolic modelling of the human gut microbiome reveals changes in the glyoxylate and dicarboxylate metabolism in metabolic disorders. 25(7):104513–104513, 2022. [DOI].
Kaspar Valgepea, Gert Talbo, Nobuaki Takemori, Ayako Takemori, Christina Ludwig, Vishnuvardhan Mahamkali, Alexander P. Mueller, Ryan Tappel, Michael Köpke, Séan D. Simpson, Lars K. Nielsen, and Esteban Marcellin. Absolute proteome quantification in the gas-fermenting acetogen clostridium autoethanogenum. 7(2):e0002622–e0002622, 2022. [DOI].
Lokanand Koduru, Meiyappan Lakshmanan, Yi Qing Lee, Pooi-Leng Ho, Pei Yu Lim, Wei Xuan Ler, Say Kong Ng, Dongseok Kim, Doo‐Sang Park, M. Shakeera Banu, Dave Siak‐Wei Ow, and Dong‐Yup Lee. Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies. 41(10):111735–111735, 2022. [DOI].
Lorena Azevedo de Lima, Henri Ingelman, Kush Brahmbhatt, Kristina Reinmets, Craig Barry, Audrey Harris, Esteban Marcellin, Michael Köpke, and Kaspar Valgepea. Faster growth enhances low carbon fuel and chemical production through gas fermentation. 10():879578–879578, 2022. [DOI].
Faiz Khan Mohammad, Meghana V. Palukuri, Shruti Shivakumar, Raghunathan Rengaswamy, and Swagatika Sahoo. A computational framework for studying gut-brain axis in autism spectrum disorder. 13():760753–760753, 2022. [DOI].
Neema Jamshidi and Sanjay K. Nigám. Drug transporters oat1 and oat3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions. 12(1):18308–18308, 2022. [DOI].
Jun Feng, Xiaolong Guo, Feifei Cai, Hongxin Fu, and Jufang Wang. Model-based driving mechanism analysis for butyric acid production in clostridium tyrobutyricum. 15(1):71–71, 2022. [DOI].
Craig Woods, Christopher M. Humphreys, Claudio Tomi-Andrino, Anne M. Henstra, Michael Köpke, Séan D. Simpson, Klaus Winzer, and Nigel P. Minton. Required gene set for autotrophic growth of clostridium autoethanogenum. 88(7):e0247921–e0247921, 2022. [DOI].
Juan D. Tibocha‐Bonilla, Cristal Zúñiga, Asama Lekbua, Colton J. Lloyd, Kevin Rychel, Katie Short, and Karsten Zengler. Predicting stress response and improved protein overproduction in bacillus subtilis. 8(1):50–50, 2022. [DOI].
Lidan Bai, Qi You, Chenyang Zhang, Jun Sun, Long Liu, Hengyang Lu, and Qidong Chen. Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models. 3(2):193–206, 2022. [DOI].
Nahikari López-López, David San León, Sonia de Castro, Roberto Díez‐Martínez, Manuel Iglesias-Bexiga, Marı́a-José Camarasa, Margarita Menéndez, Juan Nogales, and Junkal Garmendia. Interrogation of essentiality in the reconstructed haemophilus influenzae metabolic network identifies lipid metabolism antimicrobial targets: preclinical evaluation of a fabh β-ketoacyl-acp synthase inhibitor. 7(2):e0145921–e0145921, 2022. [DOI].
Aidin Behravan, Atieh Hashemi, and Sayed‐Amir Marashi. A constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiepex-scfv production by escherichia coli. 179():108339–108339, 2022. [DOI].
Jørn Carlsen, H Henriksen, Igor Marín de Mas, and Pär I. Johansson. An explorative metabolomic analysis of the endothelium in pulmonary hypertension. 12(1):13284–13284, 2022. [DOI].
Claudio Tomi-Andrino, Alina Pandele, Klaus Winzer, John R. King, Ruman Rahman, and Dong‐Hyun Kim. Metabolic modeling-based drug repurposing in glioblastoma. 12(1):11189–11189, 2022. [DOI].
Abhijit Paul, Salman Azhar, Phonindra Nath Das, Nandadulal Bairagi, and Samrat Chatterjee. Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (t2d) using a genome-scale metabolic modeling. 144():105365–105365, 2022. [DOI].
Bingqing He, Chen Cai, Tim McCubbin, Jorge Carrasco Muriel, Nikolaus Sonnenschein, Shihu Hu, Zhiguo Yuan, and Esteban Marcellin. A genome-scale metabolic model of methanoperedens nitroreducens: assessing bioenergetics and thermodynamic feasibility. 12(4):314–314, 2022. [DOI].
Merve Kutay, Devrim Gözüaçık, and Tunahan Çakır. Cancer recurrence and omics: metabolic signatures of cancer dormancy revealed by transcriptome mapping of genome-scale networks. 26(5):270–279, 2022. [DOI].
Mahsa Mekanik, Reza Fotovat, Ehsan Motamedian, and Vahab Jafarian. Improvement of lutein production in auxenochlorella protothecoides using its genome-scale metabolic model and a system-oriented approach. 195(2):889–904, 2022. [DOI].
Kaustubh R. Sawant, Aditya Sarnaik, Prashant Savvashe, Nima Hajinajaf, Parker Poole, Arul M. Varman, Arvind Lali, and Reena Pandit. One cell-two wells bio-refinery: demonstrating cyanobacterial chassis for co-production of heterologous and natural hydrocarbons. 363():127921–127921, 2022. [DOI].
Joshua E. Goldford, Ashish B. George, Avi I. Flamholz, and Daniel Segrè. Protein cost minimization promotes the emergence of coenzyme redundancy. 119(14):e2110787119–e2110787119, 2022. [DOI].
Ryutaro Kawai, Yoshihiro Toya, and Hiroshi Shimizu. Metabolic pathway design for growth-associated phenylalanine production using synthetically designed mutualism. 45(9):1539–1546, 2022. [DOI].
Kristina Simons, Olivia Thibault, Rayna Nolen, Yu Umeki, Patricia C. Faulkner, Lene H. Petersen, and David Hala. A systems toxicological analysis of oxybenzone effects on the metabolic physiology of embryo-larval zebrafish (danio rerio). 9():100308–100308, 2022. [DOI].
Subham Choudhury, Michaël Moret, Pierre Salvy, Daniel Weilandt, Vassily Hatzimanikatis, and Ljubiša Mišković. Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks. ():, 2022. [DOI].
Soumendu Mahapatra, Smrutishree Mohanty, Rasmita Mishra, and Punit Prasad. An overview of cancer and the human microbiome. 191(1):83–139, 2022. [DOI].
Amirhossein Shahbazinia, Saber Salehkaleybar, and Matin Hashemi. Paralingam: parallel causal structure learning for linear non-gaussian acyclic models. ():, 2022. [DOI].
Chen Yang, Boyuan Xue, Yiming Zhang, Shaojie Wang, and Haijia Su. Metabolic flux simulation of microbial systems based on optimal planning algorithms. 4(2):146–159, 2022. [DOI].
Ehsan Salehabadi, Ehsan Motamedian, and Seyed Abbas Shojaosadati. Reconstruction of a generic genome-scale metabolic network for chicken: investigating network connectivity and finding potential biomarkers. 17(3):e0254270–e0254270, 2022. [DOI].
Aidin Behravan, Atieh Hashemi, Sayed‐Amir Marashi, and Hamideh Fouladiha. Genome-scale metabolic model-based engineering of escherichia coli enhances recombinant single-chain antibody fragment production. 44(10):1231–1242, 2022. [DOI].
Daniela Reyes-González, Haydee De Luna-Valenciano, José Utrilla, Michael Sieber, Rafael Peña‐Miller, and Ayari Fuentes-Hernández. Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia. 9(5):212008–212008, 2022. [DOI].
Fernández-Niño Miguel and Burgos-Toro Daniela. Engineering microbial biofactories for a sustainable future. ():25–58, 2022. [DOI].
Dalal Kasir, Sébastien Besseau, Marc Clastre, Audrey Oudin, Monzer Hamzé, Vincent Courdavault, Marwan Osman, and Nicolas Papon. Synthetic biology in the candida (ctg) clade. ():337–375, 2022. [DOI].
Aidin Behravan, Atieh Hashemi, Sayed‐Amir Marashi, and Hamideh Fouladiha. Genome- scale metabolic model based engineering of escherichia coli enhances recombinant single chain antibody fragment production. ():, 2022. [DOI].
Clémence FRIOUX and Anne SIEGEL. Problèmes d’optimisation combinatoire pour l’étude du métabolisme. ():51–96, 2022. [DOI].
Carlos E. Mejía-Gómez, Rigoberto Ríos‐Estepa, Luis A González, and Norman Balcázar. An experimental and in silico analysis of lacticaseibacillus paracasei isolated from whey shows an association between lactate production and amino acid catabolism. 94(2):e20211071–e20211071, 2022. [DOI].
Müberra Fatma Cesur, Kiran Raosaheb Patil, and Tunahan Çakır. A genome-scale metabolic model of drosophila melanogaster for integrative analysis of brain diseases. ():, 2022. [DOI].
Kristina Simons, Olivia Thibault, Rayna Nolen, Yu Umeki, Patricia Faulkner, Lene H. Petersen, and David Hala. A systems toxicological analysis of oxybenzone effects on the metabolic physiology of embryo-larval zebrafish (danio rerio). ():, 2022. [DOI].
Sriya Priyadarshani, Paritosh Patel, and Sushree Sangita Sahoo. Role of system biology in microbial system. ():43–56, 2022. [DOI].
Maureen C. McCann and Patrick M. Reed. U.s. scientific leadership: addressing energy, ecosystems, climate, and sustainable prosperity. ():, 2022. [DOI].
Feiran Li, Le Yuan, Hongzhong Lu, Gang Li, Yu Chen, Martin K. M. Engqvist, Eduard J. Kerkhoven, and Jens Nielsen. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. 5(8):662–672, 2022. [DOI].
Dominik Ternes, Mina Tsenkova, Vitaly I. Pozdeev, Marianne Meyers, Eric Koncina, Sura Atatri, Martine Schmitz, Jessica Karta, Maryse Schmoetten, Almut Heinken, Fabien Rodriguez, Catherine Delbrouck, Anthoula Gaigneaux, Aurélien Ginolhac, Tam T. D. Nguyen, Léa Grandmougin, Audrey Frachet-Bour, Camille Martin‐Gallausiaux, Maria Pires Pacheco, Lorie Neuberger-Castillo, Paulo Miranda, Nikolaus Zuegel, Jean-Yves Ferrand, Manon Gantenbein, Thomas Sauter, Daniel J. Slade, Ines Thiele, Johannes Meiser, Serge Haan, Paul Wilmes, and Elisabeth Letellier. The gut microbial metabolite formate exacerbates colorectal cancer progression. 4(4):458–475, 2022. [DOI].
Ruibing Chen, Jiaoqi Gao, Wei Yu, Xianghui Chen, Xiaoxin Zhai, Yu Chen, Lei Zhang, and Yongjin J. Zhou. Engineering cofactor supply and recycling to drive phenolic acid biosynthesis in yeast. 18(5):520–529, 2022. [DOI].
Iván Domenzain, Benjamín J. Sánchez, Mihail Anton, Eduard J. Kerkhoven, Aarón Millán-Oropeza, Céline Henry, Verena Siewers, John P. Morrissey, Nikolaus Sonnenschein, and Jens Nielsen. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using gecko 2.0. 13(1):3766–3766, 2022. [DOI].
Jason Yu, Clara Correia‐Melo, Francisco Zorrilla, Lucía Herrera-Domínguez, Mary Wu, Johannes Hartl, Kate Campbell, Sonja Blasche, Marco Kreidl, Anna-Sophia Egger, Christoph B. Messner, Vadim Demichev, Anja Freiwald, Michael Mülleder, Michael Howell, Judith Berman, Kiran Raosaheb Patil, Mohammad Tauqeer Alam, and Markus Ralser. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. 7(4):542–555, 2022. [DOI].
Yameng Xu, Xinglong Wang, Chenyang Zhang, Xuan Zhou, Xianhao Xu, Luyao Han, Xueqin Lv, Yanfeng Liu, Song Liu, Jianghua Li, Guocheng Du, Jian Chen, Rodrigo Ledesma‐Amaro, and Long Liu. De novo biosynthesis of rubusoside and rebaudiosides in engineered yeasts. 13(1):3040–3040, 2022. [DOI].
Laura M. Helleckes, Johannes Hemmerich, Wolfgang Wiechert, Eric von Lieres, and Alexander Grünberger. Machine learning in bioprocess development: from promise to practice. 41(6):817–835, 2022. [DOI].
Philipp Keller, Michael Reiter, Patrick Kiefer, Thomas Gassler, Lucas Hemmerle, Philipp Christen, Εlad Noor, and Julia A. Vorholt. Generation of an escherichia coli strain growing on methanol via the ribulose monophosphate cycle. 13(1):5243–5243, 2022. [DOI].
Yuan‐Hang Du, Min-Yu Wang, Linhui Yang, Lingling Tong, Dong‐Sheng Guo, and Xiao‐Jun Ji. Optimization and scale-up of fermentation processes driven by models. 9(9):473–473, 2022. [DOI].
Robert D. Hall, John C. D’Auria, A. C. Silva Ferreira, Yves Gibon, Dariusz Kruszka, Puneet Mishra, and H.J. van de Zedde. High-throughput plant phenotyping: a role for metabolomics? 27(6):549–563, 2022. [DOI].
Priyanka Baloni, Matthias Arnold, Luna Buitrago, Kwangsik Nho, Herman Moreno, Kevin Huynh, Barbara Brauner, Gregory Louie, Alexandra Kueider‐Paisley, Karsten Suhre, Andrew J. Saykin, Kim Ekroos, Peter J. Meikle, Leroy Hood, Nathan D. Price, Matthias Arnold, Colette Blach, Rima Kaddurah‐Daouk, Murali Doraiswamy, Siamak MahmoudianDehkordi, Kathleen A. Welsh‐Bohmer, Brenda L. Plassman, Jan Krumsiek, Richa Batra, Andrew J. Saykin, Jingwen Yan, Shannon L. Risacher, Peter J. Meikle, Tingting Wang, M. Arfan Ikram, Shahzad Ahmad, Thomas Hankemeier, Ivan A. Hernandez, Almut Heinken, Filippo Martinelli Boneschi, Ines Thiele, Johannes Hertel, Tim Hensen, Tim Hulshof, Lindsay A. Farrer, Rhoda Au, Wendy Wei Qiao Qiu, Thor D. Stein, Naama Karu, Kamil Borkowski, John C. Newman, Wei Jia, Guoxiang Xie, Jingye Wang, Runmin Wei, Dan Rader, Mitchel A. Kling, Pilhan Kim, P. Murali Doraiswamy, Cory C. Funk, A. Iván Hernández, Gabi Kastenmüller, Rebecca Baillie, Xianlin Han, Rima Kaddurah‐Daouk, and Rima Kaddurah-Daouk. Multi-omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in alzheimer’s disease. 5(1):1074–1074, 2022. [DOI].
Xuan Cao, Wei Yu, Yu Chen, Shan Yang, Zongbao K. Zhao, Jens Nielsen, Hongwei Luan, and Yongjin J. Zhou. Engineering yeast for high-level production of diterpenoid sclareol. 75():19–28, 2022. [DOI].
Feiran Li, Yu Chen, Qi Qi, Yanyan Wang, Le Yuan, Mingtao Huang, Ibrahim E. Elsemman, Amir Feizi, Eduard J. Kerkhoven, and Jens Nielsen. Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints. 13(1):2969–2969, 2022. [DOI].
Shengbo Wu, Jie Feng, Chunjiang Liu, Hao Wu, Zekai Qiu, Jianjun Ge, Shuyang Sun, Xia Hong, Yukun Li, Xiaona Wang, Aidong Yang, Fei Guo, and Jianjun Qiao. Machine learning aided construction of the quorum sensing communication network for human gut microbiota. 13(1):3079–3079, 2022. [DOI].
Benjamin P. Kellman, Anne Richelle, Jeong‐Yeh Yang, Digantkumar Chapla, Austin W.T. Chiang, Julia A. Najera, Chenguang Liang, Annalee Fürst, Bokan Bao, Natália Massaco Koga, Mahmoud A. Mohammad, Anders Bech Bruntse, Morey W. Haymond, Kelley W. Moremen, Lars Bode, and Nathan E. Lewis. Elucidating human milk oligosaccharide biosynthetic genes through network-based multi-omics integration. 13(1):2455–2455, 2022. [DOI].
Zhendong Li, Cong Gao, Chao Ye, Liang Guo, Jia Liu, Xiulai Chen, Wei Song, Jing Wu, and Li Liu. Systems engineering of escherichia coli for high-level shikimate production. 75():1–11, 2022. [DOI].
Kunle Odunsi, Feng Qian, Amit A. Lugade, Han Yu, Melissa A. Geller, Steven P. Fling, Judith C. Kaiser, Andreanne M. Lacroix, Leonard D’Amico, Nirasha Ramchurren, Chihiro Morishima, Mary L. Disis, Lucas Dennis, Patrick Danaher, Sarah Warren, Van Anh Nguyen, Sudharshan Ravi, Takemasa Tsuji, Spencer R. Rosario, Wenjuan Zha, Alan D. Hutson, Song Liu, Shashikant Lele, Emese Zsíros, AJ Robert McGray, Jessie L Chiello, Richard C. Koya, Thinle Chodon, Carl D. Morrison, Vasanta Putluri, Nagireddy Putluri, Donald E. Mager, Rudiyanto Gunawan, Martin A. Cheever, Sebastiano Battaglia, and Junko Matsuzaki. Metabolic adaptation of ovarian tumors in patients treated with an ido1 inhibitor constrains antitumor immune responses. 14(636):eabg8402–eabg8402, 2022. [DOI].
Almut Heinken and Ines Thiele. Microbiome modelling toolbox 2.0: efficient, tractable modelling of microbiome communities. 38(8):2367–2368, 2022. [DOI].
Yougan Cheng, Ronny Straube, Abed E. Alnaif, Lu Huang, Tarek A. Leil, and Brian J. Schmidt. Virtual populations for quantitative systems pharmacology models. 2486():129–179, 2022. [DOI].
Maxime R.F. Gosselin, Virginie Mournetas, Małgorzata Borczyk, Suraj Verma, Annalisa Occhipinti, Justyna Róg, Łukasz Bożycki, Michał Korostyński, Samuel C. Robson, Claudio Angione, Christian Pinset, and Dariusz C. Górecki. Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts. 11():, 2022. [DOI].
Anoop T. Ambikan, Hong Yang, Shuba Krishnan, Sara Svensson Akusjärvi, Soham Gupta, Magda Lourda, Maike Sperk, Muhammad Arif, Cheng Zhang, Hampus Nordqvist, Sivasankaran Munusamy Ponnan, Anders Sönnerborg, Carl Johan Treutiger, Liam O’Mahony, Adil Mardinoğlu, Rui Benfeitas, and Ujjwal Neogi. Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with covid-19 severity. 13(8):665–681.e4, 2022. [DOI].
Santosh Lamichhane, Partho Sen, Alex M. Dickens, Marina Amaral Alves, Taina Härkönen, Jarno Honkanen, Tommi Vatanen, Ramnik J. Xavier, Tuulia Hyötyläinen, Mikael Knip, and Matej Orešič. Dysregulation of secondary bile acid metabolism precedes islet autoimmunity and type 1 diabetes. 3(10):100762–100762, 2022. [DOI].
Lisha Qu, Xiang Xiu, Guoyun Sun, Chenyang Zhang, Haiquan Yang, Yanfeng Liu, Jianghua Li, Guocheng Du, Xueqin Lv, and Long Liu. Engineered yeast for efficient de novo synthesis of 7‐dehydrocholesterol. 119(5):1278–1289, 2022. [DOI].
Nicole Pearcy, Marco Garavaglia, Thomas Millat, James P. Gilbert, Yoseb Song, Hassan Hartman, Craig Woods, Claudio Tomi-Andrino, Rajesh Reddy Bommareddy, Byung‐Kwan Cho, David A. Fell, Mark G. Poolman, John R. King, Klaus Winzer, Jamie Twycross, and Nigel P. Minton. A genome-scale metabolic model of cupriavidus necator h16 integrated with tradis and transcriptomic data reveals metabolic insights for biotechnological applications. 18(5):e1010106–e1010106, 2022. [DOI].
Dalimil Bujdoš, Barbora Popelářová, Daniel C. Volke, Pablo I. Nikel, Nikolaus Sonnenschein, and Pavel Dvořák. Engineering of pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model. 75():29–46, 2022. [DOI].
Belén Hirmas, Naschla Gasaly, Guillermo Orellana, Marco A. Vega, Pedro A. Saa, Martín Gotteland, and Daniel Garrido. Metabolic modeling and bidirectional culturing of two gut microbes reveal cross-feeding interactions and protective effects on intestinal cells. 7(5):e0064622–e0064622, 2022. [DOI].
Raymond T. Ng, J. Lee, Priyanka Baloni, Christian Diener, James R. Heath, and Yapeng Su. Constraint-based reconstruction and analyses of metabolic models: open-source python tools and applications to cancer. 12():914594–914594, 2022. [DOI].
Parsa Ghadermazi, Angela Re, Luca Ricci, and Siu Hung Joshua Chan. Metabolic engineering interventions for sustainable 2,3-butanediol production in gas-fermenting clostridium autoethanogenum. 7(2):e0111121–e0111121, 2022. [DOI].
Jiangong Lu, Xinyu Bi, Yanfeng Liu, Xueqin Lv, Jianghua Li, Guocheng Du, and Long Liu. In silico cell factory design driven by comprehensive genome-scale metabolic models: development and challenges. 3(2):207–222, 2022. [DOI].
Juan C. Villada, Maria F. Duran, Chee Kent Lim, Lisa Y. Stein, and Patrick K. H. Lee. Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in methylomicrobium album bg8. 7(2):e0007322–e0007322, 2022. [DOI].
K. B. Arun, A. N. Anoopkumar, Raveendran Sindhu, Parameswaran Binod, Embalil Mathachan Aneesh, Aravind Madhavan, and Mukesh Kumar Awasthi. Synthetic biology for sustainable food ingredients production: recent trends. 3(1):137–149, 2022. [DOI].
John R. Casey, Rene Boiteau, Martin K. M. Engqvist, Zoe V. Finkel, Gang Li, Justin D. Liefer, Christian L. Müller, Nathalie Munoz Munoz, and Michael J. Follows. Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology. 8(3):eabl4930–eabl4930, 2022. [DOI].
Abhinav Achreja, Tao Yu, Anjali Mittal, Srinadh Choppara, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman, Sarah Owen, Reva Kulkarni, Michele L. Dziubinski, Frank Weinberg, Hye Kyong Kweon, Chitra Subramanian, Max S. Wicha, Sofía D. Merajver, Sunitha Nagrath, Kathleen R. Cho, Analisa DiFeo, Xiongbin Lu, and Deepak Nagrath. Metabolic collateral lethal target identification reveals mthfd2 paralogue dependency in ovarian cancer. 4(9):1119–1137, 2022. [DOI].
Prerna Bhalla, Raghunathan Rengaswamy, Devarajan Karunagaran, G. K. Suraishkumar, and Swagatika Sahoo. Metabolic modeling of host–microbe interactions for therapeutics in colorectal cancer. 8(1):1–1, 2022. [DOI].
Antoine Régimbeau, Marko Budinich, Abdelhalim Larhlimi, Juan José Pierella Karlusich, Olivier Aumont, Laurent Mémery, Chris Bowler, and Damien Eveillard. Contribution of genome‐scale metabolic modelling to niche theory. 25(6):1352–1364, 2022. [DOI].
Vetle Simensen, Christian Schulz, Emil Karlsen, Signe Bråtelund, Idun Burgos, Lilja Brekke Thorfinnsdottir, Laura García-Calvo, Per Bruheim, and Eivind Almaas. Experimental determination of escherichia coli biomass composition for constraint-based metabolic modeling. 17(1):e0262450–e0262450, 2022. [DOI].
Zhepu Ruan, Mengjun Xu, Youwen Xing, Qi Jiang, Bingang Yang, Jiandong Jiang, and Xihui Xu. Interspecies metabolic interactions in a synergistic consortium drive efficient degradation of the herbicide bromoxynil octanoate. 70(37):11613–11622, 2022. [DOI].
Philipp Schneider, Pavlos Stephanos Bekiaris, Axel von Kamp, and Steffen Klamt. Straindesign: a comprehensive python package for computational design of metabolic networks. 38(21):4981–4983, 2022. [DOI].
Francesco Balzerani, Daniel Hinojosa-Nogueira, Xabier Cendoya, Telmo Blasco, Sergio Pérez-Burillo, Iñigo Apaolaza, M. Pilar Francino, José Ángel Rufián‐Henares, and Francisco J. Planes. Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods. 8(1):24–24, 2022. [DOI].
João R. C. Ramos, Gil Poiares‐Oliveira, Patrick Dumas, and Rui Oliveira. Genome-scale modeling of chinese hamster ovary cells by hybrid semi-parametric flux balance analysis. 45(11):1889–1904, 2022. [DOI].
Ines Thiele and Ronan M. T. Fleming. Whole-body metabolic modelling predicts isoleucine dependency of sars-cov-2 replication. 20():4098–4109, 2022. [DOI].
Partho Sen, Olivier Govaere, Tim Sinioja, Aidan McGlinchey, Dawei Geng, Vlad Ratziu, Elisabetta Bugianesi, Jörn M. Schattenberg, Antonio Vidal‐Puig, Michael Allison, Simon Cockell, Ann K. Daly, Tuulia Hyötyläinen, Quentin M. Anstee, and Matej Orešič. Quantitative modeling of human liver reveals dysregulation of glycosphingolipid pathways in nonalcoholic fatty liver disease. 25(9):104949–104949, 2022. [DOI].
Carles Foguet, Yu Xu, Scott C. Ritchie, Samuel A. Lambert, Elodie Persyn, Artika P. Nath, Emma E. Davenport, David J. Roberts, Dirk S. Paul, Emanuele Di Angelantonio, John Danesh, Adam S. Butterworth, Christopher Yau, and Michael Inouye. Genetically personalised organ-specific metabolic models in health and disease. 13(1):7356–7356, 2022. [DOI].
Jong Kwang Hong, Dong‐Hyuk Choi, Seo‐Young Park, Yaron Silberberg, Fumi Shozui, E. Nakamura, Takashi Kayahara, and Dong‐Yup Lee. Data-driven and model-guided systematic framework for media development in cho cell culture. 73():114–123, 2022. [DOI].
Satyajit Beura, Pritam Kundu, Amit Kumar Das, and Amit Ghosh. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. 149():105997–105997, 2022. [DOI].
Francine Piubeli. Systems biology: new insight into antibiotic resistance. 10(12):2362–2362, 2022. [DOI].
Saratram Gopalakrishnan, Chintan Joshi, Miguel Á. Valderrama-Gómez, Elçin Içten, Pablo Rolandi, William Johnson, Cleo Kontoravdi, and Nathan E. Lewis. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. 75():181–191, 2022. [DOI].
Antonino Baez, Ashish K. Sharma, A. L. Bryukhanov, Eric Anderson, Leba Rudack, Roberto Olivares‐Hernández, David N. Quan, and Joseph Shiloach. Iron availability enhances the cellular energetics of aerobic escherichia coli cultures while upregulating anaerobic respiratory chains. 71():11–20, 2022. [DOI].
Hock Chuan Yeo, Seo‐Young Park, Tessa Rui Min Tan, Say Kong Ng, Meiyappan Lakshmanan, and Dong‐Yup Lee. Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of chinese hamster ovary cell cultures. 119(7):1740–1754, 2022. [DOI].
Gabriela Diaz-Tang, Estefania Marin Meneses, Kavish Patel, Sophia Mirkin, Laura García-Diéguez, Camryn Pajon, Ivana Barraza, Vijay Patel, Helana Ghali, Angelica P. Tracey, Christopher A. Blanar, Allison J. Lopatkin, and Robert P. Smith. Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics. 8(50):, 2022. [DOI].
Padhmanand Sudhakar, Tahila Andrighetti, Sare Verstockt, Clara Caenepeel, Marc Ferrante, João Sabino, Bram Verstockt, and Séverine Vermeire. Integrated analysis of microbe-host interactions in crohn’s disease reveals potential mechanisms of microbial proteins on host gene expression. 25(5):103963–103963, 2022. [DOI].
João B. Xavier, Jonathan M. Monk, Saugat Poudel, Charles J. Norsigian, Anand V. Sastry, Chen Liao, José Maurício S. Bento, Marc A. Suchard, Mario L. Arrieta‐Ortiz, Eliza J. R. Peterson, Nitin S. Baliga, Thomas Stoeger, Felicia Ruffin, Reese Richardson, Catherine A. Gao, Thomas D. Horvath, Anthony M. Haag, Qinglong Wu, Tor Savidge, and Michael R. Yeaman. Mathematical models to study the biology of pathogens and the infectious diseases they cause. 25(4):104079–104079, 2022. [DOI].
Jungyeon Kim, Yu Eun Cheong, Sora Yu, Yong-Su Jin, and Kyoung Heon Kim. Strain engineering and metabolic flux analysis of a probiotic yeast saccharomyces boulardii for metabolizing l-fucose, a mammalian mucin component. 21(1):204–204, 2022. [DOI].
Gabriela Canto-Encalada, Diego Tec-Campos, Juan D. Tibocha‐Bonilla, Karsten Zengler, Alejandro Zepeda, and Cristal Zúñiga. Flux balance analysis of the ammonia-oxidizing bacterium nitrosomonas europaea atcc19718 unravels specific metabolic activities while degrading toxic compounds. 18(2):e1009828–e1009828, 2022. [DOI].
Qusheng Jin, Qiong Wu, Benjamin M. Shapiro, and Shannon E. McKernan. Limited mechanistic link between the monod equation and methanogen growth: a perspective from metabolic modeling. 10(2):e0225921–e0225921, 2022. [DOI].
Jiangong Lu, Yaokang Wu, Deng Chen, Yanfeng Liu, Xueqin Lv, Jianghua Li, Guocheng Du, and Long Liu. Model-based dynamic engineering of escherichia coli for n-acetylglucosamine overproduction. 3():15–24, 2022. [DOI].
Shouyong Jiang, Irene Otero‐Muras, Julio R. Banga, Yong Wang, Marcus Kaiser, and Natalio Krasnogor. Optdesign: identifying optimum design strategies in strain engineering for biochemical production. 11(4):1531–1541, 2022. [DOI].
Alan R. Pacheco, Charlie Pauvert, Dileep Kishore, and Daniel Segrè. Toward fair representations of microbial interactions. 7(5):e0065922–e0065922, 2022. [DOI].
Paula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Sandra Castillo, Payam Ghiachi, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramón González, Pilar Morales, and Kiran Raosaheb Patil. Predictive evolution of metabolic phenotypes using model‐designed environments. 18(10):e10980–e10980, 2022. [DOI].
Minsuk Kim, Jaeyun Sung, and Nicholas Chia. Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling. 70():12–22, 2022. [DOI].
Elle M. Barnes and Susannah G. Tringe. Exploring the roles of microbes in facilitating plant adaptation to climate change. 479(3):327–335, 2022. [DOI].
Barbara Bourgade, Christopher M. Humphreys, James Millard, Nigel P. Minton, and M. Ahsanul Islam. Design, analysis, and implementation of a novel biochemical pathway for ethylene glycol production in clostridium autoethanogenum. 11(5):1790–1800, 2022. [DOI].
Simone Schito, Rico Zuchowski, Daniel Bergen, Daniel Strohmeier, Bastian Wollenhaupt, Philipp Menke, Johannes Seiffarth, Katharina Nöh, Dietrich Kohlheyer, Michael Bott, Wolfgang Wiechert, Meike Baumgart, and Stephan Noack. Communities of niche-optimized strains (conos) – design and creation of stable, genome-reduced co-cultures. 73():91–103, 2022. [DOI].
Feng‐Sheng Wang, Kelin Chen, and Sz-Wei Chu. Human/sars-cov-2 genome-scale metabolic modeling to discover potential antiviral targets for covid-19. 133():104273–104273, 2022. [DOI].
Marilena D. A. Pantziri and Maria I. Klapa. Standardization of human metabolic stoichiometric models: challenges and directions. 2():, 2022. [DOI].
Romeu Viana, Diogo Couceiro, Tiago Carreiro, Óscar Dias, Isabel Rocha, and Miguel C. Teixeira. A genome-scale metabolic model for the human pathogen candida parapsilosis and early identification of putative novel antifungal drug targets. 13(2):303–303, 2022. [DOI].
Defei Liu, Zixiang Xu, Jingen Li, Qian Liu, Qianqian Yuan, Yanmei Guo, Hongwu Ma, and Chaoguang Tian. Reconstruction and analysis of genome‐scale metabolic model for thermophilic fungus myceliophthora thermophila. 119(7):1926–1937, 2022. [DOI].
Giuseppe Magazzù, Guido Zampieri, and Claudio Angione. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods. 151(Pt A):106244–106244, 2022. [DOI].
Patrick F. Suthers and Costas D. Maranas. Examining organic acid production potential and growth‐coupled strategies in issatchenkia orientalis using constraint‐based modeling. 38(5):e3276–e3276, 2022. [DOI].
Müberra Fatma Cesur, Tunahan Çakır, and Pınar Pir. Genome-wide analysis of yeast metabolic cycle through metabolic network models reveals superiority of integrated atac-seq data over rna-seq data. 7(3):e0134721–e0134721, 2022. [DOI].
Teresa J. Clark and Jörg Schwender. Elucidation of triacylglycerol overproduction in the c4 bioenergy crop sorghum bicolor by constraint-based analysis. 13():, 2022. [DOI].
Evangelia Vayena, Anush Chiappino-Pepe, Homa MohammadiPeyhani, Yannick Francioli, Noushin Hadadi, Meriç Ataman, Jasmin Hafner, Stavros Pavlou, and Vassily Hatzimanikatis. A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions. 119(46):e2211197119–e2211197119, 2022. [DOI].
Shivendra G. Tewari, Bobby Kwan, Rubayet Elahi, Krithika Rajaram, Jaques Reifman, Sean T. Prigge, Akhil B. Vaidya, and Anders Wallqvist. Metabolic adjustments of blood-stage plasmodium falciparum in response to sublethal pyrazoleamide exposure. 12(1):1167–1167, 2022. [DOI].
Feng Xu, Ju Lu, Xiang Ke, Minghao Shao, Mingzhi Huang, and Ju Chu. Reconstruction of the genome-scale metabolic model of saccharopolyspora erythraea and its application in the overproduction of erythromycin. 12(6):509–509, 2022. [DOI].
Iman Shahidi Pour Savizi, Nader Maghsoudi, Ehsan Motamedian, Nathan E. Lewis, and Seyed Abbas Shojaosadati. Valine feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of cho cells. 106(3):1113–1126, 2022. [DOI].
Kristaps Berzins, Reinis Muiznieks, Matiss Ricards Baumanis, Inese Strazdiņa, Karlis Shvirksts, Santa Prikule, Vytautas Galvanauskas, Daniel Pleißner, Agris Pentjuss, Māra Grūbe, Uldis Kalnenieks, and Egils Stalidzāns. Kinetic and stoichiometric modeling-based analysis of docosahexaenoic acid (dha) production potential by crypthecodinium cohnii from glycerol, glucose and ethanol. 20(2):115–115, 2022. [DOI].
Abhishek Subramanian, Pooya Zakeri, Mira Mousa, Halima Alnaqbi, Fatima Alshamsi, Leo Bettoni, Ernesto Damiani, Habiba Alsafar, Yvan Saeys, and Peter Carmeliet. Angiogenesis goes computational – the future way forward to discover new angiogenic targets? 20():5235–5255, 2022. [DOI].
Miroslava Čuperlović‐Culf, Thao Nguyen-Tran, and Steffany A. L. Bennett. Machine learning and hybrid methods for metabolic pathway modeling. 2553():417–439, 2022. [DOI].
Tamizhini Loganathan and C. George Priya Doss. The influence of machine learning technologies in gut microbiome research and cancer studies - a review. 311(Pt A):121118–121118, 2022. [DOI].
Natalia I. García‐Tomsig, Marta Robledo, George C. diCenzo, Alessio Mengoni, Vicenta Millán, Alexandra Peregrina, Alejandro Uceta, and José I. Jiménez‐Zurdo. Pervasive rna regulation of metabolism enhances the root colonization ability of nitrogen-fixing symbiotic α-rhizobia. 13(1):e0357621–e0357621, 2022. [DOI].
Richard C. Law, Aliya Lakhani, Samantha O’Keeffe, Sevcan Erşan, and Junyoung O. Park. Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes. 75():102701–102701, 2022. [DOI].
Thomas Sauter, Tamara Bintener, Ali Kishk, Luana Presta, Tessy Prohaska, D Guignard, Ni Zeng, Claudia Cipriani, Sundas Arshad, Thomas Pfau, Patricia Martins Conde, and Maria Pires Pacheco. Project-based learning course on metabolic network modelling in computational systems biology. 18(1):e1009711–e1009711, 2022. [DOI].
Stefano Camborda, Jan-Niklas Weder, and Nadine Töpfer. Cobramod: a pathway-centric curation tool for constraint-based metabolic models. 38(9):2654–2656, 2022. [DOI].
Carolin C. M. Schulte, Vinoy K. Ramachandran, Antonis Papachristodoulou, and Philip S. Poole. Genome-scale metabolic modelling of lifestyle changes in rhizobium leguminosarum. 7(1):e0097521–e0097521, 2022. [DOI].
Kadir Kocabaş, Alina Arif, Reaz Uddin, and Tunahan Çakır. Dual transcriptome based reconstruction of salmonella-human integrated metabolic network to screen potential drug targets. 17(5):e0268889–e0268889, 2022. [DOI].
Raja Murugan, Archanaa Sundararaghavan, Navdeep Kaur Dhami, Abhijit Mukherjee, and G. K. Suraishkumar. Importance of carbon to nitrogen ratio in microbial cement production: insights through experiments and genome-scale metabolic modelling. 186():108573–108573, 2022. [DOI].
Han Bin, Zeyu Dai, and Zhimin Li. Computer-based design of a cell factory for high-yield cytidine production. 11(12):4123–4133, 2022. [DOI].
Seyed Babak Loghmani, Nadine Veith, Sven Sahle, Frank Bergmann, Brett G. Olivier, and Ursula Kummer. Inspecting the solution space of genome-scale metabolic models. 12(1):43–43, 2022. [DOI].
Lokanand Koduru, Meiyappan Lakshmanan, Shawn Hoon, Dong‐Yup Lee, Yuan Kun Lee, and Dave Siak‐Wei Ow. Systems biology of gut microbiota-human receptor interactions: toward anti-inflammatory probiotics. 13():846555–846555, 2022. [DOI].
Tadeja Režen, Alexandre Martins, Miha Mraz, Nikolaj Zimic, Damjana Rozman, and Miha Moškon. Integration of omics data to generate and analyse covid-19 specific genome-scale metabolic models. 145():105428–105428, 2022. [DOI].
Philipp Wendering and Zoran Nikoloski. Commit: consideration of metabolite leakage and community composition improves microbial community reconstructions. 18(3):e1009906–e1009906, 2022. [DOI].
Philipp Wendering and Zoran Nikoloski. Genome-scale modeling specifies the metabolic capabilities of rhizophagus irregularis. 7(1):e0121621–e0121621, 2022. [DOI].
Laurence Legon, Christophe Corre, Declan G. Bates, and Ahmad A. Mannan. Gcfront: a tool for determining a pareto front of growth-coupled cell factory designs. 38(14):3657–3659, 2022. [DOI].
Iñigo Apaolaza, Edurne San José‐Eneriz, Luis V. Valcárcel, Xabier Agirre, Felipe Prósper, and Francisco J. Planes. A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism. 18(3):e1009395–e1009395, 2022. [DOI].
Sebastian Huß, Rika Judd, Kaan Koper, Hiroshi Maéda, and Zoran Nikoloski. An automated workflow that generates atom mappings for large‐scale metabolic models and its application to arabidopsis thaliana. 111(5):1486–1500, 2022. [DOI].
Mehdi Dehghan Manshadi, Payam Setoodeh, and Habil Zare. Rapid-sl identifies synthetic lethal sets with an arbitrary cardinality. 12(1):14022–14022, 2022. [DOI].
Brendan Fu‐Long Sieow, Ryan De Sotto, Zhi Ren Darren Seet, In Young Hwang, and Matthew Wook Chang. Synthetic biology meets machine learning. 2553():21–39, 2022. [DOI].
Seungyoon Nam and Yong‐Min Lee. Genome-scale metabolic model analysis of metabolic differences between lauren diffuse and intestinal subtypes in gastric cancer. 14(9):2340–2340, 2022. [DOI].
Menglei Xia, Di Wang, Yiming Xia, Haijiao Shi, Zhongyu Tian, Yu Zheng, and Min Wang. Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production. 21(1):130–130, 2022. [DOI].
Pattsarun Cheawchanlertfa, Suwalak Chitcharoen, Nachon Raethong, Qing Liu, Pramote Chumnanpuen, Panyawarin Soommat, Yuanda Song, Mattheos Koffas, Kobkul Laoteng, and Wanwipa Vongsangnak. Enhancing genome-scale model by integrative exometabolome and transcriptome: unveiling carbon assimilation towards sphingolipid biosynthetic capability of cordyceps militaris. 8(8):887–887, 2022. [DOI].
Tobias Müller, Simon Schick, Jonathan Beck, Georg A. Sprenger, and Ralf Takors. Synthetic mutualism in engineered e. coli mutant strains as functional basis for microbial production consortia. 23(1):e2100158–e2100158, 2022. [DOI].
Kathryne C. Ford, Joshua A. M. Kaste, Yair Shachar‐Hill, and Michaela A. TerAvest. Flux-balance analysis and mobile crispri-guided deletion of a conditionally essential gene in shewanella oneidensis mr-1. 11(10):3405–3413, 2022. [DOI].
Hatice Büşra Lüleci̇, Dilara Uzuner, Tunahan Çakır, and Madhav Thambisetty. Computational approaches to assess abnormal metabolism in alzheimer’s disease using transcriptomics. 2561():173–189, 2022. [DOI].
Maria Pires Pacheco, Jimmy Ji, Tessy Prohaska, María Moscardó Garcia, and Thomas Sauter. Scfastcormics: a contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell rnaseq data. 12(12):1211–1211, 2022. [DOI].
Claudia Cipriani, Maria Pires Pacheco, Ali Kishk, Maryem Wachich, Daniel Abankwa, Elisabeth Schaffner‐Reckinger, and Thomas Sauter. Bruceine d identified as a drug candidate against breast cancer by a novel drug selection pipeline and cell viability assay. 15(2):179–179, 2022. [DOI].
Clara Correia‐Melo, Stephan Kamrad, Christoph B. Messner, Roland Tengölics, Lucía Herrera-Domínguez, St John Townsend, Mohammad Tauqeer Alam, Anja Freiwald, Kate Campbell, Simran Kaur Aulakh, Łukasz Szyrwiel, Jason Yu, Aleksej Zelezniak, Vadim Demichev, Michael Muelleder, Balázs Papp, and Markus Ralser. Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan. ():, 2022. [DOI].
Eunice A. Ferreira, Catarina C. Pacheco, João S. Rodrigues, Filipe Pinto, Pedro Lamosa, David Fuente, Javier F. Urchueguía, and Paula Tamagnini. Heterologous production of glycine betaine using synechocystis sp. pcc 6803-based chassis lacking native compatible solutes. 9():821075–821075, 2022. [DOI].
Lavanya Raajaraam and Karthik Raman. A computational framework to identify metabolic engineering strategies for the co-production of metabolites. 9():779405–779405, 2022. [DOI].
H Henriksen, Igor Marín de Mas, Helena Herand, Joseph D. Krocker, Charles E. Wade, and Pär I. Johansson. Metabolic systems analysis identifies a novel mechanism contributing to shock in patients with endotheliopathy of trauma (eot) involving thromboxane a2 and ltc4. 15():100115–100115, 2022. [DOI].
Ines Thiele, German Preciat, and Ronan M. T. Fleming. Metaboannotator: an efficient toolbox to annotate metabolites in genome-scale metabolic reconstructions. 38(20):4831–4832, 2022. [DOI].
Carles Foguet, Yu Xu, Scott C. Ritchie, Samuel A. Lambert, Elodie Persyn, Artika P. Nath, Emma E. Davenport, David J. Roberts, Dirk S. Paul, Emanuele Di Angelantonio, John Danesh, Adam S. Butterworth, Christopher Yau, and Michael Inouye. Genetically personalised organ-specific metabolic models in health and disease. ():, 2022. [DOI].
Ali Kishk, Maria Pires Pacheco, Tony Heurtaux, Lasse Sinkkonen, Jun Pang, Sabrina Fritah, Simone P. Niclou, and Thomas Sauter. Review of current human genome-scale metabolic models for brain cancer and neurodegenerative diseases. 11(16):2486–2486, 2022. [DOI].
Sankalpa Venkatraghavan, Sathvik Anantakrishnan, and Karthik Raman. Probing patterning in microbial consortia with a cellular automaton for spatial organisation. 12(1):17159–17159, 2022. [DOI].
Philipp Wendering, Marius Arend, Zahra Razaghi-Moghadamkashani, and Zoran Nikoloski. Data integration across conditions improves turnover number estimates and metabolic predictions. ():, 2022. [DOI].
Jingjing Li, Yifei Gou, Jiarui Yang, Lingxuan Zhao, Bin Wang, Tong Hao, and Jinsheng Sun. Genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis. 23(1):475–475, 2022. [DOI].
Petronella R. Hove, Nora Jean Nealon, Siu Hung Joshua Chan, Shea M. Boyer, Hannah B. Haberecht, and Elizabeth P. Ryan. Integrated profiling of gram-positive and gram-negative probiotic genomes, proteomes and metabolomes revealed small molecules with differential growth inhibition of antimicrobial-resistant pathogens. 20(5):788–810, 2022. [DOI].
Cláudia Marlene Bauer, Caroline Schmitz, Melissa Fontes Landell, and Marcelo Maraschin. Biosynthesis of fatty acids and biosurfactants by the yeast yarrowia lipolytica with emphasis on metabolic networks and bioinformatics. 6(1):e2022001–e2022001, 2022. [DOI].
Nirvana Nursimulu, Alan M Moses, and John Parkinson. Architect: a tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation. 18(9):e1010452–e1010452, 2022. [DOI].
Freddy Castillo-Alfonso, Alejandro Quintana-Menéndez, Gabriel Vigueras-Ramírez, Alfonso Mauricio Sales-Cruz, Luis Manuel Rosales-Colunga, and Roberto Olivares‐Hernández. Analysis of the propionate metabolism in bacillus subtilis during 3-indolacetic production. 10(12):2352–2352, 2022. [DOI].
Yoseb Song, Jiyun Bae, Jongoh Shin, Sangrak Jin, Seulgi Kang, Hyeonsik Lee, Suhyung Cho, and Byung‐Kwan Cho. Systems biology on acetogenic bacteria for utilizing c1 feedstocks. 180():57–90, 2022. [DOI].
Supreeta Vijayakumar, Giuseppe Magazzù, Pradip Moon, Annalisa Occhipinti, and Claudio Angione. A practical guide to integrating multimodal machine learning and metabolic modeling. 2399():87–122, 2022. [DOI].
Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian K. Blaby, and Xiaoning Qian. Protocol for condition-dependent metabolite yield prediction using the trimer pipeline. 3(1):101184–101184, 2022. [DOI].
Kristina Grausa, Ivars Mozga, Kārlis Pleiko, and Agris Pentjuss. Integrative gene expression and metabolic analysis tool igemrna. 12(4):586–586, 2022. [DOI].
Takeyuki Tamura. Trimming gene deletion strategies for growth-coupled production in constraint-based metabolic networks: trimgdel. 20(2):1540–1549, 2022. [DOI].
Seo‐Young Park, Dong‐Hyuk Choi, Jinsung Song, Uiseon Park, Hyeran Cho, Bee Hak Hong, Fumi Shozui, Yaron Silberberg, and Dong‐Yup Lee. Characterizing basal and feed media effects on mammalian cell cultures by systems engineering approaches. 55(7):31–36, 2022. [DOI].
Heewon Jung, Hyun‐Seob Song, and Christof Meile. Complab v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics. ():, 2022. [DOI].
Kirk Smith, Nicole Rhoads, and Sriram Chandrasekaran. Protocol for carom: a machine learning tool to predict post-translational regulation from metabolic signatures. 3(4):101799–101799, 2022. [DOI].
Alicja K. Warda, Eugene Dempsey, Sofia D. Forssten, C. Anthony Ryan, John F. Cryan, Elaine Patterson, Mairead O’Riordan, Carol‐Anne O'Shea, Finola Keohane, Grainne Meehan, Orlagh O’Connor, R. Paul Ross, and Catherine Stanton. Cross-sectional observational study protocol: missing microbes in infants born by caesarean section (mimic): antenatal antibiotics and mode of delivery. 12(11):e064398–e064398, 2022. [DOI].
Emil Ljungqvist and Martin Gustavsson. Genome-scale reconstruction and metabolic modelling of the fast-growing thermophile geobacillus sp. lc300. 15():e00212–e00212, 2022. [DOI].
Gayathri Sambamoorthy and Karthik Raman. Deciphering the evolution of microbial interactions: in silico studies of two-member microbial communities. ():, 2022. [DOI].
Xingcun Fan, Jingru Zhou, Jianye Xia, and Xuefeng Yan. Genome‐scale metabolic model's multi‐objective solving algorithm based on the inflexion point of pareto front including maximum energy utilization and its application in aspergillus niger ds03043. 119(6):1539–1555, 2022. [DOI].
Ezgi Tanıl, Nehir Kızılilsoley, and Emrah Nıkerel. Genome-scale metabolic model guided subtyping lung cancer towards personalized diagnosis. 55(20):641–646, 2022. [DOI].
Kristina Grausa, Shahida Anusha Siddiqui, Norbert Lameyer, Karin Wiesotzki, Sergiy Smetana, and Agris Pentjuss. Metabolic modeling ofhermetia illucenslarvae resource allocation for high-value fatty acid production. ():, 2022. [DOI].
Luis V. Valcárcel, Edurne San José‐Eneriz, Raquel Ordóñez, Iñigo Apaolaza, Ana Valcárcel, Leire Gárate, Jesús F. San Miguel, Antonio Pineda‐Lucena, Xabier Agirre, Felipe Prósper, and Francisco J. Planes. Gmcstool: automated network-based tool to search for metabolic vulnerabilities in cancer. ():, 2022. [DOI].
Arianna Basile, Almut Heinken, Johannes Hertel, Larry Smarr, Weizhong Li, Laura Treu, Giorgio Valle, Stefano Campanaro, and Ines Thiele. Longitudinal flux balance analyses of a patient with crohn’s disease highlight microbiome metabolic alterations. ():, 2022. [DOI].
Tamara Bintener, Maria Pires Pacheco, Ali Kishk, Jeff Didier, and Thomas Sauter. Drug target prediction using context-specific metabolic models reconstructed from rfastcormics. 2535():221–240, 2022. [DOI].
Ye Zhang, Dehua Liu, and Zhen Chen. Genome-scale modeling and systems metabolic engineering of vibrio natriegens for the production of 1,3-propanediol. 2553():209–220, 2022. [DOI].
Hamza Umut Karakurt and Pınar Pir. in silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered gaba, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways. ():, 2022. [DOI].
Prerna Bhalla, Subasree Sridhar, Justin Kullu, Sriya Veerapaneni, Swagatika Sahoo, Nirav Bhatt, and G. K. Suraishkumar. Modeling reactive species metabolism in colorectal cancer for identifying metabolic targets and devising therapeutics. ():, 2022. [DOI].
Saratram Gopalakrishnan, Chintan Joshi, Miguel Valderrama Gomez, Elçin Içten, Pablo Rolandi, William Johnson, Cleo Kontoravdi, and Nathan E. Lewis. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. ():, 2022. [DOI].
Cecilia Noecker, Juan M. Sánchez, Jordan E. Bisanz, Veronica Escalante, Margaret Alexander, Kai Trepka, Almut Heinken, Yuanyuan Liu, Dylan Dodd, Ines Thiele, Brian C. DeFelice, and Peter J. Turnbaugh. Systems biology illuminates alternative metabolic niches in the human gut microbiome. ():, 2022. [DOI].
Maurício Alexander de Moura Ferreira, Wendel Batista da Silveira, and Zoran Nikoloski. Parrot: prediction of enzyme abundances using protein-constrained metabolic models. ():, 2022. [DOI].
Upasana Jhariya and Sukdeb Pal. Proteomic, genomic, and metabolomic understanding and designing for bioremediation of environmental contaminants. ():415–435, 2022. [DOI].
Johann F. Jadebeck, Wolfgang Wiechert, and Katharina Nöh. Chrrt: boosting coordinate hit-and-run with rounding by thinning. ():, 2022. [DOI].
Ines Thiele and Ronan M. T. Fleming. Whole-body metabolic modelling predicts isoleucine dependency of sars-cov-2 replication. ():, 2022. [DOI].
Eric Libby, Christopher P. Kempes, and Jordan G. Okie. Metabolic compatibility and the rarity of prokaryote endosymbioses. ():, 2022. [DOI].
Hiroki Suyama, Laurence Don Wai Luu, Ling Zhong, Mark J. Raftery, and Ruiting Lan. Integrating proteomic data with metabolic modelling provides insight into key pathways ofbordetella pertussisbiofilms. ():, 2022. [DOI].
Andisheh Dadashi and Derek Martinez. Flux balance network expansion predicts stage-specific human peri_implantation embryo metabolism. 20(04):2250010–2250010, 2022. [DOI].
Ning Qin, Lingyun Li, Xu Ji, Rui Pereira, Yu Chen, Shile Yin, Chaokun Li, Xiaozhen Wan, Hao Luo, Yueping Zhang, Genlai Dong, Yiming Zhang, Shuobo Shi, Christer Larsson, Yun Chen, Tianwei Tan, Zihe Liu, and Jens Nielsen. Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates. ():, 2022. [DOI].
Sebastián N. Mendoza, Pedro A. Saa, Bas Teusink, and Eduardo Agosín. Metabolic modeling of wine fermentation at genome scale. 2399():395–454, 2022. [DOI].
Kengo Watanabe, Tomasz Wilmanski, Priyanka Baloni, Max Robinson, Gonzalo G. Garcia, Michael R. Hoopmann, Mukul K. Midha, David Baxter, Michal Maes, Seamus R. Morrone, Kelly M. Crebs, Charu Kapil, Ulrike Kusebauch, Jack Wiedrick, Jodi Lapidus, Jennifer C. Lovejoy, Andrew T. Magis, Christopher Lausted, Jared C. Roach, Gustavo Glusman, Steven R. Cummings, Nicholas J. Schork, Nathan D. Price, Leroy Hood, Richard A. Miller, Robert L. Moritz, and Noa Rappaport. Systems-level patterns in biological processes are changed under prolongevity interventions and across biological age. ():, 2022. [DOI].
Handan Çetin, Zeynep Petek Çakar, and Kutlu Ö. Ülgen. Understanding the adaptive laboratory evolution of multiple stress‐resistant yeast strains by genome‐scale modeling. 39(8):449–465, 2022. [DOI].
Nunthaphan Vikromvarasiri, Shuhei Noda, Tomokazu Shirai, and Akihiko Kondo. Investigation of two metabolic engineering approaches for (r,r)-2,3-butanediol production from glycerol in bacillus subtilis. ():, 2022. [DOI].
Nantia Leonidou, Alina Renz, Reihaneh Mostolizadeh, and Andreas Dräger. New workflow predicts drug targets against sars-cov-2 via metabolic changes in infected cells. ():, 2022. [DOI].
Håvard Molversmyr, Ove Øyås, Filip Rotnes, and Jon Olav Vik. Transcriptomics data integration for context-specific modeling of atlantic salmon metabolism: functional evaluation of methods based on metabolic tasks. ():, 2022. [DOI].
Joshua A. M. Kaste and Yair Shachar‐Hill. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling. ():, 2022. [DOI].
Ibrahim Fakih, Jeanne Got, Carlos Eduardo Robles-Rodríguez, Anne Siegel, Évelyne Forano, and Rafael Muñoz‐Tamayo. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacteriumfibrobacter succinogeness85. ():, 2022. [DOI].
Marius Arend, David Zimmer, Rudan Xu, Frederick Sommer, Timo Mühlhaus, and Zoran Nikoloski. Proteomics and constraint-based modelling reveal enzyme kinetic properties ofchlamydomonas reinhardtiion a genome scale. ():, 2022. [DOI].
Kulwadee Thanamit, Franziska Hoerhold, Marcus Oswald, and Rainer Koenig. Linear programming based gene expression model (lpm-gem) predicts the carbon source for bacillus subtilis. 23(1):226–226, 2022. [DOI].
Armin Dadras, Sayed‐Amir Marashi, Kaveh Kavousi, and Ali Mohammad Banaei‐Moghaddam. Computational modeling of the evolutionary transition from c3 to c4 photosynthesis. ():, 2022. [DOI].
Min Wang, Menglei Xia, Di Wang, Yiming Xia, Haijiao Shi, Zhongyu Tian, and Yu Zheng. Oxidoreduction potential controlling for increasing the fermentability of enzymatically hydrolyzed steam-exploded corn stover for butanol production. ():, 2022. [DOI].
Mohd Firdaus Abdul‐Wahab, Shruti Pavagadhi, Hitesh Tikariha, and Sanjay Swarup. Microbiome engineering for next‐generation precision agriculture. ():137–153, 2022. [DOI].
Irina Alecu, Carmen Daniela Sosa‐Miranda, Jagdeep K. Sandhu, Steffany A. L. Bennett, and Miroslava Čuperlović‐Culf. Cell culture metabolomics and lipidomics. ():415–456, 2022. [DOI].
Prerna Bhalla, Swagatika Sahoo, Raghunathan Rengaswamy, Devarajan Karunagaran, and G. K. Suraishkumar. Constraint-based modeling to understand ros-mediated effects in cancer. ():2209–2229, 2022. [DOI].
Emil Karlsen, Marianne Gylseth, Christian Schulz, and Eivind Almaas. A study of a diauxic growth experiment using an expanded dynamic flux balance framework. ():, 2022. [DOI].
Pooya Borzou, Jafar Ghaisari, Iman Izadi, Yasin Eshraghi, and Yousof Gheisari. A novel strategy for dynamic modelling of genome-scale interaction networks. ():, 2022. [DOI].
William T. Scott, David Henriques, Eddy J. Smid, Richard A. Notebaart, and Eva Balsa‐Canto. Dynamic genome-scale modeling ofsaccharomyces cerevisiaeunravels mechanisms for ester formation during alcoholic fermentation. ():, 2022. [DOI].
Tania Alonso-Vásquez, Christopher Riccardi, Iacopo Passeri, and Marco Fondi. Modelling the metabolic consequences of antimicrobial exposure. ():, 2022. [DOI].
Lamiaa H.S. Hassan, Imran Ahmad, Mostafa El Sheekh, and Norhayati Abdullah. In silico models on algal cultivation and processing. ():228–255, 2022. [DOI].
Andreas Kremling. Modeling approaches to microbial metabolism. 2399():455–485, 2022. [DOI].
Thomas Sevrin, Lisa Strasser, Camille Ternet, Philipp Junk, Miriam Caffarini, Simona Catozzi, Giorgio Oliviero, Kieran Wynne, Christina Kiel, and Philip J. Luthert. Whole-cell energy modeling reveals quantitative changes of predicted energy flows in ras mutant cancer cell lines. ():, 2022. [DOI].
Dalimil Bujdoš, Barbora Popelářová, Daniel C. Volke, Pablo I. Nikel, Nikolaus Sonnenschein, and Pavel Dvořák. Engineering of pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model. ():, 2022. [DOI].
Mike Sportiello, Alexis Poindexter, Emma C. Reilly, Adam Geber, Kris Lambert Emo, Taylor N. Jones, and David J. Topham. Mouse memory cd8 t cell subsets defined by tissue resident memory (trm) integrin expression exhibit distinct metabolic profiles. ():, 2022. [DOI].
Herbert Yao, Sanjeev Dahal, and Laurence Yang. Novel context-specific genome-scale modelling explores the potential of chlamydomonas reinhardtii for synthetic biology applications. ():, 2022. [DOI].
Christopher Riccardi, Marzia Calvanese, Veronica Ghini, Tania Alonso-Vásquez, Elena Perrin, Paola Turano, Giorgio Giurato, Alessandro Weisz, Ermenegilda Parrilli, Maria Luisa Tutino, and Marco Fondi. Metabolic robustness to growth temperature of a cold adapted bacterium. ():, 2022. [DOI].
Anonymous. Comment on egusphere-2022-1016. ():, 2022. [DOI].
Delphine Nègre, Abdelhalim Larhlimi, and Samuel Bertrand. Reconciliation and evolution ofpenicillium rubensgenome-scale metabolic networks – what about specialised metabolism? ():, 2022. [DOI].
Maria De La Fuente Ruiz. Comment on egusphere-2022-1016. ():, 2022. [DOI].
Issalapap Kaothong. Enhanced triacylglycerol production in chlorella vulgaris by flux balance analysis on metabolic network. ():, 2022. [DOI].
Zhihan WANG, Yao Ruan, Qingye Zhang, and Hongyu Zhang. Metabolic network flux balance analysis of the microgravity influence on the growth of arabidopsis thaliana. 42(5):941–941, 2022. [DOI].
Jingjing Li, Yifei Gou, Jiarui Yang, Lingxuan Zhao, Bin Wang, Tong Hao, and Jinsheng Sun. Additional file 2 of genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis. ():, 2022. [DOI].
Jingjing Li, Yifei Gou, Jiarui Yang, Lingxuan Zhao, Bin Wang, Tong Hao, and Jinsheng Sun. Additional file 6 of genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis. ():, 2022. [DOI].
Jingjing Li, Yifei Gou, Jiarui Yang, Lingxuan Zhao, Bin Wang, Tong Hao, and Jinsheng Sun. Additional file 5 of genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis. ():, 2022. [DOI].
Jingjing Li, Yifei Gou, Jiarui Yang, Lingxuan Zhao, Bin Wang, Tong Hao, and Jinsheng Sun. Additional file 3 of genome-scale metabolic network model of eriocheir sinensis icrab4665 and nutritional requirement analysis. ():, 2022. [DOI].

Analeigha V. Colarusso, Isabella M. Goodchild-Michelman, Maya Rayle, and Ali R. Zomorrodi. Computational modeling of metabolism in microbial communities on a genome-scale. 26():46–57, 2021. [DOI].
N. T. Devika, Ashok Kumar Jangam, Vinaya Kumar Katneni, Prasanna Kumar Patil, Suganya Nathamuni, and M. S. Shekhar. In silico prediction of novel probiotic species limiting pathogenic vibrio growth using constraint-based genome scale metabolic modeling. 11():752477–752477, 2021. [DOI].
Jitin Singla and Kate L. White. A community approach to whole-cell modeling. 26():33–38, 2021. [DOI].
Siti Zulaiha. Genetic engineering of microalgae lipid biosynthesis for sustainable biodiesel production. 11(3):072–077, 2021. [DOI].
Priyanka Narad, G. Naresh, and Abhishek Sengupta. Metabolomics and flux balance analysis. ():337–365, 2021. [DOI].
Behnaz Nowrouzi and Leonardo Rios‐Solis. Redox metabolism for improving whole-cell p450-catalysed terpenoid biosynthesis. 42(8):1213–1237, 2021. [DOI].
Abhijit Paul, Rajat Anand, Sonali Porey Karmakar, Surender Rawat, Nandadulal Bairagi, and Samrat Chatterjee. Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models. 11(1):213–213, 2021. [DOI].
Yara Seif and Bernhard Ø. Palsson. Path to improving the life cycle and quality of genome-scale models of metabolism. 12(9):842–859, 2021. [DOI].
Andrew Walakira, Damjana Rozman, Tadeja Režen, Miha Mraz, and Miha Moškon. Guided extraction of genome-scale metabolic models for the integration and analysis of omics data. 19():3521–3530, 2021. [DOI].
Alan R. Pacheco and Daniel Segrè. An evolutionary algorithm for designing microbial communities via environmental modification. 18(179):20210348–20210348, 2021. [DOI].
S. Ranganayaki, Neema Jamshidi, Mohamad Aiyaz, Rashmi Santhoshkumar, Narayanappa Gayathri, Pulleri Kandi Harsha, Balasundaram Padmanabhan, and M. M. Srinivas Bharath. Inhibition of mitochondrial complex ii in neuronal cells triggers unique pathways culminating in autophagy with implications for neurodegeneration. 11(1):1483–1483, 2021. [DOI].
Elisabeth Zelle, Nina Pfelzer, Marco Oldiges, Abigail Koch-Koerfges, Michael Bott, Katharina Nöh, and Wolfgang Wiechert. An energetic profile of corynebacterium glutamicum underpinned by measured biomass yield on atp. 65():66–78, 2021. [DOI].
Bouchra Ezzamouri, Saeed Shoaie, and Rodrigo Ledesma‐Amaro. Synergies of systems biology and synthetic biology in human microbiome studies. 12():681982–681982, 2021. [DOI].
Nicolai Panikov. Genome-scale reconstruction of microbial dynamic phenotype: successes and challenges. 9(11):2352–2352, 2021. [DOI].
Gurneet S. Sangha, Craig J. Goergen, Steven J. Prior, Sushant M. Ranadive, and Alisa Morss Clyne. Preclinical techniques to investigate exercise training in vascular pathophysiology. 320(4):H1566–H1600, 2021. [DOI].
Lachlan Munro and Douglas B. Kell. Intelligent host engineering for metabolic flux optimisation in biotechnology. 478(20):3685–3721, 2021. [DOI].
Þóra Björg Sigmarsdóttir, Sarah McGarrity, James T. Yurkovich, Óttar Rolfsson, and Ólafur E. Sigurjónsson. Analyzing metabolic states of adipogenic and osteogenic differentiation in human mesenchymal stem cells via genome scale metabolic model reconstruction. 9():642681–642681, 2021. [DOI].
German Preciat, Agnieszka B. Wegrzyn, Ines Thiele, Thomas Hankemeier, Ronan M. T. Fleming, and Ronan M.T. Fleming. Xomicstomodel: omics data integration and generation of thermodynamically consistent metabolic models. ():, 2021. [DOI].
Hameri Tuure, Georgios Fengos, and Vassily Hatzimanikatis. The effects of model complexity and size on metabolic flux distribution and control: case study in escherichia coli. 22(1):134–134, 2021. [DOI].
Anush Chiappino-Pepe and Vassily Hatzimanikatis. Phenomapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models. 2(1):100280–100280, 2021. [DOI].
Philippe Bogaerts and Alain Vande Wouwer. How to tackle underdeterminacy in metabolic flux analysis? a tutorial and critical review. 9(9):1577–1577, 2021. [DOI].
Ali Navid. A beginner’s guide to the cobra toolbox. 2349():339–365, 2021. [DOI].
Hunor Bartos, Márta Balázs, Ildikó Hajnalka Kuzman, Szabolcs Lányi, and Ildikó Miklóssy. Production of high added-value chemicals in basfia succiniciproducens: role of medium composition. 13(6):3513–3513, 2021. [DOI].
Amnah Alzahmi, Sarah Daakour, Diana C. El Assal, Bushra Dohai, Amphun Chaiboonchoe, Weiqi Fu, David R. Nelson, Alexandra Mystikou, and Kourosh Salehi‐Ashtiani. High-throughput metabolic profiling for model refinements of microalgae. (178):, 2021. [DOI].
Amnah Alzahmi, Sarah Daakour, Diana C. El Assal, Bushra Dohai, Amphun Chaiboonchoe, Weiqi Fu, David R. Nelson, Alexandra Mystikou, and Kourosh Salehi‐Ashtiani. High-throughput metabolic profiling for model refinements of microalgae. (178):, 2021. [DOI].
Ali Navid. Curating cobra models of microbial metabolism. 2349():321–338, 2021. [DOI].
Bernhard Ø. Palsson. Genome‐scale models. ():23–71, 2021. [DOI].
Mohammad Mazharul Islam, Andrea Goertzen, Pankaj K. Singh, and Rajib Saha. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. ():, 2021. [DOI].
Till Tiso, Tanja Narančić, Ren Wei, Éric Pollet, Niall Beagan, Katja Schröder, Annett Honak, Mengying Jiang, Shane T. Kenny, Nick Wierckx, Rémi Perrin, Luc Avérous, Wolfgang Zimmermann, Kevin E. O’Connor, and Lars M. Blank. Towards bio-upcycling of polyethylene terephthalate. 66():167–178, 2021. [DOI].
Dorinês Rosário, Gholamreza Bidkhori, Sunjae Lee, Janis R. Bedarf, Falk Hildebrand, Emmanuelle Le Chatelier, Mathias Uhlén, S. Dusko Ehrlich, Gordon Proctor, Ullrich Wüllner, Adil Mardinoğlu, and Saeed Shoaie. Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in parkinson’s disease. 34(9):108807–108807, 2021. [DOI].
Lekha T. Pazhamala, Himabindu Kudapa, Wolfram Weckwerth, A. Harvey Millar, and Rajeev K. Varshney. Systems biology for crop improvement. 14(2):e20098–e20098, 2021. [DOI].
Omid Oftadeh, Pierre Salvy, María Masid, Maxime Curvat, Ljubiša Mišković, and Vassily Hatzimanikatis. A genome-scale metabolic model of saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics. 12(1):4790–4790, 2021. [DOI].
Karim Azer, Chanchala Kaddi, Jeffrey S. Barrett, Jane P. F. Bai, Sean T. McQuade, Nathaniel J. Merrill, Benedetto Piccoli, Susana R. Neves, Luca Marchetti, Rosario Lombardo, Silvia Parolo, Selva Rupa Christinal Immanuel, and Nitin S. Baliga. History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications. 12():637999–637999, 2021. [DOI].
David J. Sanders, S Inniss, Gregory Sebepos‐Rogers, Farooq Rahman, and Andrew Smith. The role of the microbiome in gastrointestinal inflammation. 41(6):, 2021. [DOI].
Yuan Zhu, Ying Li, Ya Xu, Jian Zhang, Linlin Ma, Qingsheng Qi, and Qian Wang. Development of bifunctional biosensors for sensing and dynamic control of glycolysis flux in metabolic engineering. 68():142–151, 2021. [DOI].
Zachary B. Haiman, Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, and Bernhard Ø. Palsson. Masspy: building, simulating, and visualizing dynamic biological models in python using mass action kinetics. 17(1):e1008208–e1008208, 2021. [DOI].
David Henriques, Romain Minebois, Sebastián N. Mendoza, Laura G. Macías, Roberto Pérez‐Torrado, Eladio Barrio, Bas Teusink, Amparo Querol, and Eva Balsa‐Canto. A multiphase multiobjective dynamic genome-scale model shows different redox balancing among yeast species of the saccharomyces genus in fermentation. 6(4):e0026021–e0026021, 2021. [DOI].
Marie Schöpping, Paula Gaspar, Ana Rute Neves, Carl Johan Franzén, and Ahmad A. Zeidan. Identifying the essential nutritional requirements of the probiotic bacteria bifidobacterium animalis and bifidobacterium longum through genome-scale modeling. 7(1):47–47, 2021. [DOI].
Song‐Min Schinn, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. A genome‐scale metabolic network model and machine learning predict amino acid concentrations in chinese hamster ovary cell cultures. 118(5):2118–2123, 2021. [DOI].
Karl Kochanowski, Hiroyuki Okano, Vadim Patsalo, James R. Williamson, Uwe Sauer, and Terence Hwa. Global coordination of metabolic pathways in escherichia coli by active and passive regulation. 17(4):e10064–e10064, 2021. [DOI].
Nhung Pham, Maarten Reijnders, María Suárez‐Diez, Bart Nijsse, Jan Springer, Gerrit Eggink, and Peter J. Schaap. Genome-scale metabolic modeling underscores the potential of cutaneotrichosporon oleaginosus atcc 20509 as a cell factory for biofuel production. 14(1):2–2, 2021. [DOI].
Punit Kumar, Kashyap Kumar Dubey, Zeba Usmani, Minaxi Sharma, and Vijai Kumar Gupta. Biotechnological and industrial applications of streptomyces metabolites. 16(1):244–264, 2021. [DOI].
Song‐Min Schinn, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. 66():21–30, 2021. [DOI].
Kangsan Kim, Donghui Choe, Yoseb Song, Minjeong Kang, Seung‐Goo Lee, Dae‐Hee Lee, and Byung‐Kwan Cho. Engineering bacteroides thetaiotaomicron to produce non-native butyrate based on a genome-scale metabolic model-guided design. 68():174–186, 2021. [DOI].
Ali Kishk, Maria Pires Pacheco, and Thomas Sauter. Dccov: repositioning of drugs and drug combinations for sars-cov-2 infected lung through constraint-based modeling. 24(11):103331–103331, 2021. [DOI].
Dohyeon Kim, Young-Shin Kim, and Sung Ho Yoon. Development of a genome-scale metabolic model and phenome analysis of the probiotic escherichia coli strain nissle 1917. 22(4):2122–2122, 2021. [DOI].
Ying Li, He Xian, Ya Xu, Yuan Zhu, Zhijie Sun, Qian Wang, and Qingsheng Qi. Fine tuning the glycolytic flux ratio of ep-bifido pathway for mevalonate production by enhancing glucose-6-phosphate dehydrogenase (zwf) and crispri suppressing 6-phosphofructose kinase (pfka) in escherichia coli. 20(1):32–32, 2021. [DOI].
Raja Rajeswary Thanmalagan, Aiswarya Jayaprakash, Abhijeet Deb Roy, A. Annamalai, and P.T.V. Lakshmi. A review on applications of plant network biology to understand the drought stress response in economically important cereal crops. 29():100345–100345, 2021. [DOI].
Hossein Firoozabadi, Mohammad Mahdi Mardanpour, and Ehsan Motamedian. A system-oriented strategy to enhance electron production of synechocystis sp. pcc6803 in bio-photovoltaic devices: experimental and modeling insights. 11(1):12294–12294, 2021. [DOI].
Zongjie Dai, Yan Zhu, Hongjun Dong, Chunhua Zhao, Yanping Zhang, and Yin Li. Enforcing atp hydrolysis enhanced anaerobic glycolysis and promoted solvent production in clostridium acetobutylicum. 20(1):149–149, 2021. [DOI].
Lady Johanna Forero-Rodríguez, Jonathan Josephs‐Spaulding, Stefano Flor, Andrés Pinzón, and Christoph Kaleta. Parkinson’s disease and the metal–microbiome–gut–brain axis: a systems toxicology approach. 11(1):71–71, 2021. [DOI].
Vladimir Porokhin, Sara A. Amin, Trevor B. Nicks, Venkatesh Endalur Gopinarayanan, Nikhil U. Nair, and Soha Hassoun. Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity. 12():e00170–e00170, 2021. [DOI].
Patricia Martins Conde, Thomas Pfau, Maria Pires Pacheco, and Thomas Sauter. A dynamic multi-tissue model to study human metabolism. 7(1):5–5, 2021. [DOI].
You‐Tyun Wang, Min‐Ru Lin, Wei‐Chen Chen, Wu‐Hsiung Wu, and Feng‐Sheng Wang. Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers. 11(8):2078–2094, 2021. [DOI].
Mahdi Jalili, Martin Scharm, Olaf Wolkenhauer, Mehdi Damaghi, and Ali Salehzadeh‐Yazdi. Exploring the metabolic heterogeneity of cancers: a benchmark study of context-specific models. 11(6):496–496, 2021. [DOI].
María Moscardó Garcia, Maria Pires Pacheco, Tamara Bintener, Luana Presta, and Thomas Sauter. Importance of the biomass formulation for cancer metabolic modeling and drug prediction. 24(10):103110–103110, 2021. [DOI].
Hilal Taymaz‐Nikerel and Alvaro R. Lara. Vitreoscilla haemoglobin: a tool to reduce overflow metabolism. 10(1):43–43, 2021. [DOI].
German Preciat, Agnieszka B. Wegrzyn, Edinson Lucumi Moreno, Cornelius C.W. Willacey, Jennifer Modamio, Fátima Liliana Monteiro, Diana C. El Assal, Alissa Schurink, Miguel A. P. Oliveira, Zhi Zhang, Ben Cousins, Hulda S. Haraldsdóttir, Siham Hachi, Susanne Zach, Germán Leparc, Yin Tat Lee, Bastian Hengerer, Santosh Vempala, Michael A. Saunders, Amy C. Harms, Enrico Glaab, Jens C. Schwamborn, Ines Thiele, Thomas Hankemeier, and Ronan M. T. Fleming. Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism. ():, 2021. [DOI].
Ratchaprapa Kamsen, Saowalak Kalapanulak, Porntip Chiewchankaset, and Treenut Saithong. Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava. 11(1):8758–8758, 2021. [DOI].
James M. Clomburg, Ángela Cintolesi, and Ramón González. in silico and in vivo analyses reveal key metabolic pathways enabling the fermentative utilization of glycerol in escherichia coli. 15(1):289–304, 2021. [DOI].
Claudio Tomi-Andrino, Rupert Norman, Thomas Millat, Philippe Soucaille, Klaus Winzer, David A. Barrett, John R. King, and Dong‐Hyun Kim. Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions. 17(1):e1007694–e1007694, 2021. [DOI].
Davide Lagoa, José P. Faria, Filipe Liu, Emanuel Cunha, Christopher S. Henry, and Óscar Dias. transyt, an innovative framework for identifying transport systems. ():, 2021. [DOI].
Fernando Medeiros Filho, Ana Paula Barbosa do Nascimento, Maiana de Oliveira Cerqueira e Costa, Thiago Castanheira Merigueti, Márcio Argollo de Menezes, Marisa Nicolás, Marcelo Trindade dos Santos, Ana Paula D’Alincourt Carvalho-Assef, and Fabrício Alves Barbosa da Silva. A systematic strategy to find potential therapeutic targets for pseudomonas aeruginosa using integrated computational models. 8():728129–728129, 2021. [DOI].
Olga Yaneth Echeverri-Peña, Diego Salazar-Barreto, Alexander Rodríguez‐López, Janneth González, Carlos Javier Alméciga-Díaz, Cristian H. Verano-Guevara, and Luis A. Barrera. Use of a neuron-glia genome-scale metabolic reconstruction to model the metabolic consequences of the arylsulphatase a deficiency through a systems biology approach. 7(7):e07671–e07671, 2021. [DOI].
Neeraj Sinha, Evert M. van Schothorst, Guido Hooiveld, Jaap Keijer, Vítor A. P. Martins dos Santos, and María Suárez‐Diez. Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism. 22(1):574–574, 2021. [DOI].
Marc Griesemer and Ali Navid. Mofa: multi-objective flux analysis for the cobra toolbox. ():, 2021. [DOI].
Priyanka Gehlot, Nidhi Pareek, and Vivekanand Vivekanand. Development of biofertilizers and microbial consortium an approach to sustainable agriculture practices. ():315–348, 2021. [DOI].
Omid Oftadeh, Pierre Salvy, María Masid, Maxime Curvat, Ljubiša Mišković, and Vassily Hatzimanikatis. A genome-scale metabolic model ofsaccharomyces cerevisiaethat integrates expression constraints and reaction thermodynamics. ():, 2021. [DOI].
Eunice van Pelt‐KleinJan, Daan H. de Groot, and Bas Teusink. Understanding fba solutions under multiple nutrient limitations. 11(5):257–257, 2021. [DOI].
Anna Trigos, Benjamin Goudey, Justin Bedő, Thomas Conway, Noel G. Faux, and Kelly L. Wyres. Collateral sensitivity to β-lactam drugs in drug-resistant tuberculosis is driven by the transcriptional wiring of blai operon genes. 6(3):e0024521–e0024521, 2021. [DOI].
MacLean G. Kohlmeier, Ben A. Bailey-Elkin, Brian L. Mark, and Ivan J. Oresnik. Characterization of the sorbitol dehydrogenase smos from sinorhizobium meliloti 1021. 77(3):380–390, 2021. [DOI].
Ankur Gupta, Ajay Kumar, Rajat Anand, Nandadulal Bairagi, and Samrat Chatterjee. Genome scale metabolic model driven strategy to delineate host response to mycobacterium tuberculosis infection. 17(2):296–306, 2021. [DOI].
David Hala, Patricia C. Faulkner, Kai He, Manoj Kamalanathan, Mikeelee Brink, Kristina Simons, Meltem Apaydin, Béatrice V. Hernout, Lene H. Petersen, Ivan Ivanov, and Xiaoning Qian. An integrated in vivo and in silico analysis of the metabolism disrupting effects of cpi-613 on embryo-larval zebrafish (danio rerio). 248():109084–109084, 2021. [DOI].
Mahsa Sadat Razavi Borghei, Meysam Mobasheri, and Tabassom Sobati. Genome scale constraint-based metabolic reconstruction of propionibacterium freudenreichii dsm 20271. ():, 2021. [DOI].
Luis Caspeta, Eduard J. Kerkhoven, Alfredo Martı́nez, and Jens Nielsen. The yeastgemmap: a process diagram to assist yeast systems‐metabolic studies. 118(12):4800–4814, 2021. [DOI].
Albert Tafur Rangel, Luis H. Reyes, Jorge M. Gómez, and Andrés Fernando González Barrios. Optimization of glycerol consumption in wild‐type escherichia coli using central carbon modeling as an alternative approach. 15(3):825–839, 2021. [DOI].
Joshua E. Goldford, Ashish B. George, Avi I. Flamholz, and Daniel Segrè. Protein cost minimization promotes the emergence of coenzyme redundancy. ():, 2021. [DOI].
Ehsan Salehabadi, Ehsan Motamedian, and Seyed Abbas Shojaosadati. Reconstruction of a generic genome-scale metabolic network for chicken: investigating network connectivity and finding potential biomarkers. ():, 2021. [DOI].
Julia Koblitz, Sabine Will, S. Alexander Riemer, Thomas Ulas, Meina Neumann‐Schaal, and Dietmar Schomburg. The metano modeling toolbox mmtb: an intuitive, web-based toolbox introduced by two use cases. 11(2):113–113, 2021. [DOI].
Craig Woods, Christopher M. Humphreys, Claudio Tomi-Andrino, Anne M. Henstra, Michael Köpke, Séan D. Simpson, Klaus Winzer, and Nigel P. Minton. Application of transposon-insertion sequencing to determine gene essentiality in the acetogen clostridium autoethanogenum. ():, 2021. [DOI].
Yuki Kuriya, Mai Inoue, Masaki Yamamoto, Masahiro Murata, and Michihiro Araki. Knowledge extraction from literature and enzyme sequences complements fba analysis in metabolic engineering. 16(12):e2000443–e2000443, 2021. [DOI].
Angela Dixon. Designing predictive mathematical models for the metabolic pathways associated with polyhydroxybutyrate synthesis in escherichia coli. ():, 2021. [DOI].
Ali Kishk, Maria Pires Pacheco, and Thomas Sauter. Dccov: repositioning of drugs and drug combinations for sars-cov-2 infected lung through constraint-based modelling. ():, 2021. [DOI].
Lokanand Koduru, Meiyappan Lakshmanan, Pei Yu Lim, Pooi-Leng Ho, M. Shakeera Banu, Doo‐Sang Park, Dave Siak‐Wei Ow, and Dong‐Yup Lee. Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet-induced and strain-specific probiotic idiosyncrasies. ():, 2021. [DOI].
Hossein Firoozabadi, Mohammad Mahdi Mardanpour, and Ehsan Motamedian. A system-oriented strategy to enhance electron production of synechocystis sp. pcc6803 in bio-photovoltaic devices: experimental and modeling insights. ():, 2021. [DOI].
Yoshitaro Heshiki. Optimization of polyhydroxybutyrate production in recombinant escherichia coli through metabolic modeling and simulation. ():, 2021. [DOI].
David Henriques, Romain Minebois, Sebastián N. Mendoza, Laura G. Macías, Roberto Pérez‐Torrado, Eladio Barrio, Bas Teusink, Amparo Querol, and Eva Balsa‐Canto. A multi-phase multi-objective genome-scale model shows diverse redox balance strategies in yeasts. ():, 2021. [DOI].
Nhung Pham. Evaluating and deploying genome-scale metabolic models for microbial cell factories. ():, 2021. [DOI].
Lisa M. Leone. Metabolic modeling of secondary metabolism in plant systems. ():, 2021. [DOI].
Sumit Kumar Singh. Pathway pioneer: a web-based graphical tool for the organization and flux analysis of metabolic networks. ():, 2021. [DOI].
Harsh Dosi. Pathway pioneer: a web-based metabolic network layout extension. ():, 2021. [DOI].
Vimaladhasan Senthamizhan, Sunanda Subramaniam, Arjun Raghavan, and Karthik Raman. Castle: a database of synthetic lethal sets predicted from genome-scale metabolic networks. ():, 2021. [DOI].
Apostolos Chalkis, Vissarion Fisikopoulos, Marios Papachristou, and Elias Tsigaridas. Truncated log-concave sampling with reflective hamiltonian monte carlo. ():, 2021. [DOI].
David Henriques, Romain Minebois, Sebastián N. Mendoza, Laura G. Macías, Roberto Pérez‐Torrado, Eladio Barrio, Bas Teusink, Amparo Querol, and Eva Balsa‐Canto. A multi-phase multi-objective dynamic genome-scale model shows different redox balancing among yeast species in fermentation. ():, 2021. [DOI].
Goldford Je, George Ab, Avi I. Flamholz, and Daniel Segrè. Thermodynamics drives coenzyme redundancy in metabolism. ():, 2021. [DOI].
Kristopher A. Hunt, Natasha Mallette, Brent Peyton, and Ross P. Carlson. In silico analysis of functionalized hydrocarbon production using ehrlich pathway and fatty acid derivatives in an endophytic fungus. 7(6):435–435, 2021. [DOI].
Hossein Firoozabadi, Mohammad Mahdi Mardanpour, and Ehsan Motamedian. A system-oriented strategy to enhance electron production of synechocystis sp. pcc6803 in bio-photovoltaic devices: experimental and modeling insights. ():, 2021. [DOI].
Aidin Behravan, Atieh Hashemi, Sayed‐Amir Marashi, and Hamideh Fouladiha. Genome-scale metabolic model based engineering of escherichia coli enhances recombinant single chain antibody fragment production. ():, 2021. [DOI].
Romain Minebois. Study of the differences in the fermentative metabolism of s. cerevisiae, s. uvarum and s. kudriavzevii species. ():, 2021. [DOI].
Ying Li, He Xian, Ya Xu, Yuan Zhu, Zhijie Sun, Qian Wang, and Qingsheng Qi. Additional file 2 of fine tuning the glycolytic flux ratio of ep-bifido pathway for mevalonate production by enhancing glucose-6-phosphate dehydrogenase (zwf) and crispri suppressing 6-phosphofructose kinase (pfka) in escherichia coli. ():, 2021. [DOI].
Zongjie Dai, Yan Zhu, Hongjun Dong, Chunhua Zhao, Yanping Zhang, and Yin Li. Additional file 2 of enforcing atp hydrolysis enhanced anaerobic glycolysis and promoted solvent production in clostridium acetobutylicum. ():, 2021. [DOI].
Allon Wagner, Chao Wang, Johannes Fessler, David DeTomaso, Julián Ávila-Pacheco, James J. Kaminski, Sarah Zaghouani, Elena Christian, Pratiksha I. Thakore, Brandon Schellhaass, Elliot H. Akama‐Garren, Kerry A. Pierce, Vasundhara Singh, Noga Ron‐Harel, Vivian Paraskevi Douglas, Lloyd Bod, Alexandra Schnell, Daniel J. Puleston, Raymond A. Sobel, Marcia C. Haigis, Erika L. Pearce, Manoocher Soleimani, Clary B. Clish, Aviv Regev, Vijay K. Kuchroo, and Nir Yosef. Metabolic modeling of single th17 cells reveals regulators of autoimmunity. 184(16):4168–4185.e21, 2021. [DOI].
Laura Judith Marcos-Zambrano, Kanita Karađuzović-Hadžiabdić, Tatjana Lončar-Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasić Telalović, Karel Hron, Thomas Klammsteiner, Mikhail Kolev, Leo Lahti, Marta B. Lopes, Vı́ctor Moreno, Irina Naskinova, Elin Org, Inês Paciência, Γεώργιος Παπουτσόγλου, Rajesh Shigdel, Blaž Stres, Baiba Vilne, Malik Yousef, Eftim Zdravevski, Ioannis Tsamardinos, Enrique Carrillo de Santa Pau, Marcus J. Claesson, Isabel Moreno‐Indias, and Jaak Truu. Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. 12():, 2021. [DOI].
Farideh Hosseinkhani, Almut Heinken, Ines Thiele, Petrus W. Lindenburg, Amy C. Harms, and Thomas Hankemeier. The contribution of gut bacterial metabolites in the human immune signaling pathway of non-communicable diseases. 13(1):1–22, 2021. [DOI].
Ilija Dukovski, Djordje Bajić, Jeremy M. Chacón, Michael Quintin, Jean C. C. Vila, Snorre Sulheim, Alan R. Pacheco, David B. Bernstein, William J. Riehl, Kirill S. Korolev, Álvaro Sánchez, William R. Harcombe, and Daniel Segrè. A metabolic modeling platform for the computation of microbial ecosystems in time and space (comets). 16(11):5030–5082, 2021. [DOI].
Ruiwen Hu, Hai-Ming Zhao, Xihui Xu, Zhigang Wang, Ke Yu, Longfei Shu, Qingyun Yan, Bo Wu, Ce-Hui Mo, Zhili He, and Cheng Wang. Bacteria-driven phthalic acid ester biodegradation: current status and emerging opportunities. 154():106560–106560, 2021. [DOI].
Anurag Passi, Juan D. Tibocha‐Bonilla, Manish Kumar, Diego Tec-Campos, Karsten Zengler, and Cristal Zúñiga. Genome-scale metabolic modeling enables in-depth understanding of big data. 12(1):14–14, 2021. [DOI].
David B. Bernstein, Snorre Sulheim, Eivind Almaas, and Daniel Segrè. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. 22(1):64–64, 2021. [DOI].
Vijay R. Varma, Hatice Büşra Lüleci̇, Anup Mammen Oommen, Sudhir Varma, Chad Blackshear, Michael Griswold, Yang An, Jackson A. Roberts, Richard O’Brien, Olga Pletniková, Juan C. Troncoso, David A. Bennett, Tunahan Çakır, Cristina Legido‐Quigley, and Madhav Thambisetty. Abnormal brain cholesterol homeostasis in alzheimer’s disease—a targeted metabolomic and transcriptomic study. 7(1):11–11, 2021. [DOI].
J. Lee, Yapeng Su, Priyanka Baloni, Daniel Chen, Ana Jimena Pavlovitch-Bedzyk, Dan Yuan, Venkata R. Duvvuri, Raymond T. Ng, Jongchan Choi, Jingyi Xie, Rongyu Zhang, Kim M. Murray, Sergey A. Kornilov, Brett Smith, Andrew T. Magis, Dave S.�B. Hoon, Jennifer Hadlock, Jason D. Goldman, Nathan D. Price, Raphaël Gottardo, Mark M. Davis, Leroy Hood, Philip D. Greenberg, and James R. Heath. Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with covid-19. 40(1):110–120, 2021. [DOI].
Carolin C. M. Schulte, Khushboo Borah, Rachel M. Wheatley, Jason J. Terpolilli, Gerhard Saalbach, Nick Crang, Daan H. de Groot, R. George Ratcliffe, Nicholas J. Kruger, Antonis Papachristodoulou, and Philip S. Poole. Metabolic control of nitrogen fixation in rhizobium-legume symbioses. 7(31):, 2021. [DOI].
Santosh Lamichhane, Partho Sen, Marina Amaral Alves, Henrique Caracho Ribeiro, Peppi Raunioniemi, Tuulia Hyötyläinen, and Matej Orešič. Linking gut microbiome and lipid metabolism: moving beyond associations. 11(1):55–55, 2021. [DOI].
Almut Heinken, Johannes Hertel, and Ines Thiele. Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis. 7(1):19–19, 2021. [DOI].
Akshit Goyal, Tong Wang, Veronika Dubinkina, and Sergei Maslov. Ecology-guided prediction of cross-feeding interactions in the human gut microbiome. 12(1):1335–1335, 2021. [DOI].
Alexander Chou, Seung Hwan Lee, Fayin Zhu, James M. Clomburg, and Ramón González. An orthogonal metabolic framework for one-carbon utilization. 3(10):1385–1399, 2021. [DOI].
Xueqin Lv, Yaokang Wu, Mengyue Gong, Jieying Deng, Yang Gu, Yanfeng Liu, Jianghua Li, Guocheng Du, Rodrigo Ledesma‐Amaro, Long Liu, and Jian Chen. Synthetic biology for future food: research progress and future directions. 3():100025–100025, 2021. [DOI].
Yu Chen, Eunice van Pelt‐KleinJan, Berdien van Olst, Sieze Douwenga, Sjef Boeren, Herwig Bachmann, Douwe Molenaar, Jens Nielsen, and Bas Teusink. Proteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments. 17(4):e10093–e10093, 2021. [DOI].
Almut Heinken, Arianna Basile, and Ines Thiele. Advances in constraint-based modelling of microbial communities. 27():100346–100346, 2021. [DOI].
St. Elmo Wilken, Jonathan M. Monk, Patrick A. Leggieri, Christopher E. Lawson, Thomas S. Lankiewicz, Susanna Seppälä, Chris Daum, Jerry Jenkins, Anna Lipzen, Stephen J. Mondo, Kerrie Barry, Igor V. Grigoriev, John K. Henske, Michael K. Theodorou, Bernhard Ø. Palsson, Linda Petzold, and Michelle O’Malley. Experimentally validated reconstruction and analysis of a genome-scale metabolic model of an anaerobic neocallimastigomycota fungus. 6(1):, 2021. [DOI].
Barbara Bourgade, Nigel P. Minton, and M. Ahsanul Islam. Genetic and metabolic engineering challenges of c1-gas fermenting acetogenic chassis organisms. 45(2):, 2021. [DOI].
Zihe Liu, Junyang Wang, and Jens Nielsen. Yeast synthetic biology advances biofuel production. 65():33–39, 2021. [DOI].
Hui-Yin Chang, Sean Colby, Xiuxia Du, Javier D. Gomez, Maximilian J. Helf, Katerina Kechris, Christine R. Kirkpatrick, Shuzhao Li, Gary J. Patti, Ryan Renslow, Shankar Subramaniam, Mukesh Verma, Jianguo Xia, and Jamey D. Young. A practical guide to metabolomics software development. 93(4):1912–1923, 2021. [DOI].
Padhmanand Sudhakar, Kathleen Machiels, Bram Verstockt, Tamás Korcsmáros, and Séverine Vermeire. Computational biology and machine learning approaches to understand mechanistic microbiome-host interactions. 12():618856–618856, 2021. [DOI].
Jiufu Qin, Anastasia Krivoruchko, Boyang Ji, Yu Chen, Mette Kristensen, Emre Özdemir, Jay D. Keasling, Michael K. Jensen, and Jens Nielsen. Engineering yeast metabolism for the discovery and production of polyamines and polyamine analogues. 4(6):498–509, 2021. [DOI].
Telmo Blasco, Sergio Pérez-Burillo, Francesco Balzerani, Daniel Hinojosa-Nogueira, Alberto Lerma-Aguilera, Silvia Pastoriza, Xabier Cendoya, Ángel Rubio, María José Gosalbes, Nuria Jiménez‐Hernández, M. Pilar Francino, Iñigo Apaolaza, José Ángel Rufián‐Henares, and Francisco J. Planes. An extended reconstruction of human gut microbiota metabolism of dietary compounds. 12(1):4728–4728, 2021. [DOI].
Nicholas S. Kruyer, Matthew J. Realff, Wenting Sun, Caroline L. Genzale, and Pamela Peralta‐Yahya. Designing the bioproduction of martian rocket propellant via a biotechnology-enabled in situ resource utilization strategy. 12(1):6166–6166, 2021. [DOI].
Gianvito Pio, Paolo Mignone, Giuseppe Magazzù, Guido Zampieri, Michelangelo Ceci, and Claudio Angione. Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction. 38(2):487–493, 2021. [DOI].
Anne Richelle, Benjamin P. Kellman, Alexander T. Wenzel, Austin W.T. Chiang, Tyler Reagan, Jahir M. Gutierrez, Chintan Joshi, Shangzhong Li, Joanne K. Liu, Helen O. Masson, Jooyong Lee, Zerong Li, Laurent Heirendt, Christophe Trefois, Edwin F. Juarez, Tyler Bath, David Borland, Jill P. Mesirov, Kimberly Robasky, and Nathan E. Lewis. Model-based assessment of mammalian cell metabolic functionalities using omics data. 1(3):100040–100040, 2021. [DOI].
Yu Chen, Feiran Li, Jiwei Mao, Yun Chen, and Jens Nielsen. Yeast optimizes metal utilization based on metabolic network and enzyme kinetics. 118(12):, 2021. [DOI].
Piyush Nanda and Amit Ghosh. Genome scale-differential flux analysis reveals deregulation of lung cell metabolism on sars-cov-2 infection. 17(4):e1008860–e1008860, 2021. [DOI].
Mohammad Karim Khaleghi, Iman Shahidi Pour Savizi, Nathan E. Lewis, and Seyed Abbas Shojaosadati. Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters. 16(11):e2100212–e2100212, 2021. [DOI].
Hongzhong Lu, Feiran Li, Le Yuan, Iván Domenzain, Rosemary Yu, Hao Wang, Gang Li, Yu Chen, Boyang Ji, Eduard J. Kerkhoven, and Jens Nielsen. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. 17(10):e10427–e10427, 2021. [DOI].
Michael A. Henson. Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling. 21(7):489–501, 2021. [DOI].
Pablo Rodríguez-Mier, Nathalie Poupin, Carlo De Blasio, Laurent Le Cam, and Fabien Jourdan. Dexom: diversity-based enumeration of optimal context-specific metabolic networks. 17(2):e1008730–e1008730, 2021. [DOI].
Nadeem Joudeh, Athanasios Saragliadis, Christian Schulz, André Voigt, Eivind Almaas, and Dirk Linke. Transcriptomic response analysis of escherichia coli to palladium stress. 12():741836–741836, 2021. [DOI].
Maziya Ibrahim and Karthik Raman. Two-species community design of lactic acid bacteria for optimal production of lactate. 19():6039–6049, 2021. [DOI].
Iván Domenzain, Feiran Li, Eduard J. Kerkhoven, and Verena Siewers. Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. 21(1):, 2021. [DOI].
Rudolfs Petrovs, Egils Stalidzāns, and Agris Pentjuss. Imfler: a web application for interactive metabolic flux analysis and visualization. 28(10):1021–1032, 2021. [DOI].
Miroslav Kratochvíl, Laurent Heirendt, St. Elmo Wilken, Taneli Pusa, Sylvain Arreckx, Alberto Noronha, Marvin van Aalst, Venkata Satagopam, Oliver Ebenhöh, Reinhard Schneider, Christophe Trefois, and Wei Gu. Cobrexa.jl: constraint-based reconstruction and exascale analysis. 38(4):1171–1172, 2021. [DOI].
Upasana Jhariya, Shweta Srivastava, Sanchita Basu Das, Sakina Bombaywala, Sejal Mahallea, and Nishant A. Dafale. Understanding the role of genetic and protein networking involved in microbial bioremediation. ():187–219, 2021. [DOI].
Thomas Eng, Deepanwita Banerjee, Andrew K. Lau, Emily Bowden, Robin A. Herbert, Jessica Trinh, Jan‐Philip Prahl, Adam M. Deutschbauer, Deepti Tanjore, and Aindrila Mukhopadhyay. Engineering pseudomonas putida for efficient aromatic conversion to bioproduct using high throughput screening in a bioreactor. 66():229–238, 2021. [DOI].
Pedro A. Saa, Arlés Urrutia, Claudia Silva, Alberto J. M. Martín, and Daniel Garrido. Modeling approaches for probing cross-feeding interactions in the human gut microbiome. 20():79–89, 2021. [DOI].
Yurong Cheng, Pascal Schlosser, Johannes Hertel, Peggy Sekula, Peter J. Oefner, Ute Spiekerkoetter, Johanna Mielke, Daniel F. Freitag, Miriam Schmidts, Peter J. Oefner, Florian Kronenberg, Kai‐Uwe Eckardt, Florian Kronenberg, Kai‐Uwe Eckardt, Ines Thiele, Yong Li, Anna Köttgen, and Anna Köttgen. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. 12(1):964–964, 2021. [DOI].
Steven Le Feunteun, Ahmed Al-Razaz, Matthijs Dekker, Erwin George, Béatrice Laroche, and George A. van Aken. Physiologically based modeling of food digestion and intestinal microbiota: state of the art and future challenges. an infogest review. 12(1):149–167, 2021. [DOI].
Rik P. van Rosmalen, Robert W. Smith, Vítor A. P. Martins dos Santos, Christian Fleck, and María Suárez‐Diez. Model reduction of genome-scale metabolic models as a basis for targeted kinetic models. 64():74–84, 2021. [DOI].
Sudharshan Ravi and Rudiyanto Gunawan. Δfba—predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data. 17(11):e1009589–e1009589, 2021. [DOI].
Matthew L. Jenior, Jhansi L. Leslie, Deborah Powers, Elizabeth M. Garrett, Kimberly A. Walker, Mary E. Dickenson, William A. Petri, Rita Tamayo, and Jason A. Papin. Novel drivers of virulence in clostridioides difficile identified via context-specific metabolic network analysis. 6(5):e0091921–e0091921, 2021. [DOI].
Johannes Hertel, Almut Heinken, Filippo Martinelli Boneschi, and Ines Thiele. Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of fusobacterium spp. on community butyrate production. 13(1):1–23, 2021. [DOI].
Shany Ofaim, Snorre Sulheim, Eivind Almaas, Daniel Sher, and Daniel Segrè. Dynamic allocation of carbon storage and nutrient-dependent exudation in a revised genome-scale model of prochlorococcus. 12():586293–586293, 2021. [DOI].
Giuseppe Magazzù, Guido Zampieri, and Claudio Angione. Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data. 37(20):3546–3552, 2021. [DOI].
Jingru Zhou, Yingping Zhuang, and Jianye Xia. Integration of enzyme constraints in a genome-scale metabolic model of aspergillus niger improves phenotype predictions. 20(1):125–125, 2021. [DOI].
Bonnie V. Dougherty, Kristopher D. Rawls, Glynis L. Kolling, Kalyan C. Vinnakota, Anders Wallqvist, and Jason A. Papin. Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. 34(10):108836–108836, 2021. [DOI].
Mattia G. Gollub, Hans‐Michael Kaltenbach, and Jörg Stelling. Probabilistic thermodynamic analysis of metabolic networks. 37(18):2938–2945, 2021. [DOI].
Christian Schulz, Tjaša Kumelj, Emil Karlsen, and Eivind Almaas. Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition. 17(5):e1008528–e1008528, 2021. [DOI].
Nam Kyu Kang, Minsik Kim, Kwangryul Baek, Yong Keun Chang, Donald R. Ort, and Yong‐Su Jin. Photoautotrophic organic acid production: glycolic acid production by microalgal cultivation. 433():133636–133636, 2021. [DOI].
Elif Esvap and Kutlu Ö. Ülgen. Advances in genome-scale metabolic modeling toward microbial community analysis of the human microbiome. 10(9):2121–2137, 2021. [DOI].
Mario L. Arrieta‐Ortiz, Selva Rupa Christinal Immanuel, Serdar Turkarslan, Wei‐Ju Wu, Brintha Girinathan, Jay Worley, Nicholas DiBenedetto, Olga Soutourina, Johann Peltier, Bruno Dupuy, Lynn Bry, and Nitin S. Baliga. Predictive regulatory and metabolic network models for systems analysis of clostridioides difficile. 29(11):1709–1723.e5, 2021. [DOI].
Marouen Ben Guebila and Ines Thiele. Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes. 1(5):348–361, 2021. [DOI].
William T. Scott, Eddy J. Smid, David E. Block, and Richard A. Notebaart. Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts. 20(1):204–204, 2021. [DOI].
Krzysztof Ciomek and Miłosz Kadziński. Polyrun: a java library for sampling from the bounded convex polytopes. 13():100659–100659, 2021. [DOI].
Tobias B. Alter, Lars M. Blank, and Birgitta E. Ebert. Proteome regulation patterns determine escherichia coli wild-type and mutant phenotypes. 6(2):, 2021. [DOI].
Viviënne Mol, Martyn Bennett, Benjamín J. Sánchez, Beata K. Lisowska, Markus J. Herrgård, Alex Toftgaard Nielsen, David J. Leak, and Nikolaus Sonnenschein. Genome-scale metabolic modeling of p. thermoglucosidasius ncimb 11955 reveals metabolic bottlenecks in anaerobic metabolism. 65():123–134, 2021. [DOI].
Junmin Wang, Alireza Delfarah, Patrick E. Gelbach, Emma J. Fong, Paul Macklin, Shannon M. Mumenthaler, Nicholas A. Graham, and Stacey D. Finley. Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and lc-ms metabolomics. 69():175–187, 2021. [DOI].
Sven Thiele, Axel von Kamp, Pavlos Stephanos Bekiaris, Philipp Schneider, and Steffen Klamt. Cnapy: a cellnetanalyzer gui in python for analyzing and designing metabolic networks. 38(5):1467–1469, 2021. [DOI].
Iván Domenzain, Benjamín J. Sánchez, Mihail Anton, Eduard J. Kerkhoven, Aarón Millán-Oropeza, Céline Henry, Verena Siewers, John P. Morrissey, Nikolaus Sonnenschein, and Jens Nielsen. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using gecko 2.0. ():, 2021. [DOI].
Nana Y. D. Ankrah, Brandon Barker, Joan Song, Cindy Wu, John G. McMullen, and Angela E. Douglas. Predicted metabolic function of the gut microbiota of drosophila melanogaster. 6(3):, 2021. [DOI].
Pejman Salahshouri, Modjtaba Emadi‐Baygi, Mahdi Jalili, Faiz M. Khan, Olaf Wolkenhauer, and Ali Salehzadeh‐Yazdi. A metabolic model of intestinal secretions: the link between human microbiota and colorectal cancer progression. 11(7):456–456, 2021. [DOI].
Egils Stalidzāns and Elīna Dāce. Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology. 19():4770–4776, 2021. [DOI].
Priyanka Baloni, Matthias Arnold, Herman Moreno, Kwangsik Nho, Luna Buitrago, Kevin Huynh, Barbara Brauner, Gregory Louie, Alexandra Kueider‐Paisley, Karsten Suhre, Andrew J. Saykin, Kim Ekroos, Peter J. Meikle, Leroy Hood, Nathan D. Price, P. Murali Doraiswamy, Cory C. Funk, Gabi Kastenmüller, Rebecca Baillie, Xianlin Han, Rima Kaddurah‐Daouk, and Rima Kaddurah-Daouk. Multi-omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in alzheimer’s disease. ():, 2021. [DOI].
Elisabeth Yaneske, Guido Zampieri, Loris Bertoldi, Giuseppe Benvenuto, and Claudio Angione. Genome‐scale metabolic modelling of sars‐cov‐2 in cancer cells reveals an increased shift to glycolytic energy production. 595(18):2350–2365, 2021. [DOI].
Cheewin Kittikunapong, Suhui Ye, Patricia Magadán‐Corpas, Álvaro Pérez‐Valero, Claudio J. Villar, Felipe Lombó, and Eduard J. Kerkhoven. Reconstruction of a genome-scale metabolic model of streptomyces albus j1074: improved engineering strategies in natural product synthesis. 11(5):304–304, 2021. [DOI].
William T. Scott, Oscar van Mastrigt, David E. Block, Richard A. Notebaart, and Eddy J. Smid. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. 9(1):e0048521–e0048521, 2021. [DOI].
Itziar Frades, Carles Foguet, Marta Cascante, and Marcos J. Araúzo‐Bravo. Genome scale modeling to study the metabolic competition between cells in the tumor microenvironment. 13(18):4609–4609, 2021. [DOI].
Mihir V. Shah, Hadi Nazem‐Bokaee, James Antoney, Suk Woo Kang, Colin J. Jackson, and Colin Scott. Improved production of the non-native cofactor f420 in escherichia coli. 11(1):21774–21774, 2021. [DOI].
Anja Karlstaedt. Stable isotopes for tracing cardiac metabolism in diseases. 8():734364–734364, 2021. [DOI].
Dung Hoang Anh, Nguyễn Thị Hoài Thu, and Eun Yeol Lee. The ethylmalonyl-coa pathway for methane-based biorefineries: a case study of using methylosinus trichosporium ob3b, an alpha-proteobacterial methanotroph, for producing 2-hydroxyisobutyric acid and 1,3-butanediol from methane. 23(19):7712–7723, 2021. [DOI].
Theresa L. Whiteside. The role of tumor-derived exosomes (tex) in shaping anti-tumor immune competence. 10(11):3054–3054, 2021. [DOI].
Alvar J. Alonso-Lavin, Djordje Bajić, and Juan F. Poyatos. Tolerance to nadh/nad+ imbalance anticipates aging and anti-aging interventions. 24(7):102697–102697, 2021. [DOI].
Axel Theorell, Johann F. Jadebeck, Katharina Nöh, and Jörg Stelling. Polyround: polytope rounding for random sampling in metabolic networks. 38(2):566–567, 2021. [DOI].
Albert Tafur Rangel, Wendy Ríos, Daisy Mejía, Carmen Ojeda, Ross P. Carlson, Jorge M. Gómez, and Andrés Fernando González Barrios. In silico design for systems-based metabolic engineering for the bioconversion of valuable compounds from industrial by-products. 12():633073–633073, 2021. [DOI].
Nunthaphan Vikromvarasiri, Tomokazu Shirai, and Akihiko Kondo. Metabolic engineering design to enhance (r,r)-2,3-butanediol production from glycerol in bacillus subtilis based on flux balance analysis. 20(1):196–196, 2021. [DOI].
Xinhui Wang. Advances in candida albicans. ():, 2021. [DOI].
Varshit Dusad, Denise Thiel, Mauricio Barahona, Hector C. Keun, and Diego A. Oyarzún. Opportunities at the interface of network science and metabolic modeling. 8():591049–591049, 2021. [DOI].
Ana Vila-Santa, M. Ahsanul Islam, Frederico Castelo Ferreira, Kristala L. J. Prather, and Nuno P. Mira. Prospecting biochemical pathways to implement microbe-based production of the new-to-nature platform chemical levulinic acid. 10(4):724–736, 2021. [DOI].
Iván Domenzain, Benjamín J. Sánchez, Mihail Anton, Eduard J. Kerkhoven, Aarón Millán-Oropeza, Céline Henry, Verena Siewers, John P. Morrissey, Nikolaus Sonnenschein, and Jens Nielsen. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using gecko 2.0. ():, 2021. [DOI].
Ewelina Węglarz‐Tomczak, Thierry D.G.A. Mondeel, Diewertje Piebes, and Hans V. Westerhoff. Simultaneous integration of gene expression and nutrient availability for studying the metabolism of hepatocellular carcinoma cell lines. 11(4):490–490, 2021. [DOI].
Daniel Noreña-Caro, Cristal Zúñiga, Amber J. Pete, Sven A. Saemundsson, Morgan R. Donaldson, Alexandria J. Adams, Kerry M. Dooley, Karsten Zengler, and Michael G. Benton. Analysis of the cyanobacterial amino acid metabolism with a precise genome-scale metabolic reconstruction of anabaena sp. utex 2576. 171():108008–108008, 2021. [DOI].
John P. Thomas, Dezső Módos, Tamás Korcsmáros, and Johanne Brooks-Warburton. Network biology approaches to achieve precision medicine in inflammatory bowel disease. 12():760501–760501, 2021. [DOI].
Peng Liu, Shuai Wang, Chao Li, Yingping Zhuang, Jianye Xia, and Henk Noorman. Dynamic response of aspergillus niger to periodical glucose pulse stimuli in chemostat cultures. 118(6):2265–2282, 2021. [DOI].
Indu Jatain, Kashyap Kumar Dubey, Manisha Sharma, Zeba Usmani, Minaxi Sharma, and Vijai Kumar Gupta. Synthetic biology potential for carbon sequestration into biocommodities. 323():129176–129176, 2021. [DOI].
Hennicke Kamp, Judith Wahrheit, Stefan Stinchcombe, Tilmann Walk, Franz Stauber, and B. van Ravenzwaay. Succinate dehydrogenase inhibitors: in silico flux analysis and in vivo metabolomics investigations show no severe metabolic consequences for rats and humans. 150():112085–112085, 2021. [DOI].
Felipe Scott, Luz Yáñez, Raúl Conejeros, Blanca Araya, and Alberto Vergara‐Fernández. Two internal bottlenecks cause the overflow metabolism leading to poly(3-hydroxybutyrate) production in azohydromonas lata dsm1123. 9(4):105665–105665, 2021. [DOI].
Ecehan Abdi̇k and Tunahan Çakır. Systematic investigation of mouse models of parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network. 17(4):492–502, 2021. [DOI].
Chenchen Gao, Jiarui Yang, Tong Hao, Jingjing Li, and Jinsheng Sun. Reconstruction of litopenaeus vannamei genome-scale metabolic network model and nutritional requirements analysis of different shrimp commercial varieties. 12():658109–658109, 2021. [DOI].
Benjamin Allen, Nidhi Gupta, Janaka N. Edirisinghe, José P. Faria, and Christopher S. Henry. Application of the metabolic modeling pipeline in kbase to categorize reactions, predict essential genes, and predict pathways in an isolate genome. 2349():291–320, 2021. [DOI].
Kyoichi Ebata, Sawa Yamashiro, Keita Iida, and Mariko Okada. Building patient‐specific models for receptor tyrosine kinase signaling networks. 289(1):90–101, 2021. [DOI].
St. Elmo Wilken, Victor Vera Frazão, Nima P. Saadat, and Oliver Ebenhöh. The view of microbes as energy converters illustrates the trade-off between growth rate and yield. 49(4):1663–1674, 2021. [DOI].
Priyanka Baloni, Cory C. Funk, Ben Readhead, and Nathan D. Price. Systems modeling of metabolic dysregulation in neurodegenerative diseases. 60():59–65, 2021. [DOI].
Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian K. Blaby, and Xiaoning Qian. Trimer: transcription regulation integrated with metabolic regulation. 24(11):103218–103218, 2021. [DOI].
Carl Malina, Francesca Di Bartolomeo, Eduard J. Kerkhoven, and Jens Nielsen. Constraint-based modeling of yeast mitochondria reveals the dynamics of protein import and iron-sulfur cluster biogenesis. 24(11):103294–103294, 2021. [DOI].
Howard Ramírez-Malule, Víctor A. López-Agudelo, David Gómez-Ríos, Silvia Ochoa, Rigoberto Ríos‐Estepa, Stefan Junne, and Peter Neubauer. Tca cycle and its relationship with clavulanic acid production: a further interpretation by using a reduced genome-scale metabolic model of streptomyces clavuligerus. 8(8):103–103, 2021. [DOI].
Omid Ardalani, Ehsan Motamedian, and Javad Hamedi. Reconstruction and validation of genome-scale metabolic model of l. lactis subsp. lactis ncdo 2118 and in silico analysis for succinate and gamma-aminobutyric acid overproduction. 170():107967–107967, 2021. [DOI].
John I. Hendry, Hoang V. Dinh, Debolina Sarkar, Lin Wang, Anindita Bandyopadhyay, Himadri B. Pakrasi, and Costas D. Maranas. A genome-scale metabolic model of anabaena 33047 to guide genetic modifications to overproduce nylon monomers. 11(3):168–168, 2021. [DOI].
Wentao Tang, Tianwei Hao, and Guanghao Chen. Comparative metabolic modeling of multiple sulfate‐reducing prokaryotes reveals versatile energy conservation mechanisms. 118(7):2676–2693, 2021. [DOI].
Baoyu Xiang, Liping Zhao, and Menghui Zhang. Metagenome-scale metabolic network suggests folate produced by bifidobacterium longum might contribute to high-fiber-diet-induced weight loss in a prader–willi syndrome child. 9(12):2493–2493, 2021. [DOI].
Zachary Johnson, Dennis D. Krutkin, Pavlo Bohutskyi, and Marina Kalyuzhnaya. Metals and methylotrophy: via global gene expression studies. 650():185–213, 2021. [DOI].
Gholamreza Bidkhori, Sunjae Lee, Lindsey Edwards, Emmanuelle Le Chatelier, Mathieu Almeida, Bouchra Ezzamouri, Florian Plaza Oñate, Nicolas Ponte, Debbie L. Shawcross, Gordon Proctor, Lars K. Nielsen, Jens Nielsen, Mathias Uhlén, S. Dusko Ehrlich, and Saeed Shoaie. The reactobiome unravels a new paradigm in human gut microbiome metabolism. ():, 2021. [DOI].
Snorre Sulheim, Fredrik A. Fossheim, Alexander Wentzel, and Eivind Almaas. Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters. 22(1):81–81, 2021. [DOI].
Vetle Simensen, André Voigt, and Eivind Almaas. High‐quality genome‐scale metabolic model of aurantiochytrium sp. t66. 118(5):2105–2117, 2021. [DOI].
Michael A. Henson. Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent clostridioides difficile infection. 17(2):e1008782–e1008782, 2021. [DOI].
Despoina Kerselidou, Bushra Dohai, David R. Nelson, Sarah Daakour, Nicolas De Cock, Zahra Al Oula Hassoun, Dae‐Kyum Kim, Julien Olivet, Diana C. El Assal, Ashish Jaiswal, Amnah Alzahmi, Deeya Saha, Charlotte Pain, Filip Matthijssens, Pierre Lemaître, Michaël Herfs, Julien Chapuis, Bart Ghesquière, Didier Vertommen, Verena Kriechbaumer, Kèvin Knoops, Carmen López‐Iglesias, Marc van Zandvoort, Jean‐Charles Lambert, Julien Hanson, Christophe Desmet, Marc Thiry, Kyle J. Lauersen, Marc Vidal, Pieter Van Vlierberghe, Samuel Dequiedt, Kourosh Salehi‐Ashtiani, and Jean‐Claude Twizere. Alternative glycosylation controls endoplasmic reticulum dynamics and tubular extension in mammalian cells. 7(19):, 2021. [DOI].
Chao-Ting Cheng, Tsun-Yu Wang, Peirong Chen, Wu‐Hsiung Wu, Jinmei Lai, Peter Mu‐Hsin Chang, Yi‐Ren Hong, Chi‐Ying F. Huang, and Feng‐Sheng Wang. Computer-aided design for identifying anticancer targets in genome-scale metabolic models of colon cancer. 10(11):1115–1115, 2021. [DOI].
Ewelina Węglarz‐Tomczak, Demi J. Rijlaarsdam, Jakub M. Tomczak, and Stanley Brul. Gem-based metabolic profiling for human bone osteosarcoma under different glucose and glutamine availability. 22(3):1470–1470, 2021. [DOI].
Supreeta Vijayakumar and Claudio Angione. Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium synechococcus sp. pcc 7002. 2(4):100837–100837, 2021. [DOI].
Chaitra Sarathy, Marian Breuer, Martina Kutmon, Michiel Adriaens, Chris T. Evelo, and Ilja C.W. Arts. Comparison of metabolic states using genome-scale metabolic models. 17(11):e1009522–e1009522, 2021. [DOI].
Özlem Altay, Cheng Zhang, Hasan Türkez, Jens Nielsen, Mathias Uhlén, and Adil Mardinoğlu. Revealing the metabolic alterations during biofilm development of burkholderia cenocepacia based on genome-scale metabolic modeling. 11(4):221–221, 2021. [DOI].
Marco Fondi, Stefano Gonzi, Mikołaj Dziurzyński, Paola Turano, Veronica Ghini, Marzia Calvanese, Andrea Colarusso, Concetta Lauro, Ermenegilda Parrilli, and Maria Luisa Tutino. Modelling hcdkl5 heterologous expression in bacteria. 11(8):491–491, 2021. [DOI].
Selva Rupa Christinal Immanuel, Mario L. Arrieta‐Ortiz, Rene A. Ruiz, Min Pan, Adrián López García de Lomana, Eliza J. R. Peterson, and Nitin S. Baliga. Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using prime. 7(1):43–43, 2021. [DOI].
William F. Kindschuh, Federico Baldini, Martin C. Liu, Kristin D. Gerson, Jingqiu Liao, Harry H. Lee, Lauren Anton, Pawel Gajer, Jacques Ravel, Maayan Levy, Michal A. Elovitz, and Tal Korem. Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. ():, 2021. [DOI].
Donald James, P. R. Rennya, Mani Deepika Mallavarapu, Ram Chandra Panigrahi, and Hitendra Kumar Patel. Integrating pan-omics data in a systems approach for crop improvement: opportunities and challenges. ():215–246, 2021. [DOI].
Mario Latendresse, Wai Kit Ong, and Peter D. Karp. Metabolic modeling with metaflux. 2349():259–289, 2021. [DOI].
Freddy Castillo-Alfonso, Gabriel Vigueras-Ramírez, Luis Manuel Rosales-Colunga, Alberto del Monte‐Martínez, and Roberto Olivares‐Hernández. Propionate as the preferred carbon source to produce 3-indoleacetic acid in b. subtilis : comparative flux analysis using five carbon sources. 17(4):554–564, 2021. [DOI].
Sudharshan Ravi and Rudiyanto Gunawan. Δfba: predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data. ():, 2021. [DOI].
Sanjeev Dahal, Jiao Zhao, and Laurence Yang. Recent advances in genome-scale modeling of proteome allocation. 26():39–45, 2021. [DOI].
J. Saraiva, Alexander Bartholomäus, René Kallies, Marta Gomes, Marcos Bicalho, Jonas Coelho Kasmanas, Carsten Vogt, Antonis Chatzinotas, Peter F. Stadler, Óscar Dias, and Ulisses Nunes da Rocha. Ortsuite: from genomes to prediction of microbial interactions within targeted ecosystem processes. 4(12):e202101167–e202101167, 2021. [DOI].
M. Águila Ruiz‐Sola, Serena Flori, Yizhong Yuan, Gaëlle Villain, Emanuel Sanz‐Luque, Petra Redekop, Ryutaro Tokutsu, Anika Küken, Angeliki Tsichla, Georgios Kepesidis, Guillaume Allorent, Marius Arend, Fabrizio Iacono, Giovanni Finazzi, Michael Hippler, Zoran Nikoloski, Jun Minagawa, Arthur Grossman, and Dimitris Petroutsos. Light-independent regulation of algal photoprotection by co2availability. ():, 2021. [DOI].
Dafni Giannari, Cleo Han-Chen Ho, and Radhakrishnan Mahadevan. A gap-filling algorithm for prediction of metabolic interactions in microbial communities. 17(11):e1009060–e1009060, 2021. [DOI].
Jibin Liu, Anchun Cheng, Mingshu Wang, Mafeng Liu, Dekang Zhu, Qiao Yang, Ying Wu, Renyong Jia, Shun Chen, Xinxin Zhao, Shaqiu Zhang, Juan Huang, Xumin Ou, Sai Mao, Qun Gao, Xingjian Wen, Ling Zhang, Yunya Liu, Yanling Yu, Bin Tian, Leichang Pan, Mujeeb Ur Rehman, and Xiaoyue Chen. Comparative genomics and metabolomics analysis of riemerella anatipestifer strain ch-1 and ch-2. 11(1):616–616, 2021. [DOI].
Albert A. Antolín and Marta Cascante. Ai delivers michaelis constants as fuel for genome-scale metabolic models. 19(10):e3001415–e3001415, 2021. [DOI].
Fernando Cruz, João Capela, Eugénio C. Ferreira, Miguel Rocha, and Óscar Dias. bioiso: an objective-oriented application for assisting the curation of genome-scale metabolic models. ():, 2021. [DOI].
William T. Scott, Oscar van Mastrigt, David E. Block, Richard A. Notebaart, and Eddy J. Smid. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. ():, 2021. [DOI].
Devlin C. Moyer, Alan R. Pacheco, David B. Bernstein, and Daniel Segrè. Stoichiometric modeling of artificial string chemistries reveals constraints on metabolic network structure. 89(7):472–483, 2021. [DOI].
Rosemary Yu, Egor Vorontsov, Carina Sihlbom, and Jens Nielsen. Non-random organization of flux control mechanisms in yeast central metabolic pathways. ():, 2021. [DOI].
Anoop T. Ambikan, Hong Yang, Shuba Krishnan, Sara Svensson Akusjärvi, Soham Gupta, Magda Lourda, Maike Sperk, Muhammad Arif, Cheng Zhang, Hampus Nordqvist, Sivasankaran Munusamy Ponnan, Anders Sönnerborg, Carl Johan Treutiger, Liam O’Mahony, Adil Mardinoğlu, Rui Benfeitas, and Ujjwal Neogi. Multiomics personalized network analyses highlight progressive immune disruption of central metabolism associated with covid-19 severity. ():, 2021. [DOI].
Jingru Zhou, Yingping Zhuang, and Jianye Xia. Integration of enzyme constraints in a genome-scale metabolic model of aspergillus niger improves phenotype predictions. ():, 2021. [DOI].
Nan Xu, Qiyuan Yang, Xiaojing Yang, Mingqi Wang, and Minliang Guo. Reconstruction and analysis of a genome‐scale metabolic model for agrobacterium tumefaciens. 22(3):348–360, 2021. [DOI].
Alexander L. R. Lubbock and Carlos F. Lopez. Programmatic modeling for biological systems. 27():100343–100343, 2021. [DOI].
Sigurður Trausti Karvelsson, Qiong Wang, Bylgja Hilmarsdóttir, Arnar Sigurdsson, Siver Andreas Moestue, Gunhild M. Mælandsmo, Skarphéðinn Halldórsson, Steinn Guðmundsson, and Óttar Rolfsson. Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer. 7(1):36–36, 2021. [DOI].
Selva Rupa Christinal Immanuel, Mario L. Arrieta‐Ortiz, Rene A. Ruiz, Min Pan, Adrián López García de Lomana, Eliza J. R. Peterson, and Nitin S. Baliga. Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks ofmycobacterium tuberculosis. ():, 2021. [DOI].
Vetle Simensen, Christian Schulz, Emil Karlsen, Signe Bråtelund, Idun Burgos, Lilja Brekke Thorfinnsdottir, Laura García-Calvo, Per Bruheim, and Eivind Almaas. Quantification of macromolecular biomass composition for constraint-based metabolic modeling. ():, 2021. [DOI].
Maxime R.F. Gosselin, Virginie Mournetas, Małgorzata Borczyk, Suraj Verma, Annalisa Occhipinti, Justyna Róg, Łukasz Bożycki, Michał Korostyński, Samuel C. Robson, Claudio Angione, Christian Pinset, and Dariusz C. Górecki. Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts. ():, 2021. [DOI].
M. D. Gasparyan, Arnout Van Messem, and Shodhan Rao. Parameter estimation for models of chemical reaction networks from experimental data of reaction rates. 96(2):392–407, 2021. [DOI].
Albert Tafur Rangel, Abel García Oviedo, Freddy Cabrera Mojica, Jorge M. Gómez, and Andrés Fernando González Barrios. Development of an integrating systems metabolic engineering and bioprocess modeling approach for rational strain improvement. 178():108268–108268, 2021. [DOI].
Matthew L. Jenior, Jhansi L. Leslie, Deborah Powers, Elizabeth M. Garrett, Kimberly A. Walker, Mary E. Dickenson, William A. Petri, Rita Tamayo, and Jason A. Papin. Conserved virulence-linked metabolic reprogramming in clostridioides difficile identified through genome-scale metabolic network analysis. ():, 2021. [DOI].
Viviënne Mol, Martyn Bennett, Benjamín J. Sánchez, Beata K. Lisowska, Markus J. Herrgård, Alex Toftgaard Nielsen, David J. Leak, and Nikolaus Sonnenschein. Genome-scale metabolic modelling ofp. thermoglucosidasiusncimb 11955 reveals metabolic bottlenecks in anaerobic metabolism. ():, 2021. [DOI].
Takeyuki Tamura. L1 norm minimal mode-based methods for listing reaction network designs for metabolite production. E104.D(5):679–687, 2021. [DOI].
Takeyuki Tamura. Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling. 14(0):12–21, 2021. [DOI].
Aidan Pavao, Brintha Girinathan, Johann Peltier, Pamela Altamirano Silva, Bruno Dupuy, Leo L. Cheng, and Lynn Bry. Real-time hrmas13c nmr of obligately anaerobic cells identifies new metabolic targets in the pathogenclostridioides difficile. ():, 2021. [DOI].
Dafni Giannari, Cleo Han-Chen Ho, and Radhakrishnan Mahadevan. A gap-filling algorithm for prediction of metabolic interactions in microbial communities. ():, 2021. [DOI].
Takeyuki Tamura, Ai Muto-Fujita, Yukako Tohsato, and Tomoyuki Kosaka. Gene deletion algorithms for minimum reaction network design by mixed-integer linear programming for metabolite production in constraint-based models: gdel_minrn. ():, 2021. [DOI].
Almut Heinken, Stefanía Magnúsdóttir, Ronan M. T. Fleming, and Ines Thiele. Demeter: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations. 37(21):3974–3975, 2021. [DOI].
Juan Pablo Molina Ortiz, Dale D. McClure, Erin R. Shanahan, Fariba Dehghani, Andrew Holmes, and Mark Read. Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models. 13(1):1965698–1965698, 2021. [DOI].
Stuart Maudsley, Hanne Leysen, Jaana van Gastel, and Bronwen Martin. Systems pharmacology: enabling multidimensional therapeutics. ():725–769, 2021. [DOI].
Thomas Eng, Deepanwita Banerjee, Andrew K. Lau, Emily Bowden, Robin A. Herbert, Jessica Trinh, Jan‐Philip Prahl, Adam M. Deutschbauer, Deepti Tanjore, and Aindrila Mukhopadhyay. High throughput fitness profiling reveals loss of gacs-gaca regulation improves indigoidine production inpseudomonas putida. ():, 2021. [DOI].
Carolin C. M. Schulte, Khushboo Borah, Rachel M. Wheatley, Jason J. Terpolilli, Gerhard Saalbach, Nick Crang, Daan H. de Groot, R. George Ratcliffe, Nicholas J. Kruger, Antonis Papachristodoulou, and Philip S. Poole. Metabolic constraints on nitrogen fixation by rhizobia in legume nodules. ():, 2021. [DOI].
Sourav Chowdhury, Daniel C. Zielinski, Christopher Dalldorf, João V. Rodrigues, Bernhard Ø. Palsson, and Eugene I. Shakhnovich. Empowering systems-guided drug target discovery with metabolic and structural analysis. ():, 2021. [DOI].
Junmin Wang, Alireza Delfarah, Patrick E. Gelbach, Emma J. Fong, Paul Macklin, Shannon M. Mumenthaler, Nicholas A. Graham, and Stacey D. Finley. Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and lc-ms metabolomics. ():, 2021. [DOI].
Laurence Legon, Christophe Corre, Declan G. Bates, and Ahmad A. Mannan. Gcfront: a tool for determining a pareto front of growth-coupled cell factory designs. ():, 2021. [DOI].
Yougan Cheng, Ronny Straube, Abed Alnaif, Lu Huang, Tarek A. Leil, and Brian J. Schmidt. Virtual populations for quantitative systems pharmacology models. ():, 2021. [DOI].
Juan D. Tibocha‐Bonilla, Cristal Zúñiga, Jared T. Broddrick, Karsten Zengler, and Rubén Darío Godoy‐Silva. A multiscale model predicts the sensitivity ofchlorella vulgaristo light and nitrogen levels in photobioreactors. ():, 2021. [DOI].
Rashi Verma, Dibyabhaba Pradhan, Harpreet Singh, Arun K. Jain, and Luqman Ahmad Khan. Metabolic network modeling for rational drug design against candida albicans. ():, 2021. [DOI].
Puhua Niu, Maria J. Soto, Byung-Jun Yoon, Edward R. Dougherty, Francis J. Alexander, Ian K. Blaby, and Xiaoning Qian. Trimer: transcription regulation integrated with metabolic regulation. ():, 2021. [DOI].
Rosemary Yu, Hao Wang, and Jens Nielsen. Amino acid and nucleotide metabolism shape the selection of trophic levels in animals. ():, 2021. [DOI].
Eunice A. Ferreira, Catarina C. Pacheco, João S. Rodrigues, Filipe Pinto, Pedro Lamosa, David Fuente, Javier F. Urchueguía, and Paula Tamagnini. Heterologous production of glycine betaine using synechocystis sp. pcc 6803-based chassis lacking native compatible solutes. ():, 2021. [DOI].
Nana Y. D. Ankrah, Brandon Barker, Joan Song, Cindy Wu, John G. McMullen, and Angela E. Douglas. The predicted metabolic function of the gut microbiota ofdrosophila melanogaster. ():, 2021. [DOI].
Alexander B. Alleman, Florence Mus, and John W. Peters. Metabolic model of nitrogen-fixing obligate aerobe azotobacter vinelandii demonstrates adaptation to oxygen concentration and metal availability. ():, 2021. [DOI].
Miroslav Kratochvíl, Laurent Heirendt, St. Elmo Wilken, Taneli Pusa, Sylvain Arreckx, Alberto Noronha, Marvin van Aalst, Venkata Satagopam, Oliver Ebenhöh, Reinhard Schneider, Christophe Trefois, and Wei Gu. Cobrexa.jl: constraint-based reconstruction and exascale analysis. ():, 2021. [DOI].
Xiang Gao, Chao Wu, Michael Cantrell, Mélissa Cano, Jianping Yu, and Wei Xiong. What we can learn from measuring metabolic fluxes in cyanobacteria. ():89–122, 2021. [DOI].
Sankalpa Venkatraghavan, Sathvik Anantakrishnan, and Karthik Raman. Probing patterning in microbial consortia with piccaso: a cellular automaton for spatial organisation. ():, 2021. [DOI].
Juan C. Villada, Maria F. Duran, Chee Kent Lim, Lisa Y. Stein, and Patrick K. H. Lee. Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization inmethylomicrobium albumbg8. ():, 2021. [DOI].
Johannes Hertel, Almut Heinken, and Ines Thiele. Causal inference on microbiome-metabolome relations viain silico in vivoassociation pattern analyses. ():, 2021. [DOI].
Emil Ljungqvist and Martin Gustavsson. in silicoanalysis of the fast-growing thermophilegeobacillussp. lc300 using a novel genome-scale metabolic model. ():, 2021. [DOI].
Paula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Payam Ghiachi, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramón González, Pilar Morales, and Kiran Raosaheb Patil. Predictive evolution of metabolic phenotypes using model-designed selection niches. ():, 2021. [DOI].
J. Saraiva, Alexander Bartholomäus, René Kallies, Marta Gomes, Marcos Bicalho, Carsten Vogt, Antonis Chatzinotas, Peter F. Stadler, Óscar Dias, and Ulisses Nunes da Rocha. Ortsuite – from genomes to prediction of microbial interactions within targeted ecosystem processes. ():, 2021. [DOI].
Marco Fondi, Stefano Gonzi, Mikołaj Dziurzyński, Paola Turano, Veronica Ghini, Marzia Calvanese, Andrea Colarusso, Concetta Lauro, Ermenegilda Parrilli, and Maria Luisa Tutino. Modelling hcdkl5 heterologous expression in bacteria. ():, 2021. [DOI].
St. Elmo Wilken, Victor Vera Frazão, Nima P. Saadat, and Oliver Ebenhöh. The view of microbes as energy converters illustrates the trade-off between growth rate and yield. ():, 2021. [DOI].
Carolin C. M. Schulte, Vinoy K. Ramachandran, Antonis Papachristodoulou, and Philip S. Poole. Genome-scale metabolic modelling of lifestyle changes in rhizobium leguminosarum. ():, 2021. [DOI].
Antoine Régimbeau, Marko Budinich, Abdelhalim Larhlimi, Juan José Pierella Karlusich, Olivier Aumont, Laurent Mémery, Chris Bowler, and Damien Eveillard. Contribution of genome scale metabolic modeling to niche theory. ():, 2021. [DOI].
Kristina Grausa, Ivars Mozga, Kārlis Pleiko, and Agris Pentjuss. Integrative gene expression and metabolic analysis tool igemrna. ():, 2021. [DOI].
Iñigo Apaolaza, Edurne San José‐Eneriz, Luis V. Valcárcel, Xabier Agirre, Felipe Prósper, and Francisco J. Planes. A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism. ():, 2021. [DOI].
Lavanya Raajaraam and Karthik Raman. A computational framework to identify metabolic engineering strategies for the co-production of metabolites. ():, 2021. [DOI].
Iman Shahidi Pour Savizi, Nader Maghsoudi, Ehsan Motamedian, Nathan E. Lewis, and Seyed Abbas Shojaosadati. Amino acid feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of cho cells. ():, 2021. [DOI].
Philipp Wendering and Zoran Nikoloski. Genome-scale modeling specifies the metabolic capabilities of rhizophagus irregularis. ():, 2021. [DOI].
Arsh Vyas. Metabolic modeling of cystic fibrosis airway microbiota from patient samples. ():, 2021. [DOI].
Prerna Bhalla, Swagatika Sahoo, Raghunathan Rengaswamy, Devarajan Karunagaran, and G. K. Suraishkumar. Constraint-based modeling to understand ros-mediated effects in cancer. ():1–21, 2021. [DOI].
J. Saraiva, Alexander Bartholomäus, René Kallies, Marta Gomes, Marcos Bicalho, Jonas Coelho Kasmanas, Carsten Vogt, Antonis Chatzinotas, Peter F. Stadler, Óscar Dias, and Ulisses Nunes da Rocha. Ortsuite – from genomes to prediction of microbial interactions within targeted ecosystem processes. ():, 2021. [DOI].

Maria A. Wörheide, Jan Krumsiek, Gabi Kastenmüller, and Matthias Arnold. Multi-omics integration in biomedical research – a metabolomics-centric review. 1141():144–162, 2020. [DOI].
Maciek R. Antoniewicz. A guide to metabolic flux analysis in metabolic engineering: methods, tools and applications. 63():2–12, 2020. [DOI].
Svetlana Volkova, Marta R. A. Matos, Matthias Mattanovich, and Igor Marín de Mas. Metabolic modelling as a framework for metabolomics data integration and analysis. 10(8):303–303, 2020. [DOI].
Shirin Fallahi, Hans J. Skaug, and Guttorm Alendal. A comparison of monte carlo sampling methods for metabolic network models. 15(7):e0235393–e0235393, 2020. [DOI].
Shreya Anand, Koel Mukherjee, and Padmini Padmanabhan. An insight to flux-balance analysis for biochemical networks. 36(1):32–55, 2020. [DOI].
Zhuangrong Huang, Jianlin Xu, Andrew Yongky, Caitlin Morris, Ashli Polanco, Michael D. Reily, Michael Borys, Zheng Jian Li, and Seongkyu Yoon. Cho cell productivity improvement by genome-scale modeling and pathway analysis: application to feed supplements. 160():107638–107638, 2020. [DOI].
Carolina A. Contador, Siu-Kit Lo, Siu Hung Joshua Chan, and Hon‐Ming Lam. Metabolic analyses of nitrogen fixation in the soybean microsymbiont sinorhizobium fredii using constraint-based modeling. 5(1):, 2020. [DOI].
William T. Scott, Eddy J. Smid, Richard A. Notebaart, and David E. Block. Curation and analysis of a saccharomyces cerevisiae genome-scale metabolic model for predicting production of sensory impact molecules under enological conditions. 8(9):1195–1195, 2020. [DOI].
Naoya Nochino, Yoshihiro Toya, and Hiroshi Shimizu. Transcription factor arca is a flux sensor for the oxygen consumption rate in escherichia coli. 15(6):e1900353–e1900353, 2020. [DOI].
Zhuangrong Huang and Seongkyu Yoon. Identifying metabolic features and engineering targets for productivity improvement in cho cells by integrated transcriptomics and genome-scale metabolic model. 159():107624–107624, 2020. [DOI].
Zhuangrong Huang and Seongkyu Yoon. Integration of time-series transcriptomic data with genome-scale cho metabolic models for mab engineering. 8(3):331–331, 2020. [DOI].
Hamideh Fouladiha, Sayed‐Amir Marashi, Shangzhong Li, Zerong Li, Helen O. Masson, Behrouz Vaziri, and Nathan E. Lewis. Systematically gap-filling the genome-scale metabolic model of cho cells. 43(1):73–87, 2020. [DOI].
Yu Xu, Roman Holič, and Qiang Hua. Comparison and analysis of published genome-scale metabolic models of yarrowia lipolytica. 25(1):53–61, 2020. [DOI].
Mikhail A. Kulyashov, S. E. Peltek, and Ilya R. Akberdin. A genome-scale metabolic model of 2,3-butanediol production by thermophilic bacteria geobacillus icigianus. 8(7):1002–1002, 2020. [DOI].
Ahmad Ahmad, Ruchi Pathania, and Shireesh Srivastava. Biochemical characteristics and a genome-scale metabolic model of an indian euryhaline cyanobacterium with high polyglucan content. 10(5):177–177, 2020. [DOI].
Hüseyin Taş, Adam Amara, Miguel Cueva, Nadine Bongaerts, Alicia Calvo‐Villamañán, Samir Hamadache, and Konstantinos Vavitsas. Are synthetic biology standards applicable in everyday research practice? 13(5):1304–1308, 2020. [DOI].
Masakazu Toyoshima, Chiaki Yamamoto, Yoshifumi Ueno, Yoshihiro Toya, Seiji Akimoto, and Hiroshi Shimizu. Role of type i nadh dehydrogenase in synechocystis sp. pcc 6803 under phycobilisome excited red light. 304():110798–110798, 2020. [DOI].
Ushashi Banerjee, Santhosh Sankar, Amit Singh, and Nagasuma Chandra. A multi-pronged computational pipeline for prioritizing drug target strategies for latent tuberculosis. 8():593497–593497, 2020. [DOI].
Gong‐Hua Li, Shaoxing Dai, Feifei Han, Wenxing Li, Jing‐Fei Huang, and Wenzhong Xiao. Fastmm: an efficient toolbox for personalized constraint-based metabolic modeling. 21(1):67–67, 2020. [DOI].
Veronica L. Porubsky, Lucian P. Smith, and Herbert M. Sauro. Publishing reproducible dynamic kinetic models. 22(3):, 2020. [DOI].
Hamideh Fouladiha, Sayed-Amir Marashi, Shangzhong Li, Zerong Li, Helen O. Masson, Behrouz Vaziri, and Nathan E. Lewis. Systematically gap-filling the genome-scale metabolic model of cho cells. ():, 2020. [DOI].
Eliana B. Souto, Elena Sánchez‐López, Joana R. Campos, Raquel da Ana, Marta Espina, María L. García, Patrícia Severino, Fernando Batain, Thais Alves, Kessi Crescencio, Selma B. Souto, and Marco V. Chaud. Retinal drug delivery: rethinking outcomes for the efficient replication of retinal behavior. 10(12):4258–4258, 2020. [DOI].
Gargi Bhattacharjee, Nisarg Gohil, and Vijai Singh. An introduction to design of microbial strain using synthetic biology toolboxes for production of biomolecules. ():1–10, 2020. [DOI].
Alan R. Pacheco and Daniel Segrè. An evolutionary algorithm for designing microbial communities via environmental modification. ():, 2020. [DOI].
Cristina Herencias, M. Auxiliadora Prieto, and Juan Nogales. Providing new insights on the byphasic lifestyle of the predatory bacteriumbdellovibrio bacteriovorusthrough genome-scale metabolic modeling. ():, 2020. [DOI].
Nikhil Kirtipal and Asheesh Shanker. Integrating omics and microbial biotechnology for the production of biofuel. ():221–239, 2020. [DOI].
Zsolt Bodor, ANDREA FAZAKAS, Katalin Bodor, Erika R. Bérczi‐Kovács, Ildikó Miklóssy, Beáta Albert, and BEÁTA ALBERT. Using genome-scale model to predict the metabolic engineering impact on escherichia coli metabolism during succinic acid production optimization. 25(3):1666–1676, 2020. [DOI].
Feng‐Sheng Wang and Wu‐Hsiung Wu. Computer‐aided design for genetic modulation to improve biofuel production. ():173–189, 2020. [DOI].
Caroline S.M. Nakama. Desenvolvimentos de ferramentas matemáticas para modelagem de sistemas biológicos baseados em fluxos metabólicos e estimação de parâmetros de problemas mal condicionados. ():, 2020. [DOI].
Rupa Bhowmick, Piyali Ganguli, and Ram Rup Sarkar. T-cell activation and differentiation: role of signaling and metabolic cross-talk. ():153–182, 2020. [DOI].
Diego Salazar Barreto. Predicción de biomarcadores en mucopolisacaridosis empleando una red metabólica humana. ():, 2020. [DOI].
William Parra Meneses. Análisis comparativo de la topología de redes metabólicas en una muestra metagenómica de aguas termales en el parque natural de los nevados en colombia. ():, 2020. [DOI].
Yoseb Song, Jin Soo Lee, Jongoh Shin, Gyu Min Lee, Sangrak Jin, Seulgi Kang, Jung-Kul Lee, Dong Rip Kim, Eun Yeol Lee, Sun Chang Kim, Suhyung Cho, Donghyuk Kim, and Byung‐Kwan Cho. Functional cooperation of the glycine synthase-reductase and wood–ljungdahl pathways for autotrophic growth of clostridium drakei. 117(13):7516–7523, 2020. [DOI].
Jahir M. Gutierrez, Amir Feizi, Shangzhong Li, Thomas Beuchert Kallehauge, Hooman Hefzi, Lise Marie Grav, Daniel Ley, Deniz Baycın Hızal, Michael J. Betenbaugh, Björn G. Voldborg, Helene Faustrup Kildegaard, Gyun Min Lee, Bernhard Ø. Palsson, Jens Nielsen, and Nathan E. Lewis. Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. 11(1):68–68, 2020. [DOI].
Isabel Bator, Andreas Wittgens, Frank Rosenau, Till Tiso, and Lars M. Blank. Comparison of three xylose pathways in pseudomonas putida kt2440 for the synthesis of valuable products. 7():480–480, 2020. [DOI].
Aarti Krishnan, Joachim Kloehn, Matteo Lunghi, Anush Chiappino-Pepe, Benjamin S. Waldman, Damien Nicolas, Emmanuel Varesio, Adrian B. Hehl, Sebastian Lourido, Vassily Hatzimanikatis, and Dominique Soldati‐Favre. Functional and computational genomics reveal unprecedented flexibility in stage-specific toxoplasma metabolism. 27(2):290–306.e11, 2020. [DOI].
Pavlos Stephanos Bekiaris and Steffen Klamt. Automatic construction of metabolic models with enzyme constraints. 21(1):19–19, 2020. [DOI].
Pierre Salvy and Vassily Hatzimanikatis. The etfl formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. 11(1):30–30, 2020. [DOI].
Cristal Zúñiga, Tingting Li, Michael T. Guarnieri, Jackson Jenkins, Chien‐Ting Li, Kerem Bingol, Young‐Mo Kim, Michael J. Betenbaugh, and Karsten Zengler. Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth. 11(1):3803–3803, 2020. [DOI].
George C. diCenzo, Michelangelo Tesi, Thomas Pfau, Alessio Mengoni, and Marco Fondi. Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium. 11(1):2574–2574, 2020. [DOI].
Arnaud Belcour, Clémence Frioux, Méziane Aïte, Anthony Bretaudeau, Falk Hildebrand, and Anne Siegel. Metage2metabo, microbiota-scale metabolic complementarity for the identification of key species. 9():, 2020. [DOI].
Dinka Mandaković, Ángela Cintolesi, Jonathan Maldonado, Sebastián N. Mendoza, Méziane Aïte, Alexis Gaete, Francisco Saitua, Miguel L. Allende, Verónica Cambiazo, Anne Siegel, Alejandro Maass, Maurício González, and Mauricio Latorre. Genome-scale metabolic models of microbacterium species isolated from a high altitude desert environment. 10(1):5560–5560, 2020. [DOI].
Kristopher D. Rawls, Bonnie V. Dougherty, Kalyan C. Vinnakota, Venkat R. Pannala, Anders Wallqvist, Glynis L. Kolling, and Jason A. Papin. Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model. 412():115390–115390, 2020. [DOI].
Hong Zeng and Aidong Yang. Bridging substrate intake kinetics and bacterial growth phenotypes with flux balance analysis incorporating proteome allocation. 10(1):4283–4283, 2020. [DOI].
Hamideh Fouladiha, Sayed‐Amir Marashi, Fatemeh Torkashvand, Fereidoun Mahboudi, Nathan E. Lewis, and Behrouz Vaziri. A metabolic network-based approach for developing feeding strategies for cho cells to increase monoclonal antibody production. 43(8):1381–1389, 2020. [DOI].
Shaozhen Ding, Yu Tian, Pengli Cai, Dachuan Zhang, Xingxiang Cheng, Dandan Sun, Le Yuan, Junni Chen, Weizhong Tu, Dong‐Qing Wei, and Qian‐Nan Hu. Novopathfinder: a webserver of designing novel-pathway with integrating gem-model. 48(W1):W477–W487, 2020. [DOI].
Hao Tong, Anika Küken, and Zoran Nikoloski. Integrating molecular markers into metabolic models improves genomic selection for arabidopsis growth. 11(1):2410–2410, 2020. [DOI].
Francesca Antonaros, Veronica Ghini, Francesca Pulina, Giuseppe Ramacieri, Elena Cicchini, Elisa Mannini, Anna Martelli, Agnese Feliciello, Silvia Lanfranchi, Sara Onnivello, Renzo Vianello, Chiara Locatelli, Guido Cocchi, Maria Chiara Pelleri, Lorenza Vitale, Pierluigi Strippoli, Claudio Luchinat, Paola Turano, Allison Piovesan, and Maria Caracausi. Plasma metabolome and cognitive skills in down syndrome. 10(1):10491–10491, 2020. [DOI].
Till Tiso, Tanja Narančić, Ren Wei, Éric Pollet, Niall Beagan, Katja Schröder, Annett Honak, Mengying Jiang, Shane T. Kenny, Nick Wierckx, Rémi Perrin, Luc Avérous, Wolfgang Zimmermann, Kevin E. O’Connor, and Lars M. Blank. Bio-upcycling of polyethylene terephthalate. ():, 2020. [DOI].
Cristal Zúñiga, Beth B. Peacock, Bo Liang, Greg McCollum, Sonia Irigoyen, Diego Tec-Campos, Clarisse Marotz, Nien-Chen Weng, Alejandro Zepeda, Georgios Vidalakis, Kranthi K. Mandadi, James Borneman, and Karsten Zengler. Linking metabolic phenotypes to pathogenic traits among “candidatus liberibacter asiaticus” and its hosts. 6(1):24–24, 2020. [DOI].
Birthe Halmschlag, Kyra Hoffmann, René Hanke, Sastia Prama Putri, Eiichiro Fukusaki, Jochen Büchs, and Lars M. Blank. Comparison of isomerase and weimberg pathway for γ-pga production from xylose by engineered bacillus subtilis. 7():476–476, 2020. [DOI].
Hardy Rolletschek, Jörg Schwender, Christina König, Kent D. Chapman, Trevor B. Romsdahl, Christin Lorenz, Hans‐Peter Braun, Peter Denolf, Katrien Van Audenhove, E. Muñz, Nicolas Heinzel, Stefan Ortleb, Twan Rutten, S. McCorkle, Taras Borysyuk, André Guendel, Hai Shi, Michiel Vander Auwermeulen, Stéphane Bourot, and Ljudmilla Borisjuk. Cellular plasticity in response to suppression of storage proteins in the brassica napus embryo. 32(7):2383–2401, 2020. [DOI].
Emrah Özcan, Merve Seven, Burcu Şirin, Tunahan Çakır, Emrah Nıkerel, Bas Teusink, and Ebru Toksoy Öner. Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures. 118(1):223–237, 2020. [DOI].
Juan D. Tibocha‐Bonilla, Manish Kumar, Anne Richelle, Rubén Darío Godoy‐Silva, Karsten Zengler, and Cristal Zúñiga. Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes. 6(1):14–14, 2020. [DOI].
Emadoddin Karimian and Ehsan Motamedian. Acbm: an integrated agent and constraint based modeling framework for simulation of microbial communities. 10(1):8695–8695, 2020. [DOI].
Carolina Shene, Paris Paredes, Liset Flores, Allison Leyton, Juan A. Asenjo, and Yusuf Chisti. Dynamic flux balance analysis of biomass and lipid production by antarctic thraustochytrid oblongichytrium sp. rt2316‐13. 117(10):3006–3017, 2020. [DOI].
José Manuel Borrero‐de Acuña, Izabook Gutiérrez‐Urrutia, Cristian Hidalgo-Dumont, Carla Aravena-Carrasco, Matias Orellana‐Saez, Nestor Palominos-Gonzalez, Jozef B. J. H. van Duuren, Viktoria Wagner, Lars Gläser, Judith Becker, Michael Kohlstedt, Flavia C. Zacconi, Christoph Wittmann, and Ignacio Poblete‐Castro. Channelling carbon flux through the meta ‐cleavage route for improved poly(3‐hydroxyalkanoate) production from benzoate and lignin‐based aromatics in pseudomonas putida h. 14(6):2385–2402, 2020. [DOI].
Yuki Kuriya and Michihiro Araki. Dynamic flux balance analysis to evaluate the strain production performance on shikimic acid production in escherichia coli. 10(5):198–198, 2020. [DOI].
Lokanand Koduru, Hyang Kim, Meiyappan Lakshmanan, Bijayalaxmi Mohanty, Yi Qing Lee, Choong Hwan Lee, and Dong‐Yup Lee. Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, xanthomonas oryzae. 21(4):527–540, 2020. [DOI].
Gunvor Bjerkelund Røkke, Martin F. Hohmann‐Marriott, and Eivind Almaas. An adjustable algal chloroplast plug-and-play model for genome-scale metabolic models. 15(2):e0229408–e0229408, 2020. [DOI].
Anukriti Verma, Shivani Sharda, Bhawna Rathi, Pallavi Somvanshi, and Bimlesh Dhar Pandey. Elucidating potential molecular signatures through host-microbe interactions for reactive arthritis and inflammatory bowel disease using combinatorial approach. 10(1):15131–15131, 2020. [DOI].
Rashi Verma, Dibyabhaba Pradhan, Mohsin Maseet, Harpreet Singh, Arun K. Jain, and Luqman Ahmad Khan. Genome-wide screening and in silico gene knockout to predict potential candidates for drug designing against candida albicans. 80():104196–104196, 2020. [DOI].
Kyle A. Stone, Matthew Hilliard, Kiumars Badr, Alisabeth Bradford, Q. Peter He, and Jin Wang. Comparative study of oxygen-limited and methane-limited growth phenotypes of methylomicrobium buryatense 5gb1. 161():107707–107707, 2020. [DOI].
Ali Malek Shahkouhi and Ehsan Motamedian. Reconstruction of a regulated two-cell metabolic model to study biohydrogen production in a diazotrophic cyanobacterium anabaena variabilis atcc 29413. 15(1):e0227977–e0227977, 2020. [DOI].
Na-Rae Lee, Choong Hwan Lee, Dong‐Yup Lee, and Jin‐Byung Park. Genome-scale metabolic network reconstruction and in silico analysis of hexanoic acid producing megasphaera elsdenii. 8(4):539–539, 2020. [DOI].
David M. Curran, Alexandra Grote, Nirvana Nursimulu, Adam Geber, Denis Voronin, Drew R. Jones, Elodie Ghedin, and John Parkinson. Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets. 9():, 2020. [DOI].
Hoda Nouri, Hamideh Fouladiha, Hamid Moghimi, and Sayed‐Amir Marashi. A reconciliation of genome-scale metabolic network model of zymomonas mobilis zm4. 10(1):7782–7782, 2020. [DOI].
Jan Géryk, Daniel Krsička, Markéta Vlčková, Markéta Havlovičová, Milan Maçek, and Radka Kremlíková Pourová. The key role of purine metabolism in the folate-dependent phenotype of autism spectrum disorders: an in silico analysis. 10(5):184–184, 2020. [DOI].
Cristina Herencias, Sergio Salgado, M. Auxiliadora Prieto, and Juan Nogales. Providing new insights on the biphasic lifestyle of the predatory bacterium bdellovibrio bacteriovorus through genome-scale metabolic modeling. 16(9):e1007646–e1007646, 2020. [DOI].
Eileen Marie Hanna, Xiaokang Zhang, Marta Eide, Shirin Fallahi, Tomasz Furmanek, Fekadu Yadetie, Daniel C. Zielinski, Anders Goksøyr, and Inge Jonassen. Recodliver0.9: overcoming challenges in genome-scale metabolic reconstruction of a non-model species. 7():591406–591406, 2020. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Elena López‐Camacho, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Rocío López‐Vacas, Jorge M. Arevalillo, Mariana Díaz‐Almirón, Hilario Navarro, Paloma Maı́n, Enrique Espinosa, Pilar Zamora, and Juan Ángel Fresno Vara. Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer. 20(1):307–307, 2020. [DOI].
Mohammad Rezazadeh, Valiollah Babaeipour, and Ehsan Motamedian. Reconstruction, verification and in-silico analysis of a genome-scale metabolic model of bacterial cellulose producing komagataeibacter xylinus. 43(6):1017–1026, 2020. [DOI].
Tobias Ploch, Eric von Lieres, Wolfgang Wiechert, Alexander Mitsos, and Ralf Hannemann. Simulation of differential-algebraic equation systems with optimization criteria embedded in modelica. 140():106920–106920, 2020. [DOI].
William Hohenschuh, Ronald E. Hector, Frank Chaplen, and Ganti S. Murthy. Using high-throughput data and dynamic flux balance modeling techniques to identify points of constraint in xylose utilization in saccharomyces cerevisiae. 1(1):58–75, 2020. [DOI].
Marisa Nicolás, Maiana de Oliveira Cerqueira e Costa, Pablo Ivan Pereira Ramos, Marcelo Trindade dos Santos, Ernesto Pérez‐Rueda, Marcelo A. Martí, Darío Fernández Do Porto, and Adrián G. Turjanski. Integrating omics data to prioritize target genes in pathogenic bacteria. ():217–276, 2020. [DOI].
Sandra M. Correa, Saleh Alseekh, Lucía Atehortúa, Yariv Brotman, Rigoberto Ríos‐Estepa, Alisdair R. Fernie, and Zoran Nikoloski. Model‐assisted identification of metabolic engineering strategies for jatropha curcas lipid pathways. 104(1):76–95, 2020. [DOI].
Rahul Kumar and Petri‐Jaan Lahtvee. Proteome overabundance enables respiration but limitation onsets carbon overflow. ():, 2020. [DOI].
Gayathri Sambamoorthy and Karthik Raman. <scp>minreact</scp> : a systematic approach for identifying minimal metabolic networks. 36(15):4309–4315, 2020. [DOI].
Fabián Moreno, José Utrilla, Francisco Bolívar, Juan Nogales, and Adelfo Escalante. Metabolic reconstruction of pseudomonas chlororaphis atcc 9446 to understand its metabolic potential as a phenazine-1-carboxamide-producing strain. 104(23):10119–10132, 2020. [DOI].
Inmaculada García‐Romero, Juan Nogales, Eduardo Dı́az, Eduardo Santero, and Belén Floriano. Understanding the metabolism of the tetralin degrader sphingopyxis granuli strain tfa through genome-scale metabolic modelling. 10(1):8651–8651, 2020. [DOI].
Song‐Min Schinn, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. ():, 2020. [DOI].
Yazdan Asgari and Pegah Khosravi. Flux variability analysis reveals a tragedy of commons in cancer cells. 2(12):, 2020. [DOI].
Clémence Frioux, Simon M. Dittami, and Anne Siegel. Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions. 48(3):901–913, 2020. [DOI].
Lucía Trilla‐Fuertes, Ismael Ghanem, Angelo Gámez‐Pozo, Joan Maurel, Laura G-Pastrián, Marta Mendiola, Cristina Peña, Rocío López‐Vacas, Guillermo Prado-Vázquez, Elena López‐Camacho, Andrea Zapater‐Moros, Victoria Heredia-Soto, Míriam Cuatrecasas, Pilar García‐Alfonso, Jaume Capdevila, Carles Conill, Rocio García‐Carbonero, Ricardo Ramos, Claudia Fortes, Carlos Lloréns, Paolo Nanni, Juan Ángel Fresno Vara, and Jaime Feliú. Genetic profile and functional proteomics of anal squamous cell carcinoma: proposal for a molecular classification. 19(4):690–700, 2020. [DOI].
Narges Pedram, Hamid Rashedi, and Ehsan Motamedian. A systematic strategy using a reconstructed genome-scale metabolic network for pathogenstreptococcuspneumoniaed39 to find novel potential drug targets. 78(6):, 2020. [DOI].
Jyotshana Gautam and Zhaohui Xu. Construction and validation of a genome-scale metabolic network of thermotoga sp. strain rq7. 193(3):896–911, 2020. [DOI].
Eileen Marie Hanna, Xiaokang Zhang, Marta Eide, Shirin Fallahi, Tomasz Furmanek, Fekadu Yadetie, Daniel C. Zielinski, Anders Goksøyr, and Inge Jonassen. Recodliver0.9: overcoming challenges in genome-scale metabolic reconstruction of a non-model species. ():, 2020. [DOI].
Song Min Schinn, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. A genome-scale metabolic network model and machine learning predict amino acid concentrations in chinese hamster ovary cell cultures. ():, 2020. [DOI].
George C. diCenzo, Marco Galardini, and Marco Fondi. Tn-core: functionally interpreting transposon-sequencing data with metabolic network analysis. 2189():199–215, 2020. [DOI].
Karel Olavarría, Caspar Quakkelaar, Joachim van Renselaar, Dennis Langerak, Mark C.M. van Loosdrecht, and A. Wahl. Nadh-driven poly-3-hydroxybutyrate accumulation in escherichia coli: data from enzymatic assays and oxygen-limited continuous cultures. 33():106588–106588, 2020. [DOI].
Piao Zou, Yunze Zhang, Jean Bosco Nshimiyimana, Qianwen Cao, Yang Yang, Hui Geng, and Xiong Li. Reconstruction of a context-specific model based on genome-scale metabolic simulation for identification of prochloraz resistance mechanisms in penicillium digitatum. 27(6):776–785, 2020. [DOI].
Ying Li, He Xian, Ya Xu, Yuan Zhu, Zhijie Sun, Qian Wang, and Qingsheng Qi. Fine and dynamic tuning the glycolytic flux ratio of an artificial carbon saving pathway for high yield of mevalonate in escherichia coli. ():, 2020. [DOI].
Jake P. N. Hattwell, Janna Hastings, Olivia Casanueva, Horst Joachim Schirra, and Michael Witting. Using genome-scale metabolic networks for analysis, visualization, and integration of targeted metabolomics data. 2104():361–386, 2020. [DOI].
Song-Min Schinn, Carly Morrison, Wei Wei, Lin Zhang, and Nathan E. Lewis. A genome-scale metabolic network model and machine learning predict amino acid concentrations in chinese hamster ovary cell cultures. ():, 2020. [DOI].
Gayathri Sambamoorthy and Karthik Raman. Minreact: an efficient algorithm for identifying minimal metabolic networks. ():, 2020. [DOI].
Nhung Pham, Maarten Reijnders, María Suárez‐Diez, Bart Nijsse, Jan Springer, Gerrit Eggink, and Peter J. Schaap. Genome-scale metabolic modelling underscores the potential of cutaneotrichosporon oleaginosus atcc 20509 as a cell factory for biofuel production. ():, 2020. [DOI].
Nathan Braniff. Optimal experimental design applied to models of microbial gene regulation. ():, 2020. [DOI].
Yuzhong Liu, Fei Gan, Pablo Cruz‐Morales, and Jay D. Keasling. Engineering natural product biosynthetic pathways to produce commodity and specialty chemicals. ():352–376, 2020. [DOI].
Claudio Tomi-Andrino, Rupert Norman, Thomas Millat, Philippe Soucaille, Klaus Winzer, David A. Barrett, John R. King, and Dong‐Hyun Kim. Assessing the impact of physicochemical parameters in the predictive capabilities of thermodynamics-based stoichiometric approaches under mesophilic and thermophilic conditions. ():, 2020. [DOI].
Lucía Trilla‐Fuertes, Ricardo Ramos, Natalia Blanca‐López, Elena López‐Camacho, Laura Martín‐Pedraza, Pablo Ryan, Mariana Díaz‐Almirón, Carlos Lloréns, Toni Gabaldón, Andrés Moyá, Juan Ángel Fresno Vara, and Angelo Gámez‐Pozo. Functional characterization of sars-cov-2 infection suggests a complex inflammatory response and metabolic alterations. ():, 2020. [DOI].
Vladimir Porokhin, Sara A. Amin, Trevor B. Nicks, Venkatesh Endalur Gopinarayanan, Nikhil U. Nair, and Soha Hassoun. Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity. ():, 2020. [DOI].
Sander Y.A. Rodenburg. Uncovering oomycete metabolism using systems biology. ():, 2020. [DOI].
Claudio Tomi-Andrino, Rupert Norman, Thomas Millat, Philippe Soucaille, Klaus Winzer, David A. Barrett, John R. King, and Dong‐Hyun Kim. Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions. ():, 2020. [DOI].
Kiyan Shabestary. Improving cyanobacteria productivity: from theory to assay. ():, 2020. [DOI].
Nhung Pham, Maarten Reijnders, María Suárez‐Diez, Bart Nijsse, Jan Springer, Gerrit Eggink, and Peter J. Schaap. Genome-scale metabolic modelling underscores the potential of cutaneotrichosporon oleaginosus atcc 20509 as a cell factory for biofuel production. ():, 2020. [DOI].
Anonymous. 9(4):, 2020. [DOI].
Nhung Pham, Maarten Reijnders, María Suárez‐Diez, Bart Nijsse, Jan Springer, Gerrit Eggink, and Peter J. Schaap. Genome-scale metabolic modelling underscores the potential of cutaneotrichosporon oleaginosus atcc 20509 as a cell factory for biofuel production. ():, 2020. [DOI].
Cynthia Alexandra Martín Jiménez. Reconstrucción a escala genómica de una red metabólica de astrocito humano : aplicación a isquemia. ():, 2020. [DOI].
Christian Lieven, Moritz E. Beber, Brett G. Olivier, Frank Bergmann, Meriç Ataman, Parizad Babaei, Jennifer Bartell, Lars M. Blank, Siddharth Chauhan, Kevin Correia, Christian Diener, Andreas Dräger, Birgitta E. Ebert, Janaka N. Edirisinghe, José P. Faria, Adam M. Feist, Georgios Fengos, Ronan M. T. Fleming, Beatriz García-Jiménez, Vassily Hatzimanikatis, Wout van Helvoirt, Christopher S. Henry, Henning Hermjakob, Markus J. Herrgård, Ali Kaafarani, Hyun Uk Kim, Zachary A. King, Steffen Klamt, Edda Klipp, Jasper J. Koehorst, Matthias König, Meiyappan Lakshmanan, Dong‐Yup Lee, Sang Yup Lee, Sunjae Lee, Nathan E. Lewis, Filipe Liu, Hongwu Ma, Daniel Machado, Radhakrishnan Mahadevan, Paulo Maia, Adil Mardinoğlu, Gregory L. Medlock, Jonathan M. Monk, Jens Nielsen, Lars K. Nielsen, Juan Nogales, Intawat Nookaew, Bernhard Ø. Palsson, Jason A. Papin, Kiran Raosaheb Patil, Mark G. Poolman, Nathan D. Price, Osbaldo Reséndis-Antonio, Anne Richelle, Isabel Rocha, Benjamín J. Sánchez, Peter J. Schaap, Rahuman S. Malik‐Sheriff, Saeed Shoaie, Nikolaus Sonnenschein, Bas Teusink, Paulo Vilaça, Jon Olav Vik, Judith A. H. Wodke, Joana C. Xavier, Qianqian Yuan, Maksim Zakhartsev, and Cheng Zhang. Memote for standardized genome-scale metabolic model testing. 38(3):272–276, 2020. [DOI].
Jonathan L. Robinson, Pınar Kocabaş, Hao Wang, Pierre-Etienne Cholley, Daniel Cook, Avlant Nilsson, Mihail Anton, Raphaël Ferreira, Iván Domenzain, Virinchi Billa, Angelo Limeta, Karl Alex Hedin, Johan Gustafsson, Eduard J. Kerkhoven, Thomas Svensson, Bernhard Ø. Palsson, Adil Mardinoğlu, Lena Hansson, Mathias Uhlén, and Jens Nielsen. An atlas of human metabolism. 13(624):, 2020. [DOI].
M. Fata Moradali and Bernd H. A. Rehm. Bacterial biopolymers: from pathogenesis to advanced materials. 18(4):195–210, 2020. [DOI].
Joana P. Bernardes, Neha Mishra, Florian Tran, Thomas Bahmer, Lena Best, Johanna I. Blase, Dora Bordoni, Jeanette Franzenburg, Ulf Geisen, Jonathan Josephs‐Spaulding, Philipp Köhler, Axel Künstner, Elisa Rosati, Anna C. Aschenbrenner, Petra Bächer, Nathan Baran, Teide Jens Boysen, Burkhard Brandt, Niklas Bruse, Jonathan Dörr, Andreas Dräger, Gunnar Elke, David Ellinghaus, Julia Fischer, Michael Förster, André Franke, Sören Franzenburg, Norbert Frey, Anette Friedrichs, Janina Fuß, Andreas Glück, Jacob Hamm, Finn Hinrichsen, Marc P. Hoeppner, Simon Imm, Ralf Junker, Sina Kaiser, Ying H. Kan, Rainer Knoll, Christoph Lange, Georg Laue, Clemens Lier, Matthias Lindner, Γεώργιος Μαρίνος, Robert Markewitz, Jacob Nattermann, Rainer Noth, Peter Pickkers, Klaus F. Rabe, Alina Renz, Christoph Röcken, Jan Rupp, Annika Schaffarzyk, Alexander Scheffold, Jonas Schulte-Schrepping, Domagoj Schunk, Dirk Skowasch, Thomas Ulas, Klaus‐Peter Wandinger, Michael Wittig, Johannes Zimmermann, Hauke Busch, Bimba F. Hoyer, Christoph Kaleta, Jan Heyckendorf, Matthijs Kox, Jan Rybniker, Stefan Schreiber, Joachim L. Schultze, Philip Rosenstiel, Nicholas E. Banovich, Tushar Desai, Oliver Eickelberg, Muzlifa Haniffa, Péter Horváth, Jonathan A. Kropski, Robert Lafyatis, Joakim Lundeberg, Kerstin B. Meyer, Martijn C. Nawijn, Marko Nikolić, José Ordovas Montañes, Dana Pe’er, Purushothama Rao Tata, Emma L. Rawlins, Aviv Regev, Paul A. Reyfman, Christos Samakovlis, Joachim L. Schultze, Alex K. Shalek, Douglas P. Shepherd, Jason R. Spence, Sarah A. Teichmann, Fabian J. Theis, Alexander M. Tsankov, Maarten van den Berge, Michael von Papen, Jeffrey A. Whitsett, Laure‐Emmanuelle Zaragosi, and Angel Angelov. Longitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells, and plasmablasts as hallmarks of severe covid-19. 53(6):1296–1314.e9, 2020. [DOI].
Kateřina Rohlenová, Jermaine Goveia, Melissa García‐Caballero, Abhishek Subramanian, Joanna Kalucka, Lucas Treps, Kim D. Falkenberg, Laura de Rooij, Yingfeng Zheng, Lin Lin, Liliana Sokol, Laure-Anne Teuwen, Vincent Geldhof, Federico Taverna, Andreas Pircher, Lena‐Christin Conradi, Shawez Khan, Steve Stegen, Dena Panovska, Frederik De Smet, Frank J. T. Staal, René McLaughlin, Stefan Vinckier, Tine Van Bergen, Nadine Ectors, Patrik De Haes, Jian Wang, Lars Bolund, Luc Schoonjans, Tobias K. Karakach, Huanming Yang, Geert Carmeliet, Yizhi Liu, Bernard Thienpont, Mieke Dewerchin, Guy Eelen, Xuri Li, Yonglun Luo, and Peter Carmeliet. Single-cell rna sequencing maps endothelial metabolic plasticity in pathological angiogenesis. 31(4):862–877.e14, 2020. [DOI].
Xin Fang, Colton J. Lloyd, and Bernhard Ø. Palsson. Reconstructing organisms in silico: genome-scale models and their emerging applications. 18(12):731–743, 2020. [DOI].
Sarah Keating, Dagmar Waltemath, Matthias König, Fengkai Zhang, Andreas Dräger, Claudine Chaouiya, Frank Bergmann, Andrew Finney, Colin S. Gillespie, Tomáš Helikar, Stefan Hoops, Rahuman S. Malik‐Sheriff, Stuart Moodie, Ion I. Moraru, Chris J. Myers, Aurélien Naldi, Brett G. Olivier, Sven Sahle, James C. Schaff, Lucian P. Smith, Maciej J. Swat, Denis Thieffry, Leandro Watanabe, Darren J. Wilkinson, Michael L. Blinov, Kimberly Begley, James R. Faeder, Harold Gómez, Thomas M. Hamm, Yuichiro Inagaki, Wolfram Liebermeister, Allyson Lister, Daniel Lucio, Eric Mjolsness, Carole J. Proctor, Karthik Raman, Nicolás Rodríguez, Clifford A. Shaffer, Bruce E. Shapiro, Joerg Stelling, Neil Swainston, Naoki Tanimura, John Wagner, Martin Meier‐Schellersheim, Herbert M. Sauro, Bernhard Ø. Palsson, Hamid Bolouri, Hiroaki Kitano, Akira Funahashi, Henning Hermjakob, John C. Doyle, Michael Hucka, Richard R. Adams, Nicholas Allen, Bastian R. Angermann, Marco Antoniotti, Gary D. Bader, Jan Červený, Mélanie Courtot, Chris D. Cox, Piero Dalle Pezze, Emek Demir, William S. Denney, Harish Dharuri, Julien Dorier, Dirk Drasdo, Ali Ebrahim, Johannes Eichner, Johan Elf, Lukas Endler, Chris T. Evelo, Christoph Flamm, Ronan M. T. Fleming, Martina Fröhlich, Mihai Glont, Emanuel Gonçalves, Martin Golebiewski, Hovakim Grabski, Alex Gutteridge, D. Hachmeister, Leonard A. Harris, Ben Heavner, Ron Henkel, William S. Hlavacek, Bin Hu, Daniel R. Hyduke, Hidde de Jong, Nick Juty, Peter D. Karp, Jonathan R. Karr, Douglas B. Kell, Roland Keller, Ilya Kiselev, Steffen Klamt, Edda Klipp, Christian Knüpfer, Fedor Kolpakov, Falko Krause, Martina Kutmon, and Camille Laibe. <scp>sbml</scp> level 3: an extensible format for the exchange and reuse of biological models. 16(8):e9110–e9110, 2020. [DOI].
Christopher A. Voigt. Synthetic biology 2020–2030: six commercially-available products that are changing our world. 11(1):6379–6379, 2020. [DOI].
Jie Zhang, Søren D. Petersen, Tijana Radivojević, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J. Sánchez, Zak Costello, Yu Chen, M.J. Fero, Héctor García Martín, Jens Nielsen, Jay D. Keasling, and Michael K. Jensen. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. 11(1):4880–4880, 2020. [DOI].
Federico Baldini, Johannes Hertel, Estelle Sandt, Cyrille C. Thinnes, Lorieza Neuberger-Castillo, Lukas Pavelka, Fay Betsou, Rejko Krüger, Ines Thiele, Gloria Aguayo, Dominic Allen, Wim Ammerlann, Maike K. Aurich, Rudi Balling, Peter Banda, Katy Beaumont, Regina Becker, Daniela Berg, Sylvia Binck, Alexandre Bisdorff, Dheeraj Reddy Bobbili, Kathrin Brockmann, Jessica Calmes, Lorieza Castillo, Nico J. Diederich, Rene Dondelinger, Daniela Esteves, Jean-Yves Ferrand, Ronan M. T. Fleming, Manon Gantenbein, Thomas Gasser, Piotr Gawron, Lars Geffers, Virginie Giarmana, Enrico Glaab, Clarissa P. C. Gomes, Nikolai Goncharenko, Jérôme Graas, Mariela Graziano, Valentin Grouès, Anne Grünewald, Wei Gu, Gaël Hammot, Anne-Marie Hanff, Linda Hansen, Maxime Hansen, Hulda S. Haraldsdóttir, Laurent Heirendt, Sylvia Herbrink, Sascha Herzinger, Michaël Heymann, Karsten Hiller, Géraldine Hipp, Joshua Shulman, Laëtitia Huiart, Alexander Hundt, Nadine Jacoby, Jacek Jarosław, Yohan Jaroz, Pierre Kolber, Joachim Kutzera, Zied Landoulsi, Catherine Larue, Roseline Lentz, Inga Liepelt, Robert Liszka, Laura Longhino, Victoria Lorentz, Clare E. Mackay, Walter Maetzler, Katrin Marcus, Guilherme Fernandes Marques, Jan Martens, Conny Mathay, Piotr Matyjaszczyk, Patrick May, Françoise Meisch, Myriam Menster, Maura Minelli, Michel Mittelbronn, Brit Mollenhauer, Kathleen Mommaerts, Carlos S. Moreno, Friedrich Mühlschlegel, Romain Nati, Ulf Nehrbass, Sarah Nickels, Béatrice Nicolai, Jean-Paul Nicolay, Alberto Noronha, Wolfgang H. Oertel, Marek Ostaszewski, Sinthuja Pachchek, Claire Pauly, Magali Perquin, Dorothea Reiter, Isabel Rosety, Kirsten Rump, Venkata Satagopam, and Marc Schlesser. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. 18(1):62–62, 2020. [DOI].
Ines Thiele, Swagatika Sahoo, Almut Heinken, Johannes Hertel, Laurent Heirendt, Maike K. Aurich, and Ronan M. T. Fleming. Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome. 16(5):e8982–e8982, 2020. [DOI].
Priyanka Baloni, Cory C. Funk, Jingwen Yan, James T. Yurkovich, Alexandra Kueider‐Paisley, Kwangsik Nho, Almut Heinken, Wei Jia, Siamak MahmoudianDehkordi, Gregory Louie, Andrew J. Saykin, Matthias Arnold, Gabi Kastenmüller, William J. Griffiths, Ines Thiele, Rima Kaddurah‐Daouk, Nathan D. Price, Rima Kaddurah‐Daouk, Alexandra Kueider‐Paisley, Gregory Louie, P. Murali Doraiswamy, Colette Blach, Arthur Moseley, Paul M. Thompson, Siamak Mahmoudiandehkhordi, Kathleen Welsh-Balmer, Brenda L. Plassman, Andrew J. Saykin, Kwangsik Nho, Gabi Kastenmüller, Matthias Arnold, Sudeepa Bhattacharyya, Xianlin Han, Rebecca Baillie, Oliver Fiehn, Dinesh Kumar Barupal, Peter J. Meikle, Sarkis K. Mazmanian, Mitchel A. Kling, Leslie M. Shaw, John Q. Trojanowski, Jon B. Toledo, Cornelia van Duijin, Thomas Hankemier, Ines Thiele, Almut Heinken, Nathan D. Price, Cory C. Funk, Priyanka Baloni, Wei Jia, David S. Wishart, Roberta Dı́az Brinton, Rui Chang, Lindsay A. Farrer, Rhoda Au, Wendy Qiu, Peter Würtz, Lara M. Mangravite, Jan Krumsiek, John W. Newman, Bin Zhang, and Herman Moreno. Metabolic network analysis reveals altered bile acid synthesis and metabolism in alzheimer’s disease. 1(8):100138–100138, 2020. [DOI].
Sébastien Moretti, Van Du T. Tran, Florence Mehl, Mark Ibberson, and Marco Pagni. Metanetx/mnxref: unified namespace for metabolites and biochemical reactions in the context of metabolic models. 49(D1):D570–D574, 2020. [DOI].
Shan Zhang, Boris Reljić, Chao Liang, Baptiste Kerouanton, Joel Celio Francisco, Jih Hou Peh, Camille Mary, N. Suhas Jagannathan, Volodimir Olexiouk, Claire Tang, Gio Fidelito, Srikanth Nama, Ruey‐Kuang Cheng, Caroline Lei Wee, Loo Chien Wang, Paula Duek, Prabha Sampath, Lydie Lane, Enrico Petretto, Radoslaw M. Sobota, Suresh Jesuthasan, Lisa Tucker‐Kellogg, Bruno Reversade, Gerben Menschaert, Lei Sun, David A. Stroud, and Lena Ho. Mitochondrial peptide brawnin is essential for vertebrate respiratory complex iii assembly. 11(1):1312–1312, 2020. [DOI].
Deepanwita Banerjee, Thomas Eng, Andrew K. Lau, Yusuke Sasaki, Brenda Wang, Yan Chen, Jan‐Philip Prahl, Vasanth Singan, Robin A. Herbert, Yuzhong Liu, Deepti Tanjore, Christopher J. Petzold, Jay D. Keasling, and Aindrila Mukhopadhyay. Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale. 11(1):5385–5385, 2020. [DOI].
Rosemary Yu, Kate Campbell, Rui Pereira, Johan Björkeroth, Qi Qi, Egor Vorontsov, Carina Sihlbom, and Jens Nielsen. Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast. 11(1):1881–1881, 2020. [DOI].
Christopher Culley, Supreeta Vijayakumar, Guido Zampieri, and Claudio Angione. A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. 117(31):18869–18879, 2020. [DOI].
Yuliang Wang, Shuyi Ma, and Walter L. Ruzzo. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. 10(1):3490–3490, 2020. [DOI].
Arianna Basile, Stefano Campanaro, Adam Kovalovszki, Guido Zampieri, Alessandro Rossi, İrini Angelidaki, Giorgio Valle, and Laura Treu. Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis. 62():138–149, 2020. [DOI].
Irene Otero‐Muras and Pablo Carbonell. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. 63():61–80, 2020. [DOI].
Hock Chuan Yeo, Jongkwang Hong, Meiyappan Lakshmanan, and Dong‐Yup Lee. Enzyme capacity-based genome scale modelling of cho cells. 60():138–147, 2020. [DOI].
Aitor Villate, Markel San Nicolás, Mara Gallastegi, Pierre-Antoine Aulas, Maitane Olivares, Aresatz Usobiaga, Néstor Etxebarría, and Oier Aizpurua‐Olaizola. Review: metabolomics as a prediction tool for plants performance under environmental stress. 303():110789–110789, 2020. [DOI].
Ailin Liu, Yee‐Shan Ku, Carolina A. Contador, and Hon‐Ming Lam. The impacts of domestication and agricultural practices on legume nutrient acquisition through symbiosis with rhizobia and arbuscular mycorrhizal fungi. 11():583954–583954, 2020. [DOI].
Yan Chen, Deepanwita Banerjee, Aindrila Mukhopadhyay, and Christopher J. Petzold. Systems and synthetic biology tools for advanced bioproduction hosts. 64():101–109, 2020. [DOI].
Amin Espah Borujeni, Jing Zhang, Hamid Doosthosseini, Alec A. K. Nielsen, and Christopher A. Voigt. Genetic circuit characterization by inferring rna polymerase movement and ribosome usage. 11(1):5001–5001, 2020. [DOI].
Philipp Keller, Εlad Noor, Fabian Meyer, Michael Reiter, Stanislav Anastassov, Patrick Kiefer, and Julia A. Vorholt. Methanol-dependent escherichia coli strains with a complete ribulose monophosphate cycle. 11(1):5403–5403, 2020. [DOI].
Γεώργιος Μαρίνος, Christoph Kaleta, and Silvio Waschina. Defining the nutritional input for genome-scale metabolic models: a roadmap. 15(8):e0236890–e0236890, 2020. [DOI].
Javiera López, D.P. Jaramillo Bustos, Conrado Camilo, N Arenas, Pedro A. Saa, and Eduardo Agosín. Engineering saccharomyces cerevisiae for the overproduction of β-ionone and its precursor β-carotene. 8():578793–578793, 2020. [DOI].
Bianca M. Esch, Sergej Limar, André Bogdanowski, Christos Gournas, Tushar H. More, Celine Sundag, Stefan Walter, Jürgen J. Heinisch, Christer S. Ejsing, Bruno André, and Florian Fröhlich. Uptake of exogenous serine is important to maintain sphingolipid homeostasis in saccharomyces cerevisiae. 16(8):e1008745–e1008745, 2020. [DOI].
S.T.R. Moolamalla and P. K. Vinod. Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders. 125():103994–103994, 2020. [DOI].
Yoon-Mi Choi, Yi Qing Lee, Hyun‐Seob Song, and Dong‐Yup Lee. Genome scale metabolic models and analysis for evaluating probiotic potentials. 48(4):1309–1321, 2020. [DOI].
Maureen A. Carey, Andreas Dräger, Moritz E. Beber, Jason A. Papin, and James T. Yurkovich. Community standards to facilitate development and address challenges in metabolic modeling. 16(8):e9235–e9235, 2020. [DOI].
Pritam Chanda, Eduardo Costa, Jie Hu, Shravan Sukumar, John Van Hemert, and Rasna R. Walia. Information theory in computational biology: where we stand today. 22(6):627–627, 2020. [DOI].
Denny Popp and Florian Centler. Μbialsim: constraint-based dynamic simulation of complex microbiomes. 8():574–574, 2020. [DOI].
Bing Hu, Miaoxiao Wang, Shuang Geng, Liqun Wen, Mengdi Wu, Yong Nie, Yue‐Qin Tang, and Xiao‐Lei Wu. Metabolic exchange with non-alkane-consuming pseudomonas stutzeri slg510a3-8 improves n -alkane biodegradation by the alkane degrader dietzia sp. strain dq12-45-1b. 86(8):, 2020. [DOI].
Teresa J. Clark, Longyun Guo, John A. Morgan, and Jörg Schwender. Modeling plant metabolism: from network reconstruction to mechanistic models. 71(1):303–326, 2020. [DOI].
L. Şafak Yılmaz, Xuhang Li, Shivani Nanda, Bennett W. Fox, Frank C. Schroeder, and Albertha J.M. Walhout. Modeling tissue‐relevant caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. 16(10):e9649–e9649, 2020. [DOI].
Sanjeev Dahal, James T. Yurkovich, Hao Xu, Bernhard Ø. Palsson, and Laurence Yang. Synthesizing systems biology knowledge from omics using genome‐scale models. 20(17-18):e1900282–e1900282, 2020. [DOI].
Dagmar Waltemath, Martin Golebiewski, Michael L. Blinov, Padraig Gleeson, Henning Hermjakob, Michael Hucka, Esther Thea Inau, Sarah Keating, Matthias König, Olga Krebs, Rahuman S. Malik‐Sheriff, David Nickerson, Ernst Oberortner, Herbert M. Sauro, Falk Schreiber, Lucian P. Smith, Melanie I. Stefan, Ulrike Wittig, and Chris J. Myers. The first 10 years of the international coordination network for standards in systems and synthetic biology (combine). 17(2-3):, 2020. [DOI].
Patrick F. Suthers, Charles Foster, Debolina Sarkar, Lin Wang, and Costas D. Maranas. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. 63():13–33, 2020. [DOI].
Tahila Andrighetti, Balázs Bohár, Ney Lemke, Padhmanand Sudhakar, and Tamás Korcsmáros. Microbiolink: an integrated computational pipeline to infer functional effects of microbiome–host interactions. 9(5):1278–1278, 2020. [DOI].
Thea Os Andersen, Benoît J. Kunath, Live H. Hagen, Magnus Ø. Arntzen, and Phillip B. Pope. Rumen metaproteomics: closer to linking rumen microbial function to animal productivity traits. 186():42–51, 2020. [DOI].
Almut Heinken, Geeta Acharya, Dmitry A. Ravcheev, Johannes Hertel, Malgorzata Nyga, Onyedika Emmanuel Okpala, Marcus Hogan, Stefanía Magnúsdóttir, Filippo Martinelli Boneschi, German Preciat, Janaka N. Edirisinghe, Christopher S. Henry, Ronan M. T. Fleming, and Ines Thiele. Agora2: large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities. ():, 2020. [DOI].
Saeideh Khodaee, Yazdan Asgari, Mehdi Totonchi, and Mohammad Hossein Karimi‐Jafari. Imm1865: a new reconstruction of mouse genome-scale metabolic model. 10(1):6177–6177, 2020. [DOI].
Chenguang Liang, Austin W.T. Chiang, Anders Holmgaard Hansen, Johnny Arnsdorf, Sanne Schoffelen, James T. Sorrentino, Benjamin P. Kellman, Bokan Bao, Björn G. Voldborg, and Nathan E. Lewis. A markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering. 2():22–36, 2020. [DOI].
Romeu Viana, Óscar Dias, Davide Lagoa, Mónica Galocha, Isabel Rocha, and Miguel C. Teixeira. Genome-scale metabolic model of the human pathogen candida albicans: a promising platform for drug target prediction. 6(3):171–171, 2020. [DOI].
Egils Stalidzāns, Massimiliano Zanin, Paolo Tieri, Filippo Castiglione, Annikka Polster, Stefan Scheiner, Jürgen Pahle, Blaž Stres, Markus List, Jan Baumbach, Manuela Lautizi, Kristel Van Steen, and Harald Schmidt. Mechanistic modeling and multiscale applications for precision medicine: theory and practice. 3(1):36–56, 2020. [DOI].
Xiaohan Yang, June I. Medford, Kasey Markel, Patrick M. Shih, Henrique C. DePaoli, Cong T. Trinh, Alistair J. McCormick, Raphaël Ployet, Steven G. Hussey, Alexander A. Myburg, Poul Erik Jensen, Md Mahmudul Hassan, Jin Zhang, Wellington Muchero, Udaya C. Kalluri, Hengfu Yin, Renying Zhuo, Paul E. Abraham, Jin‐Gui Chen, David J. Weston, Yinong Yang, Degao Liu, Yi Li, Jessy Labbé, Bing Yang, Jun Hyung Lee, Robert W. Cottingham, Stanton Martin, Mengzhu Lu, Timothy J. Tschaplinski, Guoliang Yuan, Haiwei Lu, Priya Ranjan, Julie C. Mitchell, Stan D. Wullschleger, and Gerald A. Tuskan. Plant biosystems design research roadmap 1.0. 2020():8051764–8051764, 2020. [DOI].
Yu Chen, Yan Sun, Zhihao Liu, Fengqing Dong, Yuanyuan Li, and Yonghong Wang. Genome‐scale modeling for bacillus coagulans to understand the metabolic characteristics. 117(11):3545–3558, 2020. [DOI].
Kwanjeera Wanichthanarak, Chuthamas Boonchai, Thammaporn Kojonna, Supachitra Chadchawan, Wichian Sangwongchai, and Maysaya Thitisaksakul. Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling. 18():3555–3566, 2020. [DOI].
Jinxin Zhao, Yan Zhu, Jiru Han, Yu-Wei Lin, Michael Aichem, Jiping Wang, Ke Chen, Tony Velkov, Falk Schreiber, and Jian Li. Genome-scale metabolic modeling reveals metabolic alterations of multidrug-resistant acinetobacter baumannii in a murine bloodstream infection model. 8(11):1793–1793, 2020. [DOI].
Kent M. Rapp, Jackson Jenkins, and Michael J. Betenbaugh. Partners for life: building microbial consortia for the future. 66():292–300, 2020. [DOI].
Yan Zhu, Jing Lü, Jinxin Zhao, Xinru Zhang, Heidi H. Yu, Tony Velkov, and Jian Li. Complete genome sequence and genome-scale metabolic modelling of acinetobacter baumannii type strain atcc 19606. 310(3):151412–151412, 2020. [DOI].
Diego Tec-Campos, Cristal Zúñiga, Anurag Passi, John Del Toro, Juan D. Tibocha‐Bonilla, Alejandro Zepeda, Michael J. Betenbaugh, and Karsten Zengler. Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph azotobacter vinelandii dj. 11():e00132–e00132, 2020. [DOI].
John R. Casey and Michael J. Follows. A steady-state model of microbial acclimation to substrate limitation. 16(8):e1008140–e1008140, 2020. [DOI].
Tamara Bintener, Maria Pires Pacheco, and Thomas Sauter. Towards the routine use of in silico screenings for drug discovery using metabolic modelling. 48(3):955–969, 2020. [DOI].
Daniel C. Zielinski, Arjun Patel, and Bernhard Ø. Palsson. The expanding computational toolbox for engineering microbial phenotypes at the genome scale. 8(12):2050–2050, 2020. [DOI].
Tobias B. Alter, Lars M. Blank, and Birgitta E. Ebert. Protein allocation and enzymatic constraints explainescherichia coliwildtype and mutant phenotypes. ():, 2020. [DOI].
Ilija Dukovski, Djordje Bajić, Jeremy M. Chacón, Michael Quintin, Jean C. C. Vila, Snorre Sulheim, Alan R. Pacheco, David B. Bernstein, William J Rieh, Kirill S. Korolev, Álvaro Sánchez, William R. Harcombe, and Daniel Segrè. Computation of microbial ecosystems in time and space (comets): an open source collaborative platform for modeling ecosystems metabolism. ():, 2020. [DOI].
Archanaa Sundararaghavan, Amitava Mukherjee, Swagatika Sahoo, and G. K. Suraishkumar. Mechanism of the oxidative stress‐mediated increase in lipid accumulation by the bacterium, r. opacus pd630: experimental analysis and genome‐scale metabolic modeling. 117(6):1779–1788, 2020. [DOI].
Víctor A. López-Agudelo, Tom A. Mendum, Emma Laing, Huihai Wu, Andrés Baena, Luis F. Barrera, Dany J. V. Beste, and Rigoberto Ríos‐Estepa. A systematic evaluation of mycobacterium tuberculosis genome-scale metabolic networks. 16(6):e1007533–e1007533, 2020. [DOI].
Ratklao Siriwach, Fumio Matsuda, Kentaro Yano, and Masami Yokota Hirai. Drought stress responses in context-specific genome-scale metabolic models of arabidopsis thaliana. 10(4):159–159, 2020. [DOI].
James D. Brunner and Nicholas Chia. Minimizing the number of optimizations for efficient community dynamic flux balance analysis. 16(9):e1007786–e1007786, 2020. [DOI].
Frances Blow, Nana Y. D. Ankrah, Noah Clark, Imhoi Koo, Erik L. Allman, Qing Liu, Mallappa Anitha, Andrew D. Patterson, and Angela E. Douglas. Impact of facultative bacteria on the metabolic function of an obligate insect-bacterial symbiosis. 11(4):, 2020. [DOI].
Fernando Cruz, José P. Faria, Miguel Rocha, Isabel Rocha, and Óscar Dias. A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. 48(5):1889–1903, 2020. [DOI].
Johann F. Jadebeck, Axel Theorell, Samuel Leweke, and Katharina Nöh. Hops: high-performance library for (non-)uniform sampling of convex-constrained models. 37(12):1776–1777, 2020. [DOI].
Nikolaos Tsiantis and Julio R. Banga. Using optimal control to understand complex metabolic pathways. 21(1):472–472, 2020. [DOI].
Thomas C. Keaty and Paul A. Jensen. <scp>gapsplit</scp> : efficient random sampling for non-convex constraint-based models. 36(8):2623–2625, 2020. [DOI].
Nana Y. D. Ankrah, Rebecca A. Wilkes, Freya Q. Zhang, Dantong Zhu, Tadeo Kaweesi, Ludmilla Aristilde, and Angela E. Douglas. Syntrophic splitting of central carbon metabolism in host cells bearing functionally different symbiotic bacteria. 14(8):1982–1993, 2020. [DOI].
Sergio Garcia and Cong T. Trinh. Harnessing natural modularity of metabolism with goal attainment optimization to design a modular chassis cell for production of diverse chemicals. 9(7):1665–1681, 2020. [DOI].
Priyanka Baloni, Wikum Dinalankara, John C. Earls, Theo Knijnenburg, Donald Geman, Luigi Marchionni, and Nathan D. Price. Identifying personalized metabolic signatures in breast cancer. 11(1):20–20, 2020. [DOI].
Michel Lavoie, Blanche Saint‐Béat, Jan Strauss, Sébastien Guérin, Antoine Allard, Simon Hardy, Angela Falciatore, and Johann Lavaud. Genome-scale metabolic reconstruction and in silico perturbation analysis of the polar diatom fragilariopsis cylindrus predicts high metabolic robustness. 9(2):30–30, 2020. [DOI].
Agnieszka B. Wegrzyn, Katharina Herzog, Albert Gerding, Marcel Kwiatkowski, Justina C. Wolters, Amalia M. Dolga, A. E. M. van Lint, Ronald J. A. Wanders, Hans R. Waterham, and Barbara M. Bakker. Fibroblast‐specific genome‐scale modelling predicts an imbalance in amino acid metabolism in refsum disease. 287(23):5096–5113, 2020. [DOI].
Mojtaba Tefagh and Stephen Boyd. Swiftcore: a tool for the context-specific reconstruction of genome-scale metabolic networks. 21(1):140–140, 2020. [DOI].
Priyanka Baloni, Cory C. Funk, Jingwen Yan, James T. Yurkovich, Alexandra Kueider‐Paisley, Kwangsik Nho, Almut Heinken, Wei Jia, Siamak MahmoudianDehkordi, Gregory Louie, Andrew J. Saykin, Matthias Arnold, Gabi Kastenmüller, William J. Griffiths, Ines Thiele, Rima Kaddurah-Daouk, and Nathan D. Price. Metabolic network analysis reveals altered bile acid synthesis and cholesterol metabolism in alzheimer’s disease. ():, 2020. [DOI].
Sandrien Desmet, Marlies Brouckaert, Wout Boerjan, and Kris Morreel. Seeing the forest for the trees: retrieving plant secondary biochemical pathways from metabolome networks. 19():72–85, 2020. [DOI].
Shouyong Jiang, Yong Wang, Marcus Kaiser, and Natalio Krasnogor. Nihba: a network interdiction approach for metabolic engineering design. 36(11):3482–3492, 2020. [DOI].
Sara Ranjbar, М. Shahmansouri, Pankaj Attri, and Annemie Bogaerts. Effect of plasma-induced oxidative stress on the glycolysis pathway of escherichia coli. 127():104064–104064, 2020. [DOI].
Bashir Sajo Mienda and Andreas Dräger. Genome-scale metabolic modeling of escherichia coli and its chassis design for synthetic biology applications. 2189():217–229, 2020. [DOI].
Ricardo Andrade, Mahdi Doostmohammadi, João Santos, Marie‐France Sagot, Nuno P. Mira, and Susana Vinga. Momo - multi-objective metabolic mixed integer optimization: application to yeast strain engineering. 21(1):, 2020. [DOI].
Alyaa M. Abdel‐Haleem, Vaishnavi Ravikumar, Boyang Ji, Katsuhiko Mineta, Xin Gao, Jens Nielsen, Takashi Gojobori, and Ivan Mijakovic̀. Integrated metabolic modeling, culturing, and transcriptomics explain enhanced virulence of vibrio cholerae during coinfection with enterotoxigenic escherichia coli. 5(5):, 2020. [DOI].
Axel von Kamp and Steffen Klamt. Memo: a method for computing metabolic modules for cell-free production systems. 9(3):556–566, 2020. [DOI].
David Botero, Jonathan M. Monk, María Juliana Rodríguez Cubillos, Andrés Rodríguez Cubillos, Mariana Restrepo Benavides, Vivian Bernal‐Galeano, Alejandro Reyes, Andrés Fernando González Barrios, Bernhard Ø. Palsson, Silvia Restrepo, and Adriana Bernal. Genome-scale metabolic model of xanthomonas phaseoli pv. manihotis: an approach to elucidate pathogenicity at the metabolic level. 11():837–837, 2020. [DOI].
Maryam Khoshnejat, Kaveh Kavousi, Ali Mohammad Banaei‐Moghaddam, and Ali Akbar Moosavi‐Movahedi. Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling. 13(1):119–119, 2020. [DOI].
Beatriz García-Jiménez, Jorge Carrasco, Joaquı́n Medina, and Mark D. Wilkinson. Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering. ():, 2020. [DOI].
Deepanwita Banerjee, Thomas Eng, Andrew K. Lau, Brenda Wang, Yusuke Sasaki, Robin A. Herbert, Yan Chen, Yuzhong Liu, Jan‐Philip Prahl, Vasanth Singan, Deepti Tanjore, Christopher J. Petzold, Jay D. Keasling, and Aindrila Mukhopadhyay. Genome-scale metabolic rewiring to achieve predictable titers rates and yield of a non-native product at scale. ():, 2020. [DOI].
Alan R. Pacheco and Daniel Segrè. The effects of environmental complexity on microbial community yield and structure. ():, 2020. [DOI].
Marouen Ben Guebila. Vffva: dynamic load balancing enables large-scale flux variability analysis. 21(1):424–424, 2020. [DOI].
Yu Chen, Eunice van Pelt‐KleinJan, Berdien van Olst, Sieze Douwenga, Sjef Boeren, Herwig Bachmann, Douwe Molenaar, Jens Nielsen, and Bas Teusink. Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments. ():, 2020. [DOI].
Daniel J. Lugar, Sean G. Mack, and Ganesh Sriram. Netred, an algorithm to reduce genome-scale metabolic networks and facilitate the analysis of flux predictions. 65():207–222, 2020. [DOI].
Renliang Sun, Yizhou Xu, Hang Zhang, Qiangzhen Yang, Ke Wang, Yongyong Shi, and Zhuo Wang. Mechanistic modeling of gene regulation and metabolism identifies potential targets for hepatocellular carcinoma. 11():595242–595242, 2020. [DOI].
Christian Schulz and Eivind Almaas. Genome-scale reconstructions to assess metabolic phylogeny and organism clustering. 15(12):e0240953–e0240953, 2020. [DOI].
Bianca M. Esch, Sergej Limar, André Bogdanowski, Christos Gournas, Tushar H. More, Celine Sundag, Stefan Walter, Jürgen J. Heinisch, Christer S. Ejsing, Bruno André, and Florian Fröhlich. Uptake of exogenous serine is important to maintain sphingolipid homeostasis insaccharomyces cerevisiae. ():, 2020. [DOI].
Anne Richelle, Benjamin P. Kellman, Alexander T. Wenzel, Austin W.T. Chiang, Tyler Reagan, Jahir M. Gutierrez, Chintan Joshi, Shangzhong Li, Joanne K. Liu, Helen O. Masson, Jooyong Lee, Zerong Li, Laurent Heirendt, Christophe Trefois, Edwin F. Juarez, Tyler Bath, David Borland, Jill P. Mesirov, Kimberly Robasky, and Nathan E. Lewis. What does your cell really do? model-based assessment of mammalian cells metabolic functionalities using omics data. ():, 2020. [DOI].
Akshit Goyal, Tong Wang, Veronika Dubinkina, and Sergei Maslov. Ecology-guided prediction of cross-feeding interactions in the human gut microbiome. ():, 2020. [DOI].
Sébastien Moretti, Van Du T. Tran, Florence Mehl, Mark Ibberson, and Marco Pagni. Metanetx/mnxref - unified namespace for metabolites and biochemical reactions in the context of metabolic models. ():, 2020. [DOI].
Juan F. Poyatos. Genetic buffering and potentiation in metabolism. 16(9):e1008185–e1008185, 2020. [DOI].
Joana Saldida, Anna Paola Muntoni, Daniele De Martino, Georg Hubmann, Bastian Niebel, Anna Mareike Schmidt, Alfredo Braunstein, Andreas Milias‐Argeitis, and Matthias Heinemann. Unbiased metabolic flux inference through combined thermodynamic and13c flux analysis. ():, 2020. [DOI].
Benjamin P. Kellman, Anne Richelle, Jeong‐Yeh Yang, Digantkumar Chapla, Austin W.T. Chiang, Julia A. Najera, Bokan Bao, Natália Massaco Koga, Mahmoud A. Mohammad, Anders Bech Bruntse, Morey W. Haymond, Kelley W. Moremen, Lars Bode, and Nathan E. Lewis. Elucidating human milk oligosaccharide biosynthetic genes through network-based multiomics integration. ():, 2020. [DOI].
Priyanka Baloni, Wikum Dinalankara, John C. Earls, Theo Knijnenburg, Donald Geman, Luigi Marchionni, and Nathan D. Price. Identifying personalized metabolic signatures in breast cancer. ():, 2020. [DOI].
Jagir R. Hussan and Peter Hunter. Our natural “makeup” reveals more than it hides: modeling the skin and its microbiome. 13(1):e1497–e1497, 2020. [DOI].
Chaitra Sarathy, Martina Kutmon, Michael Lenz, Michiel Adriaens, Chris T. Evelo, and Ilja C.W. Arts. Efmviz: a cobra toolbox extension to visualize elementary flux modes in genome-scale metabolic models. 10(2):66–66, 2020. [DOI].
Bin Wang, Jiarui Yang, Chenchen Gao, Tong Hao, Jinɡjinɡ Li, and Jinsheng Sun. Reconstruction of eriocheir sinensis y-organ genome-scale metabolic network and differential analysis after eyestalk ablation. 11():532492–532492, 2020. [DOI].
Gian Marco Messa, Francesco Napolitano, Sarah H. Elsea, Diego di Bernardo, and Xin Gao. A siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data. 36(Supplement_2):i787–i794, 2020. [DOI].
Joana P. Bernardes, Neha Mishra, Florian Tran, Thomas Bahmer, Lena Best, Johanna I. Blase, Dora Bordoni, Jeanette Franzenburg, Ulf Geisen, Jonathan Josephs‐Spaulding, Philipp Köhler, Axel Künstner, Elisa Rosati, Anna C. Aschenbrenner, Petra Bächer, Nathan Baran, Teide Jens Boysen, Burkhard Brandt, Niklas Bruse, Jonathan Dörr, Andreas Dräger, Gunnar Elke, David Ellinghaus, Julia Fischer, Michael Förster, André Franke, Sören Franzenburg, Norbert Frey, Anette Friedrichs, Janina Fuß, Andreas Glück, Jacob Hamm, Finn Hinrichsen, Marc P. Hoeppner, Simon Imm, Ralf Junker, Sina Kaiser, Ying H. Kan, Rainer Knoll, Christoph Lange, Georg Laue, Clemens Lier, Matthias Lindner, Γεώργιος Μαρίνος, Robert Markewitz, Jacob Nattermann, Rainer Noth, Peter Pickkers, Klaus F. Rabe, Alina Renz, Christoph Röcken, Jan Rupp, Annika Schaffarzyk, Alexander Scheffold, Jonas Schulte-Schrepping, Domagoj Schunck, Dirk Skowasch, Thomas Ulas, Klaus‐Peter Wandinger, Michael Wittig, Johannes Zimmermann, Hauke Busch, Bimba F. Hoyer, Christoph Kaleta, Jan Heyckendorf, Matthijs Kox, Jan Rybniker, Stefan Schreiber, Joachim L. Schultze, Philip Rosenstiel, HCA Lung Biological Network (DeCOI), and the Deutsche COVID-19 Omics Initiative. Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe covid-19 trajectories. ():, 2020. [DOI].
Sean G. Mack and Ganesh Sriram. Netflow: a tool for isolating carbon flows in genome-scale metabolic networks. 12():e00154–e00154, 2020. [DOI].
James D. Brunner and Nicholas Chia. Minimizing the number of optimizations for efficient community dynamic flux balance analysis. ():, 2020. [DOI].
Zachary B. Haiman, Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, and Bernhard Ø. Palsson. Masspy: building, simulating, and visualizing dynamic biological models in python using mass action kinetics. ():, 2020. [DOI].
Woosub Shin and Joseph L. Hellerstein. Isolating structural errors in reaction networks in systems biology. 37(3):388–395, 2020. [DOI].
Petronella R. Hove, Nora Jean Nealon, Siu Hung Joshua Chan, Shea M. Boyer, Hannah B. Haberecht, and Elizabeth P. Ryan. Metabolomics and proteomics ofl. rhamnosusgg ande. colinissle probiotic supernatants identify distinct pathways that mediate growth suppression of antimicrobial-resistant pathogens. ():, 2020. [DOI].
Yizhou Xu, Hang Zhang, Qiangzhen Yang, Renliang Sun, Ke Wang, Yongyong Shi, and Zhuo Wang. Integrated regulatory-metabolic network model reveals critical mechanism and potential targets for hepatocellular carcinoma. ():, 2020. [DOI].
Matthew L. Jenior, Jhansi L. Leslie, Deborah Powers, Elizabeth M. Garrett, Kimberly A. Walker, Mary E. Dickenson, William A. Petri, Rita Tamayo, and Jason A. Papin. Novel drivers of virulence inclostridioides difficileidentified via context-specific metabolic network analysis. ():, 2020. [DOI].
Johannes Hertel, Almut Heinken, Filippo Martinelli Boneschi, and Ines Thiele. Integration of constraint-based modelling with faecal metabolomics reveals large deleterious effects offusobacteriaspecies on community butyrate production. ():, 2020. [DOI].
Claudia Patricia Sánchez Henao, Howard Ramírez-Malule, and Víctor A. López-Agudelo. Conceptos básicos de análisis de fluxes metabólicos. 23(1):, 2020. [DOI].
Masoud Ahookhosh, Ronan M. T. Fleming, and Phan Tu Vuong. Finding zeros of hölder metrically subregular mappings via globally convergent levenberg–marquardt methods. 37(1):113–149, 2020. [DOI].
Devlin C. Moyer, Alan R. Pacheco, David B. Bernstein, and Daniel Segrè. Stoichiometric modeling of artificial string chemistries. ():, 2020. [DOI].
Snorre Sulheim, Fredrik A. Fossheim, Alexander Wentzel, and Eivind Almaas. Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters. ():, 2020. [DOI].
Michael A. Henson. Interrogation of the perturbed gut microbiota in gouty arthritis patients throughin silicometabolic modeling. ():, 2020. [DOI].
Maziya Ibrahim and Karthik Raman. Two-species community design of lactic acid bacteria for optimal production of lactate. ():, 2020. [DOI].
Vetle Simensen, André Voigt, and Eivind Almaas. High-quality genome-scale metabolic model ofaurantiochytriumsp. t66. ():, 2020. [DOI].
Christian Schulz, Tjaša Kumelj, Emil Karlsen, and Eivind Almaas. Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition. ():, 2020. [DOI].
Takeyuki Tamura. Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling. ():, 2020. [DOI].
Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, and Haris Zafeiropoulos. Geometric algorithms for sampling the flux space of metabolic networks. ():, 2020. [DOI].
Alyaa M. Abdel‐Haleem, Vaishnavi Ravikumar, Boyang Ji, Katsuhiko Mineta, Xin Gao, Jens Nielsen, Takashi Gojobori, and Ivan Mijakovic̀. Integrated metabolic modeling, culturing and transcriptomics explains enhanced virulence ofv. choleraeduring co-infection with etec. ():, 2020. [DOI].
Shan Zhang, Chao Liang, Camille Mary, Baptiste Kerouanton, Joel Celio Francisco, N. Suhas Jagannathan, Volodimir Olexiouk, Jih Hou Peh, Claire Tang, Gio Fidelito, Srikanth Nama, Ruey‐Kuang Cheng, Caroline Lei Wee, Loo Chien Wang, Paula Duek, Prabha Sampath, Lydie Lane, Enrico Petretto, Radoslaw M. Sobota, Suresh Jesuthasan, Lei Sun, Lisa Tucker‐Kellogg, Bruno Reversade, Gerben Menschaert, David A. Stroud, and Lena Ho. Brawnin: a sorf-encoded peptide essential for vertebrate mitochondrial complex iii assembly. ():, 2020. [DOI].
Michael A. Henson. Computational modeling of the gut microbiota predicts metabolic mechanisms of recurrentclostridioides difficileinfection. ():, 2020. [DOI].
Daniel Noreña-Caro, Naohiro Kato, Eric Achberger, Cristal Zuniga, Teresa Gutierrez-Wing, Laura Sanchez, Mario Rivera, Natalia Da, Silva Moura, Behnam Safavinia, Amber Pete, Benjamin Peterson, Juliana Lasprilla, Monica Poveda, Janeth Rios, Lina Serna, Diana Gallego, Ivan Santa, Alejandro Herrera, N Daniel, T Heidorn, D Camsund, H-H Huang, C Durall, P Lindblad, K Sciuto, I Moro, C Gonzalez-Esquer, S Newnham, C Kerfeld, E Kupriyanova, M Sinetova, S Cho, T Antal, I Kovalenko, A Rubin, E Tyystjrvi, L Rosgaard, A De Porcellinis, J Jacobsen, J Zhou, T Zhu, Z Cai, Y Li, A Friend, R Geider, M Behrenfeld, C Still, D Shevela, R Pishchalnikov, L Eichacker, A Herrero, A Muro-Pastor, E Flores, Y Ohashi, W Shi, N Takatani, K Heimann, S Cirs, M Fillat, A Gonzlez, M Bes, A Valladares, S Lpez-Gomolln, J Hernndez, S Pellicer, J Raven, M Giordano, J Beardall, S Maberly, S Vats, S Kumar, P Ahuja, P Durai, M Batool, S Choi, E Hoiczyk, A Hansel, F Huang, E Hedman, C Funk, S Moslavac, R Bredemeier, O Mirus, K Nicolaisen, V Mariscal, R Bredemeier, F Van Eerden, D Jong, A De Vries, D Ducat, J Way, P Silver, W Hess, N Khlystov, W Chan, and A Kunjapur. Metabolic network analysis of filamentous cyanobacteria. ():, 2020. [DOI].
P.J. Gawthrop. Bond graphs unify stoichiometric analysis and thermodynamics. ():, 2020. [DOI].
Despoina Kerselidou, Bushra Dohai, David R. Nelson, Sarah Daakour, Nicolas De Cock, Dae‐Kyum Kim, Julien Olivet, Diana C. El Assal, Ashish Jaiswal, Deeya Saha, Charlotte Pain, Filip Matthijssens, Pierre Lemaître, Michaël Herfs, Julien Chapuis, Bart Ghesquière, Didier Vertommen, Verena Kriechbaumer, Kèvin Knoops, Carmen López‐Iglesias, Marc van Zandvoort, Jean‐Charles Lambert, Julien Hanson, Christophe Desmet, Marc Thiry, Kyle J. Lauersen, Marc Vidal, Pieter Van Vlierberghe, Samuel Dequiedt, Kourosh Salehi‐Ashtiani, and Jean‐Claude Twizere. Exostosin-1 glycosyltransferase regulates endoplasmic reticulum architecture and dynamics. ():, 2020. [DOI].
Ewelina Węglarz‐Tomczak, Demi J. Rijlaarsdam, Jakub M. Tomczak, and Stanley Brul. Gem-based metabolic profiling for human bone osteosarcoma under different glucose and glutamine availability. ():, 2020. [DOI].
Mario L. Arrieta‐Ortiz, Selva Rupa Christinal Immanuel, Serdar Turkarslan, Wei Wu, Brintha Girinathan, Jay Worley, Nicholas DiBenedetto, Olga Soutourina, Johann Peltier, Bruno Dupuy, Lynn Bry, and Nitin S. Baliga. Predictive regulatory and metabolic network models for systems analysis ofclostridioides difficile. ():, 2020. [DOI].
Harry Beaven and Ioly Kotta‐Loizou. Modelling oxidative stress pathways. ():277–300, 2020. [DOI].
Christian Schulz and Eivind Almaas. Genome-scale reconstructions to assess metabolic phylogeny and organism clustering. ():, 2020. [DOI].
Telmo Blasco, Sergio Pérez-Burillo, Francesco Balzerani, Alberto Lerma-Aguilera, Daniel Hinojosa-Nogueira, Silvia Pastoriza, María José Gosalbes, Nuria Jiménez‐Hernández, M. Pilar Francino, José Ángel Rufián‐Henares, Iñigo Apaolaza, and Francisco J. Planes. An extended reconstruction of human gut microbiota metabolism for personalized nutrition. ():, 2020. [DOI].
Bhanwar Lal Puniya, Zhongyuan Zhao, and Tomáš Helikar. Madrid: a pipeline for metabolic drug repurposing identification. ():, 2020. [DOI].
Cankut Çubuk. Modeling functional modules using statistical and machine learning methods. ():, 2020. [DOI].
Albert Tafur Rangel, Wendy Lorena Rios Guzman, Carmen Elvira Ojeda Cuella, Daissy Esther Mejia Perez, Ross P. Carlson, Jorge M. Gómez, and Andrés Fernando González Barrios. In silico design for systems-based metabolic engineering in escherichia coli for the bioconversion of aerobic glycerol to succinic acid. ():, 2020. [DOI].
J. Saraiva, Marta Gomes, René Kallies, Carsten Vogt, Antonis Chatzinotas, Peter F. Stadler, Óscar Dias, and Ulisses Nunes da Rocha. Ortsuite – a flexible pipeline for annotation of ecosystem processes and prediction of putative microbial interactions. ():, 2020. [DOI].

Laurent Heirendt, Sylvain Arreckx, Thomas Pfau, Sebastián N. Mendoza, Anne Richelle, Almut Heinken, Hulda S. Haraldsdóttir, Jacek Wachowiak, Sarah Keating, Vanja Vlasov, Stefanía Magnúsdóttir, Chiam Yu Ng, German Preciat, Alise Žagare, Siu Hung Joshua Chan, Maike K. Aurich, Catherine M. Clancy, Jennifer Modamio, John T. Sauls, Alberto Noronha, Aarash Bordbar, Benjamin Cousins, Diana C. El Assal, Luis V. Valcárcel, Iñigo Apaolaza, Susan Ghaderi, Masoud Ahookhosh, Marouen Ben Guebila, Andrejs Kostromins, Nicolas Sompairac, Hoai Minh Le, Ding Ma, Yuekai Sun, Lin Wang, James T. Yurkovich, Miguel A. P. Oliveira, Phan Tu Vuong, Lemmer P. El Assal, Inna Kuperstein, Andreï Zinovyev, H.S. Hinton, William A Bryant, Francisco J. Aragón Artacho, Francisco J. Planes, Egils Stalidzāns, Alejandro Maass, Santosh Vempala, Michael Hucka, Michael A. Saunders, Costas D. Maranas, Nathan E. Lewis, Thomas Sauter, Bernhard Ø. Palsson, Ines Thiele, and Ronan M. T. Fleming. Creation and analysis of biochemical constraint-based models using the cobra toolbox v.3.0. 14(3):639–702, 2019. [DOI].
Yasir Suhail, Margo P. Cain, Kiran Vanaja, Paul Kurywchak, Andre Levchenko, Raghu Kalluri, and Kshitiz Gupta. Systems biology of cancer metastasis. 9(2):109–127, 2019. [DOI].
Alan R. Pacheco, Mauricio Moel, and Daniel Segrè. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. 10(1):103–103, 2019. [DOI].
Manish Kumar, Boyang Ji, Karsten Zengler, and Jens Nielsen. Modelling approaches for studying the microbiome. 4(8):1253–1267, 2019. [DOI].
Yusuke Minato, Daryl M. Gohl, Joshua M. Thiede, Jeremy M. Chacón, William R. Harcombe, Fumito Maruyama, and Anthony D. Baughn. Genomewide assessment of mycobacterium tuberculosis conditionally essential metabolic pathways. 4(4):, 2019. [DOI].
Charles J. Norsigian, Xin Fang, Yara Seif, Jonathan M. Monk, and Bernhard Ø. Palsson. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. 15(1):1–14, 2019. [DOI].
Partho Sen and Matej Orešič. Metabolic modeling of human gut microbiota on a genome scale: an overview. 9(2):22–22, 2019. [DOI].
Leah Guthrie and Libusha Kelly. Bringing microbiome-drug interaction research into the clinic. 44():708–715, 2019. [DOI].
Simonas Marcišauskas, Boyang Ji, and Jens Nielsen. Reconstruction and analysis of a kluyveromyces marxianus genome-scale metabolic model. 20(1):551–551, 2019. [DOI].
Christopher P. Long and Maciek R. Antoniewicz. Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of e. coli metabolism. 55():249–257, 2019. [DOI].
Hong Zeng and Aidong Yang. Modelling overflow metabolism in escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways. 13(1):3–3, 2019. [DOI].
Yanfen Fu, Lian He, Jennifer Reeve, David A. C. Beck, and Mary E. Lidstrom. Core metabolism shifts during growth on methanol versus methane in the methanotroph methylomicrobium buryatense 5gb1. 10(2):, 2019. [DOI].
Masakazu Toyoshima, Yoshihiro Toya, and Hiroshi Shimizu. Flux balance analysis of cyanobacteria reveals selective use of photosynthetic electron transport components under different spectral light conditions. 143(1):31–43, 2019. [DOI].
Andreas Schwentner, André Feith, Eugenia Münch, Judith Stiefelmaier, Ira Lauer, Lorenzo Favilli, Christoph Massner, Johannes Öhrlein, B. Grund, Andrea T. Hüser, Ralf Takors, and Bastian Blombach. Modular systems metabolic engineering enables balancing of relevant pathways for l-histidine production with corynebacterium glutamicum. 12(1):65–65, 2019. [DOI].
Fangzhou Shen, Camden Cheek, and Sriram Chandrasekaran. Dynamic network modeling of stem cell metabolism. 1975():305–320, 2019. [DOI].
Yubing Han and Rachael Hageman Blair. Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to alzheimer’s disease. 20(1):386–386, 2019. [DOI].
Joel A. Paulson, Marc Martin-Casas, and Ali Mesbah. Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions. 15(8):e1007308–e1007308, 2019. [DOI].
Rotem Katzir, Ibrahim H. Polat, Michal Harel, Shir Katz, Carles Foguet, Vitaly A. Selivanov, Philippe Sabatier, Marta Cascante, Tamar Geiger, and Eytan Ruppin. The landscape of tiered regulation of breast cancer cell metabolism. 9(1):17760–17760, 2019. [DOI].
Kentaro Kamata, Yoshihiro Toya, and Hiroshi Shimizu. Effect of precise control of flux ratio between the glycolytic pathways on mevalonate production in escherichia coli. 116(5):1080–1088, 2019. [DOI].
Hong Zeng and Aidong Yang. Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant escherichia coli. 116(6):1484–1495, 2019. [DOI].
Ankit Gupta, Ahmad Ahmad, Dipesh Chothwe, Midhun K. Madhu, Shireesh Srivastava, and Vineet K. Sharma. Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph methylococcus capsulatus str. bath. 7():e6685–e6685, 2019. [DOI].
Kalaivani Paramasivan, Punil Kumar HN, and Sarma Mutturi. Systems-based saccharomyces cerevisiae strain design for improved squalene synthesis. 148():37–45, 2019. [DOI].
Jelili Oyelade, Itunuoluwa Isewon, Olufemi Aromolaran, Efosa Uwoghiren, Titilope M. Dokunmu, Solomon O. Rotimi, Oluwadurotimi Samuel Aworunse, Olawole O. Obembe, and Ezekiel Adebiyi. Computational identification of metabolic pathways ofplasmodium falciparumusing the<mml:math xmlns:mml="http://www.w3.org/1998/math/mathml" id="m1"><mml:mi>k</mml:mi></mml:math>-shortest path algorithm. 2019():1–13, 2019. [DOI].
Pandiyan Muthuramalingam, Rajendran Jeyasri, Subramanian Radhesh Krishnan, Shunmugiah Karutha Pandian, Sathishkumar Ramalingam, and Manikandan Ramesh. Integrating the bioinformatics and omics tools for systems analysis of abiotic stress tolerance in oryza sativa (l.). ():59–77, 2019. [DOI].
Sandra Castillo, Kiran Raosaheb Patil, and Paula Jouhten. Yeast genome-scale metabolic models for simulating genotype–phenotype relations. 58():111–133, 2019. [DOI].
Meisam Yousefi, Sayed‐Amir Marashi, Ali Sharifi‐Zarchi, and Sara Taleahmad. The metabolic network model of primed/naive human embryonic stem cells underlines the importance of oxidation-reduction potential and tryptophan metabolism in primed pluripotency. 9(1):71–71, 2019. [DOI].
Maryam Bagheri, Sayed‐Amir Marashi, and Mohammad Ali Amoozegar. A genome-scale metabolic network reconstruction of extremely halophilic bacterium salinibacter ruber. 14(5):e0216336–e0216336, 2019. [DOI].
Songül Yaşar Yıldız, Emrah Nıkerel, and Ebru Toksoy Öner. Genome-scale metabolic model of a microbial cell factory ( brevibacillus thermoruber 423) with multi-industry potentials for exopolysaccharide production. 23(4):237–246, 2019. [DOI].
Yusuke Minato, Daryl M. Gohl, Joshua M. Thiede, Jeremy M. Chacón, William R. Harcombe, Fumito Maruyama, and Anthony D. Baughn. Genome-wide assessment ofmycobacterium tuberculosisconditionally essential metabolic pathways. ():, 2019. [DOI].
Carolina A. Contador, Vida Rodríguez, Bárbara A. Andrews, and Juan A. Asenjo. Use of genome-scale models to get new insights into the marine actinomycete genus salinispora. 13(1):11–11, 2019. [DOI].
Nutan Chauhan and Shailza Singh. Integrative computational framework for understanding metabolic modulation in leishmania. 7():336–336, 2019. [DOI].
Stephania Gómez-Cerón, David Galindo-Betancur, and Howard Ramírez-Malule. Data set of in silico simulation for the production of clavulanic acid and cephamycin c by streptomyces clavuligerus using a genome scale metabolic model. 24():103992–103992, 2019. [DOI].
Kirk Smith, Fangzhou Shen, Ho‐Joon Lee, and Sriram Chandrasekaran. Metabolic signatures of regulation by phosphorylation and acetylation. ():, 2019. [DOI].
Sarah McGarrity, Sigurður Trausti Karvelsson, Ólafur E. Sigurjónsson, and Óttar Rolfsson. Comparative metabolic network flux analysis to identify differences in cellular metabolism. 2088():223–269, 2019. [DOI].
Ana M. Untaroiu, Maureen A. Carey, Jennifer L. Güler, and Jason A. Papin. Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies. 20(1):186–186, 2019. [DOI].
Hameri Tuure, Georgios Fengos, and Vassily Hatzimanikatis. The effects of model complexity and size on metabolic flux distribution and control. case study ine. coli. ():, 2019. [DOI].
Zixiang Xu. Egknock: identifying direct gene knockout strategies for microbial strain optimization based on metabolic network with gene-protein-reaction relationships. ():, 2019. [DOI].
Furkan Ozden, Metin Can Siper, Necmi Acarsoy, Tugrulcan Elmas, Bryan Marty, Xinjian Qi, and A. Ercument Cicek. Dorman: database of reconstructed metabolic networks. 18(4):1474–1480, 2019. [DOI].
Nicolai Panikov. Microbial growth dynamics. ():231–273, 2019. [DOI].
Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, and Harri Lähdesmäki. Bayesian metabolic flux analysis reveals intracellular flux couplings. 35(14):i548–i557, 2019. [DOI].
Dikshant Pradhan, Jason A. Papin, and Paul A. Jensen. Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models. ():, 2019. [DOI].
Nutan Chauhan and Shailza Singh. Integrative computational framework for understanding metabolic modulation in leishmania. ():, 2019. [DOI].
Md Shamsuddin Sultan Khan, Christopher I. Cazzonelli, Hiroyuki Kurata, Md Bahadur Badsha, Chun Guang Li, Gerald Müench, Amin Malik Shah Abdul Majid, Chun Guang Li, Gerald Muench, and Amin Malik Shah Abdul Majid. Metabolic similarity of plant and human: implications for efficacy and regulatory compliance of herbal therapies. 2(1):, 2019. [DOI].
Georgi Georgiev, Tommi Aho, Juha Kesseli, Olli Yli‐Harja, and Stuart Kauffman. Action and power efficiency in self-organization: the case for growth efficiency as a cellular objective in escherichia coli. ():229–244, 2019. [DOI].
Rui Pereira, Paulo Vilaça, Paulo Maia, Jens Nielsen, and Isabel Rocha. Turnover dependent phenotypic simulation: a quantitative constraint-based simulation method that accommodates all main strain design strategies. 8(5):976–988, 2019. [DOI].
Kristin J. Adolfsen and Mark P. Brynildsen. Anti‐virulence therapies through potentiating ros in bacteria. ():239–253, 2019. [DOI].
Kyle Medley, Joseph L. Hellerstein, and Herbert M. Sauro. Libsbmljs — enabling web–based sbml tools. ():, 2019. [DOI].
O.W. Davies, Malkhey Verma, A Dove, R Fischer, Y Liao, K Hoffmann, S Schillberg, N Emans, S Mayfield, S Franklin, S Chebolu, H Daniell, A Hiatt, R Cafferkey, K Bowdish, J Staub, B Garcia, J Graves, Ptj Hajdukiewicz, P Hunter, N Nehra, V Paradkar, M Schlittler, J Carroll, L Spatola, D Ward, G Ye, D Russell, S Franklin, B Ngo, E Efuet, S Mayfield, S Franklin, S Mayfield, K Ishikura, Y Takaoka, K Kato, M Sekine, K Yoshida, A Shinmyo, S Mayfield, S Franklin, R Lerner, B Rasala, M Muto, P Lee, M Jager, Rmf Cardoso, C Behnke, P Kirk, C Hokanson, R Crea, M Mendez, S Mayfield, S Ruf, D Karcher, R Bock, M Gantar, Z Svircev, N Ellstrand, D Quist, I Chapela, R Surzycki, K Greenham, K Kitayama, F Dibal, R Wagner, J Rochaix, T Ajam, S Surzycki, Y Komine, E Kikis, G Schuster, D Stern, A Novick, M Weiner, A Koch, E Dekel, U Alon, P Malakar, K Venkatesh, I Shachrai, A Zaslaver, U Alon, E Dekel, J Snoep, L Yomano, H Westerhoff, L Ingram, A Marr, H Dong, L Nilsson, C Kurland, G I Lang, A W & Murray, D Botstein, E Dekel, S Mangan, U Alon, and T Kalisky. 05(02):, 2019. [DOI].
Hongzhong Lu, Feiran Li, Benjamín J. Sánchez, Zhengming Zhu, Gang Li, Iván Domenzain, Simonas Marcišauskas, Mihail Anton, Dimitra Lappa, Christian Lieven, Moritz E. Beber, Nikolaus Sonnenschein, Eduard J. Kerkhoven, and Jens Nielsen. A consensus s. cerevisiae metabolic model yeast8 and its ecosystem for comprehensively probing cellular metabolism. 10(1):3586–3586, 2019. [DOI].
Jason H. Yang, Sarah N. Wright, Meagan Hamblin, Douglas McCloskey, Miguel A. Alcantar, Lars Schrübbers, Allison J. Lopatkin, Sangeeta Satish, Amir Nili, Bernhard Ø. Palsson, Graham C. Walker, and James J. Collins. A white-box machine learning approach for revealing antibiotic mechanisms of action. 177(6):1649–1661.e9, 2019. [DOI].
Allison J. Lopatkin, Jonathan Stokes, Erica J. Zheng, Jason H. Yang, Melissa K. Takahashi, Lingchong You, and James J. Collins. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. 4(12):2109–2117, 2019. [DOI].
Juan Nogales, Joshua Mueller, Steinn Guðmundsson, Francisco J. Canalejo, Estrella Duque, Jonathan M. Monk, Adam M. Feist, Juan L. Ramos, Wei Niu, and Bernhard Ø. Palsson. High‐quality genome‐scale metabolic modelling of pseudomonas putida highlights its broad metabolic capabilities. 22(1):255–269, 2019. [DOI].
Mustafa Saifuddin, Jennifer Bhatnagar, Daniel Segrè, and Adrien C. Finzi. Microbial carbon use efficiency predicted from genome-scale metabolic models. 10(1):3568–3568, 2019. [DOI].
Rebecca R. Stanway, Ellen Bushell, Anush Chiappino-Pepe, Magali Roques, Theo Sanderson, Blandine Franke‐Fayard, Reto Caldelari, Murielle Golomingi, Mary Nyonda, Vikash Pandey, Frank Schwach, Séverine Chevalley, Jai Ramesar, Tom Metcalf, Colin Herd, Paul‐Christian Burda, Julian C. Rayner, Dominique Soldati‐Favre, Chris J. Janse, Vassily Hatzimanikatis, Oliver Billker, and Volker T. Heussler. Genome-scale identification of essential metabolic processes for targeting the plasmodium liver stage. 179(5):1112–1128.e26, 2019. [DOI].
Boaz Styr, N Gonen, Daniel Zarhin, Antonella Ruggiero, Refaela Atsmon, Neta Gazit, Gabriella Braun, Samuel Frère, Irena Vertkin, Ilana Shapira, Michal Harel, Leore R. Heim, Maxim Katsenelson, Ohad Rechnitz, Saja Fadila, Dori Derdikman, Moran Rubinstein, Tamar Geiger, Eytan Ruppin, and Inna Slutsky. Mitochondrial regulation of the hippocampal firing rate set point and seizure susceptibility. 102(5):1009–1024.e8, 2019. [DOI].
Meghan Thommes, Taiyao Wang, Qi Zhao, Ioannis Ch. Paschalidis, and Daniel Segrè. Designing metabolic division of labor in microbial communities. 4(2):, 2019. [DOI].
Helena A. Herrmann, Beth C. Dyson, Lucy Vass, Giles N. Johnson, and Jean‐Marc Schwartz. Flux sampling is a powerful tool to study metabolism under changing environmental conditions. 5(1):32–32, 2019. [DOI].
Chiara Damiani, Davide Maspero, Marzia Di Filippo, Riccardo Colombo, Dario Pescini, Alex Graudenzi, Hans V. Westerhoff, Lilia Alberghina, Marco Vanoni, and Giancarlo Mauri. Integration of single-cell rna-seq data into population models to characterize cancer metabolism. 15(2):e1006733–e1006733, 2019. [DOI].
N. T. Devika and Karthik Raman. Deciphering the metabolic capabilities of bifidobacteria using genome-scale metabolic models. 9(1):18222–18222, 2019. [DOI].
Junyoung O. Park, Nian Liu, Kara M. Holinski, David Emerson, Kangjian Qiao, Benjamin M. Woolston, Jingyang Xu, Zbigniew Lazar, M. Ahsanul Islam, Charles Vidoudez, Peter R. Girguis, and Gregory Stephanopoulos. Synergistic substrate cofeeding stimulates reductive metabolism. 1(6):643–651, 2019. [DOI].
Manali Das, Pradipta Patra, and Amit Ghosh. Metabolic engineering for enhancing microbial biosynthesis of advanced biofuels. 119():109562–109562, 2019. [DOI].
Claudio Angione. Human systems biology and metabolic modelling: a review—from disease metabolism to precision medicine. 2019():1–16, 2019. [DOI].
Lívia S. Zaramela, Cameron Martino, Frederico Alisson‐Silva, Steven D. Rees, Sandra Diaz, Léa Chuzel, Mehul B. Ganatra, Christopher H. Taron, Patrick Secrest, Cristal Zúñiga, Jianbo Huang, Dionicio Siegel, Geoffrey Chang, Ajit Varki, and Karsten Zengler. Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates. 4(12):2082–2089, 2019. [DOI].
Cristal Zúñiga, Chien‐Ting Li, Geng Yu, Mahmoud M. Al‐Bassam, Tingting Li, Liqun Jiang, Lívia S. Zaramela, Michael T. Guarnieri, Michael J. Betenbaugh, and Karsten Zengler. Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities. 4(12):2184–2191, 2019. [DOI].
Ilaria Massaiu, Lorenzo Pasotti, Nikolaus Sonnenschein, Erlinda Rama, M. Cavaletti, Paolo Magni, Cinzia Calvio, and Markus J. Herrgård. Integration of enzymatic data in bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains. 18(1):3–3, 2019. [DOI].
Cecilia Noecker, Hsuan-Chao Chiu, Colin P McNally, and Elhanan Borenstein. Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. 4(6):, 2019. [DOI].
Lázaro Molina, Ruggero La Rosa, Juan Nogales, and Fernando Rojo. Influence of the crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in pseudomonas putida kt2440 growing in a complete medium. 21(11):4446–4459, 2019. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Mariana Díaz‐Almirón, Jorge M. Arevalillo, María Ferrer-Gómez, Hilario Navarro, Paloma Maı́n, Enrique Espinosa, Álvaro Pinto, and Juan Ángel Fresno Vara. Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities. 19(1):636–636, 2019. [DOI].
Nicolas Sompairac, Jennifer Modamio, Emmanuel Barillot, Ronan M. T. Fleming, Andreï Zinovyev, and Inna Kuperstein. Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer. 20(S4):140–140, 2019. [DOI].
Nikolay Martyushenko and Eivind Almaas. Modelexplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions. 20(1):56–56, 2019. [DOI].
Emelyne Teo, Sudharshan Ravi, Diogo Barardo, Hyung-Seok Kim, Sheng Fong, Amaury Cazenave‐Gassiot, Tsze Yin Tan, Jianhong Ching, Jean‐Paul Kovalik, Markus R. Wenk, Rudiyanto Gunawan, Philip K. Moore, Barry Halliwell, Nicholas S. Tolwinski, and Jan Gruber. Metabolic stress is a primary pathogenic event in transgenic caenorhabditis elegans expressing pan-neuronal human amyloid beta. 8():, 2019. [DOI].
Joshua E. Goldford, Hyman Hartman, Robert Marsland, and Daniel Segrè. Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism. 3(12):1715–1724, 2019. [DOI].
Lázaro Molina, Ruggero La Rosa, Juan Nogales, and Fernando Rojo. pseudomonas putida kt2440 metabolism undergoes sequential modifications during exponential growth in a complete medium as compounds are gradually consumed. 21(7):2375–2390, 2019. [DOI].
Minsuk Kim, Beom Gi Park, Eun-Jung Kim, Joonwon Kim, and Byung‐Gee Kim. In silico identification of metabolic engineering strategies for improved lipid production in yarrowia lipolytica by genome-scale metabolic modeling. 12(1):187–187, 2019. [DOI].
Jae Sung Cho, Changdai Gu, Tae Hee Han, Jae Yong Ryu, and Sang Yup Lee. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. 15():1–11, 2019. [DOI].
Matias Orellana‐Saez, Nicolas Dos Santos Pacheco, José Costa, Katterinne N. Mendez, Matthieu J. Miossec, Claudio Meneses, Eduardo Castro‐Nallar, Andrés E. Marcoleta, and Ignacio Poblete‐Castro. In-depth genomic and phenotypic characterization of the antarctic psychrotolerant strain pseudomonas sp. mpc6 reveals unique metabolic features, plasticity, and biotechnological potential. 10():1154–1154, 2019. [DOI].
Laura J. Dunphy, P. H. Yen, and Jason A. Papin. Integrated experimental and computational analyses reveal differential metabolic functionality in antibiotic-resistant pseudomonas aeruginosa. 8(1):3–14.e3, 2019. [DOI].
Alessandra Biz, Scott Proulx, Zhiqing Xu, Kavya Siddartha, Alex Mulet Indrayanti, and Radhakrishnan Mahadevan. Systems biology based metabolic engineering for non-natural chemicals. 37(6):107379–107379, 2019. [DOI].
Emrah Özcan, Sinem Selvin Selvi, Emrah Nıkerel, Bas Teusink, Ebru Toksoy Öner, and Tunahan Çakır. A genome-scale metabolic network of the aroma bacterium leuconostoc mesenteroides subsp. cremoris. 103(7):3153–3165, 2019. [DOI].
Mochamad Nurcholis, Noppon Lertwattanasakul, Nadchanok Rodrussamee, Tomoyuki Kosaka, Masayuki Murata, and Mamoru Yamada. Integration of comprehensive data and biotechnological tools for industrial applications of kluyveromyces marxianus. 104(2):475–488, 2019. [DOI].
David B. Bernstein, Floyd E. Dewhirst, and Daniel Segrè. Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome. 8():, 2019. [DOI].
Uldis Kalnenieks, Elīna Balodīte, Steffi Strähler, Inese Strazdiņa, Julia Rex, Agris Pentjuss, Katsuya Fuchino, Per Bruheim, Reinis Rutkis, Katherine M. Pappas, Robert K. Poole, Oliver Sawodny, and Katja Bettenbrock. Improvement of acetaldehyde production in zymomonas mobilis by engineering of its aerobic metabolism. 10():2533–2533, 2019. [DOI].
H Henriksen, Sarah McGarrity, Rósa Sólveig Sigurðardóttir, Travis Nemkov, Angelo D’Alessandro, Bernhard Ø. Palsson, Jakob Stensballe, Charles E. Wade, Óttar Rolfsson, and Pär I. Johansson. Metabolic systems analysis of shock-induced endotheliopathy (shine) in trauma. 272(6):1140–1148, 2019. [DOI].
Chien‐Ting Li, Jacob Stephan Yelsky, Yiqun Chen, Cristal Zúñiga, Richard Eng, Liqun Jiang, Alison J. Shapiro, Kai-Wen Huang, Karsten Zengler, and Michael J. Betenbaugh. Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity. 5(1):33–33, 2019. [DOI].
Jennifer L. Greene, James Daniell, Michael Köpke, Linda J. Broadbelt, and Keith E. J. Tyo. Kinetic ensemble model of gas fermenting clostridium autoethanogenum for improved ethanol production. 148():46–56, 2019. [DOI].
Maximilian Lularevic, Andrew J. Racher, Colin Jaques, and Alexandros Kiparissides. Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions. 116(9):2339–2352, 2019. [DOI].
Douaa Mugahid, Tugce Sengül, Xintian You, Yongbo Wang, Leif Steil, Nora Bergmann, Michaël Radkë, Andreas Ofenbauer, Manuela Gesell-Salazar, András Balogh, Stefan Kempa, Baris Tursun, Charles T. Robbins, Uwe Völker, W. Chen, Lonnie A. Nelson, and Michael Gotthardt. Proteomic and transcriptomic changes in hibernating grizzly bears reveal metabolic and signaling pathways that protect against muscle atrophy. 9(1):19976–19976, 2019. [DOI].
Oveis Jamialahmadi, Sameereh Hashemi‐Najafabadi, Ehsan Motamedian, Stefano Romeo, and Fatemeh Bagheri. A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism. 15(4):e1006936–e1006936, 2019. [DOI].
Nathan D. Schwalm, Wais Mojadedi, Elliot S. Gerlach, Marcus Benyamin, Matthew Perisin, and Katherine L. Akingbade. Developing a microbial consortium for enhanced metabolite production from simulated food waste. 5(4):98–98, 2019. [DOI].
Màrius Tomàs‐Gamisans, Cristiane Conte Paim de Andrade, Francisco Maresca, Sergi Monforte, Pau Ferrer, and Joan Albiol. Redox engineering by ectopic overexpression of nadh kinase in recombinant pichia pastoris ( komagataella phaffii ): impact on cell physiology and recombinant production of secreted proteins. 86(6):, 2019. [DOI].
Ragothaman Prathiviraj, Sheela Berchmans, and P. Chellapandi. Analysis of modularity in proteome-wide protein interaction networks of methanothermobacter thermautotrophicus strain δh and metal-loving bacteria. 10(3):179–190, 2019. [DOI].
David Hoksza, Piotr Gawron, Marek Ostaszewski, Jan Hasenauer, and Reinhard Schneider. Closing the gap between formats for storing layout information in systems biology. 21(4):1249–1260, 2019. [DOI].
Julia Tröndle, Kristin Schoppel, Arne Bleidt, Natalia Trachtmann, Georg A. Sprenger, and Dirk Weuster‐Botz. Metabolic control analysis of l-tryptophan production with escherichia coli based on data from short-term perturbation experiments. 307():15–28, 2019. [DOI].
Beatriz Peñalver Bernabé, Ines Thiele, Eugene Galdones, Anaar Siletz, Sriram Chandrasekaran, Teresa K. Woodruff, Linda J. Broadbelt, and Lonnie D. Shea. Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development. 20(1):307–307, 2019. [DOI].
Anna S. Blazier and Jason A. Papin. Reconciling high-throughput gene essentiality data with metabolic network reconstructions. 15(4):e1006507–e1006507, 2019. [DOI].
Kirubhakaran Puvendran and Guhan Jayaraman. Enhancement of acetyl-coa by acetate co-utilization in recombinant lactococcus lactis cultures enables the production of high molecular weight hyaluronic acid. 103(17):6989–7001, 2019. [DOI].
Adam J. Meyer, Ishtiaq Saaem, Adam D. Silverman, Vanessa A. Varaljay, Rebecca L. Mickol, Steven M. Blum, Alexander V. Tobias, Nathan D. Schwalm, Wais Mojadedi, Elizabeth L. Onderko, Cassandra Bristol, Shangtao Liu, Katelin Pratt, Arturo Casini, Raissa Eluere, Felix Moser, Carrie A. Drake, Maneesh K. Gupta, Nancy Kelley‐Loughnane, Julius B. Lucks, Katherine L. Akingbade, Matthew W. Lux, Sarah M. Glaven, Wendy J. Crookes‐Goodson, Michael C. Jewett, D. Benjamin Gordon, and Christopher A. Voigt. Organism engineering for the bioproduction of the triaminotrinitrobenzene (tatb) precursor phloroglucinol (pg). 8(12):2746–2755, 2019. [DOI].
Shoval Lagziel, Won Dong Lee, and Tomer Shlomi. Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches. 17(1):51–51, 2019. [DOI].
Rupert Norman, Thomas Millat, Sarah Schatschneider, Anne M. Henstra, Ronja Breitkopf, Bart Pander, Florence J. Annan, Pawel Piatek, Hassan Hartman, Mark G. Poolman, David A. Fell, Klaus Winzer, Nigel P. Minton, and Charlie Hodgman. Genome‐scale model of c. autoethanogenum reveals optimal bioprocess conditions for high‐value chemical production from carbon monoxide. 3(2):32–40, 2019. [DOI].
Romina Lasry Testa, Claudio Delpino, Vanina Estrada, and M. Soledad Díaz. In silico strategies to couple production of bioethanol with growth in cyanobacteria. 116(8):2061–2073, 2019. [DOI].
Abinaya Badri, Karthik Raman, and Guhan Jayaraman. Uncovering novel pathways for enhancing hyaluronan synthesis in recombinant lactococcus lactis: genome-scale metabolic modeling and experimental validation. 7(6):343–343, 2019. [DOI].
Aparajitha Srinivasan, S. Vijayakumar, Karthik Raman, and Smita Srivastava. Rational metabolic engineering for enhanced alpha-tocopherol production in helianthus annuus cell culture. 151():107256–107256, 2019. [DOI].
Sucheta Gokhale and Anirban Bhaduri. Provitamin d3 modulation through prebiotics supplementation: simulation based assessment. 9(1):19267–19267, 2019. [DOI].
Eun-Youn Kim, Daniel Ashlock, and Sung Ho Yoon. Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks. 20(1):, 2019. [DOI].
Sabina Żołędowska, Luana Presta, Marco Fondi, Francesca Decorosi, Luciana Giovannetti, Alessio Mengoni, and Ewa Łojkowska. Metabolic modeling of pectobacterium parmentieri scc3193 provides insights into metabolic pathways of plant pathogenic bacteria. 7(4):101–101, 2019. [DOI].
Porntip Chiewchankaset, Wanatsanan Siriwat, Malinee Suksangpanomrung, Opas Boonseng, Asawin Meechai, Morakot Tanticharoen, Saowalak Kalapanulak, and Treenut Saithong. Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through flux balance analysis. 9(1):2964–2964, 2019. [DOI].
Josi Buerger, Luisa S. Gronenberg, Hans Jasper Genee, and Morten Otto Alexander Sommer. Wiring cell growth to product formation. 59():85–92, 2019. [DOI].
Deepanwita Banerjee and Anu Raghunathan. Constraints-based analysis identifies nad+ recycling through metabolic reprogramming in antibiotic resistant chromobacterium violaceum. 14(1):e0210008–e0210008, 2019. [DOI].
Mahsa Mekanik, Ehsan Motamedian, Reza Fotovat, and Vahab Jafarian. Reconstruction of a genome-scale metabolic model for auxenochlorella protothecoides to study hydrogen production under anaerobiosis using multiple optimal solutions. 44(5):2580–2591, 2019. [DOI].
Karel Olavarría, Albert Fina, Mariana I. Velasco, Mark C.M. van Loosdrecht, and A. Wahl. Metabolism of sucrose in a non-fermentative escherichia coli under oxygen limitation. 103(15):6245–6256, 2019. [DOI].
Emelyne Teo, Sudharshan Ravi, Diogo Barardo, Hyung-Seok Kim, Sheng Fong, Amaury Cazenave‐Gassiot, Tsze Yin Tan, Jianhong Ching, Jean‐Paul Kovalik, Markus R. Wenk, Rudiyanto Gunawan, Philip K. Moore, Barry Halliwell, Nicholas S. Tolwinski, and Jan Gruber. Metabolic stress is a primary pathogenic event in transgenic caenorhabditis elegans expressing pan-neuronal human amyloid beta. ():, 2019. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Mariana Díaz‐Almirón, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Rocío López‐Vacas, Paolo Nanni, Pilar Zamora, Enrique Espinosa, and Juan Ángel Fresno Vara. Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients. 15(30):3483–3490, 2019. [DOI].
Kristopher D. Rawls, Edik M. Blais, Bonnie V. Dougherty, Kalyan C. Vinnakota, Venkat R. Pannala, Anders Wallqvist, Glynis L. Kolling, and Jason A. Papin. Genome-scale characterization of toxicity-induced metabolic alterations in primary hepatocytes. 172(2):279–291, 2019. [DOI].
Sebastián N. Mendoza, Brett G. Olivier, Douwe Molenaar, and Bas Teusink. A systematic assessment of current genome-scale metabolic reconstruction tools. ():, 2019. [DOI].
Elham Iranmanesh, Mohammad Ali Asadollahi, and Davoud Biria. Improving l-phenylacetylcarbinol production in saccharomyces cerevisiae by in silico aided metabolic engineering. 308():27–34, 2019. [DOI].
Priyanka Baloni, Vineet Sangar, James T. Yurkovich, Max Robinson, Scott L. Taylor, Christine M. Karbowski, Hisham K. Hamadeh, Yudong D. He, and Nathan D. Price. Genome-scale metabolic model of the rat liver predicts effects of diet restriction. 9(1):9807–9807, 2019. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Mariana Díaz‐Almirón, Claudia Fortes, María Ferrer-Gómez, Rocío López‐Vacas, Verónica Parra Blanco, Iván Márquez‐Rodas, Ainara Soria, Juan Ángel Fresno Vara, and Enrique Espinosa. Melanoma proteomics suggests functional differences related to mutational status. 9(1):7217–7217, 2019. [DOI].
Hamideh Fouladiha, Sayed‐Amir Marashi, Fatemeh Torkashvand, Fereidoun Mahboudi, Nathan E. Lewis, and Behrouz Vaziri. A metabolic network-based approach for developing feeding strategies for cho cells to increase monoclonal antibody production. ():, 2019. [DOI].
Xingping Sun, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, and Qingyi Chen. Hybrid optimization algorithm for bayesian network structure learning. 10(10):294–294, 2019. [DOI].
Anika Küken, Jeanne M. O. Eloundou-Mbebi, Georg Basler, and Zoran Nikoloski. Cellular determinants of metabolite concentration ranges. 15(1):e1006687–e1006687, 2019. [DOI].
Cheng Zhang, Sunjae Lee, Gholamreza Bidkhori, Rui Benfeitas, Alen Lovrić, Shuqi Chen, Mathias Uhlén, Jens Nielsen, and Adil Mardinoğlu. Rmetd2: a tool for integration of relative transcriptomics data into genome-scale metabolic models. ():, 2019. [DOI].
Krishna Kanhaiya and Dwitiya Tyagi-Tiwari. Identification of drug targets in breast cancer metabolic network. 27(6):975–986, 2019. [DOI].
Shaozhen Ding, Pengli Cai, Le Yuan, Yu Tian, Weizhong Tu, Dachuan Zhang, Xingxiang Cheng, Dandan Sun, Junni Chen, and Qian‐Nan Hu. Cf-targeter: a rational biological cell factory targeting platform for biosynthetic target chemicals. 8(10):2280–2286, 2019. [DOI].
Freyr Jóhannsson, Níels Árni Árnason, Ragna Landrö, Sveinn Guðmundsson, Ólafur E. Sigurjónsson, and Óttar Rolfsson. Metabolomics study of platelet concentrates photochemically treated with amotosalen and uva light for pathogen inactivation. 60(2):367–377, 2019. [DOI].
Pierre Salvy and Vassily Hatzimanikatis. Etfl: a formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraints. ():, 2019. [DOI].
Davide Maspero, C. Damiani, Marco Antoniotti, Alex Graudenzi, Marzia Di Filippo, Marco Vanoni, Giulio Caravagna, Riccardo Colombo, Daniele Ramazzotti, and Dario Pescini. The influence of nutrients diffusion on a metabolism-driven model of a multi-cellular system. 171(1-4):279–295, 2019. [DOI].
Khadija Mhallem Gziri and Philippe Bogaerts. Determining a unique solution to underdetermined metabolic networks via a systematic path through the most accurate fluxes. 52(1):352–357, 2019. [DOI].
María Teresita Castañeda, Sebastián Núñez, Claudio E. Voget, and Hernán De Battista. In silico optimization of lipid production in rhodosporidium toruloides by gene knockout strategies. 52(1):94–99, 2019. [DOI].
Jorge Ferreira, Vítor Vieira, and Miguel Rocha. Genome-scale metabolic models. ():420–428, 2019. [DOI].
Philippe Bogaerts and Marianne Rooman. Discopolis: an algorithm for uniform sampling of metabolic flux distributions via iterative sequences of linear programs. 52(26):269–274, 2019. [DOI].
Lucía Trilla‐Fuertes, Ismael Ghanem, Angelo Gámez‐Pozo, Joan Maurel, Laura G-Pastrián, Marta Mendiola, Cristina Peña, Rocío López‐Vacas, Guillermo Prado-Vázquez, Elena López‐Camacho, Andrea Zapater‐Moros, Victoria Heredia-Soto, Míriam Cuatrecasas, Pilar García‐Alfonso, Jaume Capdevila, Carles Conill, Rocio García‐Carbonero, Ricardo Ramos, Claudia Fortes, Carlos Lloréns, Paolo Nanni, Juan Ángel Fresno Vara, and Jaime Feliú. Genetic profile and functional proteomics of anal squamous cell carcinoma: proposal for a molecular classification. ():, 2019. [DOI].
Lake‐Ee Quek and Nigel Turner. Using the human genome-scale metabolic model recon 2 for steady-state flux analysis of cancer cell metabolism. 1928():479–489, 2019. [DOI].
Lorenza Vitale, Allison Piovesan, Francesca Antonaros, Pierluigi Strippoli, Maria Chiara Pelleri, and Maria Caracausi. Dataset of differential gene expression between total normal human thyroid and histologically normal thyroid adjacent to papillary thyroid carcinoma. 24():103835–103835, 2019. [DOI].
Tunahan Çakır. Constraint-based modeling of metabolic interactions in and between astrocytes and neurons. ():393–420, 2019. [DOI].
Reza Mohammadi, Javad Zahiri, and Mohammad Javad Niroomand. Imet: a graphical user interface software tool to merge metabolic networks. 5(6):e01766–e01766, 2019. [DOI].
Leila Hassani, Mohammad R. Moosavi, and Payam Setoodeh. A graph based approach to analyse metabolic networks for strain engineering. 7():1839–1843, 2019. [DOI].
Arnaud Belcour, Clémence Frioux, Méziane Aite, Anthony Bretaudeau, and Anne Siegel. Metage2metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species. ():, 2019. [DOI].
Jean‐Christophe Lachance, Sébastien Rodrigue, and Bernhard Ø. Palsson. The use of in silico genome-scale models for the rational design of minimal cells. ():141–175, 2019. [DOI].
Nattharat Punyasu, Saowalak Kalapanulak, and Treenut Saithong. Development of a compartmentalized model for insight into the structured metabolic pathway of carbon metabolism in cassava leaves. 13((04) 2019):605–615, 2019. [DOI].
António E. N. Ferreira, Marta Sousa Silva, and Carlos Cordeiro. Metabolic network inference from time series. ():127–133, 2019. [DOI].
DM Curran, Alexandra Grote, Nirvana Nursimulu, Adam Geber, Denis Voronin, Drew R. Jones, Elodie Ghedin, and John Parkinson. Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets. ():, 2019. [DOI].
George C. diCenzo, Michelangelo Tesi, Thomas Pfau, Alessio Mengoni, and Marco Fondi. A virtual nodule environment (vine) for modelling the inter-kingdom metabolic integration during symbiotic nitrogen fixation. ():, 2019. [DOI].
Woosub Shin and Joseph L. Hellerstein. Detecting and isolating mass balance errors in reaction based models in systems biology. ():, 2019. [DOI].
Allison Piovesan, Francesca Antonaros, Pierluigi Strippoli, Lorenza Vitale, Maria Chiara Pelleri, and Maria Caracausi. Reference quantitative transcriptome dataset for adult caenorhabditis elegans. 25():104152–104152, 2019. [DOI].
Jesse C. J. van Dam. Semantic systems biology of prokaryotes : heterogeneous data integration to understand bacterial metabolism. ():, 2019. [DOI].
Jasper J. Koehorst. A fair approach to genomics. ():, 2019. [DOI].
Antti Larjo. Computational methods for modelling and analysing biological networks. ():, 2019. [DOI].
Hadas Lapid, Assaf Marron, Smadar Szekely, and David Harel. Using reactive-system modeling techniques to create executable models of biochemical pathways. ():454–464, 2019. [DOI].
Porntip Chiewchankaset, Saowalak Kalapanulak, and Treenut Saithong. Extended utilization of constraint-based metabolic model in a long-growing crop. 7(5):259–259, 2019. [DOI].
Mohammad Hossein Moteallehi-Ardakani and Sayed‐Amir Marashi. Safeppp: a simple and fast method to find and analyze extreme points of a metabolic phenotypic phase plane. ():, 2019. [DOI].
Hongzhong Lu, Zhengming Zhu, Eduard J. Kerkhoven, and Jens Nielsen. Falconet: an r package to accelerate automatic visualisation of genome scale metabolic models. ():, 2019. [DOI].
Dmitry A. Ravcheev. Peer review #1 of "genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph methylococcus capsulatus str. bath (v0.1)". ():, 2019. [DOI].
Henrique da Costa Oliveira. Impacto da inativação de genes relacionados à síntese de produtos de fermentação (lactato e acetato) na produção de 1,3-propanodiol em linhagens de >i<escherichia>/i< >i<coli>/i< recombinantes. ():, 2019. [DOI].
Pedraza Palacios and Laura Paola. Análisis metabólico y termodinámico in silico para la biosíntesis de ácido 3-indolacético (aia) a partir de glicerol en azospirillum brasilense. ():, 2019. [DOI].
Takeyuki Tamura. Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling. ():, 2019. [DOI].
Villayat Ali, Priyanka Singh, Sonu Gupta, Vijaykumar Yogesh Muley, Malkhey Verma, R Moreno-Sanchez, E Saavedra, S Rodriguez-Enriquez, Olin Sandoval, J Kruger, G Ratcliffe, B Hahn-Hgerdal, C Wahlbom, M Grdonyi, Van Zyl W H, Otero R R C, L Jonsson, J Boatright, F Negre, X Chen, C Kish, B Wood, G Peel, S D Mcneil, M L Nuccio, D Rhodes, Y Shachar-Hill, A Hanson, A P Alonso, H Vigeolas, P Raymond, D Rolin, M Dieuaide-Noubhani, J Schwender, F Goffman, J B Ohlrogge, Shachar- Hill, P Alonso, P Raymond, D Rolin, M Dieuaide-Noubhani, A Fernie, A Roscher, Ratcliffe R G, N Kruger, B Junker, J Lonien, L Heady, A Rogers, J Schwender, D Rontein, M Dieuaide-Noubhani, E Dufourc, P Raymond, D Rolin, N D Price, J Papin, C H Schilling, B Palsson, N D Price, J L Reed, B Palsson, J L Reed, B Palsson, A Varma, B Palsson, C Schilling, H Letscher, D Palsson, S Schuster, T Dandekar, D Fell, S Schuster, D Fell, T Dandekar, M Poolman, D Fell, C Raines, J A Morgan, D Rhodes, M Poolman, H Assmus, D Fell, R Luo, S Liao, S Zeng, Y Li, Q Luo, R Heinrich, T Rapoport, H Kacser, J Burns, D Fell, Hafner R P, G C Brown, M Brand, and G Stephanopoulos. 05(02):, 2019. [DOI].
Ankit Gupta, Ahmad Ahmad, Dipesh Chothwe, Midhnu Madhu, Shireesh Srivastava, Vineet K. Sharma, Midhun K. Madhu, A Ahmad, H Hartman, S Krishnakumar, D Fell, M Poolman, S Srivastava, C Barrett, M Herrgard, B Palsson, C Barrett, N Price, B Palsson, S Becker, A Feist, M Mo, G Hannum, B Palsson, M Herrgard, R Boden, M Cunliffe, J Scanlan, H Moussard, K Kits, M Klotz, M Jetten, S Vuilleumier, J Han, L Peters, N Mikhailova, H Teshima, R Tapia, N Kyrpides, N Ivanova, I Pagani, J Cheng, L Goodwin, C Han, L Hauser, M Land, A Lapidus, S Lucas, S Pitluck, T Woyke, L Stein, J Murrell, I Bogorad, T-S Lin, J Liao, N Boyle, J Morgan, S But, V Khmelina, I Mustakhimova, I Trotsenko, V Chelliah, C Laibe, Le Novere, L Chistoserdova, J Vorholt, Lidstrom Me, J Clomburg, A Crumbley, R Gonzalez, R Conrado, R Gonzalez, Torre De La, A Metivier, F Chu, L Laurens, D Beck, P Pienkos, M Lidstrom, M Kalyuzhnaya, M Eccleston, Kelly Dp, Q Fei, M Guarnieri, L Tao, L Laurens, N Dowe, Pienkos Pt, A Feist, C Henry, J Reed, M Krummenacker, A Joyce, P Karp, L Broadbelt, V Hatzimanikatis, B Palsson, A Gilman, and L Laurens. Peer review #2 of "genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph methylococcus capsulatus str. bath (v0.1)". ():, 2019. [DOI].
Nida Tabassum Khan, Namra Jameel, Muhammad Noman Khan, J Bailey, J Nielsen, J Keasling, A Blazier, J Papin, C Henry, H Bernstein, P Weisenhorn, R Taylor, J Lee, J Zucker, H Song, N Chen, I Del Val, S Kyriakopoulos, K Polizzi, C Kontoravdi, A Manichaikul, L Ghamsari, E Hom, C Lin, R Murray, R Chang, . Thiele, B Heavner, N Price, E Pitknen, J Rousu, E Ukkonen, J Hamilton, J Reed, S Ghosh, Y Matsuoka, Y Asai, K Hsin, H Kitano, R Hoehndorf, M Dumontier, J Gennari, S Wimalaratne, B De Bono, D Cook, G Gkoutos, L Strmbck, P Lambrix, P Droste, S Miebach, S Niedenfhr, W Wiechert, K Nh, W Copeland, B Bartley, D Chandran, M Galdzicki, K Kim, S Sleight, . Sauro, F Jourdan, N Le Novere, M Hucka, H Mi, S Moodie, F Schreiber, A Sorokin, . Bergman, B Bartley, J Beal, K Clancy, G Misirli, N Roehner, E Oberortner, . Zhang, V Yadav, M De Mey, C Lim, P Ajikumar, G Stephanopoulos, J Keasling, H Chou, D Chandran, F Bergmann, H Sauro, M Wilson, R Hertzberg, L Adam, J Peccoud, M Kearse, R Moir, A Wilson, S Stones-Havas, M Cheung, S Sturrock, and . Thierer. Bioinformatics in metabolic engineering. 7(11):, 2019. [DOI].
Jens Nielsen. Faculty opinions recommendation of a white-box machine learning approach for revealing antibiotic mechanisms of action. ():, 2019. [DOI].
Anonymous. Peer review #2 of "genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph methylococcus capsulatus str. bath (v0.2)". ():, 2019. [DOI].
Almut Heinken, Dmitry A. Ravcheev, Federico Baldini, Laurent Heirendt, Ronan M. T. Fleming, and Ines Thiele. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. 7(1):75–75, 2019. [DOI].
Quanli Liu, Tao Yu, Xiaowei Li, Yu Chen, Kate Campbell, Jens Nielsen, and Yun Chen. Rewiring carbon metabolism in yeast for high level production of aromatic chemicals. 10(1):4976–4976, 2019. [DOI].
Guido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, and Claudio Angione. Machine and deep learning meet genome-scale metabolic modeling. 15(7):e1007084–e1007084, 2019. [DOI].
Stéphane Pinhal, Delphine Ropers, Johannes Geiselmann, and Hidde de Jong. Acetate metabolism and the inhibition of bacterial growth by acetate. 201(13):, 2019. [DOI].
Sebastián N. Mendoza, Brett G. Olivier, Douwe Molenaar, and Bas Teusink. A systematic assessment of current genome-scale metabolic reconstruction tools. 20(1):158–158, 2019. [DOI].
Christopher P. Long and Maciek R. Antoniewicz. High-resolution 13c metabolic flux analysis. 14(10):2856–2877, 2019. [DOI].
Yu Chen and Jens Nielsen. Energy metabolism controls phenotypes by protein efficiency and allocation. 116(35):17592–17597, 2019. [DOI].
Viridiana Olín‐Sandoval, Jason Yu, Leonor Miller‐Fleming, Mohammad Tauqeer Alam, Stephan Kamrad, Clara Correia‐Melo, Robert Haas, Joanna Segal, David Alejandro Peña Navarro, Lucía Herrera-Domínguez, Oscar Méndez‐Lucio, Jakob Vowinckel, Michael Mülleder, and Markus Ralser. Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism. 572(7768):249–253, 2019. [DOI].
Johannes Hertel, Amy C. Harms, Almut Heinken, Federico Baldini, Cyrille C. Thinnes, Enrico Glaab, Daniel A. Vasco, Maik Pietzner, Isobel D. Stewart, Nicholas J. Wareham, Claudia Langenberg, Claudia Trenkwalder, Rejko Krüger, Thomas Hankemeier, Ronan M. T. Fleming, Brit Mollenhauer, and Ines Thiele. Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in parkinson’s disease. 29(7):1767–1777.e8, 2019. [DOI].
Alexander Eng and Elhanan Borenstein. Microbial community design: methods, applications, and opportunities. 58():117–128, 2019. [DOI].
Kacy Greenhalgh, Javier Ramiro‐Garcia, Almut Heinken, Pit Ullmann, Tamara Bintener, Maria Pires Pacheco, Joanna Bagińska, Pranjul Shah, Audrey Frachet, Rashi Halder, Joëlle V. Fritz, Thomas Sauter, Ines Thiele, Serge Haan, Elisabeth Letellier, and Paul Wilmes. Integrated in vitro and in silico modeling delineates the molecular effects of a synbiotic regimen on colorectal-cancer-derived cells. 27(5):1621–1632.e9, 2019. [DOI].
Anne Richelle, Austin W.T. Chiang, Chih‐Chung Kuo, and Nathan E. Lewis. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. 15(4):e1006867–e1006867, 2019. [DOI].
Maciek R. Antoniewicz. Synthetic methylotrophy: strategies to assimilate methanol for growth and chemicals production. 59():165–174, 2019. [DOI].
Federico Baldini, Johannes Hertel, Estelle Sandt, Cyrille C. Thinnes, Lorieza Neuberger-Castillo, Lukas Pavelka, Fay Betsou, Rejko Krüger, and Ines Thiele. Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes. ():, 2019. [DOI].
Kenan Jijakli and Paul A. Jensen. Metabolic modeling of streptococcus mutans reveals complex nutrient requirements of an oral pathogen. 4(5):, 2019. [DOI].
Yan Zhu, Jinxin Zhao, Mohd Hafidz Mahamad Maifiah, Tony Velkov, Falk Schreiber, and Jian Li. Metabolic responses to polymyxin treatment in acinetobacter baumannii atcc 19606: integrating transcriptomics and metabolomics with genome-scale metabolic modeling. 4(1):, 2019. [DOI].
André Schultz and Rehan Akbani. Sammi: a semi-automated tool for the visualization of metabolic networks. 36(8):2616–2617, 2019. [DOI].
Anne Richelle, Chintan Joshi, and Nathan E. Lewis. Assessing key decisions for transcriptomic data integration in biochemical networks. 15(7):e1007185–e1007185, 2019. [DOI].
Su H. Chu, Mengna Huang, Rachel S. Kelly, Elisa Benedetti, Jalal Siddiqui, Oana A. Zeleznik, Alexandre Pereira, David M. Herrington, Craig E. Wheelock, Jan Krumsiek, Michael J. McGeachie, Steven C. Moore, Peter Kraft, Ewy A. Mathé, and Jessica Lasky‐Su. Integration of metabolomic and other omics data in population-based study designs: an epidemiological perspective. 9(6):117–117, 2019. [DOI].
Rosina Pryor, Daniel Martínez‐Martínez, Leonor M. Quintaneiro, and Filipe Cabreiro. The role of the microbiome in drug response. 60(1):417–435, 2019. [DOI].
Yiqun Chen, Brian O. McConnell, Venkata Gayatri Dhara, Harnish Mukesh Naik, Chien‐Ting Li, Maciek R. Antoniewicz, and Michael J. Betenbaugh. An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells. 5(1):25–25, 2019. [DOI].
Benjamín J. Sánchez, Feiran Li, Eduard J. Kerkhoven, and Jens Nielsen. Slimer: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. 13(1):4–4, 2019. [DOI].
Sander Y.A. Rodenburg, Michael Seidl, Howard S. Judelson, Andrea L. Vu, Francine Govers, and Dick de Ridder. Metabolic model of thephytophthora infestans-tomato interaction reveals metabolic switches during host colonization. 10(4):, 2019. [DOI].
Masoud Ahookhosh, Francisco J. Aragón Artacho, Ronan M. T. Fleming, and Phan Tu Vuong. Local convergence of the levenberg–marquardt method under hölder metric subregularity. 45(5-6):2771–2806, 2019. [DOI].
Peter C. St. John and Yannick J. Bomble. Approaches to computational strain design in the multiomics era. 10():597–597, 2019. [DOI].
Taneli Pusa, Mariana Galvão Ferrarini, Ricardo Andrade, Arnaud Mary, Alberto Marchetti-Spaccamela, Leen Stougie, and Marie‐France Sagot. Moomin – mathematical exploration of ’omics data on a metabolic network. 36(2):514–523, 2019. [DOI].
Guan Wang, Cees Haringa, Wenjun Tang, Henk Noorman, Ju Chu, Yingping Zhuang, and Siliang Zhang. Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses. 117(3):844–867, 2019. [DOI].
Jorge Fernández-de-Cossio-Díaz and Roberto Mulet. Maximum entropy and population heterogeneity in continuous cell cultures. 15(2):e1006823–e1006823, 2019. [DOI].
Marouen Ben Guebila and Ines Thiele. Predicting gastrointestinal drug effects using contextualized metabolic models. 15(6):e1007100–e1007100, 2019. [DOI].
Elaina M. Maldonado, F. Taha, Joyeeta Rahman, and Shamima Rahman. Systems biology approaches toward understanding primary mitochondrial diseases. 10():19–19, 2019. [DOI].
Ilaria Granata, Enrico Troiano, Mara Sangiovanni, and Mario Rosario Guarracino. Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer. 20(S4):162–162, 2019. [DOI].
Nathalie Poupin, Marie Tremblay‐Franco, Aurélien Amiel, Cécile Canlet, Didier Rémond, Laurent Debrauwer, Dominique Dardevet, Ines Thiele, Maike K. Aurich, Fabien Jourdan, Isabelle Savary‐Auzeloux, and Sergio Polakof. Arterio-venous metabolomics exploration reveals major changes across liver and intestine in the obese yucatan minipig. 9(1):12527–12527, 2019. [DOI].
Alessandro Di Stefano, Marialisa Scatà, Supreeta Vijayakumar, Claudio Angione, Aurelio La Corte, and Píetro Lió. Social dynamics modeling of chrono-nutrition. 15(1):e1006714–e1006714, 2019. [DOI].
Bergthor Traustason. Amino acid requirements of the chinese hamster ovary cell metabolism during recombinant protein production. ():, 2019. [DOI].
Wentao Dong, Sun Jin Moon, Joanne K. Kelleher, and Gregory Stephanopoulos. Dissecting mammalian cell metabolism through 13c- and 2h-isotope tracing: interpretations at the molecular and systems levels. 59(6):2593–2610, 2019. [DOI].
Kristopher D. Rawls, Bonnie V. Dougherty, and Jason A. Papin. Metabolic network reconstructions to predict drug targets and off-target effects. 2088():315–330, 2019. [DOI].
José Ramón Valverde, Sonia Gullón, Clara A. García-Herrero, Iván Campoy, and Rafael P. Mellado. Dynamic metabolic modelling of overproduced protein secretion in streptomyces lividans using adaptive dfba. 19(1):233–233, 2019. [DOI].
Natalie Stanford, Martin Scharm, Paul D. Dobson, Martin Golebiewski, Michael Hucka, Varun B. Kothamachu, David Nickerson, Stuart Owen, Jürgen Pahle, Ulrike Wittig, Dagmar Waltemath, Carole Goble, Pedro Mendes, and Jacky L. Snoep. Data management in computational systems biology: exploring standards, tools, databases, and packaging best practices. 2049():285–314, 2019. [DOI].
Yoon-Mi Choi, Dong‐Hyuk Choi, Yi Qing Lee, Lokanand Koduru, Nathan E. Lewis, Meiyappan Lakshmanan, and Dong‐Yup Lee. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. ():, 2019. [DOI].
Brendan Fu‐Long Sieow, Toni Juhani Nurminen, Hua Ling, and Matthew Wook Chang. Meta‐omics‐ and metabolic modeling‐assisted deciphering of human microbiota metabolism. 14(9):e1800445–e1800445, 2019. [DOI].
Tal Pecht, Anna C. Aschenbrenner, Thomas Ulas, and A. Succurro. Modeling population heterogeneity from microbial communities to immune response in cells. 77(3):415–432, 2019. [DOI].
Miguel Ponce-de-León, Iñigo Apaolaza, Alfonso Valencia, and Francisco J. Planes. On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models. 36(6):1986–1988, 2019. [DOI].
Maureen A. Carey, Andreas Dräger, Jason A. Papin, and James T. Yurkovich. Community standards to facilitate development and address challenges in metabolic modeling. ():, 2019. [DOI].
Priyanka Baloni, Cory C. Funk, Jingwen Yan, James T. Yurkovich, Alexandra Kueider‐Paisley, Kwangsik Nho, Almut Heinken, Wei Jia, Siamak MahmoudianDehkordi, Gregory Louie, Andrew J. Saykin, Matthias Arnold, Gabi Kastenmüller, William J. Griffiths, Ines Thiele, Rima Kaddurah‐Daouk, Nathan D. Price, Rima Kaddurah-Daouk, and Nathan D. Price. Identifying differences in bile acid pathways for cholesterol clearance in alzheimer’s disease using metabolic networks of human brain regions. ():, 2019. [DOI].
Nattapat Isarankura Na Ayudhya, Kobkul Laoteng, Yuanda Song, Asawin Meechai, and Wanwipa Vongsangnak. Metabolic traits specific for lipid-overproducing strain of mucor circinelloides wj11 identified by genome-scale modeling approach. 7():e7015–e7015, 2019. [DOI].
Freyr Jóhannsson, James T. Yurkovich, Steinn Guðmundsson, Ólafur E. Sigurjónsson, and Óttar Rolfsson. Temperature dependence of platelet metabolism. ():, 2019. [DOI].
Almut Heinken and Ines Thiele. Systematic interrogation of the distinct metabolic potential in gut microbiomes of inflammatory bowel disease patients with dysbiosis. ():, 2019. [DOI].
Hock Chuan Yeo, Jongkwang Hong, Meiyappan Lakshmanan, and Dong‐Yup Lee. Enzyme capacity-based genome scale modelling of cho cells. ():, 2019. [DOI].
Thomas C. Keaty and Paul A. Jensen. Gapsplit: efficient random sampling for non-convex constraint-based models. ():, 2019. [DOI].
Denny Popp and Florian Centler. µbialsim: constraint-based dynamic simulation of complex microbiomes. ():, 2019. [DOI].
Yuliang Wang, Shuyi Ma, and Walter L. Ruzzo. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. ():, 2019. [DOI].
Jie Zhang, Søren D. Petersen, Tijana Radivojević, Andrés Ramirez, Andrés M. Perez, Eduardo Abeliuk, Benjamín J. Sánchez, Zachary Costello, Yu Chen, Mike Fero, Héctor García Martín, Jens Nielsen, Jay D. Keasling, and Michael K. Jensen. Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models. ():, 2019. [DOI].
Ewelina Węglarz‐Tomczak, Thierry D.G.A. Mondeel, Diewertje Piebes, and Hans V. Westerhoff. Simultaneous integration of gene expression and nutrient availability for studying metabolism of hepatocellular carcinoma. ():, 2019. [DOI].
Paolo Romano, Arnaud Céol, Andreas Dräger, Antonino Fiannaca, Rosalba Giugno, Massimo La Rosa, Luciano Milanesi, Ulrich Pfeffer, Riccardo Rizzo, Soo-Yong Shin, Junfeng Xia, and Alfonso Urso. The 2017 network tools and applications in biology (nettab) workshop: aims, topics and outcomes. 20(S4):125–125, 2019. [DOI].
Miguel Ponce-de-León, Iñigo Apaolaza, Alfonso Valencia, and Francisco J. Planes. On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models. ():, 2019. [DOI].
Partho Sen, Alex M. Dickens, María Asunción López-Bascón, Tuomas Lindeman, Esko Kemppainen, Santosh Lamichhane, Tuukka Rönkkö, Jorma Ilonen, Jorma Toppari, Riitta Veijola, Heikki Hyöty, Tuulia Hyötyläinen, Mikael Knip, and Matej Orešič. Metabolic alterations in human peripheral blood mononuclear cells associate with progression to islet autoimmunity and type 1 diabetes. ():, 2019. [DOI].
Agnieszka B. Wegrzyn, Katharina Herzog, Albert Gerding, Marcel Kwiatkowski, Justina C. Wolters, Amalia M. Dolga, A. E. M. van Lint, Ronald J. A. Wanders, Hans R. Waterham, and Barbara M. Bakker. Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in refsum disease. ():, 2019. [DOI].
Rui M. C. Portela, Anne Richelle, Patrick Dumas, and Moritz von Stosch. Time integrated flux analysis: exploiting the concentration measurements directly for cost-effective metabolic network flux analysis. 7(12):620–620, 2019. [DOI].
Kjerstin De Winter, Justien Ghesquière, Wim Teughels, Steffen Waldherr, and Kristel Bernaerts. Towards the construction of gsmn-based community model for an oral biofilm. 52(26):193–199, 2019. [DOI].
Alvar J. Alonso-Lavin, Djordje Bajić, and Juan F. Poyatos. Tolerance to nadh/nad+ imbalance anticipates aging and anti-aging interventions. ():, 2019. [DOI].
Shangzhong Li, Anne Richelle, and Nathan E. Lewis. Enhancing product and bioprocess attributes using genome‐scale models of cho metabolism. ():73–95, 2019. [DOI].
Marouen Ben Guebila. Achr.cu: gpu-accelerated sampling of metabolic networks. 4(37):1363–1363, 2019. [DOI].
Shouyong Jiang, Yong Wang, Marcus Kaiser, and Natalio Krasnogor. Nihba: a network interdiction approach with hybrid benders algorithm for strain design. ():, 2019. [DOI].
Tahila Andrighetti, Balázs Bohár, Ney Lemke, Padhmanand Sudhakar, and Tamás Korcsmáros. Microbiolink: an integrated computational pipeline to infer functional effects of microbiome-host interactions. ():, 2019. [DOI].
Juan F. Poyatos. Genetic buffering and potentiation in metabolism. ():, 2019. [DOI].
Víctor A. López-Agudelo, Emma Laing, Tom A. Mendum, Andrés Baena, Luis F. Barrera, Dany J. V. Beste, and Rigoberto Ríos‐Estepa. A systematic evaluation of mycobacterium tuberculosis genome-scale metabolic networks. ():, 2019. [DOI].
Marouen Ben Guebila and Ines Thiele. Pan-organ model integration of metabolic and regulatory processes in type 1 diabetes. ():, 2019. [DOI].
Partho Sen, Santosh Lamichhane, Alex M. Dickens, and Matej Orešič. The role of omic technologies in the study of the human gut microbiome. ():469–481, 2019. [DOI].

Hao Wang, Simonas Marcišauskas, Benjamín J. Sánchez, Iván Domenzain, Daniel Hermansson, Rasmus Ågren, Jens Nielsen, and Eduard J. Kerkhoven. Raven 2.0: a versatile toolbox for metabolic network reconstruction and a case study on streptomyces coelicolor. 14(10):e1006541–e1006541, 2018. [DOI].
Partho Sen, Esko Kemppainen, and Matej Orešič. Perspectives on systems modeling of human peripheral blood mononuclear cells. 4():96–96, 2018. [DOI].
Veronika Boczonadi, Martin King, Anthony C. Smith, Monika Oláhová, Boglárka Bánsági, Andreas Roos, Filmon Eyassu, Christoph H. Borchers, Venkateswaran Ramesh, Hanns Lochmüller, Tuomo Polvikoski, Roger G. Whittaker, Angela Pyle, Helen Griffin, Robert W. Taylor, Patrick F. Chinnery, Alan J. Robinson, Edmund R.S. Kunji, and Rita Horváth. Mitochondrial oxodicarboxylate carrier deficiency is associated with mitochondrial dna depletion and spinal muscular atrophy–like disease. 20(10):1224–1235, 2018. [DOI].
Hai He, Christian Edlich‐Muth, Steffen N. Lindner, and Arren Bar‐Even. Ribulose monophosphate shunt provides nearly all biomass and energy required for growth of e. coli. 7(6):1601–1611, 2018. [DOI].
Ioannis Papapetridis, Maarten D. Verhoeven, Sanne J. Wiersma, Maaike Goudriaan, Antonius J. A. van Maris, and Jack T. Pronk. Laboratory evolution for forced glucose-xylose co-consumption enables identification of mutations that improve mixed-sugar fermentation by xylose-fermenting saccharomyces cerevisiae. 18(6):, 2018. [DOI].
Andreas Schwentner, André Feith, Eugenia Münch, Tobias Busche, Christian Rückert, Jörn Kalinowski, Ralf Takors, and Bastian Blombach. Metabolic engineering to guide evolution – creating a novel mode for l-valine production with corynebacterium glutamicum. 47():31–41, 2018. [DOI].
Kurt Patterson, James Yu, Jenny Landberg, Ivan Chang, Farbod Shavarebi, Virginia Bilanchone, and Suzanne Sandmeyer. Functional genomics for the oleaginous yeast yarrowia lipolytica. 48():184–196, 2018. [DOI].
Hameri Tuure, Georgios Fengos, Meriç Ataman, Ljubiša Mišković, and Vassily Hatzimanikatis. Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. 52():29–41, 2018. [DOI].
Michael Scheunemann, Siobhán M. Brady, and Zoran Nikoloski. Integration of large-scale data for extraction of integrated arabidopsis root cell-type specific models. 8(1):7919–7919, 2018. [DOI].
Kristopher D. Rawls, Bonnie V. Dougherty, Edik M. Blais, Ethan Stancliffe, Glynis L. Kolling, Kalyan C. Vinnakota, Venkat R. Pannala, Anders Wallqvist, and Jason A. Papin. A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools. 105():64–71, 2018. [DOI].
Alyaa M. Abdel‐Haleem, Hooman Hefzi, Katsuhiko Mineta, Xin Gao, Takashi Gojobori, Bernhard Ø. Palsson, Nathan E. Lewis, and Neema Jamshidi. Functional interrogation of plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting. 14(1):e1005895–e1005895, 2018. [DOI].
Hao Wang, Simonas Marcišauskas, Benjamín J. Sánchez, Iván Domenzain, Daniel Hermansson, Rasmus Ågren, Jens Nielsen, and Eduard J. Kerkhoven. Raven 2.0: a versatile toolbox for metabolic network reconstruction and a casestudy on streptomyces coelicolor. ():, 2018. [DOI].
Francine Piubeli, Manuel Salvador, Montserrat Argandoña, Joaquı́n J. Nieto, Vicente Bernal, José M. Pastor, Manuel Cánovas, and Carmen Vargas. Insights into metabolic osmoadaptation of the ectoines-producer bacterium chromohalobacter salexigens through a high-quality genome scale metabolic model. 17(1):2–2, 2018. [DOI].
Alan R. Pacheco, Mauricio Moel, and Daniel Segrè. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. ():, 2018. [DOI].
Bashir Sajo Mienda, Rabiu Salihu, Aliyu Adamu, and Idris Shehu. Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets. 13(4):455–467, 2018. [DOI].
Yun Zhang, Xiuling Shang, Shujuan Lai, Yu Zhang, Qitiao Hu, Xin Chai, Bo Wang, Shuwen Liu, and Tingyi Wen. Reprogramming one-carbon metabolic pathways to decouple <scp>l</scp>-serine catabolism from cell growth in corynebacterium glutamicum. 7(2):635–646, 2018. [DOI].
Adam Amara, Eriko Takano, and Rainer Breitling. Development and validation of an updated computational model of streptomyces coelicolor primary and secondary metabolism. 19(1):519–519, 2018. [DOI].
Yu Zhang, Yun Zhang, Xiuling Shang, Bo Wang, Qitiao Hu, Shuwen Liu, Tingyi Wen, Shuwen Liu, and Tingyi Wen. Reconstruction of tricarboxylic acid cycle in corynebacterium glutamicum with a genome‐scale metabolic network model for trans‐4‐hydroxyproline production. 116(1):99–109, 2018. [DOI].
Miha Kastelic, D. Kopač, Uroš Novak, and Blaž Likozar. Dynamic metabolic network modeling of mammalian chinese hamster ovary (cho) cell cultures with continuous phase kinetics transitions. 142():124–134, 2018. [DOI].
Mohammad Tajparast and Dominic Frigon. Predicting the accumulation of storage compounds by rhodococcus jostii rha1 in the feast-famine growth cycles using genome-scale flux balance analysis. 13(3):e0191835–e0191835, 2018. [DOI].
Ilyas Kabimoldayev, Anh Duc Nguyen, Laurence Yang, Sunghoon Park, Eun Yeol Lee, and Donghyuk Kim. Basics of genome-scale metabolic modeling and applications on c1-utilization. 365(20):, 2018. [DOI].
Samara Silva de Souza, Julia de Vasconcellos Castro, and Luismar Marques Porto. Modeling the core metabolism of komagataeibacter hansenii atcc 23769 to evaluate nanocellulose biosynthesis. 35(3):869–886, 2018. [DOI].
Benjamin M. Shapiro, Tori M. Hoehler, and Qusheng Jin. Integrating genome-scale metabolic models into the prediction of microbial kinetics in natural environments. 242():102–122, 2018. [DOI].
M N Abd Latif and Elebeoba E. May. A multiscale agent-based model for the investigation of e. coli k12 metabolic response during biofilm formation. 80(11):2917–2956, 2018. [DOI].
W. Pinzon, Hector Vega, Janneth González, and Andrés Pinzón. Mathematical framework behind the reconstruction and analysis of genome scale metabolic models. 26(5):1593–1606, 2018. [DOI].
Ignace Tack, Philippe Nimmegeers, Simen Akkermans, Filip Logist, and Jan Van Impe. A low-complexity metabolic network model for the respiratory and fermentative metabolism of escherichia coli. 13(8):e0202565–e0202565, 2018. [DOI].
Zachary P. Metz, Tong Ding, and David J. Baumler. Using genome-scale metabolic models to compare serovars of the foodborne pathogen listeria monocytogenes. 13(6):e0198584–e0198584, 2018. [DOI].
Clémence Frioux, Torsten Schaub, Sebastian Schellhorn, Anne Siegel, and Philipp Wanko. Hybrid metabolic network completion. 19(1):83–108, 2018. [DOI].
Nicholas A. Jose, Rebecca Lau, Tami L. Swenson, Niels Klitgord, Ferrán García‐Pichel, Benjamin P. Bowen, Richard Baran, and Trent R. Northen. Flux balance modeling to predict bacterial survival during pulsed-activity events. 15(7):2219–2229, 2018. [DOI].
G. Mitsue León-Saiki, Narcís Ferrer-Ledo, David Lao‐Martil, Douwe van der Veen, René H. Wijffels, and Dirk E. Martens. Metabolic modelling and energy parameter estimation of tetradesmus obliquus. 35():378–387, 2018. [DOI].
Philip J. Luthert, Luís Serrano, and Christina Kiel. Opportunities and challenges of whole-cell and -tissue simulations of the outer retina in health and disease. 1(1):131–152, 2018. [DOI].
Zeyu Zhu, Defne Surujon, Aidan Pavao, José Bento, and Tim van Opijnen. Forecasting bacterial survival-success and adaptive evolution through multi-omics stress-response mapping, network analyses and machine learning. ():, 2018. [DOI].
John E. Pearl, M. Das, and Andrea M. Cooper. Immunological roulette: luck or something more? considering the connections between host and environment in tb. 15(3):226–232, 2018. [DOI].
Zixiang Xu, Jing Guo, Yunxia Yue, Jing Meng, and Xiao Sun. in silico genome-scale reconstruction and analysis of the shewanella loihica pv-4 metabolic network. 26(03):373–397, 2018. [DOI].
Daniela Calvetti and Erkki Somersalo. Brain energy metabolism. ():1–19, 2018. [DOI].
Tong Hao, Bin Wang, Lingxuan Zhao, Xin Feng, and Jinsheng Sun. Reconstruction and analysis of a genome-scale metabolic network for eriocheir sinensis hepatopancreas. 6():79235–79244, 2018. [DOI].
Hameri Tuure, Georgios Fengos, Meriç Ataman, Ljubiša Mišković, and Vassily Hatzimanikatis. Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. ():, 2018. [DOI].
Nilanjan Adhikari, Sk. Abdul Amin, Tarun Jha, and Achintya Saha. Application of computation in the biosynthesis of phytochemicals. ():255–276, 2018. [DOI].
David Hala, Joshua A. Cullen, Béatrice V. Hernout, and Ivan Ivanov. In silico predicted transcriptional regulatory control of steroidogenesis in spawning female fathead minnows (pimephales promelas). 455():179–190, 2018. [DOI].
Rafael S. Costa and Susana Vinga. Assessing escherichia coli metabolism models and simulation approaches in phenotype predictions: validation against experimental data. 34(6):1344–1354, 2018. [DOI].
Ana M. Untaroiu, Maureen A. Carey, Jennifer L. Güler, and Jason A. Papin. Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies. ():, 2018. [DOI].
Xiao Pan. Scale-down of cho cell fed-batch cultures. ():, 2018. [DOI].
Amel Bekkar, Julien Dorier, Isaac Crespo, Anne Niknejad, Alan Bridge, and Ioannis Xénarios. Generation of molecular models and pathways. ():274–285, 2018. [DOI].
Ankit Gupta, Ahmad Ahmad, Dipesh Chothwe, Midhun K. Madhu, Shireesh Srivastava, and Vineet K. Sharma. Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophmethylococcus capsulatusstr. bath. ():, 2018. [DOI].
Zixiang Xu, Qiaqing Wu, and Dunming Zhu. The enzymes that beyond non-oxidative glycolysis. ():, 2018. [DOI].
Graciela Mitsue León Saiki. Role of starch in tetradesmus obliquus. ():, 2018. [DOI].
Maria Pires Pacheco. Fast recontruction of compact context-specific network models. ():, 2018. [DOI].
Ben M. Maoz, Anna Herland, Edward A. Fitzgerald, Thomas Grevesse, Charles Vidoudez, Alan R. Pacheco, Sean P. Sheehy, Tae‐Eun Park, Stephanie Dauth, Robert Mannix, Nikita Budnik, Kevin L. Shores, Alexander Cho, Janna Nawroth, Daniel Segrè, Bogdan Budnik, Donald E. Ingber, and Kevin Kit Parker. A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. 36(9):865–874, 2018. [DOI].
Nam‐Hoon Kim, Murukarthick Jayakodi, Sang‐Choon Lee, Beom‐Soon Choi, Woojong Jang, Junki Lee, Hyun Hee Kim, Nomar Espinosa Waminal, Meiyappan Lakshmanan, Binh van Nguyen, Yun Sun Lee, Hyun‐Seung Park, Hyun Jo Koo, Jee Young Park, Sampath Perumal, Ho Jun Joh, Hana Lee, Jin‐Kyung Kim, In Seo Kim, Kyung‐Hee Kim, Lokanand Koduru, Kyo Bin Kang, Sang Hyun Sung, Yeisoo Yu, Daniel Park, Doil Choi, Eun–Young Seo, Seungill Kim, Young‐Chang Kim, Dong Yun Hyun, Youn‐Il Park, Changsoo Kim, Tae‐Ho Lee, Hyun Uk Kim, Moon‐Soo Soh, Yi Lee, Jun Gyo In, Heui‐Soo Kim, Yong‐Min Kim, Deok‐Chun Yang, Rod A. Wing, Dong‐Yup Lee, Andrew H. Paterson, and Tae‐Jin Yang. Genome and evolution of the shade‐requiring medicinal herb panax ginseng. 16(11):1904–1917, 2018. [DOI].
Xihui Xu, Raphy Zarecki, Shlomit Medina, Shany Ofaim, Xiaowei Liu, Chen Chen, Shunli Hu, Dan Brom, Daniella Gat, Seema Porob, Hanan Eizenberg, Zeev Ronen, Jiandong Jiang, and Shiri Freilich. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. 13(2):494–508, 2018. [DOI].
Colton J. Lloyd, Ali Ebrahim, Laurence Yang, Zachary A. King, Edward Catoiu, Edward J. O’Brien, Joanne K. Liu, and Bernhard Ø. Palsson. Cobrame: a computational framework for genome-scale models of metabolism and gene expression. 14(7):e1006302–e1006302, 2018. [DOI].
Ai‐Lin Liu, Carolina A. Contador, Kejing Fan, and Hon‐Ming Lam. Interaction and regulation of carbon, nitrogen, and phosphorus metabolisms in root nodules of legumes. 9():1860–1860, 2018. [DOI].
Wei-Chin Ho and Jianzhi Zhang. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. 9(1):350–350, 2018. [DOI].
Hilal Taymaz‐Nikerel, Muhammed Erkan Karabekmez, Serpil Eraslan, and Betül Kırdar. Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells. 8(1):13672–13672, 2018. [DOI].
Miroslava Čuperlović‐Culf. Machine learning methods for analysis of metabolic data and metabolic pathway modeling. 8(1):4–4, 2018. [DOI].
Arturo Casini, Fangyuan Chang, Raissa Eluere, Andrew King, Eric M. Young, Quentin M. Dudley, Ashty S. Karim, Katelin Pratt, Cassandra Bristol, Anthony L. Forget, Amar Ghodasara, Robert Warden‐Rothman, Rui Gan, Alexander Cristofaro, Amin Espah Borujeni, Min‐Hyung Ryu, Jian Li, Yong-Chan Kwon, He Wang, Evangelos C. Tatsis, Carlos E. Rodríguez López, Sarah E. O’Connor, Marnix H. Medema, Michael A. Fischbach, Michael C. Jewett, Christopher A. Voigt, and D. Benjamin Gordon. A pressure test to make 10 molecules in 90 days: external evaluation of methods to engineer biology. 140(12):4302–4316, 2018. [DOI].
Ludovic Cottret, Clément Frainay, Maxime Chazalviel, Floréal Cabanettes, Yoann Gloaguen, Etienne Camenen, Benjamin Merlet, Stéphanie Heux, Jean‐Charles Portais, Nathalie Poupin, Florence Vinson, and Fabien Jourdan. Metexplore: collaborative edition and exploration of metabolic networks. 46(W1):W495–W502, 2018. [DOI].
Shashi Kant Bhatia, Ravi Kant Bhatia, Yong‐Keun Choi, Eunsung Kan, Yun‐Gon Kim, and Yung‐Hun Yang. Biotechnological potential of microbial consortia and future perspectives. 38(8):1209–1229, 2018. [DOI].
Méziane Aïte, Marie Chevallier, Clémence Frioux, Camille Trottier, Jeanne Got, María Paz Cortés, Sebastián N. Mendoza, Grégory Carrier, Olivier Dameron, Nicolas Guillaudeux, Mauricio Latorre, Nicolás Loira, Gabriel V. Markov, Alejandro Maass, and Anne Siegel. Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models. 14(5):e1006146–e1006146, 2018. [DOI].
Ilya R. Akberdin, Merlin Thompson, Richard Hamilton, Nalini Desai, Danny Alexander, Calvin A. Henard, Michael T. Guarnieri, and Marina Kalyuzhnaya. Methane utilization in methylomicrobium alcaliphilum 20zr: a systems approach. 8(1):2512–2512, 2018. [DOI].
Felix Moser, Amin Espah Borujeni, Amar Ghodasara, E. S. Cameron, Yongjin Park, and Christopher A. Voigt. Dynamic control of endogenous metabolism with combinatorial logic circuits. 14(11):e8605–e8605, 2018. [DOI].
Pierre Salvy, Georgios Fengos, Meriç Ataman, Thomas Pathier, Keng Cher Soh, and Vassily Hatzimanikatis. Pytfa and mattfa: a python package and a matlab toolbox for thermodynamics-based flux analysis. 35(1):167–169, 2018. [DOI].
Elias Englund, Kiyan Shabestary, Elton P. Hudson, and Pia Lindberg. Systematic overexpression study to find target enzymes enhancing production of terpenes in synechocystis pcc 6803, using isoprene as a model compound. 49():164–177, 2018. [DOI].
Andrea De Martino and Daniele De Martino. An introduction to the maximum entropy approach and its application to inference problems in biology. 4(4):e00596–e00596, 2018. [DOI].
Robert Ramírez‐García, Nisarg Gohil, and Vijai Singh. Recent advances, challenges, and opportunities in bioremediation of hazardous materials. ():517–568, 2018. [DOI].
Osvaldo D. Kim, Miguel Rocha, and Paulo Maia. A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering. 9():1690–1690, 2018. [DOI].
Donghyuk Kim, Sang Woo Seo, Ye Gao, Hojung Nam, Gabriela I. Guzmán, Byung‐Kwan Cho, and Bernhard Ø. Palsson. Systems assessment of transcriptional regulation on central carbon metabolism by cra and crp. 46(6):2901–2917, 2018. [DOI].
George C. diCenzo, Maryam Zamani, Alice Checcucci, Marco Fondi, Joel S. Griffitts, Turlough M. Finan, and Alessio Mengoni. Multidisciplinary approaches for studying rhizobium–legume symbioses. 65(1):1–33, 2018. [DOI].
Wei Zou, Guangbin Ye, and Kaizheng Zhang. Diversity, function, and application of clostridium in chinese strong flavor baijiu ecosystem: a review. 83(5):1193–1199, 2018. [DOI].
Pranjul Mishra, Na‐Rae Lee, Meiyappan Lakshmanan, Minsuk Kim, Byung‐Gee Kim, and Dong‐Yup Lee. Genome-scale model-driven strain design for dicarboxylic acid production in yarrowia lipolytica. 12(S2):12–12, 2018. [DOI].
Kiyan Shabestary, Josefine Anfelt, Emil Ljungqvist, Michael Jahn, Lun Yao, and Elton P. Hudson. Targeted repression of essential genes to arrest growth and increase carbon partitioning and biofuel titers in cyanobacteria. 7(7):1669–1675, 2018. [DOI].
Dorinês Rosário, Rui Benfeitas, Gholamreza Bidkhori, Cheng Zhang, Mathias Uhlén, Saeed Shoaie, and Adil Mardinoğlu. Understanding the representative gut microbiota dysbiosis in metformin-treated type 2 diabetes patients using genome-scale metabolic modeling. 9():775–775, 2018. [DOI].
Emil Karlsen, Christian Schulz, and Eivind Almaas. Automated generation of genome-scale metabolic draft reconstructions based on kegg. 19(1):467–467, 2018. [DOI].
Beatriz García-Jiménez, José L. Garcı́a, and Juan Nogales. Flycop: metabolic modeling-based analysis and engineering microbial communities. 34(17):i954–i963, 2018. [DOI].
José P. Faria, Miguel Rocha, Isabel Rocha, and Christopher S. Henry. Methods for automated genome-scale metabolic model reconstruction. 46(4):931–936, 2018. [DOI].
Zhongbao Ma, Chao Ye, Wei‐Wei Deng, Mengmeng Xu, Qiong Wang, Gao‐Qiang Liu, Feng Wang, Li Liu, Zhenghong Xu, Guiyang Shi, and Zhongyang Ding. Reconstruction and analysis of a genome-scale metabolic model of ganoderma lucidum for improved extracellular polysaccharide production. 9():3076–3076, 2018. [DOI].
Yan Zhu, Tobias Czauderna, Jinxin Zhao, Matthias Klapperstueck, Mohd Hafidz Mahamad Maifiah, Mei‐Ling Han, Jing Lü, Björn Sommer, Tony Velkov, Trevor Lithgow, Jiangning Song, Falk Schreiber, and Jian Li. Genome-scale metabolic modeling of responses to polymyxins in pseudomonas aeruginosa. 7(4):, 2018. [DOI].
L. Kyselova, Diana Kreitmayer, Andreas Kremling, and Katja Bettenbrock. Type and capacity of glucose transport influences succinate yield in two-stage cultivations. 17(1):132–132, 2018. [DOI].
Annalisa Occhipinti, Filmon Eyassu, Thahira Rahman, Pattanathu Rahman, and Claudio Angione. In silico engineering of pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. 6():e6046–e6046, 2018. [DOI].
Miha Moškon, Nikolaj Zimic, and Miha Mraz. Grohar: automated visualization of genome-scale metabolic models and their pathways. 25(5):505–508, 2018. [DOI].
Timothy H. Self, David Gilbert, and Monika Heiner. Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models. ():39–58, 2018. [DOI].
Maria Caracausi, Veronica Ghini, Chiara Locatelli, Martina Mericio, Allison Piovesan, Francesca Antonaros, Maria Chiara Pelleri, Lorenza Vitale, Rosa Anna Vacca, Federica Bedetti, Maria Chiara Mimmi, Claudio Luchinat, Paola Turano, Pierluigi Strippoli, and Guido Cocchi. Plasma and urinary metabolomic profiles of down syndrome correlate with alteration of mitochondrial metabolism. 8(1):2977–2977, 2018. [DOI].
Nana Y. D. Ankrah, Bessem Chouaia, and Angela E. Douglas. The cost of metabolic interactions in symbioses between insects and bacteria with reduced genomes. 9(5):, 2018. [DOI].
George C. diCenzo, Alex B. Benedict, Marco Fondi, Graham C. Walker, Turlough M. Finan, Alessio Mengoni, and Joel S. Griffitts. Robustness encoded across essential and accessory replicons of the ecologically versatile bacterium sinorhizobium meliloti. 14(4):e1007357–e1007357, 2018. [DOI].
Elliot Rowe, Bernhard Ø. Palsson, and Zachary A. King. Escher-fba: a web application for interactive flux balance analysis. 12(1):84–84, 2018. [DOI].
Hongzhong Lu, Weiqiang Cao, Xiaoyun Liu, Yufei Sui, Liming Ouyang, Jianye Xia, Mingzhi Huang, Yingping Zhuang, Siliang Zhang, Henk Noorman, and Ju Chu. Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of aspergillus niger under industrial enzyme production condition. 8(1):14404–14404, 2018. [DOI].
Elizabeth Brunk, Roger L. Chang, Jing Xia, Hooman Hefzi, James T. Yurkovich, Donghyuk Kim, Evan Buckmiller, Harris H. Wang, Byung‐Kwan Cho, Chen Yang, Bernhard Ø. Palsson, George M. Church, and Nathan E. Lewis. Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow. 115(43):11096–11101, 2018. [DOI].
Naveen Venayak, Axel von Kamp, Steffen Klamt, and Radhakrishnan Mahadevan. Move identifies metabolic valves to switch between phenotypic states. 9(1):5332–5332, 2018. [DOI].
Anika Küken, Frederik Sommer, Liliya Yaneva-Roder, Luke C. M. Mackinder, Melanie Höhne, Stefan Geimer, Martin C. Jonikas, Michael Schroda, Mark Stitt, Zoran Nikoloski, and Tabea Mettler‐Altmann. Effects of microcompartmentation on flux distribution and metabolic pools in chlamydomonas reinhardtii chloroplasts. 7():, 2018. [DOI].
Wei Niu, Levi Kramer, Joshua Mueller, Kun Liu, and Jiantao Guo. Metabolic engineering of escherichia coli for the de novo stereospecific biosynthesis of 1,2-propanediol through lactic acid. 8():e00082–e00082, 2018. [DOI].
Jun Feng, Jing Yang, Wenwen Yang, Jie Chen, Min Jiang, and Xiang Zou. Metabolome- and genome-scale model analyses for engineering of aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses. 11(1):94–94, 2018. [DOI].
Kok Siong Ang, Meiyappan Lakshmanan, Na‐Rae Lee, and Dong‐Yup Lee. Metabolic modeling of microbial community interactions for health, environmental and biotechnological applications. 19(8):712–722, 2018. [DOI].
María Teresita Castañeda, Sebastián Núñez, Fabricio Garelli, Claudio E. Voget, and Hernán De Battista. Comprehensive analysis of a metabolic model for lipid production in rhodosporidium toruloides. 280():11–18, 2018. [DOI].
Sergio Garcia and Cong T. Trinh. Multiobjective strain design: a framework for modular cell engineering. 51():110–120, 2018. [DOI].
Sarah McGarrity, Ósk Anuforo, Haraldur Halldórsson, Andreas Bergmann, Skarphéðinn Halldórsson, Sirus Palsson, H Henriksen, Pär I. Johansson, and Óttar Rolfsson. Metabolic systems analysis of lps induced endothelial dysfunction applied to sepsis patient stratification. 8(1):6811–6811, 2018. [DOI].
Ulf W. Liebal, Lars M. Blank, and Birgitta E. Ebert. Co2 to succinic acid – estimating the potential of biocatalytic routes. 7():e00075–e00075, 2018. [DOI].
Markus Janasch, Johannes Asplund‐Samuelsson, Ralf Steuer, and Elton P. Hudson. Kinetic modeling of the calvin cycle identifies flux control and stable metabolomes insynechocystiscarbon fixation. 70(3):973–983, 2018. [DOI].
Kelly Botero, Silvia Restrepo, and Andrés Pinzón. A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism. 19(S8):863–863, 2018. [DOI].
Alex Graudenzi, Davide Maspero, Marzia Di Filippo, Marco Gnugnoli, Claudio Isella, Giancarlo Mauri, Enzo Médico, Marco Antoniotti, and C. Damiani. Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. 87():37–49, 2018. [DOI].
Henrik Cordes, Christoph Thiel, Vanessa Baier, Lars M. Blank, and Lars Kuepfer. Integration of genome-scale metabolic networks into whole-body pbpk models shows phenotype-specific cases of drug-induced metabolic perturbation. 4(1):10–10, 2018. [DOI].
Felipe Scott, Pamela E. Wilson, Raúl Conejeros, and Vassilios S. Vassiliadis. Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation. 119():152–170, 2018. [DOI].
Julia Tröndle, Natalia Trachtmann, Georg A. Sprenger, and Dirk Weuster‐Botz. Fed‐batch production of <scp>l</scp>‐tryptophan from glycerol using recombinant escherichia coli. 115(12):2881–2892, 2018. [DOI].
Tjaša Kumelj, Snorre Sulheim, Alexander Wentzel, and Eivind Almaas. Predicting strain engineering strategies using iks1317: a genome‐scale metabolic model of streptomyces coelicolor. 14(4):e1800180–e1800180, 2018. [DOI].
Wei Zou, Guangbin Ye, Jing Zhang, Changqing Zhao, Xingxiu Zhao, and Kaizheng Zhang. Genome-scale metabolic reconstruction and analysis for clostridium kluyveri. 61(8):605–613, 2018. [DOI].
Hanseol Kim, Sinyeon Kim, and Sung Ho Yoon. Metabolic network reconstruction and phenome analysis of the industrial microbe, escherichia coli bl21(de3). 13(9):e0204375–e0204375, 2018. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Jorge M. Arevalillo, Mariana Díaz‐Almirón, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Hilario Navarro, Rosa Aras‐López, Irene Dapía, Rocío López‐Vacas, Paolo Nanni, Sara Llorente-Armijo, Pedro Arias, Alberto M. Borobia, Paloma Maı́n, Jaime Feliú, Enrique Espinosa, and Juan Ángel Fresno Vara. Molecular characterization of breast cancer cell response to metabolic drugs. 9(11):9645–9660, 2018. [DOI].
Ayca Cankorur‐Cetinkaya, Nathalie Narraidoo, Ceyda Kasavi, Nigel K.H. Slater, David B. Archer, and Stephen G. Oliver. Process development for the continuous production of heterologous proteins by the industrial yeast, komagataella phaffii. 115(12):2962–2973, 2018. [DOI].
Freyr Jóhannsson, Steinn Guðmundsson, Giuseppe Paglia, Sveinn Guðmundsson, Bernhard Ø. Palsson, Ólafur E. Sigurjónsson, and Óttar Rolfsson. Systems analysis of metabolism in platelet concentrates during storage in platelet additive solution. 475(13):2225–2240, 2018. [DOI].
León F. Toro, Laura Pinilla, Claudio Avignone–Rossa, and Rigoberto Ríos‐Estepa. An enhanced genome-scale metabolic reconstruction of streptomyces clavuligerus identifies novel strain improvement strategies. 41(5):657–669, 2018. [DOI].
Howard Ramírez-Malule, Stefan Junne, Mariano Nicolás Cruz Bournazou, Peter Neubauer, and Rigoberto Ríos‐Estepa. Streptomyces clavuligerus shows a strong association between tca cycle intermediate accumulation and clavulanic acid biosynthesis. 102(9):4009–4023, 2018. [DOI].
Jahir M. Gutierrez, Amir Feizi, Shangzhong Li, Thomas Beuchert Kallehauge, Hooman Hefzi, Lise Marie Grav, Daniel Ley, Deniz Baycın Hızal, Michael J. Betenbaugh, Björn G. Voldborg, Helene Faustrup Kildegaard, Gyun Min Lee, Bernhard Ø. Palsson, Jens Nielsen, and Nathan E. Lewis. Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. ():, 2018. [DOI].
Kento Tokuyama, Yoshihiro Toya, Takaaki Horinouchi, Chikara Furusawa, Fumio Matsuda, and Hiroshi Shimizu. Application of adaptive laboratory evolution to overcome a flux limitation in an escherichia coli production strain. 115(6):1542–1551, 2018. [DOI].
Iñigo Apaolaza, Luis V. Valcárcel, and Francisco J. Planes. gmcs: fast computation of genetic minimal cut sets in large networks. 35(3):535–537, 2018. [DOI].
George C. diCenzo, Klaudia Dębiec-Andrzejewska, Jan Krzysztoforski, Witold Uhrynowski, Alessio Mengoni, Camilla Fagorzi, Adrian Gόrecki, Łukasz Dziewit, Tomasz Bajda, Grzegorz Rzepa, and Łukasz Drewniak. Genomic and biotechnological characterization of the heavy-metal resistant, arsenic-oxidizing bacterium ensifer sp. m14. 9(8):379–379, 2018. [DOI].
José Aguilar-Rodríguez and Andreas Wagner. Metabolic determinants of enzyme evolution in a genome-scale bacterial metabolic network. 10(11):3076–3088, 2018. [DOI].
Amornpan Klanchui, Sudarat Dulsawat, Kullapat Chaloemngam, Supapon Cheevadhanarak, Peerada Prommeenate, and Asawin Meechai. An improved genome-scale metabolic model of arthrospira platensis c1 (iak888) and its application in glycogen overproduction. 8(4):84–84, 2018. [DOI].
Rienk A. Rienksma, Peter J. Schaap, Vítor A. P. Martins dos Santos, and María Suárez‐Diez. Modeling the metabolic state of mycobacterium tuberculosis upon infection. 8():264–264, 2018. [DOI].
Nan Xu, Chao Ye, and Li Liu. Genome-scale biological models for industrial microbial systems. 102(8):3439–3451, 2018. [DOI].
Carolina Shene, Juan A. Asenjo, and Yusuf Chisti. Metabolic modelling and simulation of the light and dark metabolism of chlamydomonas reinhardtii. 96(5):1076–1088, 2018. [DOI].
Matthew Perisin and Christian Sund. Human gut microbe co-cultures have greater potential than monocultures for food waste remediation to commodity chemicals. 8(1):15594–15594, 2018. [DOI].
Junhua Wang, Cheng Wang, Huanhuan Liu, Haishan Qi, Hong Chen, and Jianping Wen. Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology. 38(7):1106–1120, 2018. [DOI].
Mustafa Sertbaş and Kutlu Ö. Ülgen. Unlocking human brain metabolism by genome-scale and multiomics metabolic models: relevance for neurology research, health, and disease. 22(7):455–467, 2018. [DOI].
George C. diCenzo, Alessio Mengoni, and Marco Fondi. Tn-core: a toolbox for integrating tn-seq gene essentiality data and constraint-based metabolic modeling. 8(1):158–169, 2018. [DOI].
David Botero, Iván Valdés, M Antonio Jesús Rodríguez, Diana Henao, Giovanna Danies, Andrés González, and Silvia Restrepo. A genome-scale metabolic reconstruction of phytophthora infestans with the integration of transcriptional data reveals the key metabolic patterns involved in the interaction of its host. 9():244–244, 2018. [DOI].
Takeyuki Tamura. Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: gridprod. 19(1):325–325, 2018. [DOI].
Yazdan Asgari, Pegah Khosravi, Zahra Zabihinpour, and Mahnaz Habibi. Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis. 10(2):113–120, 2018. [DOI].
Selva Rupa Christinal Immanuel, Deepanwita Banerjee, Mayooreshwar P. Rajankar, and Anu Raghunathan. Integrated constraints based analysis of an engineered violacein pathway in escherichia coli. 171():10–19, 2018. [DOI].
José Ramón Valverde, Sonia Gullón, and Rafael P. Mellado. Modelling the metabolism of protein secretion through the tat route in streptomyces lividans. 18(1):59–59, 2018. [DOI].
Jian Li, Renliang Sun, Xinjuan Ning, Xinran Wang, and Zhuo Wang. Genome-scale metabolic model of actinosynnema pretiosum atcc 31280 and its application for ansamitocin p-3 production improvement. 9(7):364–364, 2018. [DOI].
Swagatika Sahoo, R. Ranjith Kumar, Brandon Nicolay, Omkar S. Mohite, Karthikeyan Sivaraman, Vikas Khetan, Pukhraj Rishi, Suganeswari Ganesan, Krishnakumar Subramanyan, Karthik Raman, Wayne Miles, and Sailaja Elchuri. Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma. 593(1):23–41, 2018. [DOI].
Jason J. Flowers, Matthew A. Richards, Nitin S. Baliga, Birte Meyer, and David A. Stahl. Constraint‐based modelling captures the metabolic versatility of desulfovibrio vulgaris. 10(2):190–201, 2018. [DOI].
Kristopher A. Hunt, Ryan M. Jennings, William P. Inskeep, and Ross P. Carlson. Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community. 14(9):e1006431–e1006431, 2018. [DOI].
Ambarish Nag, Peter C. St. John, Michael F. Crowley, and Yannick J. Bomble. Prediction of reaction knockouts to maximize succinate production by actinobacillus succinogenes. 13(1):e0189144–e0189144, 2018. [DOI].
Erick Armingol, Eduardo Tobar-Calfucoy, and Ricardo Cabrera. Understanding the impact of the cofactor swapping of isocitrate dehydrogenase over the growth phenotype of escherichia coli on acetate by using constraint-based modeling. 13(4):e0196182–e0196182, 2018. [DOI].
Sailaja Elchuri, Swetha Rajasekaran, and Wayne Miles. Rna-sequencing of primary retinoblastoma tumors provides new insights and challenges into tumor development. 9():170–170, 2018. [DOI].
Muhammad Azharuddin Arif, Mohd Saberi Mohamad, Muhammad Shafie Abd Latif, Safaai Deris, Muhammad Akmal Remli, Kauthar Mohd Daud, Zuwairie Ibrahim, Sigeru Omatu, and Juan M. Corchado. A hybrid of cuckoo search and minimization of metabolic adjustment to optimize metabolites production in genome-scale models. 102():112–119, 2018. [DOI].
Lokanand Koduru, Meiyappan Lakshmanan, and Dong‐Yup Lee. In silico model-guided identification of transcriptional regulator targets for efficient strain design. 17(1):167–167, 2018. [DOI].
David A. Collins and Marina Kalyuzhnaya. Navigating methane metabolism: enzymes, compartments, and networks. 613():349–383, 2018. [DOI].
Santhust Kumar, Saurabh Mahajan, and Sanjay Jain. Feedbacks from the metabolic network to the genetic network reveal regulatory modules in e. coli and b. subtilis. 13(10):e0203311–e0203311, 2018. [DOI].
Feiran Li, Wei Xie, Qianqian Yuan, Hao Luo, Peishun Li, Tao Chen, Xueming Zhao, Zhiwen Wang, and Hongwu Ma. Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea. 8(1):106–106, 2018. [DOI].
Cheng Zhang, Gholamreza Bidkhori, Rui Benfeitas, Sunjae Lee, Muhammad Arif, Mathias Uhlén, and Adil Mardinoğlu. Ess: a tool for genome-scale quantification of essentiality score for reaction/genes in constraint-based modeling. 9():1355–1355, 2018. [DOI].
Zhendong Zhuang, Mingzhi Huang, and Ju Chu. In silico reconstruction and experimental validation of saccharopolyspora erythraea genome-scale metabolic model izz1342 that accounts for 1685 orfs. 5(1):, 2018. [DOI].
Gaoyang Li, Huansheng Cao, and Ying Xu. Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes. 20(4):1590–1603, 2018. [DOI].
Alex Graudenzi, Davide Maspero, and C. Damiani. Modeling spatio-temporal dynamics of metabolic networks with cellular automata and constraint-based methods. ():16–29, 2018. [DOI].
Do‐Gyun Kim, Sung-Won Seo, Byoung–Kwan Cho, Santosh Lohumi, Soon-Jung Hong, and Wang‐Hee Lee. Review of current approaches for implementing metabolic reconstruction. 43(1):45–58, 2018. [DOI].
Emrah Özcan and Tunahan Çakır. Genome-scale brain metabolic networks as scaffolds for the systems biology of neurodegenerative diseases: mapping metabolic alterations. 21():195–217, 2018. [DOI].
Wei Zou, Xianghua Xiong, Jing Zhang, Kaizheng Zhang, Xingxiu Zhao, and Changqing Zhao. Reconstruction and analysis of a genome-scale metabolic model of methylovorus sp. mp688, a high-level pyrroloquinolone quinone producer. 172():37–42, 2018. [DOI].
Agnieszka B. Wegrzyn, Sarah Stolle, Rienk A. Rienksma, Vítor A. P. Martins dos Santos, Barbara M. Bakker, and María Suárez‐Diez. Cofactors revisited – predicting the impact of flavoprotein-related diseases on a genome scale. 1865(2):360–370, 2018. [DOI].
Ann Marie Reinhold, Geoffrey C. Poole, Clemente Izurieta, Ashley M. Helton, and Emily S. Bernhardt. Constraint-based simulation of multiple interactive elemental cycles in biogeochemical systems. 50():102–121, 2018. [DOI].
Benjamín J. Sánchez, Feiran Li, Eduard J. Kerkhoven, and Jens Nielsen. Slimer: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. ():, 2018. [DOI].
Hadi Nazem‐Bokaee and Costas D. Maranas. A prospective study on the fermentation landscape of gaseous substrates to biorenewables using methanosarcina acetivorans metabolic model. 9():1855–1855, 2018. [DOI].
David Ando and Héctor García Martín. Genome-scale 13c fluxomics modeling for metabolic engineering of saccharomyces cerevisiae. 1859():317–345, 2018. [DOI].
Meghan Thommes, Taiyao Wang, Qi Zhao, Ioannis Ch. Paschalidis, and Daniel Segrè. Designing metabolic division of labor in microbial communities. ():, 2018. [DOI].
Elliot Rowe, Bernhard Ø. Palsson, and Zachary A. King. Escher-fba: a web application for interactive flux balance analysis. ():, 2018. [DOI].
Chiara Damiani, Davide Maspero, Marzia Di Filippo, Riccardo Colombo, Dario Pescini, Alex Graudenzi, Hans V. Westerhoff, Lilia Alberghina, Marco Vanoni, and Giancarlo Mauri. Integration of single-cell rna-seq data into metabolic models to characterize tumour cell populations. ():, 2018. [DOI].
Ilya R. Akberdin, Merlin Thompson, and Marina Kalyuzhnaya. Systems biology and metabolic modeling of c1-metabolism. ():99–115, 2018. [DOI].
Igor Marín de Mas. Multiomic data integration and analysis via model-driven approaches. ():447–476, 2018. [DOI].
Neeraj Sinha, María Suárez‐Diez, Guido Hooiveld, Jaap Keijer, Vitor Martin dos Santos, and Evert M. van Schothorst. A constraint-based model analysis of enterocyte mitochondrial adaptation to dietary interventions of lipid type and lipid load. 9():749–749, 2018. [DOI].
Anna S. Blazier and Jason A. Papin. Reconciling high-throughput gene essentiality data with metabolic network reconstructions. ():, 2018. [DOI].
Nicolas Sompairac, Jennifer Modamio, Emmanuel Barillot, Ronan M. T. Fleming, Andreï Zinovyev, and Inna Kuperstein. Metabolic and signalling network map integration: application to cross-talk studies and omics data analysis in cancer. ():, 2018. [DOI].
Alessio Mancini, Filmon Eyassu, Maxwell Conway, Annalisa Occhipinti, Píetro Lió, Claudio Angione, and Sandra Pucciarelli. Ciliategem: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design. 19(S15):442–442, 2018. [DOI].
Sabina Żołędowska, Luana Presta, Marco Fondi, Francesca Decorosi, Luciana Giovannetti, Alessio Mengoni, and Ewa Łojkowska. Metabolic modeling of pectobacterium parmentieri scc3193 provides insights into metabolic pathways of plant pathogenic bacteria. ():, 2018. [DOI].
Elizabeth Brunk, Roger L. Chang, Jing Xia, Hooman Hefzi, James T. Yurkovich, Donghyuk Kim, Evan Buckmiller, Harris H. Wang, Yang Chen, Bernhard Ø. Palsson, George M. Church, and Nathan E. Lewis. Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments. ():, 2018. [DOI].
Tahereh Ghasemi-Kahrizsangi, Sayed‐Amir Marashi, and Zhaleh Hosseini. Genome-scale metabolic network models of bacillus species suggest that model improvement is necessary for biotechnological applications. 16(3):164–172, 2018. [DOI].
David B. Bernstein, Floyd E. Dewhirst, and Daniel Segrè. Quantifying biosynthetic network robustness across the human oral microbiome. ():, 2018. [DOI].
Cecilia Noecker, Hsuan-Chao Chiu, Colin P McNally, and Elhanan Borenstein. Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. ():, 2018. [DOI].
Tahereh Ghasemi-Kahrizsangi, Sayed‐Amir Marashi, and Zhaleh Hosseini. Genome-scale metabolic network models of bacillus species suggest that model improvement is necessary for biotechnological applications. 16(3):e1684–e1684, 2018. [DOI].
Laura J. Dunphy, P. H. Yen, and Jason A. Papin. Network analysis reveals differential metabolic functionality in antibiotic-resistantpseudomonas aeruginosa. ():, 2018. [DOI].
Meghana V. Palukuri, Shruti Shivakumar, Swagatika Sahoo, and Raghunathan Rengaswamy. Computational framework for exploring the interplay of diet and gut microbiota in autism. ():, 2018. [DOI].
Jeanne M. O. Eloundou-Mbebi, Anika Küken, Georg Basler, and Zoran Nikoloski. Cellular determinants of metabolite concentration ranges. ():, 2018. [DOI].
Agnieszka B. Wegrzyn, Sarah Stolle, Rienk A. Rienksma, Vítor A. P. Martins dos Santos, Barbara M. Bakker, and María Suárez‐Diez. Cofactors revisited – predicting the impact of flavoprotein-related diseases on a genome scale. ():, 2018. [DOI].
Meiyappan Lakshmanan, Lokanand Koduru, and Dong‐Yup Lee. Software applications for phenotype analysis and strain design of cellular systems. ():771–791, 2018. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Jorge M. Arevalillo, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Mariana Díaz‐Almirón, Hilario Navarro, Paloma Maı́n, Enrique Espinosa, Pilar Zamora, and Juan Ángel Fresno Vara. Probabilistic graphical models and computational metabolic models applied to the analysis of metabolomics data in breast cancer. ():370221, 2018. [DOI].
Abinaya Badri, Karthik Raman, and Guhan Jayaraman. Uncovering novel pathways for enhancing hyaluronan synthesis in recombinantlactococcus lactis: genome-scale metabolic modelling and experimental validation. ():, 2018. [DOI].
Kyle A. Stone. Exploring phenotypes of a promising industrial methanotroph: an experimental and computational study. ():, 2018. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Mariana Díaz‐Almirón, Jorge M. Arevalillo, María Ferrer-Gómez, Hilario Navarro, Paloma Maı́n, Enrique Espinosa, Álvaro Pinto, and Juan Ángel Fresno Vara. Novel molecular classification of muscle-invasive bladder cancer opens new treatment opportunities. ():, 2018. [DOI].
Sergio Garcia and Cong T. Trinh. Multiobjective strain design: a framework for modular cell engineering. ():, 2018. [DOI].
James P. Gilbert, Nicole Pearcy, Rupert Norman, Thomas Millat, Klaus Winzer, John R. King, Charlie Hodgman, Nigel P. Minton, and Jamie Twycross. Gsmodutils: a python based framework for test-driven genome scale metabolic model development. ():, 2018. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Mariana Díaz‐Almirón, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Rocío López‐Vacas, Paolo Nanni, Pilar Zamora, Enrique Espinosa, and Juan Ángel Fresno Vara. Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients. ():, 2018. [DOI].
Tuo Zang. Quantitative characterization of paediatric burn blister fluid. ():, 2018. [DOI].
Clémence Frioux, Torsten Schaub, Sebastian Schellhorn, Anne Siegel, and Philipp Wanko. Hybrid metabolic network completion. ():, 2018. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Elena López‐Camacho, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Rocío López‐Vacas, Jorge M. Arevalillo, Mariana Díaz‐Almirón, Hilario Navarro, Paloma Maı́n, Enrique Espinosa, Pilar Zamora, and Juan Ángel Fresno Vara. Computational metabolomics hints at the relevance of glutamine metabolism in breast cancer. ():, 2018. [DOI].
Villayat Ali, Priyanka Singh, Sonu Kumar Gupta, Yogesh Mauley, and Malkhey Verma. Metabolic flux analysis in plant metabolic network. ():, 2018. [DOI].
Annalisa Occhipinti, Filmon Eyassu, Thahira Rahman, Shafiqur Rahman, Claudio Corresp, Claudio Angione, Pattanathu Rahman, A Mohammad, F Lpine, E Dziel, H Amani, C Angione, M Conway, P Li, C Angione, P Li, C Angione, C Angione, N Pratanwanich, P Li, C Angione, M Aurich, Rmt Fleming, I Thiele, P Babaei, T Ghasemi-Kahrizsangi, S Marashi, F Bahia, G Almeida, L Andrade, C Campos, L Queiroz, . Silva R Lv, N Parachin, Jennifer Bartell, John Phillip Yen, Joanna Varga, Jason Goldberg, J Beuker, A Steier, A Wittgens, F Rosenau, M Henkel, R Hausmann, Emeline Bouffartigues, Gwendoline Gicquel, Alexis Bazire, Laurne Fito-Boncompte, Laure Taupin, E Brunk, K George, J Alonso-Gutierrez, M Thompson, E Baidoo, G Wang, C Petzold, D Mccloskey, J Monk, L Yang, E O'brien, N Cabrera-Valladares, A-P Richardson, C Olvera, L Trevio, E Dziel, F Lpine, G Sobern-Chvez, R Caspi, H Foerster, C Fulcher, P Kaipa, M Krummenacker, M Latendresse, S Paley, S Rhee, A Shearer, C Tissier, T Walk, M Cha, N Lee, M Kim, M Kim, S Lee, S Chandrasekaran, Na Price, Q Chen, M Bao, X Fan, S Liang, P Sun, Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond Lun, Brian Weiner, Maha Farhat, Tan Yun Cheng, and D Moody. Peer review #1 of "in silico engineering of pseudomonas metabolism reveals new biomarkers for increased biosurfactant production (v0.2)". ():, 2018. [DOI].
Anonymous. Peer review #2 of "in silico engineering of pseudomonas metabolism reveals new biomarkers for increased biosurfactant production (v0.1)". ():, 2018. [DOI].
Jean‐Marc Schwartz and Zita Soons. Metabolic models. ():438–444, 2018. [DOI].
Anonymous. Peer review #1 of "in silico engineering of pseudomonas metabolism reveals new biomarkers for increased biosurfactant production (v0.1)". ():, 2018. [DOI].
Elizabeth Brunk, Swagatika Sahoo, Daniel C. Zielinski, Ali Altunkaya, Andreas Dräger, Nathan Mih, Francesco Gatto, Avlant Nilsson, German Preciat, Maike K. Aurich, Andreas Prlić, Anand V. Sastry, Anna Dröfn Daníelsdóttir, Almut Heinken, Alberto Noronha, Peter W. Rose, S.K. Burley, Ronan M. T. Fleming, Jens Nielsen, Ines Thiele, and Bernhard Ø. Palsson. Recon3d enables a three-dimensional view of gene variation in human metabolism. 36(3):272–281, 2018. [DOI].
Alberto Noronha, Jennifer Modamio, Yohan Jarosz, Elisabeth Guerard, Nicolas Sompairac, German Preciat, Anna Dröfn Daníelsdóttir, Max Krecké, Diane Merten, Hulda S. Haraldsdóttir, Almut Heinken, Laurent Heirendt, Stefanía Magnúsdóttir, Dmitry A. Ravcheev, Swagatika Sahoo, Piotr Gawron, Lucia Friscioni, Beatriz García Santa Cruz, Mabel Prendergast, Alberto Puente, Mariana Figueiredo Rodrigues, Akansha Roy, Mouss Rouquaya, Luca Wiltgen, Alise Žagare, Elisabeth John, Maren Krueger, Inna Kuperstein, Andreï Zinovyev, Reinhard Schneider, Ronan M. T. Fleming, and Ines Thiele. The virtual metabolic human database: integrating human and gut microbiome metabolism with nutrition and disease. 47(D1):D614–D624, 2018. [DOI].
Federico Baldini, Almut Heinken, Laurent Heirendt, Stefanía Magnúsdóttir, Ronan M. T. Fleming, and Ines Thiele. The microbiome modeling toolbox: from microbial interactions to personalized microbial communities. 35(13):2332–2334, 2018. [DOI].
Eugen Bauer and Ines Thiele. From network analysis to functional metabolic modeling of the human gut microbiota. 3(3):, 2018. [DOI].
Marek Ostaszewski, Stephan Gebel, Inna Kuperstein, Alexander Mazein, Andreï Zinovyev, Uğur Doğrusöz, Jan Hasenauer, Ronan M. T. Fleming, Nicolas Le Novère, Piotr Gawron, Thomas Ligon, Anna Niarakis, David Nickerson, Daniel Weindl, Rudi Balling, Emmanuel Barillot, Charles Auffray, and Reinhard Schneider. Community-driven roadmap for integrated disease maps. 20(2):659–670, 2018. [DOI].
Michaela Graf, Julia Zieringer, Thorsten Haas, Alexander Nieß, Bastian Blombach, and Ralf Takors. Physiological response of corynebacterium glutamicum to increasingly nutrient-rich growth conditions. 9():2058–2058, 2018. [DOI].
Van Du T. Tran, Sébastien Moretti, Alix T. Coste, Sara Amorim-Vaz, Dominique Sanglard, and Marco Pagni. Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis. 35(13):2258–2266, 2018. [DOI].
James T. Yurkovich, Miguel A. Alcantar, Zachary B. Haiman, and Bernhard Ø. Palsson. Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials. 14(8):e1006356–e1006356, 2018. [DOI].

Jennifer Bartell, Anna S. Blazier, P. H. Yen, Juliane Charlotte Thøgersen, Lars Jelsbak, Joanna B. Goldberg, and Jason A. Papin. Reconstruction of the metabolic network of pseudomonas aeruginosa to interrogate virulence factor synthesis. 8(1):14631–14631, 2017. [DOI].
Ali R. Zomorrodi and Daniel Segrè. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. 8(1):1563–1563, 2017. [DOI].
Xiao Pan, Ciska Dalm, René H. Wijffels, and Dirk E. Martens. Metabolic characterization of a cho cell size increase phase in fed-batch cultures. 101(22):8101–8113, 2017. [DOI].
Farhana R. Pinu and Silas G. Villas‐Bôas. Extracellular microbial metabolomics: the state of the art. 7(3):43–43, 2017. [DOI].
D. Joseph Sexton and Martín Schuster. Nutrient limitation determines the fitness of cheaters in bacterial siderophore cooperation. 8(1):230–230, 2017. [DOI].
Troy E. Sandberg, Colton J. Lloyd, Bernhard Ø. Palsson, and Adam M. Feist. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. 83(13):, 2017. [DOI].
Yu Zhang, Jingyi Cai, Xiuling Shang, Bo Wang, Shuwen Liu, Xin Chai, Tianwei Tan, Yun Zhang, and Tingyi Wen. A new genome-scale metabolic model of corynebacterium glutamicum and its application. 10(1):169–169, 2017. [DOI].
Christopher P. Long, Jacqueline E. Gonzalez, Robert M. Cipolla, and Maciek R. Antoniewicz. Metabolism of the fast-growing bacterium vibrio natriegens elucidated by 13c metabolic flux analysis. 44():191–197, 2017. [DOI].
Andreas Wagner. Information theory, evolutionary innovations and evolvability. 372(1735):20160416–20160416, 2017. [DOI].
Francisco Saitua, Paulina Torres, José Ricardo Pérez‐Correa, and Eduardo Agosín. Dynamic genome-scale metabolic modeling of the yeast pichia pastoris. 11(1):27–27, 2017. [DOI].
Nicolás Loira, Sebastián N. Mendoza, María Paz Cortés, Natalia Palacios‐Rojas, Dante Travisany, Alex Di Genova, Natalia Gajardo, Nicole Ehrenfeld, and Alejandro Maass. Reconstruction of the microalga nannochloropsis salina genome-scale metabolic model with applications to lipid production. 11(1):66–66, 2017. [DOI].
Homa Majd, Martin King, Anthony C. Smith, and Edmund R.S. Kunji. Pathogenic mutations of the human mitochondrial citrate carrier slc25a1 lead to impaired citrate export required for lipid, dolichol, ubiquinone and sterol synthesis. 1859(1):1–7, 2017. [DOI].
Ab Rauf Shah, Ahmad Ahmad, Shireesh Srivastava, and B. M. Jaffar Ali. Reconstruction and analysis of a genome-scale metabolic model of nannochloropsis gaditana. 26():354–364, 2017. [DOI].
Maureen A. Carey, Jason A. Papin, and Jennifer L. Güler. Novel plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance. 18(1):543–543, 2017. [DOI].
Mahesh C. Sharma, Naeem Shaikh, Shailendra Yadav, Sushma Singh, and Prabha Garg. A systematic reconstruction and constraint-based analysis of leishmania donovani metabolic network: identification of potential antileishmanial drug targets. 13(5):955–969, 2017. [DOI].
R. Adam Thompson and Cong T. Trinh. Overflow metabolism and growth cessation in clostridium thermocellum dsm1313 during high cellulose loading fermentations. 114(11):2592–2604, 2017. [DOI].
Lauren T. Cordova, Robert M. Cipolla, Adti Swarup, Christopher P. Long, and Maciek R. Antoniewicz. 13c metabolic flux analysis of three divergent extremely thermophilic bacteria: geobacillus sp. lc300, thermus thermophilus hb8, and rhodothermus marinus dsm 4252. 44():182–190, 2017. [DOI].
M. Ahsanul Islam, Noushin Hadadi, Meriç Ataman, Vassily Hatzimanikatis, and Gregory Stephanopoulos. Exploring biochemical pathways for mono-ethylene glycol (meg) synthesis from synthesis gas. 41():173–181, 2017. [DOI].
ShengShee Thor, J.R. Peterson, and Zaida Luthey‐Schulten. Genome-scale metabolic modeling of archaea lends insight into diversity of metabolic function. 2017():1–18, 2017. [DOI].
Jennifer L. Greene, Andreas Wäechter, Keith E. J. Tyo, and Linda J. Broadbelt. Acceleration strategies to enhance metabolic ensemble modeling performance. 113(5):1150–1162, 2017. [DOI].
Živa Bricman Rejc, Lidija Magdevska, Tilen Tršelič, Timotej Osolin, Rok Vodopivec, Jakob Mraz, Eva Pavliha, Nikolaj Zimic, Tanja Cvitanović, Damjana Rozman, Miha Moškon, and Miha Mraz. Computational modelling of genome-scale metabolic networks and its application to cho cell cultures. 88():150–160, 2017. [DOI].
Jorge Fernández-de-Cossio-Díaz, Kalet León, and Roberto Mulet. Characterizing steady states of genome-scale metabolic networks in continuous cell cultures. 13(11):e1005835–e1005835, 2017. [DOI].
Deepanwita Banerjee, Dharmeshkumar Parmar, Nivedita Bhattacharya, Avinash Ghanate, Venkateswarlu Panchagnula, and Anu Raghunathan. A scalable metabolite supplementation strategy against antibiotic resistant pathogen chromobacterium violaceum induced by nad+/nadh+ imbalance. 11(1):51–51, 2017. [DOI].
Garrett W. Birkel, Amit Ghosh, Vinay Kumar, Daniel Weaver, David Ando, Tyler W. H. Backman, Adam P. Arkin, Jay D. Keasling, and Héctor García Martín. The jbei quantitative metabolic modeling library (jqmm): a python library for modeling microbial metabolism. 18(1):219–219, 2017. [DOI].
András Hartmann, Ana Vila-Santa, Nicolai Kallscheuer, Michael Vogt, Alice Julien-Laferrière, Marie-France Sagot, Jan Marienhagen, and Susana Vinga. Optpipe - a pipeline for optimizing metabolic engineering targets. 11(1):143–143, 2017. [DOI].
Michael A. Rowland, Ahmed Abdelzaher, Preetam Ghosh, and Michael L. Mayo. Crosstalk and the dynamical modularity of feed-forward loops in transcriptional regulatory networks. 112(8):1539–1550, 2017. [DOI].
A. J. Mueller, Mandy J. Peffers, Carole J. Proctor, and Peter Clegg. Systems approaches in osteoarthritis: identifying routes to novel diagnostic and therapeutic strategies. 35(8):1573–1588, 2017. [DOI].
Jose A. Gomez and Paul I. Barton. Dynamic flux balance analysis using dfbalab. 1716():353–370, 2017. [DOI].
Maryam Iman, Tabassom Sobati, Yunes Panahi, and Meysam Mobasheri. Systems biology approach to bioremediation of nitroaromatics: constraint-based analysis of 2,4,6-trinitrotoluene biotransformation by escherichia coli. 22(8):1242–1242, 2017. [DOI].
Hamideh Fouladiha, Sayed‐Amir Marashi, Mohammad Ali Shokrgozar, Mehdi Farokhi, and Amir Atashi. Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation. 70(1):331–338, 2017. [DOI].
Stephen P. Chapman, Marcelo Trindade dos Santos, Giles N. Johnson, Maurício Vieira Kritz, and Jean‐Marc Schwartz. Cyclic decomposition explains a photosynthetic down regulation for chlamydomonas reinhardtii. 162():119–127, 2017. [DOI].
Yvonne van der Meer and Yvonne van der Meer. Sustainable bio-based materials: opportunities and challenges. ():2–6, 2017. [DOI].
Paul A. Jensen. Coupling fluxes, enzymes, and regulation in genome-scale metabolic models. 1716():337–351, 2017. [DOI].
Sarah Schatschneider, Jessica Schneider, Jochen Blom, Fabien Létisse, Karsten Niehaus, Alexander Goesmann, and Frank-Jörg Vorhölter. Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium xanthomonas campestris. 163(8):1117–1144, 2017. [DOI].
Clémence Frioux, Torsten Schaub, Sebastian Schellhorn, Anne Siegel, and Philipp Wanko. Hybrid metabolic network completion. ():308–321, 2017. [DOI].
Níels Árni Árnason and Ólafur E. Sigurjónsson. New strategies to understand platelet storage lesion. 12(4):496–500, 2017. [DOI].
Karthik Raman, Aditya Pratapa, Omkar S. Mohite, and Shankar Balachandran. Computational prediction of synthetic lethals in genome-scale metabolic models using fast-sl. 1716():315–336, 2017. [DOI].
Ali R. Zomorrodi and Daniel Segrè. Intracellular metabolic circuits shape inter-species microbial interactions. ():, 2017. [DOI].
Paulo Vilaça, Paulo Maia, Hugo Giesteira, Isabel Rocha, and Miguel Rocha. Analyzing and designing cell factories with optflux. 1716():37–76, 2017. [DOI].
Abraham Alemayehu Labena, Yuan‐Nong Ye, Chuan Dong, Fa-Z Zhang, and Feng‐Biao Guo. Sser: species specific essential reactions database. 11(1):50–50, 2017. [DOI].
Nick Fyson, Min Kyung Kim, Desmond S. Lun, and Caroline Colijn. Gene-centric constraint of metabolic models. ():, 2017. [DOI].
Daniel Osorio, Janneth González, and Andrés Pinzón. Minval: an r package for minimal validation of stoichiometric reactions. 9(1):114–114, 2017. [DOI].
Maureen A. Carey, Jason A. Papin, and Jennifer L. Güler. Novelplasmodium falciparummetabolic network reconstruction identifies shifts associated with clinical antimalarial resistance. ():, 2017. [DOI].
Paul D. Dobson, Pedro Mendes, Douglas B. Kell, and Neil Swainston. A metabolic reaction balancing web service for computational systems biology. ():, 2017. [DOI].
Kalyani Korla and Nagasuma Chandra. A systems perspective of signalling networks in host–pathogen interactions. 97(1):41–57, 2017. [DOI].
Elisa de Medeiros and Elisa de Medeiros. Conceptual design of a self-sufficient hybrid biorefinery for syngas production and fermentation to ethanol. ():25–31, 2017. [DOI].
Aravind Madhavan, Raveendran Sindhu, K. B. Arun, Ashok Pandey, and Parameswaran Binod. Advances and tools in engineering yeast for pharmaceutical production. ():29–49, 2017. [DOI].
Janaka N. Edirisinghe, José P. Faria, Nomi L. Harris, Benjamin Allen, and Christopher S. Henry. Reconstruction and analysis of central metabolism in microbes. 1716():111–129, 2017. [DOI].
Yvonne van der Meer, Ali R. Mulakhudair, William Zimmerman, Parkia Timoriana, Maxim Muravyov, Н. В. Фомченко, Elisa De Medeiros, John A. Posada, Henk Noorman, Patrícia Osseweijer, Rubens Maciel Filho, Rubens Filho, J Langeveld, J Dixon, J Jaworski, M Weiss, J Haufe, M Carus, M Brando, S Bringezu, B Hermann, M Patel, P Pawelzik, M Carus, J Hotchkiss, R Narayan, S Selke, M Wellisch, M Weiss, B Wicke, M Patel, M Van Dael, N Mrquez, P Reumerman, L Pelkmans, T Kuppens, S Van Passel, A Souza, M Watanabe, O Cavalett, J.-S Seo, A Flamholz, E Noor, A Bar-Even, W Liebermeister, R Milo, P Rogers, K Lee, D Tribe, K Deanda, M Zhang, C Eddy, S Picataggio, U Kalnenieks, N Galinina, M Toma, R Poole, G Miller, A Humphrey, H Ishikawa, H Nobayashi, H Tanaka, S Bringer, R Finn, H Sahm, M Bredwell, R Worden, M Al-Mashhadani, H Bandulasena, W Zimmerman, J Swings, J Ley, P Rogers, K Lee, M Skotnicki, D Tribe, G Sprenger, G Cook, J Russell, A Brugarolas, M Gosalvez, B Correa, B Blanco, M Delrio, V Victor, N Guayerbas, S Medina, De La Fluente, M Cuendet, K Hostettmann, O Potterat, Kja Davies, M Delsingore, S Lin, L Dezwart, Jhn Meerman, Jnm Commandeur, and Npe Vermenlen. Biotech france 2017 conference proceedings. ():, 2017. [DOI].
Do‐Gyun Kim, Wang‐Hee Lee, Sung-Won Seo, and Hye‐Sun Park. Metabolic reconstruction for developing a tissue-specific model for cattle glycolysis using cobra. 25(02):231–246, 2017. [DOI].
Juan Camilo Roncallo Sarmiento. Avaliação da expressão de genes da via de weimberg sobre o catabolismo de xilose na linhagem produtora de phamcl pseudomonas sp. lfm046. ():, 2017. [DOI].
Ruben Heck. Metabolic modeling to understand and redesign microbial systems. ():, 2017. [DOI].
G. J. Sharma and Gurumayum Jitendra Sharma. Antioxidant and cytotoxic properties of non-conventional food plant, parkia timoriana (dc.) merr. ():15–21, 2017. [DOI].
Ali R. Mulakhudair and Ali R. Mulakhudair. Aerobic fermentation performance of zymomonas mobilis mediated by microbubbles generated by fluidic oscillation. ():7–14, 2017. [DOI].
Maxim Muravyov and Maxim Muravyov. A two-step process for improving of refractory sulfide concentrate biooxidation. ():22–24, 2017. [DOI].
Maria Siurana Paula. Modelling and multiobjective optimization for simulation of cyanobacterial metabolism. ():, 2017. [DOI].
Nathan E. Lewis, Neema Jamshidi, Ines Thiele, and Bernhard Ø. Palsson. Metabolic systems biology. ():1–23, 2017. [DOI].
Nicholas A. Jose, Rebecca Lau, Tami L. Swenson, Niels Klitgord, Ferrán García‐Pichel, Benjamin P. Bowen, Richard Baran, and Trent R. Northen. Flux balance modelling to predict bacterial survival during pulsed activity events. ():, 2017. [DOI].
Jonathan M. Monk, Colton J. Lloyd, Elizabeth Brunk, Nathan Mih, Anand V. Sastry, Zachary A. King, Rikiya Takeuchi, Wataru Nomura, Zhen Zhang, Hirotada Mori, Adam M. Feist, and Bernhard Ø. Palsson. Iml1515, a knowledgebase that computes escherichia coli traits. 35(10):904–908, 2017. [DOI].
Benjamín J. Sánchez, Cheng Zhang, Avlant Nilsson, Petri‐Jaan Lahtvee, Eduard J. Kerkhoven, and Jens Nielsen. Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints. 13(8):935–935, 2017. [DOI].
Pavel Dvořák, Pablo I. Nikel, Jiřı́ Damborský, and Vı́ctor de Lorenzo. Bioremediation 3.0: engineering pollutant-removing bacteria in the times of systemic biology. 35(7):845–866, 2017. [DOI].
Sebastian Weis, Ana Rita Carlos, Maria Raquel Moita, Sumnima Singh, Birte Blankenhaus, Sílvia Cardoso, Rasmus Larsen, Sofia Rebelo, Sascha Schäuble, Laura del Barrio, Gilles Mithieux, Fabienne Rajas, Sandro Lindig, Michael Bauer, and Miguel P. Soares. Metabolic adaptation establishes disease tolerance to sepsis. 169(7):1263–1275.e14, 2017. [DOI].
Sjoerd Opdam, Anne Richelle, Benjamin P. Kellman, Shanzhong Li, Daniel C. Zielinski, and Nathan E. Lewis. A systematic evaluation of methods for tailoring genome-scale metabolic models. 4(3):318–329.e6, 2017. [DOI].
Noora Ottman, Mark Davids, María Suárez‐Diez, Sjef Boeren, Peter J. Schaap, Vítor A. P. Martins dos Santos, Hauke Smidt, Clara Belzer, and Willem M. de Vos. Genome-scale model and omics analysis of metabolic capacities of akkermansia muciniphila reveal a preferential mucin-degrading lifestyle. 83(18):, 2017. [DOI].
Petri‐Jaan Lahtvee, Benjamín J. Sánchez, Agata Smialowska, Sergo Kasvandik, Ibrahim E. Elsemman, Francesco Gatto, and Jens Nielsen. Absolute quantification of protein and mrna abundances demonstrate variability in gene-specific translation efficiency in yeast. 4(5):495–504.e5, 2017. [DOI].
Ran Chao, Shekhar Mishra, Tong Si, and Huimin Zhao. Engineering biological systems using automated biofoundries. 42():98–108, 2017. [DOI].
Xiulai Chen, Cong Gao, Liang Guo, Guipeng Hu, Qiuling Luo, Jia Liu, Jens Nielsen, Jian Chen, and Li Liu. Dceo biotechnology: tools to design, construct, evaluate, and optimize the metabolic pathway for biosynthesis of chemicals. 118(1):4–72, 2017. [DOI].
Kaspar Valgepea, Renato de Souza Pinto Lemgruber, Kieran Meaghan, Robin Palfreyman, Tanus Abdalla, Björn D. Heijstra, James B. Y. H. Behrendorff, Ryan Tappel, Michael Köpke, Séan D. Simpson, Lars K. Nielsen, and Esteban Marcellin. Maintenance of atp homeostasis triggers metabolic shifts in gas-fermenting acetogens. 4(5):505–515.e5, 2017. [DOI].
Jan-Philipp Schwarzhans, Tobias Luttermann, Martina Geier, Jörn Kalinowski, and Karl Friehs. Towards systems metabolic engineering in pichia pastoris. 35(6):681–710, 2017. [DOI].
Cristal Zúñiga, Lívia S. Zaramela, and Karsten Zengler. Elucidation of complexity and prediction of interactions in microbial communities. 10(6):1500–1522, 2017. [DOI].
Aarash Bordbar, James T. Yurkovich, Giuseppe Paglia, Óttar Rolfsson, Ólafur E. Sigurjónsson, and Bernhard Ø. Palsson. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. 7(1):46249–46249, 2017. [DOI].
Kaspar Valgepea, Quang K. Loi, James B. Y. H. Behrendorff, Renato de Souza Pinto Lemgruber, Manuel R. Plan, Mark P. Hodson, Michael Köpke, Lars K. Nielsen, and Esteban Marcellin. Arginine deiminase pathway provides atp and boosts growth of the gas-fermenting acetogen clostridium autoethanogenum. 41():202–211, 2017. [DOI].
Siu Hung Joshua Chan, Jingyi Cai, Lin Wang, Margaret Simons, and Costas D. Maranas. Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models. 33(22):3603–3609, 2017. [DOI].
Sami Ben Said and Dani Or. Synthetic microbial ecology: engineering habitats for modular consortia. 8():1125–1125, 2017. [DOI].
Milan J. A. Hoek and Roeland M. H. Merks. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. 11(1):56–56, 2017. [DOI].
Alyaa M. Abdel‐Haleem, Nathan E. Lewis, Neema Jamshidi, Katsuhiko Mineta, Xin Gao, and Takashi Gojobori. The emerging facets of non-cancerous warburg effect. 8():279–279, 2017. [DOI].
Nicholas S. Kruyer and Pamela Peralta‐Yahya. Metabolic engineering strategies to bio-adipic acid production. 45():136–143, 2017. [DOI].
Faraaz N.K. Yusufi, Meiyappan Lakshmanan, Ying Swan Ho, Bernard Liat Wen Loo, Pramila Ariyaratne, Yuansheng Yang, Say Kong Ng, Tessa Rui Min Tan, Hock Chuan Yeo, Hsueh Lee Lim, Sze Wai Ng, Ai Ping Hiu, Chung Ping Chow, Corrine Wan, Shuwen Chen, Gavin Teo, Song Gao, Ju Xin Chin, Xiaoan Ruan, Wing‐Kin Sung, Wei‐Shou Hu, Miranda G.S. Yap, Muriel Bardor, Niranjan Nagarajan, and Dong‐Yup Lee. Mammalian systems biotechnology reveals global cellular adaptations in a recombinant cho cell line. 4(5):530–542.e6, 2017. [DOI].
C. Damiani, Riccardo Colombo, Daniela Gaglio, Fabrizia Mastroianni, Dario Pescini, Hans V. Westerhoff, Giancarlo Mauri, Marco Vanoni, and Lilia Alberghina. A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: the warburq effect. 13(9):e1005758–e1005758, 2017. [DOI].
Santosh Pandit, Vaishnavi Ravikumar, Alyaa M. Abdel‐Haleem, Abderahmane Derouiche, V. R. S. S. Mokkapati, Carina Sihlbom, Katsuhiko Mineta, Takashi Gojobori, Xin Gao, Fredrik Westerlund, and Ivan Mijakovic̀. Low concentrations of vitamin c reduce the synthesis of extracellular polymers and destabilize bacterial biofilms. 8():2599–2599, 2017. [DOI].
Màrius Tomàs‐Gamisans, Pau Ferrer, and Joan Albiol. Fine‐tuning the p. pastoris imt1026 genome‐scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources. 11(1):224–237, 2017. [DOI].
Hulda S. Haraldsdóttir, Ben Cousins, Ines Thiele, Ronan M. T. Fleming, and Santosh Vempala. Chrr: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. 33(11):1741–1743, 2017. [DOI].
Axel von Kamp, Sven Thiele, Oliver Hädicke, and Steffen Klamt. Use of cellnetanalyzer in biotechnology and metabolic engineering. 261():221–228, 2017. [DOI].
Evan Appleton, Curtis Madsen, Nicholas Roehner, and Douglas Densmore. Design automation in synthetic biology. 9(4):a023978–a023978, 2017. [DOI].
Christopher P. Long, Jacqueline E. Gonzalez, Adam M. Feist, Bernhard Ø. Palsson, and Maciek R. Antoniewicz. Fast growth phenotype of e. coli k-12 from adaptive laboratory evolution does not require intracellular flux rewiring. 44():100–107, 2017. [DOI].
Aleksandr Demidenko, Ilya R. Akberdin, Marco N. Allemann, Eric E. Allen, and Marina Kalyuzhnaya. Fatty acid biosynthesis pathways in methylomicrobium buryatense 5g(b1). 7():2167–2167, 2017. [DOI].
Thomas J. J. Mueller, Justin Ungerer, Himadri B. Pakrasi, and Costas D. Maranas. Identifying the metabolic differences of a fast-growth phenotype in synechococcus utex 2973. 7(1):41569–41569, 2017. [DOI].
Agris Pentjuss, Egils Stalidzāns, Jānis Liepiņš, Agnese Kokina, Jekaterina Martynova, Pēteris Zikmanis, Ivars Mozga, Rita Scherbaka, Hassan Hartman, Mark G. Poolman, David A. Fell, and Armands Vīgants. Model-based biotechnological potential analysis of kluyveromyces marxianus central metabolism. 44(8):1177–1190, 2017. [DOI].
Luana Presta, Emanuele Bosi, Leila Mansouri, Lenie Dijkshoorn, Renato Fani, and Marco Fondi. Constraint-based modeling identifies new putative targets to fight colistin-resistant a. baumannii infections. 7(1):3706–3706, 2017. [DOI].
Octavio Perez‐Garcia, Cody Mankelow, Kartik Chandran, Silas G. Villas‐Bôas, and Naresh Singhal. Modulation of nitrous oxide (n2o) accumulation by primary metabolites in denitrifying cultures adapting to changes in environmental c and n. 51(23):13678–13688, 2017. [DOI].
Nana Y. D. Ankrah, Jun‐Bo Luan, and Angela E. Douglas. Cooperative metabolism in a three-partner insect-bacterial symbiosis revealed by metabolic modeling. 199(15):, 2017. [DOI].
Govindprasad Bhutada, Martin Kavšček, Rodrigo Ledesma‐Amaro, Stéphane Thomas, Gerald N. Rechberger, Jean‐Marc Nicaud, and Klaus Natter. Sugar versus fat: elimination of glycogen storage improves lipid accumulation in yarrowia lipolytica. 17(3):, 2017. [DOI].
Cristal Zúñiga, Jennifer Levering, Maciek R. Antoniewicz, Michael T. Guarnieri, Michael J. Betenbaugh, and Karsten Zengler. Predicting dynamic metabolic demands in the photosynthetic eukaryote chlorella vulgaris. 176(1):450–462, 2017. [DOI].
Yougan Cheng, Craig J. Thalhauser, Shepard Smithline, Jyotsna Pagidala, Marko Miladinov, Heather Vezina, Manish Gupta, Tarek A. Leil, and Brian J. Schmidt. Qsp toolbox: computational implementation of integrated workflow components for deploying multi-scale mechanistic models. 19(4):1002–1016, 2017. [DOI].
Ran Yu, Octavio Perez‐Garcia, Huijie Lü, and Kartik Chandran. Nitrosomonas europaea adaptation to anoxic-oxic cycling: insights from transcription analysis, proteomics and metabolic network modeling. 615():1566–1573, 2017. [DOI].
Angelo Gámez‐Pozo, Lucía Trilla‐Fuertes, Julia Berges‐Soria, Nathalie Selevsek, Rocío López‐Vacas, Mariana Díaz‐Almirón, Paolo Nanni, Jorge M. Arevalillo, Hilario Navarro, Jonas Grossmann, Francisco Gayá, Rubén Gómez Rioja, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Paloma Maı́n, Jaime Feliú, Purificación Martínez del Prado, Pilar Zamora, Eva Ciruelos, Enrique Espinosa, and Juan Ángel Fresno Vara. Functional proteomics outlines the complexity of breast cancer molecular subtypes. 7(1):10100–10100, 2017. [DOI].
William Morrell, Garrett W. Birkel, Mark Forrer, Teresa Díaz-Faes López, Tyler W. H. Backman, Michael Dussault, Christopher J. Petzold, Edward E. K. Baidoo, Zak Costello, David Ando, Jorge Alonso-Gutiérrez, Kevin W. George, Aindrila Mukhopadhyay, Ian Vaino, Jay D. Keasling, Paul D. Adams, Nathan J. Hillson, and Héctor García Martín. The experiment data depot: a web-based software tool for biological experimental data storage, sharing, and visualization. 6(12):2248–2259, 2017. [DOI].
Qianqian Yuan, Teng Huang, Peishun Li, Tong Hao, Feiran Li, Hongwu Ma, Zhiwen Wang, Xueming Zhao, Tao Chen, and Igor Goryanin. Pathway-consensus approach to metabolic network reconstruction for pseudomonas putida kt2440 by systematic comparison of published models. 12(1):e0169437–e0169437, 2017. [DOI].
Kiyotaka Y. Hara, Jyumpei Kobayashi, Ryosuke Yamada, Daisuke Sasaki, Yuki Kuriya, Yoko Hirono-Hara, Jun Ishii, Michihiro Araki, and Akihiko Kondo. Transporter engineering in biomass utilization by yeast. 17(7):, 2017. [DOI].
Lokanand Koduru, Yu‐Jin Kim, Jeongsu Bang, Meiyappan Lakshmanan, Nam Soo Han, and Dong‐Yup Lee. Genome-scale modeling and transcriptome analysis of leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria. 7(1):15721–15721, 2017. [DOI].
Zhuo Wang, Samuel A. Danziger, Ben Heavner, Shuyi Ma, Jennifer J. Smith, Li Song, Thurston Herricks, Evangelos Simeonidis, Nitin S. Baliga, John D. Aitchison, and Nathan D. Price. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. 13(5):e1005489–e1005489, 2017. [DOI].
Anthony C. Smith, Filmon Eyassu, Jean‐Pierre Mazat, and Alan J. Robinson. Mitocore: a curated constraint-based model for simulating human central metabolism. 11(1):114–114, 2017. [DOI].
Arvand Asghari, Sayed‐Amir Marashi, and Naser Ansari‐Pour. A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia. 63(2):100–112, 2017. [DOI].
Yasutaka Hirokawa, Shingo Matsuo, Hiroyuki Hamada, Fumio Matsuda, and Taizo Hanai. Metabolic engineering of synechococcus elongatus pcc 7942 for improvement of 1,3-propanediol and glycerol production based on in silico simulation of metabolic flux distribution. 16(1):212–212, 2017. [DOI].
Songsong Wei, Xingxing Jian, Jun Chen, Cheng Zhang, and Qiang Hua. Reconstruction of genome-scale metabolic model of yarrowia lipolytica and its application in overproduction of triacylglycerol. 4(1):, 2017. [DOI].
Ding Ma, Laurence Yang, Ronan M. T. Fleming, Ines Thiele, Bernhard Ø. Palsson, and Michael A. Saunders. Reliable and efficient solution of genome-scale models of metabolism and macromolecular expression. 7(1):40863–40863, 2017. [DOI].
Jennifer Levering, Christopher L. Dupont, Andrew E. Allen, Bernhard Ø. Palsson, and Karsten Zengler. Integrated regulatory and metabolic networks of the marine diatom phaeodactylum tricornutum predict the response to rising co 2 levels. 2(1):, 2017. [DOI].
German Preciat, Lemmer R. P. El Assal, Alberto Noronha, Ines Thiele, Hulda S. Haraldsdóttir, and Ronan M. T. Fleming. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to recon 3d. 9(1):39–39, 2017. [DOI].
Sander Y.A. Rodenburg, Michael Seidl, Dick de Ridder, and Francine Govers. Genome‐wide characterization of phytophthora infestans metabolism: a systems biology approach. 19(6):1403–1413, 2017. [DOI].
Govindprasad Bhutada, Martin Kavšček, Rodrigo Ledesma‐Amaro, Stéphane Thomas, Gerald N. Rechberger, Jean‐Marc Nicaud, and Klaus Natter. Sugar versus fat: elimination of glycogen storage improves lipid accumulation inyarrowia lipolytica. 17(3):, 2017. [DOI].
Junhua Wang, Cheng Wang, Kejing Song, and Jianping Wen. Metabolic network model guided engineering ethylmalonyl-coa pathway to improve ascomycin production in streptomyces hygroscopicus var. ascomyceticus. 16(1):169–169, 2017. [DOI].
Juan Nogales, Steinn Guðmundsson, Estrella Duque, Juan L. Ramos, and Bernhard Ø. Palsson. Expanding the computable reactome inpseudomonas putidareveals metabolic cycles providing robustness. ():, 2017. [DOI].
Ove Øyås and Jörg Stelling. Genome-scale metabolic networks in time and space. 8():51–58, 2017. [DOI].
Chiara Damiani, Marzia Di Filippo, Dario Pescini, Davide Maspero, Riccardo Colombo, and Giancarlo Mauri. Popfba: tackling intratumour heterogeneity with flux balance analysis. 33(14):i311–i318, 2017. [DOI].
Yangyang Zheng, Qianqian Yuan, Xiaoyan Yang, and Hongwu Ma. Engineering escherichia coli for poly-(3-hydroxybutyrate) production guided by genome-scale metabolic network analysis. 106():60–66, 2017. [DOI].
Maike K. Aurich, Ronan M. T. Fleming, and Ines Thiele. A systems approach reveals distinct metabolic strategies among the nci-60 cancer cell lines. 13(8):e1005698–e1005698, 2017. [DOI].
Laurent Heirendt, Ines Thiele, and Ronan M. T. Fleming. Distributedfba.jl: high-level, high-performance flux balance analysis in julia. 33(9):1421–1423, 2017. [DOI].
Weihua Guo, You Lin Xu, and Xueyang Feng. Deepmetabolism: a deep learning system to predict phenotype from genome sequencing. ():, 2017. [DOI].
Cheng Wang, Jiao Liu, Huanhuan Liu, Junhua Wang, and Jianping Wen. A genome-scale dynamic flux balance analysis model of streptomyces tsukubaensis nrrl18488 to predict the targets for increasing fk506 production. 123():45–56, 2017. [DOI].
Claudio Angione. Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. 34(3):494–501, 2017. [DOI].
Filmon Eyassu and Claudio Angione. Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism. 4(10):170360–170360, 2017. [DOI].
Yi Yang, Xiao-Pan Hu, and Bin‐Guang Ma. Construction and simulation of the bradyrhizobium diazoefficiens usda110 metabolic network: a comparison between free-living and symbiotic states. 13(3):607–620, 2017. [DOI].
Chintan Joshi, Christie A. M. Peebles, and Ashok Prasad. Modeling and analysis of flux distribution and bioproduct formation in synechocystis sp. pcc 6803 using a new genome-scale metabolic reconstruction. 27():295–310, 2017. [DOI].
Manon Morin, Delphine Ropers, Eugenio Cinquemani, Jean‐Charles Portais, Brice Enjalbert, and Muriel Cocaign‐Bousquet. The csr system regulates escherichia coli fitness by controlling glycogen accumulation and energy levels. 8(5):, 2017. [DOI].
Konstantinos Vavitsas, Emil Østergaard Rue, Lára Kristín Stefánsdóttir, Thiyagarajan Gnanasekaran, Andreas Blennow, Christoph Crocoll, Steinn Guðmundsson, and Poul Erik Jensen. Responses of synechocystis sp. pcc 6803 to heterologous biosynthetic pathways. 16(1):140–140, 2017. [DOI].
Tanja Cvitanović, Matthias Reichert, Miha Moškon, Miha Mraz, Frank Lammert, and Damjana Rozman. Large‐scale computational models of liver metabolism: how far from the clinics? 66(4):1323–1334, 2017. [DOI].
Michael R. MacGillivray, Amy Ko, Emily Gruber, Miranda Sawyer, Eivind Almaas, and Allen Holder. Robust analysis of fluxes in genome-scale metabolic pathways. 7(1):268–268, 2017. [DOI].
Thierry Chénard, Frédéric Guénard, Marie‐Claude Vohl, André C. Carpentier, André Tchernof, and Rafaël Najmanovich. Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes. 11(1):60–60, 2017. [DOI].
Aida Kalantari, Tao Chen, Boyang Ji, Ivan Andreas Stančik, Vaishnavi Ravikumar, Damjan Franjević, Claire Saulou‐Bérion, Anne Goelzer, and Ivan Mijakovic̀. Conversion of glycerol to 3-hydroxypropanoic acid by genetically engineered bacillus subtilis. 8():638–638, 2017. [DOI].
Alfredo Braunstein, Anna Paola Muntoni, and Andrea Pagnani. An analytic approximation of the feasible space of metabolic networks. 8(1):14915–14915, 2017. [DOI].
Rui Ye, Mingzhi Huang, Hongzhong Lu, Jiangchao Qian, Weilu Lin, Ju Chu, Yingping Zhuang, and Siliang Zhang. Comprehensive reconstruction and evaluation of pichia pastoris genome-scale metabolic model that accounts for 1243 orfs. 4(1):22–22, 2017. [DOI].
Sara Saheb Kashaf, Claudio Angione, and Píetro Lió. Making life difficult for clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. 11(1):25–25, 2017. [DOI].
Nisarg Gohil, Happy Panchasara, Shreya K. Patel, Robert Ramírez‐García, and Vijai Singh. Book review: recent advances in yeast metabolic engineering. 5():, 2017. [DOI].
Alejandro Acevedo, Raúl Conejeros, and Germán Aroca. Ethanol production improvement driven by genome-scale metabolic modeling and sensitivity analysis in scheffersomyces stipitis. 12(6):e0180074–e0180074, 2017. [DOI].
Piyush Labhsetwar, Marcelo C. R. Melo, John A. Cole, and Zaida Luthey‐Schulten. Population fba predicts metabolic phenotypes in yeast. 13(9):e1005728–e1005728, 2017. [DOI].
R. Axayácatl González-García, Tim McCubbin, Annalena Wille, Manuel R. Plan, Lars K. Nielsen, and Esteban Marcellin. Awakening sleeping beauty: production of propionic acid in escherichia coli through the sbm operon requires the activity of a methylmalonyl-coa epimerase. 16(1):121–121, 2017. [DOI].
Simón Menéndez-Bravo, Julián Paganini, Claudio Avignone–Rossa, Hugo Gramajo, and Ana Arabolaza. Identification of fadab complexes involved in fatty acid β-oxidation in streptomyces coelicolor and construction of a triacylglycerol overproducing strain. 8():1428–1428, 2017. [DOI].
Joseph Gardner and Nanette Boyle. The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of n-fixing versus c-fixing cells in a diazotrophic cyanobacterium, trichodesmium erythraeum. 11(1):4–4, 2017. [DOI].
Sarah Beate Hintermayer and Dirk Weuster‐Botz. Experimental validation of in silico estimated biomass yields of pseudomonas putida kt2440. 12(6):, 2017. [DOI].
Ayca Cankorur‐Cetinkaya, Duygu Dikicioǧlu, and Stephen G. Oliver. Metabolic modeling to identify engineering targets for komagataella phaffii: the effect of biomass composition on gene target identification. 114(11):2605–2615, 2017. [DOI].
Paul Adamczyk and Jennifer L. Reed. Escherichia coli as a model organism for systems metabolic engineering. 6():80–88, 2017. [DOI].
Supreeta Vijayakumar, Max Conway, Píetro Lió, and Claudio Angione. Optimization of multi-omic genome-scale models: methodologies, hands-on tutorial, and perspectives. 1716():389–408, 2017. [DOI].
Aitor Blanco‐Míguez, Florentino Fdez‐Riverola, Borja Sánchez, and Anália Lourenço. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. 20(3):1032–1056, 2017. [DOI].
Óscar Dias, Miguel Rocha, Eugénio C. Ferreira, and Isabel Rocha. Reconstructing high-quality large-scale metabolic models with merlin. 1716():1–36, 2017. [DOI].
Zhaleh Hosseini and Sayed‐Amir Marashi. Discovering missing reactions of metabolic networks by using gene co-expression data. 7(1):41774–41774, 2017. [DOI].
Keith Dufault‐Thompson, Huahua Jian, Rui-Xue Cheng, Jiefu Li, Fengping Wang, and Ying Zhang. A genome-scale model of shewanella piezotolerans simulates mechanisms of metabolic diversity and energy conservation. 2(2):, 2017. [DOI].
Mehrdad Ghadiri, Mahshid Heidari, Sayed‐Amir Marashi, and Seyed Hasan Mousavi. A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth. 13(9):1888–1897, 2017. [DOI].
Anirikh Chakrabarti, Mathieu Membrez, Delphine Morin‐Rivron, Jay Siddharth, Chieh Jason Chou, Hugues Henry, Stephen J. Bruce, Sylviane Métairon, Frédéric Raymond, Bertrand Bétrisey, Carole Loyer, Scott J. Parkinson, and Mojgan Masoodi. Transcriptomics-driven lipidomics (tdl) identifies the microbiome-regulated targets of ileal lipid metabolism. 3(1):33–33, 2017. [DOI].
Maria Pires Pacheco and Thomas Sauter. The fastcore family: for the fast reconstruction of compact context-specific metabolic networks models. 1716():101–110, 2017. [DOI].
Eun Sung Jung, Hye Min Park, Seung Min Hyun, Jong Cheol Shon, Meiyappan Lakshmanan, Minsoo Noh, Hock Chuan Yeo, Kwang‐Hyeon Liu, Dong‐Yup Lee, Jae Sung Hwang, and Choong Hwan Lee. Integrative metabolomic analysis reveals diet supplementation with green tea alleviates uvb-damaged mouse skin correlated with ascorbate metabolism and urea cycle. 13(7):, 2017. [DOI].
David Ando and Héctor García Martín. Two-scale 13c metabolic flux analysis for metabolic engineering. 1671():333–352, 2017. [DOI].
Marco Fondi, Eva Pinatel, Adelfia Talà, Fabrizio Damiano, Clarissa Consolandi, Benedetta Mattorre, Daniela Fico, Mariangela Testini, Giuseppe Egidio De Benedetto, Luisa Siculella, Gianluca De Bellis, Pietro Alifano, and Clelia Peano. Time-resolved transcriptomics and constraint-based modeling identify system-level metabolic features and overexpression targets to increase spiramycin production in streptomyces ambofaciens. 8():835–835, 2017. [DOI].
Michael P. Weiner, Julia Tröndle, Christoph Albermann, Georg A. Sprenger, and Dirk Weuster‐Botz. Metabolic control analysis of l -phenylalanine production from glycerol with engineered e. coli using data from short-term steady-state perturbation experiments. 126():86–100, 2017. [DOI].
Xi Wang, Liang Yu, and Shulin Chen. Up finder: a cobra toolbox extension for identifying gene overexpression strategies for targeted overproduction. 5():54–59, 2017. [DOI].
Marzia Di Filippo, Chiara Damiani, Riccardo Colombo, Dario Pescini, and Giancarlo Mauri. Constraint-based modeling and simulation of cell populations. ():126–137, 2017. [DOI].
Matteo Mori, Enzo Marinari, and Andrea De Martino. A yield-cost tradeoff governs escherichia coli's decision between fermentation and respiration in carbon-limited growth. ():, 2017. [DOI].
Tjerko Kamminga, Simen‐Jan Slagman, Jetta J. E. Bijlsma, Vítor A. P. Martins dos Santos, María Suárez‐Diez, and Peter J. Schaap. Metabolic modeling of energy balances in mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate. 114(10):2339–2347, 2017. [DOI].
Lisa Katharina Blaß, Christian Weyler, and Elmar Heinzle. Network design and analysis for multi-enzyme biocatalysis. 18(1):366–366, 2017. [DOI].
William J. Kelly, Sorelle Veigne, Xianhua Li, Shyam Sundar Subramanian, Zuyi Huang, and Eugene Schaefer. Optimizing performance of semi‐continuous cell culture in an ambr15™ microbioreactor using dynamic flux balance modeling. 34(2):420–431, 2017. [DOI].
Steinn Guðmundsson, Lucía Agudo, and Juan Nogales. Applications of genome-scale metabolic models of microalgae and cyanobacteria in biotechnology. ():93–111, 2017. [DOI].
James T. Yurkovich, Benjamin J. Yurkovich, Andreas Dräger, Bernhard Ø. Palsson, and Zachary A. King. A padawan programmer’s guide to developing software libraries. 5(5):431–437, 2017. [DOI].
Yu Chen, Yonghong Wang, and Jens Nielsen. Systematic inference of functional phosphorylation events in yeast metabolism. 33(13):1995–2001, 2017. [DOI].
Brendan King, Terry Farrah, Matthew A. Richards, Michael Mundy, Evangelos Simeonidis, and Nathan D. Price. Probannoweb and probannopy: probabilistic annotation and gap-filling of metabolic reconstructions. 34(9):1594–1596, 2017. [DOI].
Chao Ye, Nan Xu, Chuan Dong, Yuannong Ye, Xuan Zou, Xiulai Chen, Feng‐Biao Guo, and Li Liu. Imgmd: a platform for the integration and standardisation of in silico microbial genome-scale metabolic models. 7(1):727–727, 2017. [DOI].
Ehsan Motamedian, Elham Taheri, and Fatemeh Bagheri. Proliferation inhibition of cisplatin‐resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data. 50(6):, 2017. [DOI].
Hugh M. Purdy and Jennifer L. Reed. Evaluating the capabilities of microbial chemical production using genome-scale metabolic models. 2():91–97, 2017. [DOI].
Colton J. Lloyd, Ali Ebrahim, Laurence Yang, Zachary King, Edward Catoiu, Edward J. O’Brien, Joanne K. Liu, and Bernhard O. Palsson. Cobrame: a computational framework for genome-scale models of metabolism and gene expression. ():, 2017. [DOI].
Jimmy G. Lafontaine Rivera, Matthew K. Theisen, Po‐Wei Chen, and James C. Liao. Kinetically accessible yield (kay) for redirection of metabolism to produce exo-metabolites. 41():144–151, 2017. [DOI].
Paulo Maia, Isabel Rocha, and Miguel Rocha. Identification of robust strain designs via tandem pfba/lmoma phenotype prediction. ():1661–1668, 2017. [DOI].
Iñigo Apaolaza, Edurne San José‐Eneriz, Xabier Agirre, Felipe Prósper, and Francisco J. Planes. Cobra methods and metabolic drug targets in cancer. 5(1):e1389672–e1389672, 2017. [DOI].
Cecilio Valadez‐Cano, Roberto Olivares‐Hernández, Osbaldo Reséndis-Antonio, Alexander DeLuna, and Luis Delaye. Natural selection drove metabolic specialization of the chromatophore in paulinella chromatophora. 17(1):99–99, 2017. [DOI].
Marco Fondi and Renato Fani. Constraint-based metabolic modelling of marine microbes and communities. 34():1–10, 2017. [DOI].
Neeraj Sinha, María Suárez‐Diez, Evert M. van Schothorst, Jaap Keijer, Vítor A. P. Martins dos Santos, and Guido Hooiveld. Predicting the murine enterocyte metabolic response to diets that differ in lipid and carbohydrate composition. 7(1):8784–8784, 2017. [DOI].
Xingxing Jian, Ningchuan Li, Qian Chen, and Qiang Hua. Model-guided identification of novel gene amplification targets for improving succinate production in escherichia coli nzn111. 9(10):830–835, 2017. [DOI].
George C. diCenzo and Turlough M. Finan. Techniques for large-scale bacterial genome manipulation and characterization of the mutants with respect to in silico metabolic reconstructions. 1716():291–314, 2017. [DOI].
Julia Tröndle, Christoph Albermann, Michael P. Weiner, Georg A. Sprenger, and Dirk Weuster‐Botz. Phosphoenolpyruvate transporter enables targeted perturbation during metabolic analysis of l‐phenylalanine production with escherichia coli. 13(5):e1700611–e1700611, 2017. [DOI].
Sascha Schäuble, Anne-Kristin Stavrum, Mathias Bockwoldt, Pål Puntervoll, and Ines Heiland. Sbmlmod: a python-based web application and web service for efficient data integration and model simulation. 18(1):314–314, 2017. [DOI].
Aurélien F. A. Moumbock, Conrad V. Simoben, Ludger A. Wessjohann, Wolfgang Sippl, Stefan Günther, and Fidele Ntie‐Kang. Computational studies and biosynthesis of natural products with promising anticancer properties. ():, 2017. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Jorge M. Arevalillo, Mariana Díaz‐Almirón, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Hilario Navarro, Rosa Aras‐López, Irene Dapía, Rocío López‐Vacas, Paolo Nanni, Sara Llorente-Armijo, Pedro Arias, Alberto M. Borobia, Paloma Maı́n, Jaime Feliú, Enrique Espinosa, and Juan Ángel Fresno Vara. Molecular characterization of breast cancer cell response to metabolic drugs. ():, 2017. [DOI].
Jonathan M. Monk and Emanuele Bosi. Integration of comparative genomics with genome-scale metabolic modeling to investigate strain-specific phenotypical differences. 1716():151–175, 2017. [DOI].
Tiziano Vignolini, Alessio Mengoni, and Marco Fondi. Template-assisted metabolic reconstruction and assembly of hybrid bacterial models. 1716():177–196, 2017. [DOI].
George C. diCenzo, Alessio Mengoni, and Marco Fondi. Tn-core: context-specific reconstruction of core metabolic models using tn-seq data. ():, 2017. [DOI].
Romina Lasry Testa, Claudio Delpino, Vanina Estrada, and M. Soledad Díaz. Metabolic network design of synechocystis sp. pcc 6803 to obtain bioethanol under autotrophic conditions. ():2857–2862, 2017. [DOI].
George C. diCenzo, Alex B. Benedict, Marco Fondi, Graham C. Walker, Turlough M. Finan, Alessio Mengoni, and Joel S. Griffitts. Robustness encoded across essential and accessory replicons in an ecologically versatile bacterium. ():, 2017. [DOI].
Elizabeth Brunk, Roger L. Chang, Jing Xia, Hooman Hefzi, James T. Yurkovich, Donghyuk Kim, Evan Buckmiller, Harris H. Wang, Yang Chen, Bernhard Ø. Palsson, George M. Church, and Nathan E. Lewis. Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments. ():, 2017. [DOI].
Anthony C. Smith, Filmon Eyassu, Jean‐Pierre Mazat, and Alan J. Robinson. Mitocore: a curated constraint-based model for simulating human central metabolism. ():, 2017. [DOI].
Brendan King, Terry Farrah, Matthew A. Richards, Michael Mundy, Evangelos Simeonidis, and Nathan D. Price. Probannoweb and probannopy: probabilistic annotation and gap-filling of metabolic reconstructions. ():, 2017. [DOI].
Takeyuki Tamura. Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: gridprod. ():, 2017. [DOI].
Nathan Mih, Elizabeth Brunk, Ke Chen, Edward Catoiu, Ananth Sastry, Erol Kavvas, Jonathan M. Monk, Zhen Zhang, and Bernhard Ø. Palsson. Ssbio: a python framework for structural systems biology. ():, 2017. [DOI].
Anu Raghunathan and Neema Jamshidi. Integrated host-pathogen metabolic reconstructions. 1716():197–217, 2017. [DOI].
Sander Y.A. Rodenburg, Michael Seidl, Dick de Ridder, and Francine Govers. Genome-wide characterization of phytophthora infestans metabolism: a systems biology approach. ():, 2017. [DOI].
Kumar Gaurav and Yasha Hasija. Comparative analysis of metabolic network of pathogens. 12(2):139–150, 2017. [DOI].
Chao Ye, Nan Xu, Xiulai Chen, and Li Liu. Metabolic model reconstruction and analysis of an artificial microbial ecosystem. 1716():219–238, 2017. [DOI].
Laurent Heirendt, Sylvain Arreckx, Thomas Pfau, Sebastián N. Mendoza, Anne Richelle, Almut Heinken, Hulda S. Haraldsdóttir, Jacek Wachowiak, Sarah Keating, Vanja Vlasov, Stefanía Magnúsdóttir, Chiam Yu Ng, German Preciat, Alise Žagare, Siu Hung Joshua Chan, Maike K. Aurich, Catherine M. Clancy, Jennifer Modamio, John T. Sauls, Alberto Noronha, Aarash Bordbar, Benjamin Cousins, Diana C. El Assal, Luis V. Valcárcel, Iñigo Apaolaza, Susan Ghaderi, Masoud Ahookhosh, Marouen Ben Guebila, Andrejs Kostromins, Nicolas Sompairac, Hoai Minh Le, Ding Ma, Yuekai Sun, Lin Wang, James T. Yurkovich, Miguel A.P. Oliveira, Phan Tu Vuong, Lemmer P. El Assal, Inna Kuperstein, Andreï Zinovyev, H.S. Hinton, William A Bryant, Francisco J. Aragón Artacho, Francisco J. Planes, Egils Stalidzāns, Alejandro Maass, Santosh Vempala, Michael Hucka, Michael A. Saunders, Costas D. Maranas, Nathan E. Lewis, Thomas Sauter, Bernhard Ø. Palsson, Ines Thiele, and Ronan M. T. Fleming. Creation and analysis of biochemical constraint-based models: the cobra toolbox v3.0. ():, 2017. [DOI].
Tobias Österlund, Marija Cvijović, and Erik Kristiansson. Integrative analysis of omics data. ():1–24, 2017. [DOI].
Bonnie V. Dougherty, Thomas J. Moutinho, and Jason A. Papin. Accelerating the drug development pipeline with genome‐scale metabolic network reconstructions. ():139–162, 2017. [DOI].
Mingyuan Tian, Prashant Kumar, Sanjan T. P. Gupta, and Jennifer L. Reed. Metabolic modeling for design of cell factories. ():71–107, 2017. [DOI].
J. R. Watson. Modelling sponge-symbiont metabolism. ():, 2017. [DOI].
Gabriel Bosque Chacón. Network analysis and modeling in systems biology. ():, 2017. [DOI].
Luana Presta, Emanuele Bosi, Leila Mansouri, Lenie Dijkshoorn, Renato Fani, and Marco Fondi. Constraint-based modeling identifies new putative targets to fight colistin-resistanta. baumanniiinfections. ():, 2017. [DOI].
Anika Küken and Zoran Nikoloski. Genome-scale modeling of photorespiratory pathway manipulation. 1653():195–202, 2017. [DOI].
Lucía Trilla‐Fuertes, Angelo Gámez‐Pozo, Guillermo Prado-Vázquez, Andrea Zapater‐Moros, Mariana Díaz‐Almirón, Claudia Fortes, Rocío López‐Vacas, Iván Márquez‐Rodas, Ainara Soria, Juan Ángel Fresno Vara, and Enrique Espinosa. Melanoma proteomics unravels major differences related to mutational status. ():, 2017. [DOI].
Tjerko Kamminga. Systems analysis of mycoplasma hyopneumoniae to improve vaccine production. ():, 2017. [DOI].
Anna Trigos, BW Goudey, Justin Bedő, TC Conway, NG Faux, and KL Wyres. Collateral sensitivity to β-lactam drugs in drug-resistant tuberculosis is driven by the transcriptional wiring of blai operon genes. ():, 2017. [DOI].
Е. В. Лихошвай. Современные тенденции развития биологии водных экосистем. 2(4):3–14, 2017. [DOI].
Dipali Singh. Genome scale metabolic modelling of phaeodactylum tricornutum. ():, 2017. [DOI].
Van Du T. Tran, Sébastien Moretti, Alix T. Coste, Sara Amorim-Vaz, Dominique Sanglard, and Marco Pagni. Gene set enrichment analysis using rna-seq-based condition-specific metabolic networks. ():200964, 2017. [DOI].
Phillip Yen. History of antibiotic adaptation influences evolutionary dynamics during subsequent treatment in pseudomonas aeruginosa. ():, 2017. [DOI].
William A Bryant, Régis Stentz, Gwénaëlle Le Gall, Michael J.E. Sternberg, Simon R. Carding, and Thomas Wilhelm. In silico analysis of the small molecule content of outer membrane vesicles produced by bacteroides thetaiotaomicron indicates an extensive metabolic link between microbe and host. 8():2440–2440, 2017. [DOI].

Esteban Marcellin, James B. Y. H. Behrendorff, Shilpa Nagaraju, Sashini DeTissera, Simón Segovia, Robin Palfreyman, James Daniell, Cuauhtémoc Licona‐Cassani, Lake‐Ee Quek, Robert Speight, Mark P. Hodson, Séan D. Simpson, Wayne P. Mitchell, Michael Köpke, and Lars K. Nielsen. Low carbon fuels and commodity chemicals from waste gases – systematic approach to understand energy metabolism in a model acetogen. 18(10):3020–3028, 2016. [DOI].
James A. Cotton, Sasisekhar Bennuru, Alexandra Grote, Bhavana Harsha, Alan Tracey, Robin N. Beech, Stephen R. Doyle, Matthew Dunn, Julie C. Dunning Hotopp, Nancy Holroyd, Taisei Kikuchi, Olivia J. Lambert, Amruta S. Mhashilkar, Prudence Mutowo, Nirvana Nursimulu, José M. C. Ribeiro, Matthew B. Rogers, Eleanor Stanley, Lakshmipuram S. Swapna, Isheng Jason Tsai, Thomas R. Unnasch, Denis Voronin, John Parkinson, Thomas B. Nutman, Elodie Ghedin, Matthew Berriman, and Sara Lustigman. The genome of onchocerca volvulus, agent of river blindness. 2(2):16216–16216, 2016. [DOI].
Jacqueline E. Gonzalez, Christopher P. Long, and Maciek R. Antoniewicz. Comprehensive analysis of glucose and xylose metabolism in escherichia coli under aerobic and anaerobic conditions by 13c metabolic flux analysis. 39():9–18, 2016. [DOI].
Aditya M. Kunjapur, Jason C. Hyun, and Kristala L. J. Prather. Deregulation of s-adenosylmethionine biosynthesis and regeneration improves methylation in the e. coli de novo vanillin biosynthesis pathway. 15(1):61–61, 2016. [DOI].
Stefano Andreozzi, Anirikh Chakrabarti, Keng Cher Soh, Anthony P. Burgard, Tae Hoon Yang, Stephen Van Dien, Ljubiša Mišković, and Vassily Hatzimanikatis. Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant e. coli using large-scale kinetic models. 35():148–159, 2016. [DOI].
Tobias Großkopf, Jessika Consuegra, Joël Gaffé, John C. Willison, Richard E. Lenski, Orkun S. Soyer, and Dominique Schneider. Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment. 16(1):163–163, 2016. [DOI].
Anna Lechner, Elizabeth Brunk, and Jay D. Keasling. The need for integrated approaches in metabolic engineering. 8(11):a023903–a023903, 2016. [DOI].
Bin Du, Daniel C. Zielinski, Erol Kavvas, Andreas Dräger, Justin Tan, Zhen Zhang, Kayla E. Ruggiero, Garri A. Arzumanyan, and Bernhard Ø. Palsson. Evaluation of rate law approximations in bottom-up kinetic models of metabolism. 10(1):40–40, 2016. [DOI].
Eliza J. R. Peterson, Shuyi Ma, David R. Sherman, and Nitin S. Baliga. Network analysis identifies rv0324 and rv0880 as regulators of bedaquiline tolerance in mycobacterium tuberculosis. 1(8):16078–16078, 2016. [DOI].
Helena Mendes‐Soares, Michael Mundy, Luis Mendes Soares, and Nicholas Chia. Mminte: an application for predicting metabolic interactions among the microbial species in a community. 17(1):343–343, 2016. [DOI].
Elizabeth Brunk, Nathan Mih, Jonathan M. Monk, Zhen Zhang, Edward J. O’Brien, Spencer Bliven, Ke Chen, Roger L. Chang, Philip E. Bourne, and Bernhard Ø. Palsson. Systems biology of the structural proteome. 10(1):26–26, 2016. [DOI].
Keisuke Wada, Yoshihiro Toya, Satomi Banno, Katsunori Yoshikawa, Fumio Matsuda, and Hiroshi Shimizu. 13c-metabolic flux analysis for mevalonate-producing strain of escherichia coli. 123(2):177–182, 2016. [DOI].
L Zieliński, Anthony C. Smith, Alexander Smith, and Alan J. Robinson. Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling. 31():45–55, 2016. [DOI].
Kyeong Rok Choi, Jae Ho Shin, Jae Sung Cho, Dongsoo Yang, and Sang Yup Lee. Systems metabolic engineering of escherichia coli. 7(1):, 2016. [DOI].
Huili Yuan, C. Y. Maurice Cheung, P.A.J. Hilbers, and N.A.W. van Riel. Flux balance analysis of plant metabolism: the effect of biomass composition and model structure on model predictions. 7():537–537, 2016. [DOI].
Rui Pereira, Jens Nielsen, and Isabel Rocha. Improving the flux distributions simulated with genome-scale metabolic models of saccharomyces cerevisiae. 3():153–163, 2016. [DOI].
Eli Bogart and Christopher R. Myers. Multiscale metabolic modeling of c4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves. 11(3):e0151722–e0151722, 2016. [DOI].
Atefeh Taherian Fard, Sriganesh Srihari, Jessica C. Mar, and Mark A. Ragan. Not just a colourful metaphor: modelling the landscape of cellular development using hopfield networks. 2(1):16001–16001, 2016. [DOI].
Janaka N. Edirisinghe, Pamela Weisenhorn, Neal Conrad, Fangfang Xia, Ross Overbeek, Rick Stevens, and Christopher S. Henry. Modeling central metabolism and energy biosynthesis across microbial life. 17(1):568–568, 2016. [DOI].
Marzia Di Filippo, Riccardo Colombo, Chiara Damiani, Dario Pescini, Daniela Gaglio, Marco Vanoni, Lilia Alberghina, and Giancarlo Mauri. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. 62():60–69, 2016. [DOI].
Sandra Castillo, Dorothee Barth, Mikko Arvas, Tiina Pakula, Esa Pitkänen, Peter Blomberg, Tuulikki Seppänen‐Laakso, Heli Nygrén, Dhinakaran Sivasiddarthan, Merja Penttilä, and Merja Oja. Whole-genome metabolic model of trichoderma reesei built by comparative reconstruction. 9(1):252–252, 2016. [DOI].
Wei-Chin Ho and Jianzhi Zhang. Adaptive genetic robustness ofescherichia colimetabolic fluxes. 33(5):1164–1176, 2016. [DOI].
Xian‐Hua Li, Yanhong Liu, Qian Jia, Virginia LaMacchia, Kathryn O’Donoghue, and Zuyi Huang. A systems biology approach to investigate the antimicrobial activity of oleuropein. 43(12):1705–1717, 2016. [DOI].
Bashir Sajo Mienda. Genome-scale metabolic models as platforms for strain design and biological discovery. 35(9):1863–1873, 2016. [DOI].
Helena Mendes‐Soares and Nicholas Chia. Community metabolic modeling approaches to understanding the gut microbiome: bridging biochemistry and ecology. 105():102–109, 2016. [DOI].
Janne Wallenius, Hannu Maaheimo, and Tero Eerikäinen. Carbon 13-metabolic flux analysis derived constraint-based metabolic modelling of clostridium acetobutylicum in stressed chemostat conditions. 219():378–386, 2016. [DOI].
Luis Tobalina, Jon Pey, Alberto Rezola, and Francisco J. Planes. Assessment of fba based gene essentiality analysis in cancer with a fast context-specific network reconstruction method. 11(5):e0154583–e0154583, 2016. [DOI].
Amphun Chaiboonchoe, Lila Ghamsari, Bushra Dohai, Patrick P. Ng, Basel Khraiwesh, Ashish Jaiswal, Kenan Jijakli, Joseph Koussa, David R. Nelson, Hong Cai, Xinping Yang, Roger L. Chang, Jason A. Papin, Haiyuan Yu, Balaji Santhanam, and Kourosh Salehi‐Ashtiani. Systems level analysis of the chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. 12(8):2394–2407, 2016. [DOI].
Andreas Wagner. The white-knight hypothesis, or does the environment limit innovations? 32(2):131–140, 2016. [DOI].
В. Б. Паничкин, В. А. Лившиц, I. V. Biryukova, and Sergey V. Mashko. Metabolic engineering of escherichia coli for l-tryptophan production. 52(9):783–809, 2016. [DOI].
Oveis Jamialahmadi, Ehsan Motamedian, and Sameereh Hashemi‐Najafabadi. Bikegg: a cobra toolbox extension for bridging the bigg and kegg databases. 12(11):3459–3466, 2016. [DOI].
Bhanwar Lal Puniya, Deepika Kulshreshtha, Inna Mittal, Ahmed Mobeen, and Srinivasan Ramachandran. Integration of metabolic modeling with gene co-expression reveals transcriptionally programmed reactions explaining robustness in mycobacterium tuberculosis. 6(1):24916–24916, 2016. [DOI].
Rachhpal S. Kahlon. Pseudomonas for industrial biotechnology. ():281–342, 2016. [DOI].
Ildikó Miklóssy, Zsolt Bodor, Réka Sinkler, Kálmán Csongor Orbán, Szabolcs Lányi, and Beáta Albert. in silico and in vivo stability analysis of a heterologous biosynthetic pathway for 1,4-butanediol production in metabolically engineered e. coli. 35(9):1874–1889, 2016. [DOI].
Sarah McGarrity, Haraldur Halldórsson, Sirus Palsson, Pär I. Johansson, and Óttar Rolfsson. Understanding the causes and implications of endothelial metabolic variation in cardiovascular disease through genome-scale metabolic modeling. 3():10–10, 2016. [DOI].
Sayed‐Rzgar Hosseini and Andreas Wagner. The potential for non-adaptive origins of evolutionary innovations in central carbon metabolism. 10(1):97–97, 2016. [DOI].
Nicholas Ribaudo, Xian‐Hua Li, Brett Davis, Thomas K. Wood, and Zuyi Huang. A genome‐scale modeling approach to quantify biofilm component growth of salmonella typhimurium. 82(1):154–166, 2016. [DOI].
Anonymous. Current challenges in modeling cellular metabolism. ():, 2016. [DOI].
Abinaya Badri, Aparajitha Srinivasan, and Karthik Raman. In silico approaches to metabolic engineering. ():161–200, 2016. [DOI].
Yuqi Zhao, Yanjie Wang, Lei Zou, and Jing‐Fei Huang. Reconstruction and applications of consensus yeast metabolic network based on <scp>rna</scp> sequencing. 6(4):264–275, 2016. [DOI].
Helen S. Toogood, Shirley Tait, Adrian J. Jervis, Aisling Ní Cheallaigh, Luke Humphreys, Eriko Takano, John M. Gardiner, and Nigel S. Scrutton. Natural product biosynthesis in escherichia coli. 575():247–270, 2016. [DOI].
Milsee Mol, Vineetha Mandlik, and Shailza Singh. Microbial chassis assisting retrosynthesis. ():1–10, 2016. [DOI].
Zhaobin Xu, Nicholas Ribaudo, Xian‐Hua Li, Thomas K. Wood, and Zuyi Huang. A genome-scale modeling approach to investigate the antibiotics-triggered perturbation in the metabolism of <italic>pseudomonas aeruginosa</italic>. 2(3):39–42, 2016. [DOI].
Helena Mendes‐Soares, Michael Mundy, Luis Mendes Soares, and Nicholas Chia. Mminte: an application for predicting metabolic interactions among the microbial species in a community. ():, 2016. [DOI].
Almut Heinken, Dmitry A. Ravcheev, and Ines Thiele. Systems biology of bacteria‐host interactions. ():113–137, 2016. [DOI].
Francis Isidore G. Totañes, Sanu Shameer, David R. Westhead, Fabien Jourdan, and Glenn A. McConkey. Understanding protozoan parasite metabolism and identifying drug targets through constraint‐based modeling. ():487–512, 2016. [DOI].
Martins Mednis. Comparative analysis of biochemical network reconstructions. 4(4):, 2016. [DOI].
Abel Folch Fortuny. Chemometric approaches for systems biology. ():, 2016. [DOI].
Matthew Biggs. Novel computational tools for linking genotypes to microbial community phenotypes. ():, 2016. [DOI].
Borja Sánchez, Susana Delgado, Aitor Blanco‐Míguez, Anália Lourenço, Miguel Gueimonde, and Abelardo Margollés. Probiotics, gut microbiota, and their influence on host health and disease. 61(1):, 2016. [DOI].
Stefanía Magnúsdóttir, Almut Heinken, Laura Kutt, Dmitry A. Ravcheev, Eugen Bauer, Alberto Noronha, Kacy Greenhalgh, Christian Jäger, Joanna Bagińska, Paul Wilmes, Ronan M. T. Fleming, and Ines Thiele. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. 35(1):81–89, 2016. [DOI].
Eugeni Belda, Ruben Heck, María José López Sánchez, Stéphane Cruveiller, Valérie Barbe, Claire M. Fraser, Hans‐Peter Klenk, Jörn Petersen, Anne Morgat, Pablo I. Nikel, David Vallenet, Zoé Rouy, Agnieszka Sekowska, Vítor A. P. Martins dos Santos, Vı́ctor de Lorenzo, Antoine Danchin, and Claudine Médigue. The revisited genome of pseudomonas putida kt2440 enlightens its value as a robust metabolic chassis. 18(10):3403–3424, 2016. [DOI].
Neil Swainston, Kieran Smallbone, Hooman Hefzi, Paul D. Dobson, Judy McKinley Brewer, Michael Hanscho, Daniel C. Zielinski, Kok Siong Ang, Natalie J. Gardiner, Jahir M. Gutierrez, Sarantos Kyriakopoulos, Meiyappan Lakshmanan, Shangzhong Li, Joanne K. Liu, Verónica S. Martínez, Camila A. Orellana, Lake‐Ee Quek, Alex Thomas, Jürgen Zanghellini, Nicole Borth, Dong‐Yup Lee, Lars K. Nielsen, Douglas B. Kell, Nathan E. Lewis, and Pedro Mendes. Recon 2.2: from reconstruction to model of human metabolism. 12(7):109–109, 2016. [DOI].
Hooman Hefzi, Kok Siong Ang, Michael Hanscho, Aarash Bordbar, David E. Ruckerbauer, Meiyappan Lakshmanan, Camila A. Orellana, Deniz Baycin‐Hizal, Yingxiang Huang, Daniel Ley, Verónica S. Martínez, Sarantos Kyriakopoulos, Natalia E. Jiménez, Daniel C. Zielinski, Lake‐Ee Quek, Tune Wulff, Johnny Arnsdorf, Shangzhong Li, Jae Seong Lee, Giuseppe Paglia, Nicolás Loira, Philipp N. Spahn, Lasse Ebdrup Pedersen, Jahir M. Gutierrez, Zachary A. King, Anne Mathilde Lund, Harish Nagarajan, Alex Thomas, Alyaa M. Abdel‐Haleem, Jürgen Zanghellini, Helene Faustrup Kildegaard, Björn G. Voldborg, Ziomara P. Gerdtzen, Michael J. Betenbaugh, Bernhard Ø. Palsson, Mikael Rørdam Andersen, Lars K. Nielsen, Nicole Borth, Dong‐Yup Lee, and Nathan E. Lewis. A consensus genome-scale reconstruction of chinese hamster ovary cell metabolism. 3(5):434–443.e8, 2016. [DOI].
Matteo Mori, Terence Hwa, Olivier Martin, Andrea De Martino, and Enzo Marinari. Constrained allocation flux balance analysis. 12(6):e1004913–e1004913, 2016. [DOI].
André Schultz and Amina A. Qutub. Reconstruction of tissue-specific metabolic networks using corda. 12(3):e1004808–e1004808, 2016. [DOI].
John T. Sauls, Dongyang Li, and Suckjoon Jun. Adder and a coarse-grained approach to cell size homeostasis in bacteria. 38():38–44, 2016. [DOI].
Jared T. Broddrick, Benjamin E. Rubin, David Welkie, Niu Du, Nathan Mih, Spencer Diamond, Jenny J. Lee, Susan S. Golden, and Bernhard Ø. Palsson. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis. 113(51):, 2016. [DOI].
Cristal Zúñiga, Chien‐Ting Li, Tyler Huelsman, Jennifer Levering, Daniel C. Zielinski, Brian O. McConnell, Christopher P. Long, Eric P. Knoshaug, Michael T. Guarnieri, Maciek R. Antoniewicz, Michael J. Betenbaugh, and Karsten Zengler. Genome-scale metabolic model for the green alga chlorella vulgaris utex 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth conditions. 172(1):589–602, 2016. [DOI].
Jennifer Levering, Jared T. Broddrick, Christopher L. Dupont, Graham Peers, Karen Beeri, Joshua J. Mayers, Alessandra A. Gallina, Andrew E. Allen, Bernhard Ø. Palsson, and Karsten Zengler. Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom. 11(5):e0155038–e0155038, 2016. [DOI].
Aarash Bordbar, Pär I. Johansson, Giuseppe Paglia, Scott J. Harrison, Kristine Wichuk, Manuela Magnúsdóttir, Sóley Valgeirsdóttir, Mikkel Gybel‐Brask, Sisse Rye Ostrowski, Sirus Palsson, Óttar Rolfsson, Ólafur E. Sigurjónsson, Morten Hansen, Sveinn Guðmundsson, and Bernhard Ø. Palsson. Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes. 56(4):852–862, 2016. [DOI].
John I. Hendry, Charulata B. Prasannan, Aditi Joshi, Santanu Dasgupta, and Pramod P. Wangikar. Metabolic model of synechococcus sp. pcc 7002: prediction of flux distribution and network modification for enhanced biofuel production. 213():190–197, 2016. [DOI].
Robert Häsler, Raheleh Sheibani‐Tezerji, Anupam Sinha, Matthias Barann, Ateequr Rehman, Daniela Esser, Konrad Aden, Carolin Knecht, Berenice Brandt, Susanna Nikolaus, Sascha Schäuble, Christoph Kaleta, André Franke, Christoph Fretter, Werner Müller, Marc‐Thorsten Hütt, Michael Krawczak, Stefan Schreiber, and Philip Rosenstiel. Uncoupling of mucosal gene regulation, mrna splicing and adherent microbiota signatures in inflammatory bowel disease. 66(12):2087–2097, 2016. [DOI].
Ying Li, Xi Wang, Xizhen Ge, and Pingfang Tian. High production of 3-hydroxypropionic acid in klebsiella pneumoniae by systematic optimization of glycerol metabolism. 6(1):26932–26932, 2016. [DOI].
George C. diCenzo, Alice Checcucci, Marco Bazzicalupo, Alessio Mengoni, Carlo Viti, Łukasz Dziewit, Turlough M. Finan, Marco Galardini, and Marco Fondi. Metabolic modelling reveals the specialization of secondary replicons for niche adaptation in sinorhizobium meliloti. 7(1):12219–12219, 2016. [DOI].
Kiyan Shabestary and Elton P. Hudson. Computational metabolic engineering strategies for growth-coupled biofuel production by synechocystis. 3():216–226, 2016. [DOI].
Mohammad Tauqeer Alam, Aleksej Zelezniak, Michael Mülleder, Pavel V. Shliaha, Roland F. Schwarz, Floriana Capuano, Jakob Vowinckel, Elahe Radmaneshfar, Antje Krüger, Enrica Calvani, Steve Michel, Stefan T. Börno, Stefan Christen, Kiran Raosaheb Patil, Bernd Timmermann, Kathryn S. Lilley, and Markus Ralser. The metabolic background is a global player in saccharomyces gene expression epistasis. 1(3):15030–15030, 2016. [DOI].
Weiqi Fu, Amphun Chaiboonchoe, Basel Khraiwesh, David R. Nelson, Dina Al-Khairy, Alexandra Mystikou, Amnah Alzahmi, and Kourosh Salehi‐Ashtiani. Algal cell factories: approaches, applications, and potentials. 14(12):225–225, 2016. [DOI].
Jasper J. Koehorst, Jesse C. J. van Dam, Ruben Heck, Edoardo Saccenti, Vítor A. P. Martins dos Santos, María Suárez‐Diez, and Peter J. Schaap. Comparison of 432 pseudomonas strains through integration of genomic, functional, metabolic and expression data. 6(1):38699–38699, 2016. [DOI].
Jozef Anné, Anastassios Economou, and Kristel Bernaerts. Protein secretion in gram-positive bacteria: from multiple pathways to biotechnology. 404():267–308, 2016. [DOI].
Alberto Noronha, Anna Dröfn Daníelsdóttir, Piotr Gawron, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson, Sigurður Brynjólfsson, Reinhard Schneider, Ines Thiele, and Ronan M. T. Fleming. Reconmap: an interactive visualization of human metabolism. 33(4):605–607, 2016. [DOI].
Màrius Tomàs‐Gamisans, Pau Ferrer, and Joan Albiol. Integration and validation of the genome-scale metabolic models of pichia pastoris: a comprehensive update of protein glycosylation pathways, lipid and energy metabolism. 11(1):e0148031–e0148031, 2016. [DOI].
Xiaolin Zhang, Christopher J. Tervo, and Jennifer L. Reed. Metabolic assessment of e. coli as a biofactory for commercial products. 35():64–74, 2016. [DOI].
Adam P. Arkin, Rick Stevens, Robert W. Cottingham, Sergei Maslov, Christopher S. Henry, Paramvir Dehal, Doreen Ware, Fernando Pérez, Nomi L. Harris, Shane Canon, Michael W. Sneddon, Matthew Henderson, William J. Riehl, Dan Gunter, Dan Murphy-Olson, Stephen Y. Chan, Roy T. Kamimura, Thomas Brettin, Folker Meyer, Dylan Chivian, David J. Weston, Elizabeth M. Glass, Brian H. Davison, Sunita Kumari, Benjamin Allen, Jason K. Baumohl, Aaron A. Best, Ben Bowen, Steven E. Brenner, Christopher Bun, John‐Marc Chandonia, Jer-Ming Chia, Ric Colasanti, Neal Conrad, James J. Davis, Matthew DeJongh, Scott Devoid, Emily Dietrich, Meghan M. Drake, Inna Dubchak, Janaka N. Edirisinghe, Gang Fang, José P. Faria, Paul M Frybarger, Wolfgang Gerlach, Mark Gerstein, James Gurtowski, Holly L Haun, Fei He, Rashmi Jain, Marcin P. Joachimiak, Kevin Keegan, Shinnosuke Kondo, Vivek Kumar, Miriam Land, Marissa Mills, Pavel S. Novichkov, Taeyun Oh, Gary J. Olsen, Bob Olson, Bruce Parrello, Shiran Pasternak, Erik Pearson, Sarah S Poon, Gavin A Price, Srividya Ramakrishnan, Priya Ranjan, Pamela C. Ronald, Michael C. Schatz, Samuel M. D. Seaver, Maulik Shukla, Roman A. Sutormin, Mustafa Syed, James Thomason, Nathan Tintle, Daifeng Wang, Fangfang Xia, Hyunseung Yoo, and Shinjae Yoo. The doe systems biology knowledgebase (kbase). ():, 2016. [DOI].
Manon Morin, Delphine Ropers, Fabien Létisse, Sandrine Laguerre, Jean‐Charles Portais, Muriel Cocaign‐Bousquet, and Brice Enjalbert. The post‐transcriptional regulatory system csr controls the balance of metabolic pools in upper glycolysis of escherichia coli. 100(4):686–700, 2016. [DOI].
Hulda S. Haraldsdóttir and Ronan M. T. Fleming. Identification of conserved moieties in metabolic networks by graph theoretical analysis of atom transition networks. 12(11):e1004999–e1004999, 2016. [DOI].
Daniel Machado, Markus J. Herrgård, and Isabel Rocha. Stoichiometric representation of gene–protein–reaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction. 12(10):e1005140–e1005140, 2016. [DOI].
Daniel Cuevas, Janaka N. Edirisinghe, Chris Henry, Ross Overbeek, Taylor O’Connell, and Robert A. Edwards. From dna to fba: how to build your own genome-scale metabolic model. 7():907–907, 2016. [DOI].
Silvio Waschina, Glen G D’Souza, Christian Kost, and Christoph Kaleta. Metabolic network architecture and carbon source determine metabolite production costs. 283(11):2149–2163, 2016. [DOI].
Matthew A. Richards, Thomas J. Lie, Juan Zhang, Stephen W. Ragsdale, John A. Leigh, and Nathan D. Price. Exploring hydrogenotrophic methanogenesis: a genome scale metabolic reconstruction of methanococcus maripaludis. 198(24):3379–3390, 2016. [DOI].
Christopher P. Long, Jacqueline E. Gonzalez, Nicholas R. Sandoval, and Maciek R. Antoniewicz. Characterization of physiological responses to 22 gene knockouts in escherichia coli central carbon metabolism. 37():102–113, 2016. [DOI].
Elias Björnson, Jan Borén, and Adil Mardinoğlu. Personalized cardiovascular disease prediction and treatment—a review of existing strategies and novel systems medicine tools. 7():2–2, 2016. [DOI].
Laurence Yang, Ding Ma, Ali Ebrahim, Colton J. Lloyd, Michael A. Saunders, and Bernhard Ø. Palsson. Solveme: fast and reliable solution of nonlinear me models. 17(1):391–391, 2016. [DOI].
Ankita Juneja, Frank Chaplen, and Ganti S. Murthy. Genome scale metabolic reconstruction of chlorella variabilis for exploring its metabolic potential for biofuels. 213():103–110, 2016. [DOI].
Miroslava Čuperlović‐Culf and Adrian S. Culf. Applied metabolomics in drug discovery. 11(8):759–770, 2016. [DOI].
Dávid Malatinszky, Ralf Steuer, and Patrik R. Jones. A comprehensively curated genome-scale two-cell model for the heterocystous cyanobacterium anabaena sp. pcc 7120. 173(1):509–523, 2016. [DOI].
Elsje Pienaar, William M. Matern, Jennifer J. Linderman, Joel S. Bader, and Denise E. Kirschner. Multiscale model of mycobacterium tuberculosis infection maps metabolite and gene perturbations to granuloma sterilization predictions. 84(5):1650–1669, 2016. [DOI].
Kumari Sonal Choudhary, Neha Rohatgi, Skarphéðinn Halldórsson, Eiríkur Briem, Þórarinn Guðjónsson, Steinn Guðmundsson, and Óttar Rolfsson. Egfr signal-network reconstruction demonstrates metabolic crosstalk in emt. 12(6):e1004924–e1004924, 2016. [DOI].
Hadi Nazem‐Bokaee, Saratram Gopalakrishnan, James G. Ferry, Thomas K. Wood, and Costas D. Maranas. Assessing methanotrophy and carbon fixation for biofuel production by methanosarcina acetivorans. 15(1):10–10, 2016. [DOI].
Miguel A. Campodonico, Daniela Vaisman, Jean Franco Castro, Valeria Razmilic, Francesca Mercado, Bárbara A. Andrews, Adam M. Feist, and Juan A. Asenjo. Acidithiobacillus ferrooxidans's comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications. 3():84–96, 2016. [DOI].
Georgios Savoglidis, Aline Xavier da Silveira dos Santos, Isabelle Riezman, Paolo Angelino, Howard Riezman, and Vassily Hatzimanikatis. A method for analysis and design of metabolism using metabolomics data and kinetic models: application on lipidomics using a novel kinetic model of sphingolipid metabolism. 37():46–62, 2016. [DOI].
Marouen Ben Guebila and Ines Thiele. Model-based dietary optimization for late-stage, levodopa-treated, parkinson’s disease patients. 2(1):16013–16013, 2016. [DOI].
Pedro A. Saa and Lars K. Nielsen. Ll-achrb: a scalable algorithm for sampling the feasible solution space of metabolic networks. 32(15):2330–2337, 2016. [DOI].
Jin Chen and Michael A. Henson. In silico metabolic engineering of clostridium ljungdahlii for synthesis gas fermentation. 38():389–400, 2016. [DOI].
Mariana Galvão Ferrarini, Franciele Maboni Siqueira, Scheila Gabriele Mucha, Tony Palama, Élodie Jobard, Bénédicte Elena‐Herrmann, Ana Tereza Ribeiro de Vasconcelos, Florence Tardy, Irene Silveira Schrank, Arnaldo Zaha, and Marie‐France Sagot. Insights on the virulence of swine respiratory tract mycoplasmas through genome-scale metabolic modeling. 17(1):353–353, 2016. [DOI].
Maria Pires Pacheco, Thomas Pfau, and Thomas Sauter. Benchmarking procedures for high-throughput context specific reconstruction algorithms. 6():410–410, 2016. [DOI].
Di Liu, Ni Wan, Fuzhong Zhang, Yinjie Tang, and Stephen Gang Wu. Enhancing fatty acid production in escherichia coli by vitreoscilla hemoglobin overexpression. 114(2):463–467, 2016. [DOI].
Thibault Nidelet, Pascale Brial, Carole Camarasa, and Sylvie Dequin. Diversity of flux distribution in central carbon metabolism of s. cerevisiae strains from diverse environments. 15(1):58–58, 2016. [DOI].
R.P. Vivek-Ananth and Areejit Samal. Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. 147():1–10, 2016. [DOI].
Juliane Gebauer, Christoph Gentsch, Johannes Mansfeld, Kathrin Schmeißer, Silvio Waschina, Susanne Brandes, Lukas Klimmasch, Nicola Zamboni, Kim Zarse, Stefan Schuster, Michael Ristow, Sascha Schäuble, and Christoph Kaleta. A genome-scale database and reconstruction of caenorhabditis elegans metabolism. 2(5):312–322, 2016. [DOI].
Hongzhong Lu, Weiqiang Cao, Liming Ouyang, Jianye Xia, Mingzhi Huang, Ju Chu, Yingping Zhuang, Siliang Zhang, and Henk Noorman. Comprehensive reconstruction and in silico analysis of aspergillus niger genome‐scale metabolic network model that accounts for 1210 orfs. 114(3):685–695, 2016. [DOI].
Filomena Sannino, Maria Elisa Giuliani, U. Salvatore, Gennaro Antonio Apuzzo, Donatella de Pascale, Renato Fani, Marco Fondi, Gennaro Marino, Maria Luisa Tutino, and Ermenegilda Parrilli. A novel synthetic medium and expression system for subzero growth and recombinant protein production in pseudoalteromonas haloplanktis tac125. 101(2):725–734, 2016. [DOI].
Wanwipa Vongsangnak, Amornpan Klanchui, Iyarest Tawornsamretkit, Witthawin Tatiyaborwornchai, Kobkul Laoteng, and Asawin Meechai. Genome-scale metabolic modeling of mucor circinelloides and comparative analysis with other oleaginous species. 583(2):121–129, 2016. [DOI].
Douglas McCloskey, Jamey D. Young, Sibei Xu, Bernhard Ø. Palsson, and Adam M. Feist. Modeling method for increased precision and scope of directly measurable fluxes at a genome-scale. 88(7):3844–3852, 2016. [DOI].
Emrah Özcan and Tunahan Çakır. Reconstructed metabolic network models predict flux-level metabolic reprogramming in glioblastoma. 10():156–156, 2016. [DOI].
Paula Jouhten, Tomasz Boruta, Sergej Andrejev, Filipa Pereira, Isabel Rocha, and Kiran Raosaheb Patil. Yeast metabolic chassis designs for diverse biotechnological products. 6(1):29694–29694, 2016. [DOI].
Xi Wang, Xiaochao Xiong, Na Sa, Sanja Roje, and Shulin Chen. Metabolic engineering of enhanced glycerol-3-phosphate synthesis to increase lipid production in synechocystis sp. pcc 6803. 100(13):6091–6101, 2016. [DOI].
Daniel Hartleb, Florian Jarre, and Martin J. Lercher. Improved metabolic models for e. coli and mycoplasma genitalium from globalfit, an algorithm that simultaneously matches growth and non-growth data sets. 12(8):e1005036–e1005036, 2016. [DOI].
Ruben Heck, Mathias Ganter, Vítor A. P. Martins dos Santos, and Joerg Stelling. Efficient reconstruction of predictive consensus metabolic network models. 12(8):e1005085–e1005085, 2016. [DOI].
Lanqing Dang, Jiao Liu, Cheng Wang, Huanhuan Liu, and Jianping Wen. Enhancement of rapamycin production by metabolic engineering in streptomyces hygroscopicus based on genome-scale metabolic model. 44(2):259–270, 2016. [DOI].
Zhihao Wang, Siu Hung Joshua Chan, Suresh Sudarsan, Lars M. Blank, Peter Ruhdal Jensen, and Christian Solem. Elucidation of the regulatory role of the fructose operon reveals a novel target for enhancing the nadph supply in corynebacterium glutamicum. 38():344–357, 2016. [DOI].
Xingxing Jian, Shengguo Zhou, Cheng Zhang, and Qiang Hua. In silico identification of gene amplification targets based on analysis of production and growth coupling. 145():1–8, 2016. [DOI].
Kristopher A. Hunt, Ryan deM. Jennings, William P. Inskeep, and Ross P. Carlson. Stoichiometric modelling of assimilatory and dissimilatory biomass utilisation in a microbial community. 18(12):4946–4960, 2016. [DOI].
Yunze Zhang, Huxing Chen, Jiamei Liu, Ge Geng, Deli Liu, Hui Geng, and Xiong Li. Genome sequencing and biodegradation characteristics of the n-butyl benzyl phthalate degrading bacterium-rhodococcus sp. hs-d2. 128():56–62, 2016. [DOI].
R. Adam Thompson, Sanjeev Dahal, Sergio Garcia, Intawat Nookaew, and Cong T. Trinh. Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of clostridium thermocellum dsm 1313 implementing an adjustable cellulosome. 9(1):194–194, 2016. [DOI].
Ehsan Motamedian, M. Saeidi, and Seyed Abbas Shojaosadati. Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of zymomonas mobilis zm1. 12(4):1241–1249, 2016. [DOI].
Fredrick M. Mobegi, Aldert Zomer, Marien I. de Jonge, and Sacha A. F. T. van Hijum. Advances and perspectives in computational prediction of microbial gene essentiality. 16(2):70–79, 2016. [DOI].
Xiaoyan Yang, Qianqian Yuan, Yangyang Zheng, Hongwu Ma, Tao Chen, and Xueming Zhao. An engineered non-oxidative glycolysis pathway for acetone production in escherichia coli. 38(8):1359–1365, 2016. [DOI].
Reza Mohammadi, Jalil Fallah-Mehrabadi, Gholamreza Bidkhori, Javad Zahiri, Mohammad Javad Niroomand, and Ali Masoudi‐Nejad. A systems biology approach to reconcile metabolic network models with application to synechocystis sp. pcc 6803 for biofuel production. 12(8):2552–2561, 2016. [DOI].
Jun Feng, Jing Yang, Xiaorong Li, Meijin Guo, Bochu Wang, Shang‐Tian Yang, and Xiang Zou. Reconstruction of a genome-scale metabolic model and in silico analysis of the polymalic acid producer aureobasidium pullulans cctcc m2012223. 607():1–8, 2016. [DOI].
Pablo Carbonell. Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data. 34(2):219–234, 2016. [DOI].
Marco Fondi, Emanuele Bosi, Luana Presta, Diletta Natoli, and Renato Fani. Modelling microbial metabolic rewiring during growth in a complex medium. 17(1):970–970, 2016. [DOI].
Ronan M. T. Fleming, Nikos Vlassis, Ines Thiele, and Michael A. Saunders. Conditions for duality between fluxes and concentrations in biochemical networks. 409():1–10, 2016. [DOI].
Salma Sohrabi-Jahromi, Sayed‐Amir Marashi, and Shiva Kalantari. A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis. 27(3-4):158–167, 2016. [DOI].
Luis Tobalina, Jon Pey, and Francisco J. Planes. Direct calculation of minimal cut sets involving a specific reaction knock-out. 32(13):2001–2007, 2016. [DOI].
Xingxing Jian, Ningchuan Li, Cheng Zhang, and Qiang Hua. In silico profiling of cell growth and succinate production in escherichia coli nzn111. 3(1):48–48, 2016. [DOI].
Amornpan Klanchui, Nachon Raethong, Peerada Prommeenate, Wanwipa Vongsangnak, and Asawin Meechai. Cyanobacterial biofuels: strategies and developments on network and modeling. 160():75–102, 2016. [DOI].
Jason Whitham, Mark J. Schulte, Benjamin G. Bobay, José M. Bruno-Bárcena, Mari S. Chinn, Michael C. Flickinger, Joel J. Pawlak, and Amy M. Grunden. Characterization of clostridium ljungdahlii ota1: a non-autotrophic hyper ethanol-producing strain. 101(4):1615–1630, 2016. [DOI].
Milan JA van Hoek and Roeland M. H. Merks. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. ():, 2016. [DOI].
Lili Liu, Zijun Zhang, Taotao Sheng, and Ming Chen. Def: an automated dead-end filling approach based on quasi-endosymbiosis. 33(3):405–413, 2016. [DOI].
André Schultz, Sudhir Mehta, Chuanbo Hu, Fieke W. Hoff, Terzah M. Horton, Steven M. Kornblau, and Amina A. Qutub. Identifying cancer specific metabolic signatures using constraint-based models. 22():485–496, 2016. [DOI].
Cristiana G. O. Dal’Molin and Lars K. Nielsen. Algae genome-scale reconstruction, modelling and applications. ():591–598, 2016. [DOI].
Regiane Kawasaki, Rafael A. Baraúna, Artur Silva, Marta S. P. Carepo, Rui Oliveira, Rodolfo Marques, Rommel Thiago Jucá Ramos, and Maria Paula Cruz Schneider. Reconstruction of the fatty acid biosynthetic pathway ofexiguobacterium antarcticumb7 based on genomic and bibliomic data. 2016():1–9, 2016. [DOI].
Sanjeev Dahal, Suresh Poudel, and R. Adam Thompson. Genome-scale modeling of thermophilic microorganisms. 160():103–119, 2016. [DOI].
Semidán Robaina‐Estévez and Zoran Nikoloski. Metabolic network constrains gene regulation of c4photosynthesis: the case of maize. 57(5):933–943, 2016. [DOI].
Do‐Gyun Kim, Byoung–Kwan Cho, and Wang‐Hee Lee. A novel approach in analyzing agriculture and food systems: review of modeling and its applications. 43(2):163–175, 2016. [DOI].
Xudong Wu and Guohui Li. Prevalent accumulation of non-optimal codons through somatic mutations in human cancers. 11(8):e0160463–e0160463, 2016. [DOI].
Meghana Chitale, Ishita Khan, and Daisuke Kihara. Missing gene identification using functional coherence scores. 6(1):31725–31725, 2016. [DOI].
Donghyuk Kim, Sang Woo Seo, Hojung Nam, Gabriela I. Guzmán, Ye Gao, and Bernhard Ø. Palsson. Systems assessment of transcriptional regulation on central carbon metabolism by cra and crp. ():, 2016. [DOI].
Balaji Balagurunathan, Vishist Kumar Jain, Crystal Jing Ying Tear, Chanyuen Lim, and Hua Zhao. In silico design of anaerobic growth-coupled product formation in escherichia coli: experimental validation using a simple polyol, glycerol. 40(3):361–372, 2016. [DOI].
Maike K. Aurich, Ronan M. T. Fleming, and Ines Thiele. Metabotools: a comprehensive toolbox for analysis of genome-scale metabolic models. 7():327–327, 2016. [DOI].
Thomas Pfau, Nils Christian, Shyam Kumar Masakapalli, Lee Sweetlove, Mark G. Poolman, and Oliver Ebenhöh. The intertwined metabolism ofmedicago truncatulaand its nitrogen fixing symbiontsinorhizobium melilotielucidated by genome-scale metabolic models. ():, 2016. [DOI].
Wouter Daniels, Jeroen Bouvin, Tobias Busche, Jörn Kalinowski, and Kristel Bernaerts. Finding targets for genome reduction in streptomyces lividans tk24 using flux balance analysis. 49(26):252–257, 2016. [DOI].
Kevin Schwahn, Anika Küken, Daniel J. Kliebenstein, Alisdair R. Fernie, and Zoran Nikoloski. Observability of plant metabolic networks is reflected in the correlation of metabolic profiles. 172(2):pp.00900.2016–pp.00900.2016, 2016. [DOI].
Cameron Cotten and Jennifer L. Reed. Applications of constraint-based models for biochemical production. ():201–226, 2016. [DOI].
Amornpan Klanchui, Wanwipa Vongsangnak, Kobkul Laoteng, and Asawin Meechai. In silico analysis of mucor circinelloides genome-scale model for enhancing lipid production. ():14–18, 2016. [DOI].
Fangzhou Shen, Jian Li, Ying Zhu, and Zhuo Wang. Systematic investigation of metabolic reprogramming in different cancers based on tissue-specific metabolic models. 14(05):1644001–1644001, 2016. [DOI].
Neha Chaudhary, Kristin Tøndel, Rakesh Bhatnagar, Vítor A. P. Martins dos Santos, and Jacek Puchałka. Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling. 12(3):994–1005, 2016. [DOI].
R.P. Vivek-Ananth and Areejit Samal. Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. ():, 2016. [DOI].
Zhuo Wang, Samuel A. Danziger, Ben Heavner, Shuyi Ma, Jennifer J. Smith, Song Li, Thurston Herricks, Evangelos Simeonidis, Nitin S. Baliga, John D. Aitchison, and Nathan D. Price. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. ():, 2016. [DOI].
Meng Liang, Xiaowei Zhou, and Chunping Xu. Systems biology in biofuel. 1(11):, 2016. [DOI].
Alberto Noronha, Anna Dröfn Daníelsdóttir, Freyr Jóhannsson, Soffía Jónsdóttir, Sindri Jarlsson, Jón Pétur Gunnarsson, Sigur dhur Brynjólfsson, Piotr Gawron, Reinhard Schneider, Ines Thiele, and Ronan M. T. Fleming. Reconmap: an interactive visualisation of human metabolism. ():, 2016. [DOI].
Ding Ma, Laurence Yang, Ronan M. T. Fleming, Ines Thiele, Bernhard Ø. Palsson, and Michael A. Saunders. Quadruple-precision solution of genome-scale models of metabolism and macromolecular expression. ():, 2016. [DOI].
R. Adam Thompson. In silico driven metabolic engineering towards enhancing biofuel and biochemical production. ():, 2016. [DOI].
Morales Pérez and Yeimy Liceth. Constraint-based metabolic models and their application in industrial biotechnology. ():, 2016. [DOI].
Jesse R. Walsh. Computational methods for integrated analysis of omics and pathway data. ():, 2016. [DOI].
Ding Ma, Laurence Yang, Ronan M. T. Fleming, Ines Thiele, Bernhard Ø. Palsson, and Michael A. Saunders. Reliable and efficient solution of genome-scale models of metabolism and macromolecular expression. ():, 2016. [DOI].
Edik M. Blais. Reconciled rat and human metabolic networks for comparative toxicogenomics analyses. ():, 2016. [DOI].
Agris Pentjuss, Jdzieni Skaidrojums, U Kalnenieks, A Pentjuss, R Rutkis, E Stalidzans, D Fell, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, I Odzina, A Pentjuss, I Odzina, A Pentjuss, A Pentjuss, I Odzina, A Gailums, A Pentjuss, U Kalnenieks, A Pentjuss, I Odzina, A Kostromins, D Fell, E Stalidzans, U Kalnenieks, A Pentjuss, O Rubenis, D Bauze, L Aprupe, B Lace, U Kalnenieks, A Pentjuss, R Rutkis, E Stalidzans, D Fell, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, A Pentjuss, S Abedon, E Almaas, B Kovcs, T Vicsek, Z Oltvai, A.-L Barabsi, O Babur, U Dogrusoz, E Demir, C Sander, E Baena-Gonzlez, F Rolland, J Thevelein, J Sheen, T Barrett, S Wilhite, P Ledoux, C Evangelista, I Kim, M Tomashevsky, K Marshall, K Phillippy, P Sherman, M Holko, A Yefanov, H Lee, N Zhang, C Robertson, N Serova, S Davis, A Soboleva, J Bates, D Chivian, A Arkin, D Beard, K Vinnakota, F Wu, S Becker, B Palsson, P Belford, F Bergmann, R Vallabhajosyula, H Sauro, B Bernstein, J Stamatoyannopoulos, J Costello, B Ren, A Milosavljevic, A Meissner, M Kellis, and M Marra. Stoichiometric models creation and analysis procedures for metabolic engineering = stehiometrisko modeļu izveides un analīzes procedūras metaboliskajai inženierijai. ():, 2016. [DOI].

Sheng Ren, Anna A. Hinzman, Emily L. Kang, Rhonda D. Szczesniak, and Long Lu. Computational and statistical analysis of metabolomics data. 11(6):1492–1513, 2015. [DOI].
Goldy De Bhowmick, Lokanand Koduru, and Ramkrishna Sen. Metabolic pathway engineering towards enhancing microalgal lipid biosynthesis for biofuel application—a review. 50():1239–1253, 2015. [DOI].
Matthew B. Biggs, Gregory L. Medlock, Glynis L. Kolling, and Jason A. Papin. Metabolic network modeling of microbial communities. 7(5):317–334, 2015. [DOI].
Evangelos Simeonidis and Nathan D. Price. Genome-scale modeling for metabolic engineering. 42(3):327–338, 2015. [DOI].
Philipp N. Spahn, Anders Holmgaard Hansen, Henning Gram Hansen, Johnny Arnsdorf, Helene Faustrup Kildegaard, and Nathan E. Lewis. A markov chain model for n-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering. 33():52–66, 2015. [DOI].
Shuyi Ma, K Minch, Tige R. Rustad, Samuel J. Hobbs, Suk-Lin Zhou, David R. Sherman, and Nathan D. Price. Integrated modeling of gene regulatory and metabolic networks in mycobacterium tuberculosis. 11(11):e1004543–e1004543, 2015. [DOI].
John Varga, Mariette Barbier, Xavier Mulet, Piotr Bielecki, Jennifer Bartell, Joshua P. Owings, Inmaculada Martínez-Ramos, Lauren Hittle, Michael R. Davis, F. Heath Damron, George W. Liechti, Jacek Puchałka, Vítor A. P. Martins dos Santos, Robert K. Ernst, Jason A. Papin, Sebastián Albertí, Antonio Oliver, and Joanna B. Goldberg. Genotypic and phenotypic analyses of a pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains. 16(1):883–883, 2015. [DOI].
Lisa Maria Dersch, Véronique Beckers, and Christoph Wittmann. Green pathways: metabolic network analysis of plant systems. 34():1–24, 2015. [DOI].
Andrew Damiani, Q. Peter He, Thomas W. Jeffries, and Jin Wang. Comprehensive evaluation of two genome‐scale metabolic network models for scheffersomyces stipitis. 112(6):1250–1262, 2015. [DOI].
Parizad Babaei, Sayed‐Amir Marashi, and Sedigheh Asad. Genome-scale reconstruction of the metabolic network in pseudomonas stutzeri a1501. 11(11):3022–3032, 2015. [DOI].
Devin H. Currie, Babu Raman, Christopher M. Gowen, Timothy J. Tschaplinski, Miriam Land, Steven D. Brown, Sean F. Covalla, Dawn M. Klingeman, Zamin K. Yang, Nancy L. Engle, Courtney M. Johnson, Miguel Rodríguez, A. Joe Shaw, William R. Kenealy, Lee R. Lynd, Stephen S. Fong, Jonathan R. Mielenz, Brian H. Davison, David A. Hogsett, and Christopher D. Herring. Genome-scale resources for thermoanaerobacterium saccharolyticum. 9(1):30–30, 2015. [DOI].
Mohammad Tajparast and Dominic Frigon. Genome-scale metabolic model of rhodococcus jostii rha1 (imt1174) to study the accumulation of storage compounds during nitrogen-limited condition. 9(1):43–43, 2015. [DOI].
Diego Salazar, Alexander Rodríguez‐López, Angélica María Herreño, Hector Barbosa, Juliana Herrera, Andrea Ardila, George E. Barreto, Janneth González, and Carlos Javier Alméciga-Díaz. Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network. 117(2):129–139, 2015. [DOI].
Anna‐Lena Hillje, Elisabeth Beckmann, Maria Angeliki S. Pavlou, Christian Jaeger, Maria Pires Pacheco, Thomas Sauter, Jens C. Schwamborn, and Lars Lewejohann. The neural stem cell fate determinant trim32 regulates complex behavioral traits. 9():75–75, 2015. [DOI].
Hamideh Fouladiha, Sayed‐Amir Marashi, and Mohammad Ali Shokrgozar. Reconstruction and validation of a constraint‐based metabolic network model for bone marrow‐derived mesenchymal stem cells. 48(4):475–485, 2015. [DOI].
Zahra Azimzadeh Irani, Amir Maghsoudi, Seyed Abbas Shojaosadati, and Ehsan Motamedian. Development and in silico analysis of a new nitrogen-limited feeding strategy for fed-batch cultures of pichia pastoris based on a simple ph-control system. 98():1–9, 2015. [DOI].
Carolina A. Contador, V. Rodríguez, Bárbara A. Andrews, and Juan A. Asenjo. Genome-scale reconstruction of salinispora tropica cnb-440 metabolism to study strain-specific adaptation. 108(5):1075–1090, 2015. [DOI].
Hailin Meng, Zhiqiang Xiong, Shujie Song, Jianfeng Wang, and Yong Wang. Construction of polyketide overproducing escherichia coli strains via synthetic antisense rnas based on in silico fluxome analysis and comparative transcriptome analysis. 11(4):530–541, 2015. [DOI].
Michael Binns, Pedro de Atauri, Anestis Vlysidis, Marta Cascante, and Constantinos Theodoropoulos. Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of actinobacillus succinogenes. 16(1):49–49, 2015. [DOI].
Javier Ferrer Valenzuela, Luis Andrés Pinuer, Apolinaria García, and Rodrigo Bórquez. Metabolic fluxes in lactic acid bacteria—a review. 29(2):185–217, 2015. [DOI].
Franziska Erlmeier, Annette Feuchtinger, Daniela Borgmann, Martina Rudelius, Michael Autenrieth, Axel Walch, and Gregor Weirich. Supremacy of modern morphometry in typing renal oncocytoma and malignant look-alikes. 144(2):147–156, 2015. [DOI].
Sayed‐Rzgar Hosseini, Aditya Barve, and Andreas Wagner. Exhaustive analysis of a genotype space comprising 1015 central carbon metabolisms reveals an organization conducive to metabolic innovation. 11(8):e1004329–e1004329, 2015. [DOI].
Bin Wang, Qianji Ning, Tong Hao, Ailing Yu, and Jinsheng Sun. Reconstruction and analysis of a genome-scale metabolic model for eriocheir sinensis eyestalks. 12(1):246–252, 2015. [DOI].
Lian He, Stephen Gang Wu, Ni Wan, Adrienne C. Reding, and Yinjie Tang. Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution function. 14(1):206–206, 2015. [DOI].
Carolina A. Contador, Carolina Shene, A. Olivera, Yasuo Yoshikuni, Alejandro H. Buschmann, Bárbara A. Andrews, and Juan A. Asenjo. Analyzing redox balance in a synthetic yeast platform to improve utilization of brown macroalgae as feedstock. 2():76–84, 2015. [DOI].
Bashir Sajo Mienda and Mohd Shahir Shamsir. in silicodeletion ofptsggene inescherichia coligenome-scale model predicts increased succinate production from glycerol. 33(11):2380–2389, 2015. [DOI].
Lisha K. Parambil and Debasis Sarkar. in silico analysis of bioethanol overproduction by genetically modified microorganisms in coculture fermentation. 2015():1–11, 2015. [DOI].
Michael P. Weiner, Julia Tröndle, Christoph Albermann, Georg A. Sprenger, and Dirk Weuster‐Botz. Perturbation experiments: approaches for metabolic pathway analysis in bioreactors. 152():91–136, 2015. [DOI].
Kourosh Salehi‐Ashtiani, Joseph Koussa, Bushra Dohai, Amphun Chaiboonchoe, Hong Cai, Kelly Dougherty, David R. Nelson, Kenan Jijakli, and Basel Khraiwesh. Toward applications of genomics and metabolic modeling to improve algal biomass productivity. ():173–189, 2015. [DOI].
Brandon Barker, Lin Xu, and Zhenglong Gu. Dynamic epistasis under varying environmental perturbations. 10(1):e0114911–e0114911, 2015. [DOI].
Katja Tummler, Clemens Kühn, and Edda Klipp. Dynamic metabolic models in context: biomass backtracking. 7(8):940–951, 2015. [DOI].
María Antonia Zamora, Andrés Pinzón, María Mercedes Zambrano, Silvia Restrepo, Linda J. Broadbelt, Matthew Moura, Johana Husserl, and Andrés Fernando González Barrios. A comparison between functional frequency and metabolic flows framed by biogeochemical cycles in metagenomes: the case of “el coquito” hot spring located at colombia's national nevados park. 313():259–265, 2015. [DOI].
David Hala, Lene H. Petersen, Dalma Martinović‐Weigelt, and Duane B. Huggett. in silicoanalysis of perturbed steroidogenesis and gonad growth in fathead minnows (p. promelas) exposed to 17α-ethynylestradiol. 61(3):122–138, 2015. [DOI].
Joachim von Wulffen, Patrick C. F. Buchholz, Oliver Sawodny, and Ronny Feuer. Modeling the metabolism of escherichia coli under oxygen gradients with dynamically changing flux bounds. 18():1–6, 2015. [DOI].
Jennifer Bartell. Comparative systems analysis of opportunistic gram-negative pathogens. ():, 2015. [DOI].
Bashir Sajo Mienda and Mohd Shahir Shamsir. Model-guided in silico overexpression of adhe gene predicts increased ethanol production in escherichia coli from xylose. 3(3):39–43, 2015. [DOI].
Stephen Lincoln, Ian S. Rogers, and Ranjan Srivastava. Metabolic design and engineering through ant colony optimization. ():225–232, 2015. [DOI].
Claudio Angione, Giovanni Carapezza, Jole Costanza, Píetro Lió, and Giuseppe Nicosia. Design and strain selection criteria for bacterial communication networks. ():, 2015. [DOI].
Zachary A. King, Justin S. Lu, Andreas Dräger, Philip Miller, Stephen Federowicz, Joshua A. Lerman, Ali Ebrahim, Bernhard Ø. Palsson, and Nathan E. Lewis. Bigg models: a platform for integrating, standardizing and sharing genome-scale models. 44(D1):D515–D522, 2015. [DOI].
Edward J. O’Brien, Jonathan M. Monk, and Bernhard Ø. Palsson. Using genome-scale models to predict biological capabilities. 161(5):971–987, 2015. [DOI].
Peter D. Karp, Mario Latendresse, Suzanne Paley, Markus Krummenacker, Quang Ong, Richard Billington, Anamika Kothari, Daniel Weaver, Thomas Lee, Pallavi Subhraveti, Aaron Spaulding, Carol A. Fulcher, Ingrid M. Keseler, and Ron Caspi. Pathway tools version 19.0 update: software for pathway/genome informatics and systems biology. 17(5):877–890, 2015. [DOI].
Douglas B. Kell, Neil Swainston, Pınar Pir, and Stephen G. Oliver. Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis. 33(4):237–246, 2015. [DOI].
Luca Gerosa, Bart Rudolf Boudewijn Haverkorn van Rijsewijk, Dimitris Christodoulou, Karl Kochanowski, Thomas Schmidt, Εlad Noor, and Uwe Sauer. Pseudo-transition analysis identifies the key regulators of dynamic metabolic adaptations from steady-state data. 1(4):270–282, 2015. [DOI].
Martin Kavšček, Govindprasad Bhutada, Tobias Madl, and Klaus Natter. Optimization of lipid production with a genome-scale model of yarrowia lipolytica. 9(1):72–72, 2015. [DOI].
Nicholas D. Gold, Christopher M. Gowen, François-Xavier Lussier, Sarat C. Cautha, Radhakrishnan Mahadevan, and Vincent J. J. Martin. Metabolic engineering of a tyrosine-overproducing yeast platform using targeted metabolomics. 14(1):73–73, 2015. [DOI].
Almut Heinken and Ines Thiele. Anoxic conditions promote species-specific mutualism between gut microbes in silico. 81(12):4049–4061, 2015. [DOI].
Cristiana Gomes de Oliveira Dal’Molin, Lake‐Ee Quek, Pedro A. Saa, and Lars K. Nielsen. A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. 6():4–4, 2015. [DOI].
Erik M. Quandt, Jimmy Gollihar, Zachary D. Blount, Andrew D. Ellington, George Georgiou, and Jeffrey E. Barrick. Fine-tuning citrate synthase flux potentiates and refines metabolic innovation in the lenski evolution experiment. 4():, 2015. [DOI].
Josefine Anfelt, Danuta Kaczmarzyk, Kiyan Shabestary, Björn Renberg, Johan Rockberg, Jens Nielsen, Mathias Uhlén, and Elton P. Hudson. Genetic and nutrient modulation of acetyl-coa levels in synechocystis for n-butanol production. 14(1):167–167, 2015. [DOI].
Almut Heinken and Ines Thiele. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. 6(2):120–130, 2015. [DOI].
Aarash Bordbar, Douglas McCloskey, Daniel C. Zielinski, Nikolaus Sonnenschein, Neema Jamshidi, and Bernhard Ø. Palsson. Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. 1(4):283–292, 2015. [DOI].
Haiqin Chen, Guangfei Hao, Lei Wang, Hongchao Wang, Zhennan Gu, Li Liu, Hao Zhang, Wei Chen, and Yong Q. Chen. Identification of a critical determinant that enables efficient fatty acid synthesis in oleaginous fungi. 5(1):11247–11247, 2015. [DOI].
Almut Heinken and Ines Thiele. Systems biology of host–microbe metabolomics. 7(4):195–219, 2015. [DOI].
Jérôme Izard, Cindy DC Gomez Balderas, Delphine Ropers, Stéphan Lacour, Xiaohu Song, Yifan Yang, Ariel B. Lindner, Johannes Geiselmann, and Hidde de Jong. A synthetic growth switch based on controlled expression of rna polymerase. 11(11):840–840, 2015. [DOI].
Aarthi Ravikrishnan and Karthik Raman. Critical assessment of genome-scale metabolic networks: the need for a unified standard. 16(6):1057–1068, 2015. [DOI].
Ben Heavner and Nathan D. Price. Comparative analysis of yeast metabolic network models highlights progress, opportunities for metabolic reconstruction. 11(11):e1004530–e1004530, 2015. [DOI].
Aditya Pratapa, Shankar Balachandran, and Karthik Raman. Fast-sl: an efficient algorithm to identify synthetic lethal sets in metabolic networks. 31(20):3299–3305, 2015. [DOI].
Héctor García Martín, Vinay Kumar, Daniel Weaver, Amit Ghosh, Victor Chubukov, Aindrila Mukhopadhyay, Adam P. Arkin, and Jay D. Keasling. A method to constrain genome-scale models with 13c labeling data. 11(9):e1004363–e1004363, 2015. [DOI].
Thomas Bayer, Sofia Milker, Thomas Wiesinger, Florian Rudroff, and Marko D. Mihovilovič. Designer microorganisms for optimized redox cascade reactions – challenges and future perspectives. 357(8):1587–1618, 2015. [DOI].
Wout Megchelenbrink, Rotem Katzir, Xiaowen Lu, Eytan Ruppin, and Richard A. Notebaart. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. 112(39):12217–12222, 2015. [DOI].
Eugen Bauer, Cédric C. Laczny, Stefanía Magnúsdóttir, Paul Wilmes, and Ines Thiele. Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. 3(1):35–35, 2015. [DOI].
Takeyuki Tamura, Wei Lu, and Tatsuya Akutsu. Computational methods for modification of metabolic networks. 13():376–381, 2015. [DOI].
John A. Cole, Lars Köhler, Jamila Hedhli, and Zaida Luthey‐Schulten. Spatially-resolved metabolic cooperativity within dense bacterial colonies. 9(1):15–15, 2015. [DOI].
Christopher J. Petzold, Leanne Jade G. Chan, Melissa Nhan, and Paul D. Adams. Analytics for metabolic engineering. 3():135–135, 2015. [DOI].
Chao Ye, Nan Xu, Haiqin Chen, Yong Q. Chen, Wei Chen, and Li Liu. Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus mortierella alpina. 9(1):1–1, 2015. [DOI].
Quỳnh Anh Nguyễn, Jens Schneider, Gajendar Komati Reddy, and Volker F. Wendisch. Fermentative production of the diamine putrescine: system metabolic engineering of corynebacterium glutamicum. 5(2):211–231, 2015. [DOI].
Meiyappan Lakshmanan, Sun‐Hyung Lim, Bijayalaxmi Mohanty, Jae Kwang Kim, Sun‐Hwa Ha, and Dong‐Yup Lee. Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis. 169(4):pp.01379.2015–pp.01379.2015, 2015. [DOI].
Kristin J. Adolfsen and Mark P. Brynildsen. Futile cycling increases sensitivity toward oxidative stress in escherichia coli. 29():26–35, 2015. [DOI].
Justin Tan, Cristal Zúñiga, and Karsten Zengler. Unraveling interactions in microbial communities - from co-cultures to microbiomes. 53(5):295–305, 2015. [DOI].
Ali Ebrahim, Eivind Almaas, Eugen Bauer, Aarash Bordbar, Anthony P. Burgard, Roger L. Chang, Andreas Dräger, Iman Famili, Adam M. Feist, Ronan M. T. Fleming, Stephen S. Fong, Vassily Hatzimanikatis, Markus J. Herrgård, Allen Holder, Michael Hucka, Daniel R. Hyduke, Neema Jamshidi, Sang Yup Lee, Nicolas Le Novère, Joshua A. Lerman, Nathan E. Lewis, Ding Ma, Radhakrishnan Mahadevan, Costas D. Maranas, Harish Nagarajan, Ali Navid, Jens Nielsen, Lars K. Nielsen, Juan Nogales, Alberto Noronha, Csaba Pál, Bernhard Ø. Palsson, Jason A. Papin, Kiran Raosaheb Patil, Nathan D. Price, Jennifer L. Reed, Michael A. Saunders, Ryan S. Senger, Nikolaus Sonnenschein, Yuekai Sun, and Ines Thiele. Do genome‐scale models need exact solvers or clearer standards? 11(10):831–831, 2015. [DOI].
Lucas Marmiesse, Rémi Peyraud, and Ludovic Cottret. Flexflux: combining metabolic flux and regulatory network analyses. 9(1):93–93, 2015. [DOI].
Stanislav Kopřiva, Alexander Calderwood, Silke C. Weckopp, and Anna Kopřivová. Plant sulfur and big data. 241():1–10, 2015. [DOI].
Chao Ye, Weihua Qiao, Xiaobin Yu, Xiao‐Jun Ji, He Huang, Jackie L. Collier, and Li Liu. Reconstruction and analysis of the genome-scale metabolic model of schizochytrium limacinum sr21 for docosahexaenoic acid production. 16(1):799–799, 2015. [DOI].
Bianca Habermann, José Villaveces, and Prasanna S. Koti. Tools for visualization and analysis of molecular networks, pathways, and -omics data. 8():11–11, 2015. [DOI].
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee. Reconstruction of genome-scale human metabolic models using omics data. 7(8):859–868, 2015. [DOI].
Zhenquan Lin, Yan Zhang, Qianqian Yuan, Qiaojie Liu, Yifan Li, Zhiwen Wang, Hongwu Ma, Tao Chen, and Xueming Zhao. Metabolic engineering of escherichia coli for poly(3-hydroxybutyrate) production via threonine bypass. 14(1):185–185, 2015. [DOI].
Claudio Angione and Píetro Lió. Predictive analytics of environmental adaptability in multi-omic network models. 5(1):15147–15147, 2015. [DOI].
Jahir M. Gutierrez and Nathan E. Lewis. Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome‐scale modeling. 10(7):939–949, 2015. [DOI].
Ruggero La Rosa, Juan Nogales, and Fernando Rojo. The <scp>crc</scp> / <scp>crcz</scp> ‐ <scp>crcy</scp> global regulatory system helps the integration of gluconeogenic and glycolytic metabolism in <scp> p </scp> seudomonas putida. 17(9):3362–3378, 2015. [DOI].
James D. Winkler, Andrea L. Halweg‐Edwards, and Ryan T. Gill. The laser database: formalizing design rules for metabolic engineering. 2():30–38, 2015. [DOI].
Daniel C. Zielinski, Fabian V. Filipp, Aarash Bordbar, Kasper Jensen, Jeffrey W. Smith, Markus J. Herrgård, Monica L. Mo, and Bernhard Ø. Palsson. Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. 6(1):7101–7101, 2015. [DOI].
Henning Knoop and Ralf Steuer. A computational analysis of stoichiometric constraints and trade-offs in cyanobacterial biofuel production. 3():47–47, 2015. [DOI].
Minsuk Kim, Jeong Sang Yi, Meiyappan Lakshmanan, Dong‐Yup Lee, and Byung‐Gee Kim. Transcriptomics‐based strain optimization tool for designing secondary metabolite overproducing strains of streptomyces coelicolor. 113(3):651–660, 2015. [DOI].
Ratul Chowdhury, Anupam Chowdhury, and Costas D. Maranas. Using gene essentiality and synthetic lethality information to correct yeast and cho cell genome-scale models. 5(4):536–570, 2015. [DOI].
Cheng Zhang, Boyang Ji, Adil Mardinoğlu, Jens Nielsen, and Qiang Hua. Logical transformation of genome-scale metabolic models for gene level applications and analysis. 31(14):2324–2331, 2015. [DOI].
Maike K. Aurich and Ines Thiele. Computational modeling of human metabolism and its application to systems biomedicine. 1386():253–281, 2015. [DOI].
M. Ahsanul Islam, Karsten Zengler, Elizabeth A. Edwards, Radhakrishnan Mahadevan, and Gregory Stephanopoulos. Investigating moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model. 7(8):869–882, 2015. [DOI].
Tomáš Zavřel, Henning Knoop, Ralf Steuer, Patrik R. Jones, Jan Červený, and Martin Trtílek. A quantitative evaluation of ethylene production in the recombinant cyanobacterium synechocystis sp. pcc 6803 harboring the ethylene-forming enzyme by membrane inlet mass spectrometry. 202():142–151, 2015. [DOI].
Komi Nambou, Xingxing Jian, Xin-Kai Zhang, Liujing Wei, Jiajia Lou, Catherine Madzak, and Qiang Hua. Flux balance analysis inspired bioprocess upgrading for lycopene production by a metabolically engineered strain of yarrowia lipolytica. 5(4):794–813, 2015. [DOI].
Jie Mei, Nan Xu, Chao Ye, Li Liu, and Jianrong Wu. Reconstruction and analysis of a genome-scale metabolic network of corynebacterium glutamicum s9114. 575(2):615–622, 2015. [DOI].
Radhakrishnan Mahadevan, Axel von Kamp, and Steffen Klamt. Genome-scale strain designs based on regulatory minimal cut sets. 31(17):2844–2851, 2015. [DOI].
Michael A. Henson. Genome-scale modelling of microbial metabolism with temporal and spatial resolution. 43(6):1164–1171, 2015. [DOI].
Susanne Alff-Tuomala, Laura Salusjärvi, Dorothee Barth, Merja Oja, Merja Penttilä, Juha‐Pekka Pitkänen, Laura Ruohonen, and Paula Jouhten. Xylose-induced dynamic effects on metabolism and gene expression in engineered saccharomyces cerevisiae in anaerobic glucose-xylose cultures. 100(2):969–985, 2015. [DOI].
Abhishek Subramanian, Jitesh Jhawar, and Ram Rup Sarkar. Dissecting leishmania infantum energy metabolism - a systems perspective. 10(9):e0137976–e0137976, 2015. [DOI].
Natalie Stanford, Pierre Millard, and Neil Swainston. Robokod: microbial strain design for (over)production of target compounds. 3():17–17, 2015. [DOI].
Rupa Bhowmick, Abhishek Subramanian, and Ram Rup Sarkar. Exploring the differences in metabolic behavior of astrocyte and glioblastoma: a flux balance analysis approach. 9(4):159–177, 2015. [DOI].
Julian Brandl and Mikael Rørdam Andersen. Current state of genome-scale modeling in filamentous fungi. 37(6):1131–1139, 2015. [DOI].
Ahmad A. Mannan, Yoshihiro Toya, Kazuyuki Shimizu, Johnjoe McFadden, Andrzej Kierzek, and Andrea Rocco. Integrating kinetic model of e. coli with genome scale metabolic fluxes overcomes its open system problem and reveals bistability in central metabolism. 10(10):e0139507–e0139507, 2015. [DOI].
Joshua J. Hamilton, Montserrat Calixto Contreras, and Jennifer L. Reed. Thermodynamics and h2 transfer in a methanogenic, syntrophic community. 11(7):e1004364–e1004364, 2015. [DOI].
Ehsan Motamedian, Ghazaleh Ghavami, and Soroush Sardari. Investigation on metabolism of cisplatin resistant ovarian cancer using a genome scale metabolic model and microarray data. 18(3):267–76, 2015. [DOI].
Yanfen Fu, Jong Moon Yoon, Laura R. Jarboe, and Jacqueline V. Shanks. Metabolic flux analysis of escherichia coli mg1655 under octanoic acid (c8) stress. 99(10):4397–4408, 2015. [DOI].
Brett G. Olivier, Maciej J. Swat, and Martijn J. Moné. Modeling and simulation tools: from systems biology to systems medicine. 1386():441–463, 2015. [DOI].
Ding Ma and Michael A. Saunders. Solving multiscale linear programs using the simplex method in quadruple precision. ():223–235, 2015. [DOI].
Thomas Pfau, Maria Pires Pacheco, and Thomas Sauter. Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond. 17(6):bbv100–bbv100, 2015. [DOI].
Haythem Latif, Merve Şahin, Janna Tarasova, Yekaterina Tarasova, Vasiliy A. Portnoy, Juan Nogales, and Karsten Zengler. Adaptive evolution of thermotoga maritima reveals plasticity of the abc transporter network. 81(16):5477–5485, 2015. [DOI].
James T. Yurkovich and Bernhard Ø. Palsson. Solving puzzles with missing pieces: the power of systems biology. 104(1):2–7, 2015. [DOI].
Chao Shi, Jian Yin, Zhe Liu, Jianxin Wu, Qi Zhao, Jian Ren, and Nan Yao. A systematic simulation of the effect of salicylic acid on sphingolipid metabolism. 6():186–186, 2015. [DOI].
Zhaobin Xu, Sabina Islam, Thomas K. Wood, and Zuyi Huang. An integrated modeling and experimental approach to study the influence of environmental nutrients on biofilm formation ofpseudomonas aeruginosa. 2015():1–12, 2015. [DOI].
Matthew Moura, Justin D. Finkle, Sarah C. Stainbrook, Jennifer L. Greene, Linda J. Broadbelt, and Keith E. J. Tyo. Evaluating enzymatic synthesis of small molecule drugs. 33():138–147, 2015. [DOI].
Elias W. Krumholz and Igor G. L. Libourel. Sequence-based network completion reveals the integrality of missing reactions in metabolic networks. 290(31):19197–19207, 2015. [DOI].
Georg Basler. Computational prediction of essential metabolic genes using constraint-based approaches. 1279():183–204, 2015. [DOI].
David Knies, Philipp Wittmüß, Sebastian Appel, Oliver Sawodny, Michael Ederer, and Ronny Feuer. Modeling and simulation of optimal resource management during the diurnal cycle in emiliania huxleyi by genome-scale reconstruction and an extended flux balance analysis approach. 5(4):659–676, 2015. [DOI].
Miguel Ponce-de-León, Jorge Calle-Espinosa, Juli Peretό, and Francisco Montero. Consistency analysis of genome-scale models of bacterial metabolism: a metamodel approach. 10(12):e0143626–e0143626, 2015. [DOI].
Abdul Hakim Mohamed Salleh, Mohd Saberi Mohamad, Safaai Deris, Sigeru Omatu, Florentino Fdez‐Riverola, and Juan M. Corchado. Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis. 20(4):685–693, 2015. [DOI].
Matthew B. Biggs and Jason A. Papin. Metabolic network-guided binning of metagenomic sequence fragments. 32(6):867–874, 2015. [DOI].
Sayed‐Amir Marashi and Zhaleh Hosseini. On correlated reaction sets and coupled reaction sets in metabolic networks. 13(04):1571003–1571003, 2015. [DOI].
Anand Gavai, Farahaniza Supandi, Hannes Hettling, Paul Murrell, Jack A. M. Leunissen, and Hans van Beek. Using bioconductor package biggr for metabolic flux estimation based on gene expression changes in brain. 10(3):e0119016–e0119016, 2015. [DOI].
Hai Shi and Jörg Schwender. Mathematical models of plant metabolism. 37():143–152, 2015. [DOI].
Lin Liu, Fangzhou Shen, Changpeng Xin, and Zhuo Wang. Multi‐scale modeling of arabidopsis thaliana response to different co2 conditions: from gene expression to metabolic flux. 58(1):2–11, 2015. [DOI].
Feifei Han, Gong‐Hua Li, Shaoxing Dai, and Jing‐Fei Huang. Genome-wide metabolic model to improve understanding of cd4 + t cell metabolism, immunometabolism and application in drug design. 12(2):431–443, 2015. [DOI].
Karthik Sekar and Keith E. J. Tyo. Regulatory effects on central carbon metabolism from poly-3-hydroxybutryate synthesis. 28():180–189, 2015. [DOI].
Neema Jamshidi and Anu Raghunathan. Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods. 6():1032–1032, 2015. [DOI].
Óttar Rolfsson and Bernhard Ø. Palsson. Decoding the jargon of bottom‐up metabolic systems biology. 37(6):588–591, 2015. [DOI].
Yali Wang, Nan Xu, Chao Ye, Li Liu, Zhongping Shi, and Jing Wu. Reconstruction and in silico analysis of an actinoplanes sp. se50/110 genome-scale metabolic model for acarbose production. 6():, 2015. [DOI].
Octavio Perez‐Garcia, Kartik Chandran, Silas G. Villas‐Bôas, and Naresh Singhal. Assessment of nitric oxide (no) redox reactions contribution to nitrous oxide (n2o) formation during nitrification using a multispecies metabolic network model. 113(5):1124–1136, 2015. [DOI].
Tijana Marinković and Matej Orešič. Modeling strategies to study metabolic pathways in progression to type 1 diabetes – challenges and opportunities. 589():131–137, 2015. [DOI].
Roberto Gallardo, Alejandro Acevedo, Julián Quintero, Iván Paredes, Raúl Conejeros, and Germán Aroca. In silico analysis of clostridium acetobutylicum atcc 824 metabolic response to an external electron supply. 39(2):295–305, 2015. [DOI].
Juan Nogales and Lucía Agudo. A practical protocol for integration of transcriptomics data into genome-scale metabolic reconstructions. ():135–152, 2015. [DOI].
Leanne S. Whitmore and Ping Ye. Dissecting germ cell metabolism through network modeling. 10(9):e0137607–e0137607, 2015. [DOI].
Longfei Mao, Averina Nicolae, Miguel A. P. Oliveira, Feng He, Siham Hachi, and Ronan M. T. Fleming. A constraint-based modelling approach to metabolic dysfunction in parkinson's disease. 13():484–491, 2015. [DOI].
Suthat Phaiphinit, Sittiporn Pattaradilokrat, Chidchanok Lursinsap, and Kitiporn Plaimas. In silico multiple-targets identification for heme detoxification in the human malaria parasite plasmodium falciparum. 37():237–244, 2015. [DOI].
Duschak VG. Synthetic biology: computational modeling bridging the gap between in vitro and in vivo reactions. 03(03):, 2015. [DOI].
Daichi Yukihira, Yoshinori Fujimura, Hiroyuki Wariishi, and Daisuke Miura. Bacterial metabolism in immediate response to nutritional perturbation with temporal and network view of metabolites. 11(9):2473–2482, 2015. [DOI].
Bashir Sajo Mienda, Mohd Shahir Shamsir, and Mohd Shahir Shamsir. Model-driven in silico glpc gene knockout predicts increased succinate production from glycerol in escherichia coli. 2(2):40–48, 2015. [DOI].
David E. Ruckerbauer, Christian Jungreuthmayer, and Jürgen Zanghellini. Predicting genetic engineering targets with elementary flux mode analysis: a review of four current methods. 32(6):534–546, 2015. [DOI].
Julia Lischke, Christine Lang, Oliver Sawodny, and Ronny Feuer. Impairment of energy metabolism in cardiomyocytes caused by 5-fu catabolites can be compensated by administration of amino acids. 79():5363–5366, 2015. [DOI].
Nima Abedpour and Markus Kollmann. Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks. 9(1):88–88, 2015. [DOI].
Na-Rae Lee, Ji-Yeong Yun, Sun-Mee Lee, and Jin‐Byung Park. Cyclohexanone-induced stress metabolism of escherichia coli and corynebacterium glutamicum. 20(6):1088–1098, 2015. [DOI].
Max Conway, Claudio Angione, and Píetro Lió. Iterative multi level calibration of metabolic networks. 11(1):93–105, 2015. [DOI].
Pratish Gawand, Fatumina Said Abukar, Naveen Venayak, Siavash Partow, Adilson E. Motter, and Radhakrishnan Mahadevan. Sub-optimal phenotypes of double-knockout mutants of escherichia coli depend on the order of gene deletions. 7(8):930–939, 2015. [DOI].
Bashir Sajo Mienda, Mohd Shahir Shamsir, and Mohd Shahir Shamsir. An overview of pathway prediction tools for synthetic design of microbial chemical factories. 2(1):1–14, 2015. [DOI].
Hadi Nazem‐Bokaee, Ryan S. Senger, and Ryan S. Senger. Tomi-fba: a genome-scale metabolic flux based algorithm to select optimum hosts and media formulations for expressing pathways of interest. 2(4):335–374, 2015. [DOI].
Daniele De Martino, Matteo Mori, and V. Parisi. Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding. 10(4):e0122670–e0122670, 2015. [DOI].
Arne C. Reimers. Obstructions to sampling qualitative properties. 10(8):e0135636–e0135636, 2015. [DOI].
Seong-Joo Hong and Choul‐Gyun Lee. Microalgal systems biology for biofuel production. ():3–21, 2015. [DOI].
Weiwei Wang, Yanda Li, and Xiaowo Wang. Op‐synthetic: identification of optimal genetic manipulations for the overproduction of native and non‐native metabolites. 2(3):100–109, 2015. [DOI].
Jiun Y. Yen, Glenda E. Gillaspy, and Ryan S. Senger. Systems metabolic engineering of arabidopsis for increased cellulose production. 29(S1):, 2015. [DOI].
Miguel Á. Valderrama-Gómez, Sander Wagner, and Andreas Kremling. Computer-guided metabolic engineering. ():153–184, 2015. [DOI].
John W. May. Cheminformatics for genome-scale metabolic reconstructions. ():, 2015. [DOI].
Rui C. Martins and Nuno Fachada. Finite element procedures for enzyme, chemical reaction and 'in-silico' genome scale networks. ():, 2015. [DOI].
Andrew Damiani. Control engineering perspective on genome-scale metabolic modeling. ():, 2015. [DOI].
Freyr Sverrisson. Mat á styrkgildum umbrotsefna í efnaskiptalíkönum með monte carlo hermun. ():, 2015. [DOI].
Stéphane Pinhal. Mécanismes de l'inhibition de la croissance par l'acétate chez escherichia coli. ():, 2015. [DOI].
João R. C. Ramos. Analysis of metabolic flux distributions in relation to the extracellular environment in avian cells. ():, 2015. [DOI].
Gabriele Fontanarosa, Giulia Menichetti, Enrico Giampieri, Gastone Castellani, Giovanni Martinelli, and Daniel Remondini. Network approaches for analysis and modeling of the human metabolism. 15(02):1540026–1540026, 2015. [DOI].
Md. Bahadur Badsha. Development of complementary elementary modeanalysis for integration of heterogeneous biological data into a complex metabolic network. ():, 2015. [DOI].
E. Mendes and A. FURIGO. Estudo em escala genômica da clostridium acetobutylicum atcc 824 para a bioprodução de hidrogênio e criação de um modelo regulatório. ():12816–12823, 2015. [DOI].
Shuyi Ma. Systems biology of lung diseases: a multi-perspective view from host and pathogen. ():, 2015. [DOI].
Tyler Huelsman. Genome-scale reconstruction of the chlorella vulgaris utex 395 metabolic network. ():, 2015. [DOI].
Yufang Wang and Dewu Ding. Revealing the structure and function of p. pastoris metabolic network using petri nets. 129():360–363, 2015. [DOI].
Amit Kumar. Understanding physiology of diseases and cell lines using omics based approaches. ():, 2015. [DOI].
Ronan M. T. Fleming, Nikos Vlassis, Ines Thiele, and Michael A. Saunders. Conditions for duality between fluxes and concentrations in biochemical networks. ():, 2015. [DOI].
Mark Reuter, Duncan J. H. Gaskin, and Aline Métris. A proposed essential gene discovery pipeline: a campylobacter jejuni case study. 1279():167–181, 2015. [DOI].

Edward T. Chouchani, Victoria R. Pell, Edoardo Gaude, Dunja Aksentijević, Stephanie Y. Sundier, Ellen L. Robb, Angela Logan, Sergiy M. Nadtochiy, Emily Ord, Anthony C. Smith, Filmon Eyassu, Rachel Shirley, Chou-Hui Hu, Anna Dare, Andrew M. James, Sebastian Rogatti, Richard C. Hartley, Simon Eaton, Ana S.H. Costa, Paul S. Brookes, Sean M. Davidson, Michael R. Duchen, Kourosh Saeb‐Parsy, Michael J. Shattock, Alan J. Robinson, Lorraine M. Work, Christian Frezza, Thomas Krieg, and Michael P. Murphy. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ros. 515(7527):431–435, 2014. [DOI].
Stephen Sarria, Betty L. Wong, Héctor García Martín, Jay D. Keasling, and Pamela Peralta‐Yahya. Microbial synthesis of pinene. 3(7):466–475, 2014. [DOI].
Matthew N. Benedict, James R. Henriksen, William W. Metcalf, Rachel J. Whitaker, and Nathan D. Price. Itep: an integrated toolkit for exploration of microbial pan-genomes. 15(1):8–8, 2014. [DOI].
Michael A. Henson and Timothy J. Hanly. Dynamic flux balance analysis for synthetic microbial communities. 8(5):214–229, 2014. [DOI].
Samuel M. D. Seaver, Svetlana Gerdes, Océane Frelin, Claudia Lerma‐Ortiz, Louis Mt Bradbury, Rémi Zallot, Ghulam Hasnain, Thomas D. Niehaus, Basma Yacoubi, Shiran Pasternak, Robert Olson, Gordon D. Pusch, Ross Overbeek, Rick Stevens, Valérie de Crécy‐Lagard, Doreen Ware, Andrew D. Hanson, and Christopher S. Henry. High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the plantseed resource. 111(26):9645–9650, 2014. [DOI].
Javier Carrera, Raíssa Estrela, Jing Luo, Navneet Rai, Αθανάσιος Τσουκαλάς, and Ilias Tagkopoulos. An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of <scp>e</scp> scherichia coli . 10(7):735–735, 2014. [DOI].
Md. Bahadur Badsha, Ryo Tsuboi, and Hiroyuki Kurata. Complementary elementary modes for fast and efficient analysis of metabolic networks. 90():121–130, 2014. [DOI].
Germán Plata, Christopher S. Henry, and Dennis Vitkup. Long-term phenotypic evolution of bacteria. 517(7534):369–372, 2014. [DOI].
Andreas Dräger and Bernhard Ø. Palsson. Improving collaboration by standardization efforts in systems biology. 2():61–61, 2014. [DOI].
Óscar Dias, Rui Pereira, Andreas Gombert, Eugénio C. Ferreira, and Isabel Rocha. Iod907, the first genome‐scale metabolic model for the milk yeast kluyveromyces lactis. 9(6):776–790, 2014. [DOI].
Octavio Perez‐Garcia, Silas G. Villas‐Bôas, Simon Swift, Kartik Chandran, and Naresh Singhal. Clarifying the regulation of no/n2o production in nitrosomonas europaea during anoxic–oxic transition via flux balance analysis of a metabolic network model. 60():267–277, 2014. [DOI].
Nadeera Jayasinghe, Ashley E. Franks, Kelly P. Nevin, and Radhakrishnan Mahadevan. Metabolic modeling of spatial heterogeneity of biofilms in microbial fuel cells reveals substrate limitations in electrical current generation. 9(10):1350–1361, 2014. [DOI].
Mario Latendresse. Efficiently gap-filling reaction networks. 15(1):225–225, 2014. [DOI].
Zachary A. King and Adam M. Feist. Optimal cofactor swapping can increase the theoretical yield for chemical production in escherichia coli and saccharomyces cerevisiae. 24():117–128, 2014. [DOI].
Amphun Chaiboonchoe, Bushra Dohai, Hong Cai, David R. Nelson, Kenan Jijakli, and Kourosh Salehi‐Ashtiani. Microalgal metabolic network model refinement through high-throughput functional metabolic profiling. 2():68–68, 2014. [DOI].
Yanfeng Liu, Hyun‐Dong Shin, Jianghua Li, and Long Liu. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects. 99(3):1109–1118, 2014. [DOI].
Zhiqiang Sun, Hailin Meng, Jing Li, Jianfeng Wang, Qian Li, Yong Wang, and Yansheng Zhang. Identification of novel knockout targets for improving terpenoids biosynthesis in saccharomyces cerevisiae. 9(11):e112615–e112615, 2014. [DOI].
Rienk A. Rienksma, María Suárez‐Diez, Lucie Spina, Peter J. Schaap, and Vítor A. P. Martins dos Santos. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets. 26(6):610–622, 2014. [DOI].
Yi Li, Guojian Zhang, and Blaine A. Pfeifer. Current and emerging options for taxol production. 148():405–425, 2014. [DOI].
Björn H. Junker. Flux analysis in plant metabolic networks: increasing throughput and coverage. 26():183–188, 2014. [DOI].
J.M. González-Martínez, Abel Folch‐Fortuny, Francisco Llaneras, Marta Tortajada, Jesús Picó, and Alberto Ferrer. Metabolic flux understanding of pichia pastoris grown on heterogenous culture media. 134():89–99, 2014. [DOI].
Parizad Babaei, Tahereh Ghasemi-Kahrizsangi, and Sayed‐Amir Marashi. Modeling the differences in biochemical capabilities ofpseudomonasspecies by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences? 2014():1–11, 2014. [DOI].
Benjamín J. Sánchez, José Ricardo Pérez‐Correa, and Eduardo Agosín. Construction of robust dynamic genome-scale metabolic model structures of saccharomyces cerevisiae through iterative re-parameterization. 25():159–173, 2014. [DOI].
Joseph Koussa, Amphun Chaiboonchoe, and Kourosh Salehi‐Ashtiani. Computational approaches for microalgal biofuel optimization: a review. 2014():1–12, 2014. [DOI].
Xiaoyang Wang, Chuanbo Zhang, Meiling Wang, and Wenyu Lu. Genome-scale metabolic network reconstruction of saccharopolyspora spinosa for spinosad production improvement. 13(1):41–41, 2014. [DOI].
Stephen S. Fong. Computational approaches to metabolic engineering utilizing systems biology and synthetic biology. 11(18):28–34, 2014. [DOI].
Lü Jing, Farah Fathiah Muzaffar Shah, Mohd Saberi Mohamad, Nur Laily Hamran, Abdul Hakim Mohamed Salleh, Safaai Deris, and Hany Alashwal. Database and tools for metabolic network analysis. 19(4):568–585, 2014. [DOI].
Rafael S. Costa, André Veríssimo, and Susana Vinga. Ki mosys: a web-based repository of experimental data for kinetic models of biological systems. 8(1):85–85, 2014. [DOI].
Gilles Vieira, Marc Carnicer, Jean‐Charles Portais, and Stéphanie Heux. Findpath: a matlab solution for in silico design of synthetic metabolic pathways. 30(20):2986–2988, 2014. [DOI].
Abel Folch‐Fortuny, Marta Tortajada, J.M. Prats-Montalbán, Francisco Llaneras, Jesús Picó, and Alberto Ferrer. Mcr-als on metabolic networks: obtaining more meaningful pathways. 142():293–303, 2014. [DOI].
Juan Nogales. A practical protocol for genome-scale metabolic reconstructions. ():197–221, 2014. [DOI].
Yue‐Dong Gao, Yuqi Zhao, and Jing‐Fei Huang. Metabolic modeling of commonescherichia colistrains in human gut microbiome. 2014():1–11, 2014. [DOI].
Michael P. Weiner, Julia Tröndle, Christoph Albermann, Georg A. Sprenger, and Dirk Weuster‐Botz. Improvement of constraint‐based flux estimation during l‐phenylalanine production with escherichia coli using targeted knock‐out mutants. 111(7):1406–1416, 2014. [DOI].
Xinsen Wu, Xiaoyang Wang, and Wenyu Lu. Genome-scale reconstruction of a metabolic network for gluconobacter oxydans 621h. 117():10–14, 2014. [DOI].
Robert Nilsson, Fredric Bauer, Sennai Mesfun, Christian Hulteberg, Joakim Lundgren, Sune Wännström, Ulrika Rova, and Kris A. Berglund. Techno-economics of carbon preserving butanol production using a combined fermentative and catalytic approach. 161():263–269, 2014. [DOI].
Priyanka P. Sadhukhan and Anu Raghunathan. Investigating host–pathogen behavior and their interaction using genome-scale metabolic network models. 1184():523–562, 2014. [DOI].
Josh Quarterman, Soo Rin Kim, Pan‐Jun Kim, and Yong‐Su Jin. Enhanced hexose fermentation by saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening. 194():48–57, 2014. [DOI].
Ming‐Tsang Wu and Christina Chan. Prediction of therapeutic microrna based on the human metabolic network. 30(8):1163–1171, 2014. [DOI].
Alberto Noronha, Paulo Vilaça, and Miguel Rocha. An integrated network visualization framework towards metabolic engineering applications. 15(1):420–420, 2014. [DOI].
Lisha K. Parambil and Debasis Sarkar. Probing the bioethanol production potential of scheffersomyces (pichia) stipitis using validated genome-scale model. 36(12):2443–2451, 2014. [DOI].
Mathieu Almeida and Mihai Pop. High-throughput sequencing as a tool for exploring the human microbiome. ():55–66, 2014. [DOI].
Sriram Chandrasekaran. Predicting phenotype from genotype through reconstruction and integrative modeling of metabolic and regulatory networks. ():307–325, 2014. [DOI].
K. P. Lisha and Debasis Sarkar. In silico analysis of bioethanol production from glucose/xylose mixtures during fed-batch fermentation of co-culture and mono-culture systems. 19(5):879–891, 2014. [DOI].
Rafael S. Costa, Son Hoang Nguyen, András Hartmann, and Susana Vinga. Exploring the cellular objective in flux balance constraint-based models. ():211–224, 2014. [DOI].
Erin R. Shellman, Yu Chen, Xiaoxia Nina Lin, Charles Burant, and Santiago Schnell. Metabolic network motifs can provide novel insights into evolution: the evolutionary origin of eukaryotic organelles as a case study. 53():242–250, 2014. [DOI].
Erno Lindfors, Paula Jouhten, Merja Oja, Eija Rintala, Matej Orešič, and Merja Penttilä. Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of saccharomyces cerevisiae. 8(1):16–16, 2014. [DOI].
Leonid Chindelevitch, Jason Trigg, Aviv Regev, and Bonnie Berger. An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. 5(1):4893–4893, 2014. [DOI].
Paula Jouhten and Hannu Maaheimo. Labelling analysis for 13c mfa using nmr spectroscopy. 1191():143–164, 2014. [DOI].
James R. Collett, Pimphan A. Meyer, and Susanne B. Jones. Preliminary economics for hydrocarbon fuel production from cellulosic sugars. ():, 2014. [DOI].
Guojian Zhang and Blaine A. Pfeifer. Production of therapeutic products. ():261–276, 2014. [DOI].
DANIELA CAROLINA FLÓREZ PARRA, VANESSA RAMIREZ TOVAR, and Andrés Fernando González Barrios. Elucidating the role of metabolites as quorum sensing signals using phase plane analysis: the case of indole in escherichia coli. 22(04):523–531, 2014. [DOI].
JULIAN TRIANA DOPICO. Model-based analysis and metabolic design of a cyanobacterium for bio-products synthesis. ():, 2014. [DOI].
Wei Suong Teo. Engineering synthetic promoters as dynamic controllers in saccharomyces cerevisiae. ():, 2014. [DOI].
Binbin Chen. Systems-level characterization and transporter engineering in saccharomyces cerevisiae for improved hydrocarbon tolerance. ():, 2014. [DOI].
Tamás Turányi and Alison S. Tomlin. Reaction pathway analysis. ():53–60, 2014. [DOI].
Victor Chubukov, Luca Gerosa, Karl Kochanowski, and Uwe Sauer. Coordination of microbial metabolism. 12(5):327–340, 2014. [DOI].
Daniel Machado and Markus J. Herrgård. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. 10(4):e1003580–e1003580, 2014. [DOI].
Anna Kubacka, María Suárez‐Diez, David Rojo, Rafael Bargiela, Sergio Ciordia, Inés Zapico, Juan Pablo Albar, Coral Barbas, Vítor A. P. Martins dos Santos, Marcos Fernández–García, and Manuel Ferrer. Understanding the antimicrobial mechanism of tio2-based nanocomposite films in a pathogenic bacterium. 4(1):4134–4134, 2014. [DOI].
Nikos Vlassis, Maria Pires Pacheco, and Thomas Sauter. Fast reconstruction of compact context-specific metabolic network models. 10(1):e1003424–e1003424, 2014. [DOI].
Irina Borodina and Jens Nielsen. Advances in metabolic engineering of yeastsaccharomyces cerevisiaefor production of chemicals. 9(5):609–620, 2014. [DOI].
Irina Borodina, Kanchana Rueksomtawin Kildegaard, Niels Bjerg Jensen, Thomas Blicher, Jérôme Maury, Svetlana Sherstyk, Konstantin Schneider, Pedro Lamosa, Markus J. Herrgård, Inger Rosenstand, Fredrik Öberg, Jochen Förster, and Jens Nielsen. Establishing a synthetic pathway for high-level production of 3-hydroxypropionic acid in saccharomyces cerevisiae via β-alanine. 27():57–64, 2014. [DOI].
Almut Heinken, M. Tanweer Khan, Giuseppe Paglia, Dmitry A. Rodionov, Hermie J. M. Harmsen, and Ines Thiele. Functional metabolic map of faecalibacterium prausnitzii, a beneficial human gut microbe. 196(18):3289–3302, 2014. [DOI].
Achuthanunni Chokkathukalam, Dong‐Hyun Kim, Michael P. Barrett, Rainer Breitling, and Darren J. Creek. Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks. 6(4):511–524, 2014. [DOI].
Joris Beld, D. John Lee, and Michael D. Burkart. Fatty acid biosynthesis revisited: structure elucidation and metabolic engineering. 11(1):38–59, 2014. [DOI].
Miguel A. Campodonico, Bárbara A. Andrews, Juan A. Asenjo, Bernhard Ø. Palsson, and Adam M. Feist. Generation of an atlas for commodity chemical production in escherichia coli and a novel pathway prediction algorithm, gem-path. 25():140–158, 2014. [DOI].
Tige R. Rustad, K Minch, Shuyi Ma, Jessica K. Winkler, Samuel J. Hobbs, Mark J. Hickey, William Brabant, Serdar Turkarslan, Nathan D. Price, Nitin S. Baliga, and David R. Sherman. Mapping and manipulating the mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network. 15(11):502–502, 2014. [DOI].
Jose A. Gomez, Kai Höffner, and Paul I. Barton. Dfbalab: a fast and reliable matlab code for dynamic flux balance analysis. 15(1):409–409, 2014. [DOI].
Ines Thiele, Nikos Vlassis, and Ronan M. T. Fleming. <scp>fast</scp>g<scp>ap</scp>f<scp>ill</scp>: efficient gap filling in metabolic networks. 30(17):2529–2531, 2014. [DOI].
Byoung‐Jin Kim, Won Jun Kim, Dong In Kim, and Sang Yup Lee. Applications of genome-scale metabolic network model in metabolic engineering. 42(3):339–348, 2014. [DOI].
Swagatika Sahoo, Maike K. Aurich, Jón J. Jónsson, and Ines Thiele. Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. 5():91–91, 2014. [DOI].
SemidÃ¡n Robaina EstÃ©vez and Zoran Nikoloski. Generalized framework for context-specific metabolic model extraction methods. 5():491–491, 2014. [DOI].
Ibrahim E. Elsemman, Fredrik Karlsson, Saeed Shoaie, Intawat Nookaew, Taysir Hassan A. Soliman, and Jens Nielsen. Genome-scale metabolic reconstructions of bifidobacterium adolescentis l2-32 and faecalibacterium prausnitzii a2-165 and their interaction. 8(1):41–41, 2014. [DOI].
Matthew N. Benedict, Michael Mundy, Christopher S. Henry, Nicholas Chia, and Nathan D. Price. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. 10(10):e1003882–e1003882, 2014. [DOI].
Minsuk Kim, Jeong Sang Yi, Joonwon Kim, Jinu Kim, Min Woo Kim, and Byung‐Gee Kim. Reconstruction of a high‐quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production in streptomyces coelicolor a3(2). 9(9):1185–1194, 2014. [DOI].
Maike K. Aurich, Giuseppe Paglia, Óttar Rolfsson, Sigrún Hrafnsdóttir, Manuela Magnúsdóttir, Magdalena M. Stefaniak, Bernhard Ø. Palsson, Ronan M. T. Fleming, and Ines Thiele. Prediction of intracellular metabolic states from extracellular metabolomic data. 11(3):603–619, 2014. [DOI].
Paolo Cazzaniga, Chiara Damiani, Daniela Besozzi, Riccardo Colombo, Marco S. Nobile, Daniela Gaglio, Dario Pescini, Sara Molinari, Giancarlo Mauri, Lilia Alberghina, and Marco Vanoni. Computational strategies for a system-level understanding of metabolism. 4(4):1034–1087, 2014. [DOI].
Swagatika Sahoo, Hulda S. Haraldsdóttir, Ronan M. T. Fleming, and Ines Thiele. Modeling the effects of commonly used drugs on human metabolism. 282(2):297–317, 2014. [DOI].
John Blazeck, Jarrett Miller, Anny Pan, Jon Gengler, Clinton Holden, Mariam Jamoussi, and Hal S. Alper. Metabolic engineering of saccharomyces cerevisiae for itaconic acid production. 98(19):8155–8164, 2014. [DOI].
Javier Cárdenas and Nancy A. Da Silva. Metabolic engineering of saccharomyces cerevisiae for the production of triacetic acid lactone. 25():194–203, 2014. [DOI].
Tige R. Rustad, K Minch, Shuyi Ma, Jessica K. Winkler, Samuel J. Hobbs, Mark J. Hickey, William Brabant, Serdar Turkarslan, Nathan D. Price, Nitin S. Baliga, and David R. Sherman. Mapping and manipulating the mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network. 15(11):502–502, 2014. [DOI].
Stephen Federowicz, Donghyuk Kim, Ali Ebrahim, Joshua A. Lerman, Harish Nagarajan, Byung-kwan Cho, Karsten Zengler, and Bernhard Ø. Palsson. Determining the control circuitry of redox metabolism at the genome-scale. 10(4):e1004264–e1004264, 2014. [DOI].
Benjamin VanderSluis, David Hess, Colin Pesyna, Elias W. Krumholz, Tahin Syed, Balázs Szappanos, Corey Nislow, Balázs Papp, Olga G. Troyanskaya, Chad L. Myers, and Amy A. Caudy. Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection. 15(4):R64–R64, 2014. [DOI].
Hojung Nam, Miguel A. Campodonico, Aarash Bordbar, Daniel R. Hyduke, Sangwoo Kim, Daniel C. Zielinski, and Bernhard Ø. Palsson. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. 10(9):e1003837–e1003837, 2014. [DOI].
Lake‐Ee Quek, Stefanie Dietmair, Michael Hanscho, Verónica S. Martínez, Nicole Borth, and Lars K. Nielsen. Reducing recon 2 for steady-state flux analysis of hek cell culture. 184():172–178, 2014. [DOI].
Kambiz Baghalian, Mohammad‐Reza Hajirezaei, and Falk Schreiber. Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering. 26(10):3847–3866, 2014. [DOI].
Eduard J. Kerkhoven, Petri‐Jaan Lahtvee, and Jens Nielsen. Applications of computational modeling in metabolic engineering of yeast. 15(1):n/a–n/a, 2014. [DOI].
Daniel Weaver, Ingrid M. Keseler, Amanda Mackie, Ian T. Paulsen, and Peter D. Karp. A genome-scale metabolic flux model of escherichia coli k–12 derived from the ecocyc database. 8(1):79–79, 2014. [DOI].
Marco Fondi, Isabel Maida, Elena Perrin, Alessandra Mellera, Stéfano Mocali, Ermenegilda Parrilli, Maria Luisa Tutino, Píetro Lió, and Renato Fani. Genome‐scale metabolic reconstruction and constraint‐based modelling of the antarctic bacterium <scp> p </scp> seudoalteromonas haloplanktis <scp>tac</scp> 125. 17(3):751–766, 2014. [DOI].
Aarash Bordbar, Harish Nagarajan, Nathan E. Lewis, Haythem Latif, Ali Ebrahim, Stephen Federowicz, Jan Schellenberger, and Bernhard Ø. Palsson. Minimal metabolic pathway structure is consistent with associated biomolecular interactions. 10(7):737–737, 2014. [DOI].
Omer Eilam, Raphy Zarecki, Matthew Oberhardt, Luke K. Ursell, Martin Kupiec, Rob Knight, Uri Gophna, and Eytan Ruppin. Glycan degradation (glyder) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations. 5(4):, 2014. [DOI].
Ali Masoudi‐Nejad and Yazdan Asgari. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. 30():21–29, 2014. [DOI].
Adam M. Feist, Harish Nagarajan, Amelia‐Elena Rotaru, Pier‐Luc Tremblay, Tian Zhang, Kelly P. Nevin, Derek R. Lovley, and Karsten Zengler. Constraint-based modeling of carbon fixation and the energetics of electron transfer in geobacter metallireducens. 10(4):e1003575–e1003575, 2014. [DOI].
Jordan O. Hay, Hai Shi, Nicolas Heinzel, Inga Hebbelmann, Hardy Rolletschek, and Jörg Schwender. Integration of a constraint-based metabolic model of brassica napus developing seeds with 13c-metabolic flux analysis. 5():724–724, 2014. [DOI].
Monica Larocque, Thierry Chénard, and Rafaël Najmanovich. A curated c. difficile strain 630 metabolic network: prediction of essential targets and inhibitors. 8(1):117–117, 2014. [DOI].
Nan Xu, Jie Liu, Lianzhong Ai, and Li Liu. Reconstruction and analysis of the genome-scale metabolic model of lactobacillus casei lc2w. 554(2):140–147, 2014. [DOI].
Uldis Kalnenieks, Agris Pentjuss, Reinis Rutkis, Egils Stalidzāns, and David A. Fell. Modeling of zymomonas mobilis central metabolism for novel metabolic engineering strategies. 5():42–42, 2014. [DOI].
Ángela Cintolesi, James M. Clomburg, and Ramón González. In silico assessment of the metabolic capabilities of an engineered functional reversal of the β-oxidation cycle for the synthesis of longer-chain (c≥4) products. 23():100–115, 2014. [DOI].
Sanu Shameer, Flora Logan-Klumpler, Florence Vinson, Ludovic Cottret, Benjamin Merlet, Fiona Achcar, Michael Boshart, Matthew Berriman, Rainer Breitling, Frédéric Bringaud, Peter Bütikofer, Amy M. Cattanach, Bridget Bannerman-Chukualim, Darren J. Creek, Kathryn Crouch, Harry P. de Koning, Hubert Denise, Charles Ebikeme, Alan H. Fairlamb, Michael A. J. Ferguson, Michael L. Ginger, Christiane Hertz‐Fowler, Eduard J. Kerkhoven, Pascal Mäser, Paul A.M. Michels, Archana Nayak, David W. Nes, Derek P. Nolan, Christian A. Olsen, Fátima Silva-Franco, Terry Smith, Martin C. Taylor, Aloysius G. M. Tielens, Michael D. Urbaniak, Jaap J. van Hellemond, Isabel M. Vincent, Shane R. Wilkinson, Susan Wyllie, Fred R. Opperdoes, Michael P. Barrett, and Fabien Jourdan. Trypanocyc: a community-led biochemical pathways database for trypanosoma brucei. 43(D1):D637–D644, 2014. [DOI].
Paul A. Jensen and Jason A. Papin. Metdraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data. 30(9):1327–1328, 2014. [DOI].
Hong Yang, Elias W. Krumholz, Evan D. Brutinel, Nagendra P. Palani, Michael J. Sadowsky, Andrew Odlyzko, Jeffrey A. Gralnick, and Igor G. L. Libourel. Genome-scale metabolic network validation of shewanella oneidensis using transposon insertion frequency analysis. 10(9):e1003848–e1003848, 2014. [DOI].
Mahdieh Hadi and Sayed‐Amir Marashi. Reconstruction of a generic metabolic network model of cancer cells. 10(11):3014–3021, 2014. [DOI].
Joonhoon Kim and Jennifer L. Reed. Refining metabolic models and accounting for regulatory effects. 29():34–38, 2014. [DOI].
Andrea De Martino, Daniele De Martino, Roberto Mulet, and Andrea Pagnani. Identifying all moiety conservation laws in genome-scale metabolic networks. 9(7):e100750–e100750, 2014. [DOI].
András Pásztor, Pauli T. Kallio, Dávid Malatinszky, M. Akhtar, and Patrik R. Jones. A synthetic o2‐tolerant butanol pathway exploiting native fatty acid biosynthesis in escherichia coli. 112(1):120–128, 2014. [DOI].
Xin Fang, Jaques Reifman, and Anders Wallqvist. Modeling metabolism and stage-specific growth of plasmodium falciparum hb3 during the intraerythrocytic developmental cycle. 10(10):2526–2537, 2014. [DOI].
Chao Ye, Wei Zou, Nan Xu, and Li Liu. Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin c production. 182-183():61–67, 2014. [DOI].
Nicolás Loira, Anna Zhukova, and David James Sherman. Pantograph: a template-based method for genome-scale metabolic model reconstruction. 13(02):1550006–1550006, 2014. [DOI].
Alfred Fernández‐Castané, Tamás Fehér, Pablo Carbonell, Cyrille Pauthenier, and Jean‐Loup Faulon. Computer-aided design for metabolic engineering. 192():302–313, 2014. [DOI].
Neha Rohatgi, Tine Nielsen, Sara Petersen Bjørn, Ívar Axelsson, Giuseppe Paglia, Björn G. Voldborg, Bernhard Ø. Palsson, and Óttar Rolfsson. Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic acid as determined by constraint based metabolic network analysis. 9(6):e98760–e98760, 2014. [DOI].
Shubo Li, Xiang Gao, Nan Xu, Li Liu, and Jian Chen. Enhancement of acetoin production in candida glabrata by in silico-aided metabolic engineering. 13(1):55–55, 2014. [DOI].
Sarah Schatschneider, Claudia Huber, Heiko Neuweger, Tony Watt, Alfred Pühler, Wolfgang Eisenreich, Christoph Wittmann, Karsten Niehaus, and Frank‐Jörg Vorhölter. Metabolic flux pattern of glucose utilization by xanthomonas campestris pv. campestris: prevalent role of the entner–doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis. 10(10):2663–2676, 2014. [DOI].
Timo Maarleveld, Joost Boele, Frank J. Bruggeman, and Bas Teusink. A data integration and visualization resource for the metabolic network of synechocystis sp. pcc 6803. 164(3):1111–1121, 2014. [DOI].
Marco Fondi and Píetro Lió. Genome-scale metabolic network reconstruction. 1231():233–256, 2014. [DOI].
Na‐Rae Lee, Meiyappan Lakshmanan, Shilpi Aggarwal, Ji‐Won Song, Iftekhar A. Karimi, Dong‐Yup Lee, and Jin‐Byung Park. Genome-scale metabolic network reconstruction and in silico flux analysis of the thermophilic bacterium thermus thermophilus hb27. 13(1):61–61, 2014. [DOI].
Igor Marín de Mas, Esther Aguilar, Anusha Jayaraman, Ibrahim H. Polat, Alfonso Martín-Bernabé, Rohit Bharat, Carles Foguet, Enric Milà, Balázs Papp, Josep J. Centelles, and Marta Cascante. Cancer cell metabolism as new targets for novel designed therapies. 6(16):1791–1810, 2014. [DOI].
María Alejandra Álvarez. Plant secondary metabolism. ():15–31, 2014. [DOI].
Viswanadham Sridhara, Austin G. Meyer, Piyush Rai, Jeffrey E. Barrick, Pradeep Ravikumar, Daniel Segrè, and Claus O. Wilke. Predicting growth conditions from internal metabolic fluxes in an in-silico model of e. coli. 9(12):e114608–e114608, 2014. [DOI].
Daniel Veyel, Alexander Erban, Ines Fehrle, Joachim Kopka, and Michael Schroda. Rationales and approaches for studying metabolism in eukaryotic microalgae. 4(2):184–217, 2014. [DOI].
Steffen Klamt, Oliver Hädicke, and Axel von Kamp. Stoichiometric and constraint-based analysis of biochemical reaction networks. ():263–316, 2014. [DOI].
Anders Sandström, Henrik Almqvist, Diogo Portugal-Nunes, Dário Neves, Gunnar Lidén, and Marie F. Gorwa‐Grauslund. Saccharomyces cerevisiae: a potential host for carboxylic acid production from lignocellulosic feedstock? 98(17):7299–7318, 2014. [DOI].
Raphael Voges, Stephanie Corsten, Wolfgang Wiechert, and Stephan Noack. Absolute quantification of corynebacterium glutamicum glycolytic and anaplerotic enzymes by qconcat. 113():366–377, 2014. [DOI].
Meiyappan Lakshmanan, Bijayalaxmi Mohanty, Sun‐Hyung Lim, Sun‐Hwa Ha, and Dong‐Yup Lee. Metabolic and transcriptional regulatory mechanisms underlying the anoxic adaptation of rice coleoptile. 6():, 2014. [DOI].
Daniel D. Lewis, Fernando Villarreal, Fan Wu, and Cheemeng Tan. Synthetic biology outside the cell: linking computational tools to cell-free systems. 2():66–66, 2014. [DOI].
Ryan S. Senger, Jiun Y. Yen, and Stephen S. Fong. A review of genome-scale metabolic flux modeling of anaerobiosis in biotechnology. 6():33–42, 2014. [DOI].
Chintan Joshi and Ashok Prasad. Epistatic interactions among metabolic genes depend upon environmental conditions. 10(10):2578–2589, 2014. [DOI].
Yan Zhu, Dan Li, Guanhui Bao, Shaohua Wang, Shaoming Mao, Jiangning Song, Yin Li, and Yanping Zhang. Metabolic changes in klebsiella oxytoca in response to low oxidoreduction potential, as revealed by comparative proteomic profiling integrated with flux balance analysis. 80(9):2833–2841, 2014. [DOI].
María Victoria Aguilar Pontes, Ronald P. de Vries, and Miaomiao Zhou. (post-)genomics approaches in fungal research. 13(6):424–439, 2014. [DOI].
Karel Olavarría, Jessica De Ingeniis, Daniel C. Zielinski, Matías Fuentealba, Rodrigo Muñoz, Douglas McCloskey, Adam M. Feist, and Ricardo Cabrera. Metabolic impact of an nadh-producing glucose-6-phosphate dehydrogenase in escherichia coli. 160(12):2780–2793, 2014. [DOI].
Amit Kumar, Thomas F. Harrelson, Nathan E. Lewis, Emily J. Gallagher, Derek LeRoith, Joseph Shiloach, and Michael J. Betenbaugh. Multi-tissue computational modeling analyzes pathophysiology of type 2 diabetes in mkr mice. 9(7):e102319–e102319, 2014. [DOI].
Cheng Wang, Zhi-Luo Deng, Xie Zhi-ming, Xin‐Yi Chu, Ji-Wei Chang, Kong De, Baoju Li, Hongyu Zhang, and Ling‐Ling Chen. Construction of a genome‐scale metabolic network of the plant pathogen pectobacterium carotovorum provides new strategies for bactericide discovery. 589(3):285–294, 2014. [DOI].
Marianna Taffi, Nicola Paoletti, Claudio Angione, Sandra Pucciarelli, Mauro Marini, and Pietro LiÃ. Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis. 5():319–319, 2014. [DOI].
Hae Woo Lee, Andrew Christie, Jason A. Starkey, Erik K. Read, and Seongkyu Yoon. Intracellular metabolic flux analysis of <scp>cho</scp> cells supplemented with wheat hydrolysates for improved <scp>mab</scp> production and cell‐growth. 90(2):291–302, 2014. [DOI].
Ali Salehzadeh‐Yazdi, Yazdan Asgari, Ali Akbar Saboury, and Ali Masoudi‐Nejad. Computational analysis of reciprocal association of metabolism and epigenetics in the budding yeast: a genome-scale metabolic model (gsmm) approach. 9(11):e111686–e111686, 2014. [DOI].
Longfei Mao and W.S. Verwoerd. Computational comparison of mediated current generation capacity of chlamydomonas reinhardtii in photosynthetic and respiratory growth modes. 118(5):565–574, 2014. [DOI].
Pankaj Narang, Shawez Khan, Anmol Jaywant Hemrom, and Andrew M. Lynn. Metanet - a web-accessible interactive platform for biological metabolic network analysis. 8(1):130–130, 2014. [DOI].
Keesha E. Erickson, Ryan T. Gill, and Anushree Chatterjee. Constrictor: constraint modification provides insight into design of biochemical networks. 9(11):e113820–e113820, 2014. [DOI].
Maurice HT Ling and Chueh Loo Poh. A predictor for predicting escherichia colitranscriptome and the effects of gene perturbations. 15(1):140–140, 2014. [DOI].
Rafael Carreira, Pedro Evangelista, Paulo Maia, Paulo Vilaça, Marcellinus Pont, Jean-François Tomb, Isabel Rocha, and Miguel Rocha. Cbfa: phenotype prediction integrating metabolic models with constraints derived from experimental data. 8(1):, 2014. [DOI].
N.A.L. Flahaut and Willem M. de Vos. Systems biology and metabolic engineering of lactic acid bacteria for improved fermented foods. ():177–196, 2014. [DOI].
Guodong Liu, Antonio Marras, and Jens Nielsen. The future of genome‐scale modeling of yeast through integration of a transcriptional regulatory network. 2(1):30–46, 2014. [DOI].
Alejandro Acevedo, Germán Aroca, and Raúl Conejeros. Genome-scale nad(h/+) availability patterns as a differentiating feature between saccharomyces cerevisiae and scheffersomyces stipitis in relation to fermentative metabolism. 9(1):e87494–e87494, 2014. [DOI].
Zsolt Bodor, ANDREA FAZAKAS, Erika R. Bérczi‐Kovács, Szabolcs Lányi, and B. Albert. Systems biology and metabolic engineering for obtaining e. coli mutants capable to produce succinate from renewable resources. 19(4):9625–9636, 2014. [DOI].
Viswanadham Sridhara, Austin G. Meyer, Piyush Rai, Jeffrey E. Barrick, Pradeep Ravikumar, Daniel Segrè, and Claus O. Wilke. Predicting growth conditions from internal metabolic fluxes in anin-silicomodel ofe. coli. ():, 2014. [DOI].
Arne C. Müller, Frank J. Bruggeman, Brett G. Olivier, and Leen Stougie. Fast flux module detection using matroid theory. ():192–206, 2014. [DOI].
Andreas Wagner, Vardan Andriasyan, and Aditya Barve. The organization of metabolic genotype space facilitates adaptive evolution in nitrogen metabolism. 3(1):2–13, 2014. [DOI].
Pratish Gawand and Radhakrishnan Mahadevan. Engineering escherichia coli for d-ribose production from glucose-xylose mixtures. 10(2):106–114, 2014. [DOI].
Min-Kyu Jeon, Boeun Kim, Min‐Gyu Sung, and Jay H. Lee. On integrating the droop model with the flux balance model for predicting metabolic shifts in microalgae growth. 35():1534–1539, 2014. [DOI].
Octavio Perez‐Garcia, Silas G. Villas‐Bôas, and Naresh Singhal. A method to calibrate metabolic network models with experimental datasets. ():183–190, 2014. [DOI].
Mafalda Galhardo, Lasse Sinkkonen, Philipp Berninger, Jake Lin, Thomas Sauter, and Sergei Häyrynen. Transcriptomics profiling of human sgbs adipogenesis. 2():246–248, 2014. [DOI].
Zaida Luthey‐Schulten. Stochastic simulations of cellular processes: from single cells to colonies. 106(2):7a–7a, 2014. [DOI].
Sergio Bordel. Genome-scale metabolic models of yeast, methods for their reconstruction, and other applications. 1152():269–279, 2014. [DOI].
Ana Rita Brochado and Kiran Raosaheb Patil. Model-guided identification of gene deletion targets for metabolic engineering in saccharomyces cerevisiae. 1152():281–294, 2014. [DOI].
Nathalie Poupin and Fabien Jourdan. Analysing human metabolic networks using metabolomics. ():85–114, 2014. [DOI].
Mara Sangiovanni. Model checking of metabolic networks: application to metabolic diseases. ():, 2014. [DOI].
Chao Shi, Jian Yin, Zhe Liu, Jianxin Wu, Qi Zhao, Jian Ren, and Nan Yao. A systematic simulation of the effect of salicylic acid on sphingolipid metabolism. ():, 2014. [DOI].
Marion O. Adebiyi. Computational analysis of anopheles gambiae metabolism to facilitate insecticidal target and complex resistance mechanism discovery. ():, 2014. [DOI].
Fahimeh Salimi. Genome-scale characterization of metabolic interactions in a clostridial co-culture for consolidated bioprocessing. ():, 2014. [DOI].
Pratish Gawand. Metabolic engineering for substrate co-utilization. ():, 2014. [DOI].
Diego Salazar Barreto. Predicción de biomarcadores en mucopolisacaridosis empleando una red metabólica humana. ():, 2014. [DOI].
Benjamin VanderSluis. Computational analysis of genome-scale growth-interaction data in saccharomyces cerevisiae. ():, 2014. [DOI].
Kathleen A. Curran. Optimizing gene expression in saccharomyces cerevisiae for metabolic engineering applications. ():, 2014. [DOI].
Kevin Tsai and Chuan-Hsiung Chang. Diagnostics for stochastic genome-scale modeling via model slicing and debugging. 9(11):e110380–e110380, 2014. [DOI].
Pei-Jung Wu, Wu‐Hsiung Wu, Tzu-Chi Chen, Kuan‐Ting Lin, Jin-Mei Lai, Chi‐Ying F. Huang, and Feng‐Sheng Wang. Reconstruction and analysis of a signal transduction network using hela cell protein–protein interaction data. 45(6):2835–2842, 2014. [DOI].
Zhaobin Xu, Xin Fang, Thomas K. Wood, and Zuyi Huang. Modeling framework for investigating the influence of amino acids on the planktonic-biofilm transition of pseudomonas aeruginosa. 47(3):803–808, 2014. [DOI].
Orlando Rocha, Paulo Vilaça, Miguel Rocha, and Rui Mendes. Reg4optflux: an optflux plug-in that comprises meta-heuristics approaches for metabolic engineering using integrated models. 103():226–231, 2014. [DOI].
Priyanka Baloni, Soma Ghosh, and Nagasuma Chandra. Systems approaches to study infectious diseases. ():151–172, 2014. [DOI].
Jeevan Amos and Sambasiva Rao. Computational approach to explore plasmodium falciparum 3d7 seryl trna synthetase structure. ():, 2014. [DOI].
Chunli Liu and Tianwei Tan. 156. metabolically engineered escherichia coli for isoprene biosynthesis. ():, 2014. [DOI].
Benjamín J. Sánchez and Benjamín J. Sánchez. Development, calibration and validation of a dynamic genome-scale metabolic model of saccharomyces cerevisiae. ():, 2014. [DOI].
Sylvain Prigent. Thèse / université de rennes 1 sous le sceau de l'université européenne de bretagne pour le grade de docteur de l'université de rennes 1 mention : informatique. ():, 2014. [DOI].
Aditya Reddy Kolli. Action potentials as indicators of metabolic perturbations for temporal proteomic analysis. ():, 2014. [DOI].
J. Hettling. Viable models of energy metabolism. ():, 2014. [DOI].
Monica Larocque, Thierry Chénard, and Rafaël Najmanovich. A curated c. difficile strain 630 metabolic network: prediction of essential targets and inhibitors. ():, 2014. [DOI].
Anonymous. 1(5):, 2014. [DOI].
Maurice HT Ling, A Ramzi, E Palazzotto, Y Tong, S Lee, T Weber, K Choi, W Jang, D Yang, G Nguyen, Y-G Kim, J-W Ahn, Y-P Shen, F-X Niu, Z-B Yan, J King, S Edgar, K Qiao, G Naseri, Mag Koffas, M Kim, B Park, E-J Kim, J Wayman, C Glasscock, T Mansell, R Tian, Y Liu, J Chen, M Ling, C Gu, G Kim, W Kim, H Kim, Lee Sy, H Resat, L Petzold, M Pettigrew, J Strutz, J Martin, J Greene, E Kerkhoven, P-J Lahtvee, J Nielsen, J Sier, A Thumser, N Plant, J Schellenberger, R Que, Rmt Fleming, A Ebrahim, J Lerman, B Palsson, D Machado, M Herrgrd, J Edwards, B Palsson, Jgr Cardoso, K Jensen, C Lieven, S Hoops, S Sahle, R Gauges, F Bergmann, S Hoops, B Klahn, J Leipzig, L Smith, F Bergmann, D Chandran, K Choi, J Medley, M Knig, M Ling, M Ling, W Liebermeister, J Uhlendorf, E Klipp, M Hucka, F Bergmann, S Hoops, K Abou-Taleb, G Galal, S Razavi, H Gupta, K Tamano, J Yeoh, S Jayaraman, Sg-D Tan, A Hill, G Nair, C Jungreuthmayer, J Zanghellini, C-T Li, J Yelsky, Y Chen, A Feist, B Palsson, E Chang, and M Ling. 1(3):, 2014. [DOI].

Mark P. Brynildsen, Jonathan Winkler, Catherine S. Spina, I. Cody MacDonald, and James J. Collins. Potentiating antibacterial activity by predictably enhancing endogenous microbial ros production. 31(2):160–165, 2013. [DOI].
Rasmus Ågren, Li Liu, Saeed Shoaie, Wanwipa Vongsangnak, Intawat Nookaew, and Jens Nielsen. The raven toolbox and its use for generating a genome-scale metabolic model for penicillium chrysogenum. 9(3):e1002980–e1002980, 2013. [DOI].
Alison K. Hottes, Peter L. Freddolino, Anupama Khare, Zachary N. Donnell, Julia Liu, and Saeed Tavazoie. Bacterial adaptation through loss of function. 9(7):e1003617–e1003617, 2013. [DOI].
Anirikh Chakrabarti, Ljubiša Mišković, Keng Cher Soh, and Vassily Hatzimanikatis. Towards kinetic modeling of genome‐scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. 8(9):1043–1057, 2013. [DOI].
Gabriel Gelius‐Dietrich, Abdelmoneim Amer Desouki, Claus Jonathan Fritzemeier, and Martin J. Lercher. Sybil – efficient constraint-based modelling in r. 7(1):125–125, 2013. [DOI].
Scott Devoid, Ross Overbeek, Matthew DeJongh, Veronika Vonstein, Aaron A. Best, and Christopher S. Henry. Automated genome annotation and metabolic model reconstruction in the seed and model seed. 985():17–45, 2013. [DOI].
Douglas B. Kell. Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it. 280(23):5957–5980, 2013. [DOI].
Mathias Ganter, Thomas Bernard, Sébastien Moretti, Joerg Stelling, and Marco Pagni. Metanetx.org: a website and repository for accessing, analysing and manipulating metabolic networks. 29(6):815–816, 2013. [DOI].
Matthew B. Biggs and Jason A. Papin. Novel multiscale modeling tool applied to pseudomonas aeruginosa biofilm formation. 8(10):e78011–e78011, 2013. [DOI].
Wei Wu, Neema Jamshidi, Satish A. Eraly, Henry C. Liu, Kevin T. Bush, Bernhard Ø. Palsson, and Sanjay K. Nigám. Multispecific drug transporter slc22a8 (oat3) regulates multiple metabolic and signaling pathways. 41(10):1825–1834, 2013. [DOI].
Tat‐Ming Lo, Wei Suong Teo, Hua Ling, Binbin Chen, Aram Kang, and Matthew Wook Chang. Microbial engineering strategies to improve cell viability for biochemical production. 31(6):903–914, 2013. [DOI].
Pramod R. Somvanshi and K. V. Venkatesh. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. 8(1):99–116, 2013. [DOI].
Jonathan M. Dreyfuss, Jeremy Zucker, Heather M. Hood, Linda R. Ocasio, Matthew S. Sachs, and James E. Galagan. Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus neurospora crassa using farm. 9(7):e1003126–e1003126, 2013. [DOI].
Namrata Tomar and Rajat K. De. Comparing methods for metabolic network analysis and an application to metabolic engineering. 521(1):1–14, 2013. [DOI].
Tong Hao, Binbin Han, Hongwu Ma, Jing Fu, Hui Wang, Zhiwen Wang, Bincai Tang, Tao Chen, and Xueming Zhao. In silico metabolic engineering of bacillus subtilis for improved production of riboflavin, egl-237, (r,r)-2,3-butanediol and isobutanol. 9(8):2034–2034, 2013. [DOI].
Sarah Schatschneider, Marcus Persicke, Steven Alexander Watt, Gerd Hublik, Alfred Pühler, Karsten Niehaus, and Frank‐Jörg Vorhölter. Establishment, in silico analysis, and experimental verification of a large-scale metabolic network of the xanthan producing xanthomonas campestris pv. campestris strain b100. 167(2):123–134, 2013. [DOI].
Zixiang Xu, Ping Zheng, Jibin Sun, and Yanhe Ma. Reacknock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network. 8(12):e72150–e72150, 2013. [DOI].
Carl Yulun Song, Melissa A. Chiasson, Nirvana Nursimulu, Stacy Hung, James D. Wasmuth, Michael E. Grigg, and John Parkinson. Metabolic reconstruction identifies strain‐specific regulation of virulence in toxoplasma gondii. 9(1):708–708, 2013. [DOI].
Matthew C. Gonnerman, Matthew N. Benedict, Adam M. Feist, William W. Metcalf, and Nathan D. Price. Genomically and biochemically accurate metabolic reconstruction of methanosarcina barkeri fusaro, img746. 8(9):1070–1079, 2013. [DOI].
Zachary A. King and Adam M. Feist. Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strains—optswap. 9(4):236–246, 2013. [DOI].
Hyun Ju Kim, Bo Kyeng Hou, Sung Gun Lee, Joong Su Kim, Dong‐Woo Lee, Sang Jun Lee, Sang Jun Lee, and Sang Jun Lee. Genome-wide analysis of redox reactions reveals metabolic engineering targets for d-lactate overproduction in escherichia coli. 18():44–52, 2013. [DOI].
Sriram Chandrasekaran and Nathan D. Price. Metabolic constraint-based refinement of transcriptional regulatory networks. 9(12):e1003370–e1003370, 2013. [DOI].
Francisco G Vital-Lopez, Anders Wallqvist, and Jaques Reifman. Bridging the gap between gene expression and metabolic phenotype via kinetic models. 7(1):63–63, 2013. [DOI].
Zhaobin Xu, Xin Fang, Thomas K. Wood, and Zuyi Huang. A systems-level approach for investigating pseudomonas aeruginosa biofilm formation. 8(2):e57050–e57050, 2013. [DOI].
Steffen Krauser, Christian Weyler, Lisa Katharina Blaß, and Elmar Heinzle. Directed multistep biocatalysis using tailored permeabilized cells. 137():185–234, 2013. [DOI].
K. P. Lisha and Debasis Sarkar. Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production. 37(4):617–627, 2013. [DOI].
Kai Zhuang, Bhavik R. Bakshi, and Markus J. Herrgård. Multi‐scale modeling for sustainable chemical production. 8(9):973–984, 2013. [DOI].
Bevan Kai-Sheng Chung, Meiyappan Lakshmanan, Maximilian Klement, Chi Bun Ching, and Dong‐Yup Lee. Metabolic reconstruction and flux analysis of industrial pichia yeasts. 97(5):1865–1873, 2013. [DOI].
Margaret Simons, Ashish Misra, and Ganesh Sriram. Genome-scale models of plant metabolism. 1083():213–230, 2013. [DOI].
Sumanta Mukherjee, Awanti Sambarey, Prashanthi Karyala, and Nagasuma Chandra. Current trends in modeling host–pathogen interactions. 3(2):109–128, 2013. [DOI].
Ivan Lule, Pieter-Jan D’Huys, Lieve Van Mellaert, Jozef Anné, Kristel Bernaerts, and Jan Van Impe. Metabolic impact assessment for heterologous protein production in streptomyces lividans based on genome-scale metabolic network modeling. 246(1):113–121, 2013. [DOI].
Sidhartha Chaudhury, Mohamed Diwan M. AbdulHameed, Narender Singh, Gregory J. Tawa, Patrik D’haeseleer, Adam Zemła, Ali Navid, Carol E. Zhou, Matthew C. Franklin, Jonah Cheung, M. Rudolph, J. Love, John Graf, David A. Rozak, Jennifer L. Dankmeyer, Kei Amemiya, Simon Daefler, and Anders Wallqvist. Rapid countermeasure discovery against francisella tularensis based on a metabolic network reconstruction. 8(5):e63369–e63369, 2013. [DOI].
Janne Wallenius, Matti Viikilä, Shrikant A. Survase, Heikki Ojamo, and Tero Eerikäinen. Constraint-based genome-scale metabolic modeling of clostridium acetobutylicum behavior in an immobilized column. 142():603–610, 2013. [DOI].
Jacob E. Koskimaki, Anna S. Blazier, Andrés F. Clarens, and Jason A. Papin. Computational models of algae metabolism for industrial applications. 9(4):185–195, 2013. [DOI].
Wei Tong, Zhen Chen, Zhe Cao, Quanhui Wang, Jiyuan Zhang, Xue Bai, Rong Wang, and Siqi Liu. Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of thermoanaerobacter tengcongensis under temperature perturbation. 9(4):713–713, 2013. [DOI].
Sarat C. Cautha, Christopher M. Gowen, François-Xavier Lussier, Nicholas D. Gold, Vincent J. J. Martin, and Radhakrishnan Mahadevan. Model-driven design of a saccharomyces cerevisiae platform strain with improved tyrosine production capabilities. 46(31):221–226, 2013. [DOI].
Patrick Hyland, Serene Sow Mun Lock, and Radhakrishnan Mahadevan. Prediction of weak acid toxicity in saccharomyces cerevisiae using genome-scale metabolic models. 9(4):229–235, 2013. [DOI].
Jordan O. Hay and Jörg Schwender. Flux variability analysis: application to developing oilseed rape embryos using toolboxes for constraint-based modeling. 1090():301–316, 2013. [DOI].
Giuseppe Facchetti and Claudio Altafini. Partial inhibition and bilevel optimization in flux balance analysis. 14(1):344–344, 2013. [DOI].
N. C. Santos, Maria Olívia Pereira, and Anália Lourenço. Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties. 16(1):169–182, 2013. [DOI].
Hong Yang, Dominic E. Mandy, and Igor G. L. Libourel. Optimal design of isotope labeling experiments. 1083():133–147, 2013. [DOI].
Claudio Angione, Giovanni Carapezza, Jole Costanza, Píetro Lió, and Giuseppe Nicosia. Design and strain selection criteria for bacterial communication networks. 4(4):155–163, 2013. [DOI].
Lanay Tierney, Katarzyna M. Tyc, Edda Klipp, and Karl Kuchler. 3 systems biology approaches to understanding and predicting fungal virulence. ():45–74, 2013. [DOI].
Eva Grafahrend‐Belau, Astrid Junker, Falk Schreiber, and Björn H. Junker. Flux balance analysis as an alternative method to estimate fluxes without labeling. 1090():281–299, 2013. [DOI].
Zhaobin Xu, Xin Fang, Thomas K. Wood, and Zuyi Huang. Gene target identification for biofilm-associated pathogens: an application to pseudomonas aeruginosa. 2():3765–3770, 2013. [DOI].
Paul Jensén. Data integration with constraint-based genome-scale models. ():, 2013. [DOI].
Andrés Mauricio Pinzón Velasco, Silvia Restrepo, and Andrés Fernando González Barrios. Fba analysis, plant-pathogen interactions. ():733–736, 2013. [DOI].
Ali Ebrahim, Joshua A. Lerman, Bernhard Ø. Palsson, and Daniel R. Hyduke. Cobrapy: constraints-based reconstruction and analysis for python. 7(1):74–74, 2013. [DOI].
Ines Thiele, Neil Swainston, Ronan M. T. Fleming, Andreas Hoppe, Swagatika Sahoo, Maike K. Aurich, Hulda S. Haraldsdóttir, Monica L. Mo, Óttar Rolfsson, Miranda D. Stobbe, Stefan Gretar Thorleifsson, Rasmus Ågren, Christian Bölling, Sergio Bordel, Arvind K. Chavali, Paul D. Dobson, Warwick B. Dunn, Lukas Endler, David Hala, Michael Hucka, Duncan Hull, Daniel Jameson, Neema Jamshidi, Jón J. Jónsson, Nick Juty, Sarah Keating, Intawat Nookaew, Nicolas Le Novère, Naglis Malys, Alexander Mazein, Jason A. Papin, Nathan D. Price, E Selkov, Martin I. Sigurðsson, Evangelos Simeonidis, Nikolaus Sonnenschein, Kieran Smallbone, Anatoly Sorokin, Hans van Beek, Dieter Weichart, Igor Goryanin, Jens Nielsen, Hans V. Westerhoff, Douglas B. Kell, Pedro Mendes, and Bernhard Ø. Palsson. A community-driven global reconstruction of human metabolism. 31(5):419–425, 2013. [DOI].
Koyel Mitra, Anne‐Ruxandra Carvunis, Sanath Kumar Ramesh, and Trey Ideker. Integrative approaches for finding modular structure in biological networks. 14(10):719–732, 2013. [DOI].
Εlad Noor, Hulda S. Haraldsdóttir, Ron Milo, and Ronan M. T. Fleming. Consistent estimation of gibbs energy using component contributions. 9(7):e1003098–e1003098, 2013. [DOI].
Henning Knoop, Marianne Gründel, Yvonne Zilliges, Robert Lehmann, Sabrina Hoffmann, Wolfgang Lockau, and Ralf Steuer. Flux balance analysis of cyanobacterial metabolism: the metabolic network of synechocystis sp. pcc 6803. 9(6):e1003081–e1003081, 2013. [DOI].
Almut Heinken, Swagatika Sahoo, Ronan M. T. Fleming, and Ines Thiele. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. 4(1):28–40, 2013. [DOI].
Benjamin Steeb, Beatrice Claudi, Neil A. Burton, Petra Tienz, Alexander Schmidt, Hesso Farhan, Alain Mazé, and Dirk Bumann. Parallel exploitation of diverse host nutrients enhances salmonella virulence. 9(4):e1003301–e1003301, 2013. [DOI].
Finja Büchel, Nicolás Rodríguez, Neil Swainston, Clemens Wrzodek, Tobias Czauderna, Roland Keller, Florian Mittag, Michaël Schubert, Mihai Glont, Martin Golebiewski, Martijn van Iersel, Sarah Keating, Matthias Rall, Michael Wybrow, Henning Hermjakob, Michael Hucka, Douglas B. Kell, Wolfgang G. Müller, Pedro Mendes, Andreas Zell, Claudine Chaouiya, Julio Sáez-Rodríguez, Falk Schreiber, Camille Laibe, Andreas Dräger, and Nicolas Le Novère. Path2models: large-scale generation of computational models from biochemical pathway maps. 7(1):116–116, 2013. [DOI].
Hnin W. Aung, Susan A. Henry, and Larry P. Walker. Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. 9(4):215–228, 2013. [DOI].
Harish Nagarajan, Merve Şahin, Juan Nogales, Haythem Latif, Derek R. Lovley, Ali Ebrahim, and Karsten Zengler. Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of clostridium ljungdahlii. 12(1):118–118, 2013. [DOI].
Harish Nagarajan, Mallory Embree, Amelia‐Elena Rotaru, Pravin Malla Shrestha, Adam M. Feist, Bernhard Ø. Palsson, Derek R. Lovley, and Karsten Zengler. Characterization and modelling of interspecies electron transfer mechanisms and microbial community dynamics of a syntrophic association. 4(1):2809–2809, 2013. [DOI].
N.A.L. Flahaut, Anne Wiersma, Bert van de Bunt, Dirk E. Martens, Peter J. Schaap, Lolke Sijtsma, Vítor A. P. Martins dos Santos, and Willem M. de Vos. Genome-scale metabolic model for lactococcus lactis mg1363 and its application to the analysis of flavor formation. 97(19):8729–8739, 2013. [DOI].
Simon Unthan, Alexander Grünberger, Jan van Ooyen, Jochem Gätgens, Johanna Heinrich, Nicole Paczia, Wolfgang Wiechert, Dietrich Kohlheyer, and Stephan Noack. Beyond growth rate 0.6: what drives corynebacterium glutamicum to higher growth rates in defined medium. 111(2):359–371, 2013. [DOI].
Roger L. Chang, Kathleen A. Andrews, Donghyuk Kim, Zhanwen Li, Adam Godzik, and Bernhard Ø. Palsson. Structural systems biology evaluation of metabolic thermotolerance in escherichia coli. 340(6137):1220–1223, 2013. [DOI].
Ben Heavner, Kieran Smallbone, Nathan D. Price, and Larry P. Walker. Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance. 2013():bat059–bat059, 2013. [DOI].
Piyush Labhsetwar, John A. Cole, Elijah Roberts, Nathan D. Price, and Zaida Luthey‐Schulten. Heterogeneity in protein expression induces metabolic variability in a modeled escherichia coli population. 110(34):14006–14011, 2013. [DOI].
Joshua J. Hamilton and Jennifer L. Reed. Software platforms to facilitate reconstructing genome‐scale metabolic networks. 16(1):49–59, 2013. [DOI].
Yi Wang, Xiangzhen Li, Caroline B. Milne, Holger Janssen, Weiyin Lin, Gloria Phan, Huiying Hu, Yong‐Su Jin, Nathan D. Price, and Hans P. Blaschek. Development of a gene knockout system using mobile group ii introns (targetron) and genetic disruption of acid production pathways in clostridium beijerinckii. 79(19):5853–5863, 2013. [DOI].
Jennifer Bartell, P. H. Yen, John Varga, Joanna B. Goldberg, and Jason A. Papin. Comparative metabolic systems analysis of pathogenic burkholderia. 196(2):210–226, 2013. [DOI].
Judith A. H. Wodke, Jacek Puchałka, María Lluch‐Senar, Josep Marcos, Eva Yus, Miguel Godinho, Ricardo Gutiérrez–Gallego, Vítor A. P. Martins dos Santos, Luís Serrano, Edda Klipp, and Tobias Maier. Dissecting the energy metabolism in mycoplasma pneumoniae through genome‐scale metabolic modeling. 9(1):653–653, 2013. [DOI].
Di Huang, Shanshan Li, Menglei Xia, Jianping Wen, and Xiaoqiang Jia. Genome-scale metabolic network guided engineering of streptomyces tsukubaensis for fk506 production improvement. 12(1):52–52, 2013. [DOI].
Swagatika Sahoo and Ines Thiele. Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. 22(13):2705–2722, 2013. [DOI].
Kieran Smallbone and Pedro Mendes. Large-scale metabolic models: from reconstruction to differential equations. 9(4):179–184, 2013. [DOI].
Alexander Golberg, Edward Vitkin, Gregory Linshiz, Sabaa A. Khan, Nathan J. Hillson, Zohar Yakhini, and Martin L. Yarmush. Proposed design of distributed macroalgal biorefineries: thermodynamics, bioconversion technology, and sustainability implications for developing economies. 8(1):67–82, 2013. [DOI].
Young‐Mo Kim, Brian J. Schmidt, Afshan S. Kidwai, Marcus B. Jones, Brooke L. Deatherage Kaiser, Heather M. Brewer, Hugh Mitchell, Bernhard Ø. Palsson, Jason McDermott, Fred Heffron, Richard Smith, Scott N. Peterson, Charles Ansong, Daniel R. Hyduke, Thomas Metz, and Joshua Adkins. Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes. 9(6):1522–1522, 2013. [DOI].
Agris Pentjuss, Ilona Odzina, Andrejs Kostromins, David A. Fell, Egils Stalidzāns, and Uldis Kalnenieks. Biotechnological potential of respiring zymomonas mobilis: a stoichiometric analysis of its central metabolism. 165(1):1–10, 2013. [DOI].
Arne C. Müller and Alexander Bockmayr. Fast thermodynamically constrained flux variability analysis. 29(7):903–909, 2013. [DOI].
Daniele De Martino, Fabrizio Capuani, Matteo Mori, Andrea De Martino, and Enzo Marinari. Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks. 3(4):946–966, 2013. [DOI].
Jie Liu, Qian Gao, Nan Xu, and Li Liu. Genome-scale reconstruction and in silico analysis of aspergillus terreus metabolism. 9(7):1939–1939, 2013. [DOI].
Wei Zou, M. Zhou, Li Liu, and Jian Chen. Reconstruction and analysis of the industrial strain bacillus megaterium wsh002 genome-scale in silico metabolic model. 164(4):503–509, 2013. [DOI].
Roger L. Chang, Lei Xie, Philip E. Bourne, and Bernhard Ø. Palsson. Antibacterial mechanisms identified through structural systems pharmacology. 7(1):102–102, 2013. [DOI].
Longfei Mao and W.S. Verwoerd. Model-driven elucidation of the inherent capacity of geobacter sulfurreducens for electricity generation. 7(1):14–14, 2013. [DOI].
Manuel Garcia‐Albornoz and Jens Nielsen. Application of genome-scale metabolic models in metabolic engineering. 9(4):203–214, 2013. [DOI].
Shubo Li, Nan Xu, Li Liu, and Jian Chen. Engineering of carboligase activity reaction in candida glabrata for acetoin production. 22():32–39, 2013. [DOI].
Meng Liang, Andrew Damiani, Q. Peter He, and Jin Wang. Elucidating xylose metabolism of scheffersomyces stipitis for lignocellulosic ethanol production. 2(1):38–48, 2013. [DOI].
Miguel Ponce-de-León, Francisco Montero, and Juli Peretό. Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of blattabacterium cuenoti. 7(1):114–114, 2013. [DOI].
Yuekai Sun, Ronan M. T. Fleming, Ines Thiele, and Michael A. Saunders. Robust flux balance analysis of multiscale biochemical reaction networks. 14(1):240–240, 2013. [DOI].
Debjit Ray and Ping Ye. Characterization of the metabolic requirements in yeast meiosis. 8(5):e63707–e63707, 2013. [DOI].
Shayan Tabe-Bordbar and Sayed‐Amir Marashi. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of escherichia coli metabolism. 35(12):2039–2044, 2013. [DOI].
Jiun Y. Yen, Hadi Nazem‐Bokaee, Benjamin G. Freedman, Ahmad I. M. Athamneh, and Ryan S. Senger. Deriving metabolic engineering strategies from genome‐scale modeling with flux ratio constraints. 8(5):581–594, 2013. [DOI].
Colin R. Harwood, Susanne Pohl, Wendy D. Smith, and Anil Wipat. Bacillus subtilis. ():87–117, 2013. [DOI].
Pablo Carbonell, Anne‐Gaëlle Planson, and Jean‐Loup Faulon. Retrosynthetic design of heterologous pathways. 985():149–173, 2013. [DOI].
Claudia Patiño, Gustavo Jaime-Muñoz, and Osbaldo Reséndis-Antonio. Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. 3():481–481, 2013. [DOI].
Yu Qiu, Harish Nagarajan, Mallory Embree, Wendy Shieu, Elisa Ann Abate, Katy Juárez, Byung‐Kwan Cho, James G. Elkins, Kelly P. Nevin, Christian Barrett, Derek R. Lovley, Bernhard Ø. Palsson, and Karsten Zengler. Characterizing the interplay between multiple levels of organization within bacterial sigma factor regulatory networks. 4(1):1755–1755, 2013. [DOI].
Soma Ghosh, Priyanka Baloni, Sumanta Mukherjee, Praveen Anand, and Nagasuma Chandra. A multi-level multi-scale approach to study essential genes in mycobacterium tuberculosis. 7(1):132–132, 2013. [DOI].
Longfei Mao and W.S. Verwoerd. Orca: a cobra toolbox extension for model-driven discovery and analysis. 30(4):584–585, 2013. [DOI].
Ashish Misra, Matthew F. Conway, Joseph Johnnie, Tabish M. Qureshi, Bao Lige, Anne M. Derrick, Eddy C. Agbo, and Ganesh Sriram. Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast. 4():200–200, 2013. [DOI].
Yaron Goldstein and Alexander Bockmayr. A lattice-theoretic framework for metabolic pathway analysis. ():178–191, 2013. [DOI].
Luca Marchetti, Vincenzo Manca, Roberto Pagliarini, and Aliccia Bollig‐Fischer. Mp modelling for systems biology: two case studies. ():223–245, 2013. [DOI].
Finja Büchel, Sandra Saliger, Andreas Dräger, Stephanie Hoffmann, Clemens Wrzodek, Andreas Zell, and Philipp J. Kahle. Parkinson’s disease: dopaminergic nerve cell model is consistent with experimental finding of increased extracellular transport of α-synuclein. 14(1):136–136, 2013. [DOI].
Ashwini Kumar Sharma and Rainer König. Metabolic network modeling approaches for investigating the “hungry cancer”. 23(4):227–234, 2013. [DOI].
John T. Sauls and Joerg M. Buescher. Assimilating genome-scale metabolic reconstructions with modelborgifier. 30(7):1036–1038, 2013. [DOI].
Aidi Zhang, Shaoxing Dai, and Jing‐Fei Huang. Reconstruction and analysis of human kidney-specific metabolic network based on omics data. 2013():1–11, 2013. [DOI].
Nicole L. Fong, Joshua A. Lerman, Irene Lam, Bernhard Ø. Palsson, and Pep Charusanti. Reconciling asalmonella entericametabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethalppcdeletion mutant. 342(1):62–69, 2013. [DOI].
Edik M. Blais, Arvind K. Chavali, and Jason A. Papin. Linking genome-scale metabolic modeling and genome annotation. 985():61–83, 2013. [DOI].
Cristiana G. O. Dal’Molin, Lake‐Ee Quek, Robin Palfreyman, and Lars K. Nielsen. Plant genome-scale modeling and implementation. 1090():317–332, 2013. [DOI].
Longfei Mao and W.S. Verwoerd. Exploration and comparison of inborn capacity of aerobic and anaerobic metabolisms ofsaccharomyces cerevisiaefor microbial electrical current production. 4(6):420–430, 2013. [DOI].
Arnau Montagud, D. Gamermann, Pedro Fernández de Córdoba, and Javier F. Urchueguía. synechocystissp. pcc6803 metabolic models for the enhanced production of hydrogen. 35(2):184–198, 2013. [DOI].
Longfei Mao and W.S. Verwoerd. Genome-scale stoichiometry analysis to elucidate the innate capability of the cyanobacterium synechocystis for electricity generation. 40(10):1161–1180, 2013. [DOI].
Jing Meng, Zixiang Xu, Jing Guo, Yunxia Yue, and Xiao Sun. Analysis of enhanced current-generating mechanism of geobacter sulfurreducens strain via model-driven metabolism simulation. 8(9):e73907–e73907, 2013. [DOI].
Mona Yousofshahi, Ehsan Ullah, Russell Stern, and Soha Hassoun. Mc3: a steady-state model and constraint consistency checker for biochemical networks. 7(1):129–129, 2013. [DOI].
Agris Pentjuss, Oskars Rubenis, D. Bauze, Lilija Aprupe, and Baiba Lāce. Flux variability analysis approach of autism related metabolism in stoichiometric model of mitochondria. 2(2):37–42, 2013. [DOI].
Ryan S. Senger and Hadi Nazem‐Bokaee. Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm. 985():85–101, 2013. [DOI].
Stephan Pabinger and Zlatko Trajanoski. Genome-scale model management and comparison. 985():3–16, 2013. [DOI].
Agris Pentjuss and Uldis Kalnenieks. Assessment of zymomonas mobilis biotechnological potential in ethanol production by flux variability analysis. 3(1):1–5, 2013. [DOI].
Meng Liang, Q. Peter He, Thomas W. Jeffries, and Jin Wang. Elucidating xylose metabolism of scheffersomyces stipitis by integrating principal component analysis with flux balance analysis. 56():3777–3782, 2013. [DOI].
Martins Mednis and Armands Vīgants. Automatic comparison of metabolites names: impact of criteria thresholds. 2(1):1–5, 2013. [DOI].
Zixiang Xu, Xiao Sun, and Jibin Sun. Construction and analysis of the model of energy metabolism in e. coli. 8(1):e55137–e55137, 2013. [DOI].
Jurijs Meitalovs and Egils Stalidzāns. Analysis of synthetic metabolic pathways solution space. ():183–187, 2013. [DOI].
Pratish Gawand, Laurence Yang, W.R. Cluett, and Radhakrishnan Mahadevan. Metabolic model refinement using phenotypic microarray data. 985():47–59, 2013. [DOI].
Tatjana Rubina, Martins Mednis, and Egils Stalidzāns. Agreement assessment of biochemical pathway models by structural analysis of their intersection. 40():411–418, 2013. [DOI].
Brett G. Olivier. Sbml level 3 package: flux balance constraints ('fbc'). ():, 2013. [DOI].
Nathan D. Price. Genome-scale models for microbial factories. 9(4):177–178, 2013. [DOI].
Cesar A. Vargas-García, Carlos Eduardo García Sánchez, Henry Argüello, and Rodrigo Torres. Balance de flujos metabólicos en saccharomyces cerevisiae basado en compartimentalización intracelular. 15(2):18–18, 2013. [DOI].
Jurijs Meitalovs and Egils Stalidzāns. Connectivity analysis of metabolites in synthetic metabolic pathways. ():, 2013. [DOI].
John A. Cole, Michael J. Hallock, Piyush Labhsetwar, J.R. Peterson, John E. Stone, and Zaida Luthey‐Schulten. Stochastic simulations of cellular processes: from single cells to colonies. ():277–293, 2013. [DOI].
Dimitrije Jevremović. Scalable computation and analysis of elementary flux modes in metabolic networks. ():, 2013. [DOI].
Bernard Ycart, Frédéric Pont, and Jean‐Jacques Fournié. Simulation of gene regulatory networks. ():, 2013. [DOI].
铁申. Fba and fva analysis of central carbon metabolism in e. coli deletion mutants. 03(03):15–19, 2013. [DOI].
Alberto Noronha. Development of a computational platform for the visualization of metabolic models. ():, 2013. [DOI].
Anja Karlstädt. A systems biology approach to model cardiomyocyte metabolism. ():, 2013. [DOI].
Longfei Mao. Flux balance analysis to model microbial metabolism for electricity generation. ():, 2013. [DOI].
Beverly Sackler. A cross-species study of the evolutionary conservation of essential genes in bacteria. ():, 2013. [DOI].
Yuekai Sun, Ronan M. T. Fleming, Ines Thiele, and Michael A. Saunders. On lifting poorly-scaled ux balance analysis problems. ():, 2013. [DOI].
Fredric Bauer, Kris A. Berglund, Christian Hulteberg, Joakim Lundgren, Sennai Mesfun, Robert Nilsson, Ulrika Rova, and Sune Wännström. Comparative system analysis of carbon preserving fermentations for biofuels production. ():, 2013. [DOI].
Tie Shen, Jiejun Jin, and Bin Rui. Fba and fva analysis of central carbon metabolism in e. coli deletion mutants. ():, 2013. [DOI].
Matteo Mori. Unraveling bacterial growth laws. ():, 2013. [DOI].
Matthew A. Richards. Improving methanosarcina genome-scale models for strain design by incorporating cofactor specificity and free energy constraints. ():, 2013. [DOI].
Eugene Ma. An in silico investigation of the metabolic capabilities of anaeromyxobacter dehalogenans and field-scale applications. ():, 2013. [DOI].
Caroline M. Paget. Environmental systems biology of temperature adaptation in yeast. 8(16):999–999, 2013. [DOI].
Stephan Pabinger and Zlatko Trajanoski. Memosys: platform for genome-scale metabolic models. ():1–7, 2013. [DOI].

Kathleen A. Curran, John M. Leavitt, Ashty S. Karim, and Hal S. Alper. Metabolic engineering of muconic acid production in saccharomyces cerevisiae. 15():55–66, 2012. [DOI].
Yuliang Wang, James A. Eddy, and Nathan D. Price. Reconstruction of genome-scale metabolic models for 126 human tissues using mcadre. 6(1):153–153, 2012. [DOI].
Marnix H. Medema, Renske van Raaphorst, Eriko Takano, and Rainer Breitling. Computational tools for the synthetic design of biochemical pathways. 10(3):191–202, 2012. [DOI].
Chuanli Wang, Longyun Guo, Yixue Li, and Zhuo Wang. Systematic comparison of c3 and c4 plants based on metabolic network analysis. 6(Suppl 2):S9–S9, 2012. [DOI].
Milan JA van Hoek and Roeland M. H. Merks. Redox balance is key to explaining full vs. partial switching to low-yield metabolism. 6(1):22–22, 2012. [DOI].
Livnat Jerby, Lior Wolf, Carsten Denkert, Gideon Y. Stein, Mika Hilvo, Matej Orešič, Tamar Geiger, and Eytan Ruppin. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. 72(22):5712–5720, 2012. [DOI].
Nicolás Loira, Thierry Dulermo, Jean‐Marc Nicaud, and David James Sherman. A genome-scale metabolic model of the lipid-accumulating yeast yarrowia lipolytica. 6(1):35–35, 2012. [DOI].
Ali R. Zomorrodi, Patrick F. Suthers, Sridhar Ranganathan, and Costas D. Maranas. Mathematical optimization applications in metabolic networks. 14(6):672–686, 2012. [DOI].
Arvind K. Chavali, Kevin M. D’Auria, Erik L. Hewlett, Richard D. Pearson, and Jason A. Papin. A metabolic network approach for the identification and prioritization of antimicrobial drug targets. 20(3):113–123, 2012. [DOI].
Meiyappan Lakshmanan, Gerald Choon‐Huat Koh, Bevan Kai-Sheng Chung, and Dong‐Yup Lee. Software applications for flux balance analysis. 15(1):108–122, 2012. [DOI].
Mario Latendresse, Markus Krummenacker, Miles Trupp, and Peter D. Karp. Construction and completion of flux balance models from pathway databases. 28(3):388–396, 2012. [DOI].
Pengcheng Pan and Qiang Hua. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, yarrowia lipolytica. 7(12):e51535–e51535, 2012. [DOI].
Sandy MacDonald, George Lin, Calum W. Russell, Gavin H. Thomas, and Angela E. Douglas. The central role of the host cell in symbiotic nitrogen metabolism. 279(1740):2965–2973, 2012. [DOI].
Michael J. McAnulty, Jiun Y. Yen, Benjamin G. Freedman, and Ryan S. Senger. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico. 6(1):42–42, 2012. [DOI].
Anja Karlstädt, Daniela Fliegner, Georgios Kararigas, Hugo Sanchez‐Ruderisch, Vera Regitz‐Zagrosek, and Hermann−Georg Holzhütter. Cardionet: a human metabolic network suited for the study of cardiomyocyte metabolism. 6(1):114–114, 2012. [DOI].
Eva Collakova, Jiun Y. Yen, and Ryan S. Senger. Are we ready for genome-scale modeling in plants? 191-192():53–70, 2012. [DOI].
Balaji Balagurunathan, Sudhakar Jonnalagadda, Lily Tan, and Rajagopalan Srinivasan. Reconstruction and analysis of a genome-scale metabolic model for scheffersomyces stipitis. 11(1):27–27, 2012. [DOI].
B. Mikael Bergdahl, Dominik Heer, Uwe Sauer, Bärbel Hahn‐Hägerdal, and Ed Wj van Niel. Dynamic metabolomics differentiates between carbon and energy starvation in recombinant saccharomyces cerevisiae fermenting xylose. 5(1):34–34, 2012. [DOI].
Xueyang Feng, You Xu, Yixin Chen, and Yinjie Tang. Microbesflux: a web platform for drafting metabolic models from the kegg database. 6(1):94–94, 2012. [DOI].
Gunnar Sigurðsson, Ronan M. T. Fleming, Almut Heinken, and Ines Thiele. A systems biology approach to drug targets in pseudomonas aeruginosa biofilm. 7(4):e34337–e34337, 2012. [DOI].
Thomas Dandekar, Astrid Fieselmann, Saman Majeed, and Zeeshan Ahmed. Software applications toward quantitative metabolic flux analysis and modeling. 15(1):91–107, 2012. [DOI].
Ralf Steuer, Henning Knoop, and Rainer Machné. Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth. 63(6):2259–2274, 2012. [DOI].
Andreas Wagner. Metabolic networks and their evolution. 751():29–52, 2012. [DOI].
Khuram Shahzad and Juan J. Loor. Application of top-down and bottom-up systems approaches in ruminant physiology and metabolism. 13(5):379–394, 2012. [DOI].
Carmen María González-Domenech, Eugeni Belda, Rafael Patiño‐Navarrete, Andrés Moyá, Juli Peretό, and Amparo Latorre. Metabolic stasis in an ancient symbiosis: genome-scale metabolic networks from two blattabacterium cuenoti strains, primary endosymbionts of cockroaches. 12(S1):S5–S5, 2012. [DOI].
Judith M. Fontana, Elizabeth Alexander, and Mirella Salvatore. Translational research in infectious disease: current paradigms and challenges ahead. 159(6):430–453, 2012. [DOI].
Andreas Hoppe. What mrna abundances can tell us about metabolism. 2(3):614–631, 2012. [DOI].
Xuewen Chen and Yair Shachar‐Hill. Insights into metabolic efficiency from flux analysis. 63(6):2343–2351, 2012. [DOI].
Yu-Chieh Liao, Ming-Hsin Tsai, Feng‐Chi Chen, and Chao A. Hsiung. Gemsirv: a software platform for genome-scale metabolic model simulation, reconstruction and visualization. 28(13):1752–1758, 2012. [DOI].
Pieter-Jan D’Huys, Ivan Lule, Dominique Vercammen, Jozef Anné, Jan Van Impe, and Kristel Bernaerts. Genome-scale metabolic flux analysis of streptomyces lividans growing on a complex medium. 161(1):1–13, 2012. [DOI].
Paula Jouhten, Marilyn G. Wiebe, and Merja Penttilä. Dynamic flux balance analysis of the metabolism of saccharomyces cerevisiae during the shift from fully respirative or respirofermentative metabolic states to anaerobiosis. 279(18):3338–3354, 2012. [DOI].
Ana Rita Brochado, Sergej Andrejev, Costas D. Maranas, and Kiran Raosaheb Patil. Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. 8(11):e1002758–e1002758, 2012. [DOI].
Eugeni Belda, Francisco J. Silva, Juli Peretό, and Andrés Moyá. Metabolic networks of sodalis glossinidius: a systems biology approach to reductive evolution. 7(1):e30652–e30652, 2012. [DOI].
Radhakrishnan Mahadevan and Michael A. Henson. Genome-based modeling and design of metabolic interactions in microbial communities. 3(4):e201210008–e201210008, 2012. [DOI].
Erin R. Shellman, Charles Burant, and Santiago Schnell. Network motifs provide signatures that characterize metabolism. 9(3):352–352, 2012. [DOI].
Aaron Brandes, Desmond S. Lun, Kuhn Ip, Jeremy Zucker, Caroline Colijn, Brian Weiner, and James E. Galagan. Inferring carbon sources from gene expression profiles using metabolic flux models. 7(5):e36947–e36947, 2012. [DOI].
Martins Mednis and Maike K. Aurich. Application of string similarity ratio and edit distance in automatic metabolite reconciliation comparing reconstructions and models. 1(1):14–18, 2012. [DOI].
Amornpan Klanchui, Chiraphan Khannapho, Atchara Phodee, Supapon Cheevadhanarak, and Asawin Meechai. I ak692: a genome-scale metabolic model of spirulina platensis c1. 6(1):71–71, 2012. [DOI].
Piotr Zakrzewski, Marnix H. Medema, Albert Gevorgyan, Andrzej Kierzek, Rainer Breitling, and Eriko Takano. Multimeteval: comparative and multi-objective analysis of genome-scale metabolic models. 7(12):e51511–e51511, 2012. [DOI].
Hansheng Zhao, Mao Li, Kechi Fang, Wenfeng Chen, and Jing Wang. In silico insights into the symbiotic nitrogen fixation in sinorhizobium meliloti via metabolic reconstruction. 7(2):e31287–e31287, 2012. [DOI].
Rachael Hageman Blair, Daniel J. Kliebenstein, and Gary A. Churchill. What can causal networks tell us about metabolic pathways? 8(4):e1002458–e1002458, 2012. [DOI].
Lin Xu, Brandon Barker, and Zhenglong Gu. Dynamic epistasis for different alleles of the same gene. 109(26):10420–10425, 2012. [DOI].
Somedutta Barat, Benjamin Steeb, Alain Mazé, and Dirk Bumann. Extensive in vivo resilience of persistent salmonella. 7(7):e42007–e42007, 2012. [DOI].
Tie Shen, Bin Rui, Hong Zhou, Ximing Zhang, Yin Yi, Wen Han, Haoran Zheng, Jihui Wu, and Yunyu Shi. Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of e. coli to paraquat-induced oxidative stress. 9(1):121–132, 2012. [DOI].
David Hala, Lene H. Petersen, Dalma Martinović‐Weigelt, and Duane B. Huggett. Constraints-based stoichiometric analysis of hypoxic stress on steroidogenesis in fathead minnows, pimephales promelas. 215(10):1753–1765, 2012. [DOI].
Matthias Bujara and Sven Panke. In silico assessment of cell‐free systems. 109(10):2620–2629, 2012. [DOI].
Juan I. Castrillo, Pınar Pir, and Stephen G. Oliver. Yeast systems biology. ():343–365, 2012. [DOI].
Payam Setoodeh, A. Jahanmiri, and Reza Eslamloueyan. Hybrid neural modeling framework for simulation and optimization of diauxie-involved fed-batch fermentative succinate production. 81():57–76, 2012. [DOI].
Michaël Bekaert. Reconstruction of danio rerio metabolic model accounting for subcellular compartmentalisation. 7(11):e49903–e49903, 2012. [DOI].
Jianfeng Wang, Zhiqiang Xiong, Hailin Meng, Yiguang Wang, and Yong Wang. Synthetic biology triggers new era of antibiotics development. 64():95–114, 2012. [DOI].
Aaron Brandes, Desmond S. Lun, Kuhn Ip, Jeremy Zucker, Caroline Colijn, Brian Weiner, and James E. Galagan. Correction: inferring carbon sources from gene expression profiles using metabolic flux models. 7(8):, 2012. [DOI].
Francisco G Vital-Lopez, Vesna Memišević, and Bhaskar Dutta. Tutorial on biological networks. 2(4):298–325, 2012. [DOI].
Tugce Bilgin and Andreas Wagner. Design constraints on a synthetic metabolism. 7(6):e39903–e39903, 2012. [DOI].
Martins Mednis, Zintis Rove, and Vytautas Galvanauskas. Moderator - a software tool for comparison of stoichiometric models. 6():97–100, 2012. [DOI].
Martins Mednis, Valters Brusbārdis, and Vytautas Galvanauskas. Comparison of genome-scale reconstructions using moderator. 2():79–84, 2012. [DOI].
Liram Vardi, Eytan Ruppin, and Roded Sharan. A linearized constraint-based approach for modeling signaling networks. 19(2):232–240, 2012. [DOI].
Christine Klier. Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments. 46(5):2790–2798, 2012. [DOI].
Liam G. Fearnley and Lars K. Nielsen. Pathlogic-s: a scalable boolean framework for modelling cellular signalling. 7(8):e41977–e41977, 2012. [DOI].
Jing Ma, Xun Zhang, Choong Yong Ung, Yu Chen, and Baowen Li. Metabolic network analysis revealed distinct routes of deletion effects between essential and non-essential genes. 8(4):1179–1179, 2012. [DOI].
Jurijs Meitalovs. Software tool for probabilistic metabolic pathways construction. ():405–408, 2012. [DOI].
Eva Grafahrend‐Belau, Björn H. Junker, and Falk Schreiber. Plant metabolic pathways: databases and pipeline for stoichiometric analysis. ():345–366, 2012. [DOI].
Zheng Li, Alim Dewan, and M. Nazmul Karim. Optimization of bioethanol ethanol production in fed-batch fermentation. 45(15):816–821, 2012. [DOI].
Sang Yup Lee, Seung Bum Sohn, Hyun Uk Kim, Jong Myoung Park, Tae Yong Kim, Jeffrey D. Orth, and Bernhard Ø. Palsson. Genome-scale network modeling. ():1–23, 2012. [DOI].
Mariano Bizzarri, Simona Dinicola, and Cesare Manetti. Systems biology approach to metabolomics in cancer studies. ():3–37, 2012. [DOI].
António Roldão, Il‐Kwon Kim, and Jens Nielsen. Bridging omics technologies with synthetic biology in yeast industrial biotechnology. ():271–327, 2012. [DOI].
Joo Sang Lee, Takashi Nishikawa, and Adilson E. Motter. Why optimal states recruit fewer reactions in metabolic networks. 32(8):2937–2950, 2012. [DOI].
Eleftheria Tzamali, Vangelis Sakkalis, and Kostas Marias. The effects of near optimal growth solutions in genome-scale human cancer metabolic model. 324():626–631, 2012. [DOI].
Carol Milena Barreto-Rodriguez, Jessica Paola Ramirez-Angulo, Jorge M. Gómez, Luke E. K. Achenie, and Andrés Fernando González Barrios. Optimization of the bioconversion of glycerol to ethanol using &lt;i&gt;escherichia coli&lt;/i&gt; by implementing a bi-level programming framework for proposing gene transcription control strategies based on genetic algorithms. 03(04):336–343, 2012. [DOI].
Andreas Wagner. Genotype networks and evolutionary innovations in biological systems. ():251–264, 2012. [DOI].
Hooman Hefzi, Bernhard Ø. Palsson, and Nathan E. Lewis. Reconstruction of genome-scale metabolic networks. ():229–250, 2012. [DOI].
Porntip Chiewchankaset, Yanee Srimarut, Amornpan Klanchui, Peter Kurdi, Vethachai Plengvidhya, and Asawin Meechai. Systematic identification of lactobacillus plantarum auxotrophs for fermented nham using genome-scale metabolic model. 162(2-3):327–335, 2012. [DOI].
Arnau Montagud. Modelling and analysis of biological systems to obtain biofuels. ():, 2012. [DOI].
Dewu Ding and Ying Long. Revisiting modules (and centers) in staphylococcus aureus metabolic network with link clustering. 20(01):57–66, 2012. [DOI].
Ali Navid. Development of constraint-based system-level models of microbial metabolism. 881():531–549, 2012. [DOI].
Chuanli Wang, Longyun Guo, and Zhuo Wang. Comparative analysis of c3 and c4 plants using constraint-based model. 60():418–425, 2012. [DOI].
Kourosh Salehi‐Ashtiani, Jason A. Papin, and Jason Papin. Experimental definition and validation of protein coding transcripts in chlamydomonas reinhardtii. ():, 2012. [DOI].
Juan Nogales, Steinn Guðmundsson, Eric M. Knight, Bernhard Ø. Palsson, and Ines Thiele. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. 109(7):2678–2683, 2012. [DOI].
Hojung Nam, Nathan E. Lewis, Joshua A. Lerman, Dae‐Hee Lee, Roger L. Chang, Donghyuk Kim, and Bernhard Ø. Palsson. Network context and selection in the evolution to enzyme specificity. 337(6098):1101–1104, 2012. [DOI].
Alisdair R. Fernie and Mark Stitt. On the discordance of metabolomics with proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions scientific correspondence. 158(3):1139–1145, 2012. [DOI].
ChiamYu Ng, Moo-Young Jung, Jinwon Lee, and Min‐Kyu Oh. Production of 2,3-butanediol in saccharomyces cerevisiae by in silico aided metabolic engineering. 11(1):68–68, 2012. [DOI].
Dave Lee, Kieran Smallbone, Warwick B. Dunn, Ettore Murabito, Catherine Winder, Douglas B. Kell, Pedro Mendes, and Neil Swainston. Improving metabolic flux predictions using absolute gene expression data. 6(1):73–73, 2012. [DOI].
Douglas B. Kell, Paul D. Dobson, Elizabeth Bilsland, and Stephen G. Oliver. The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. 18(5-6):218–239, 2012. [DOI].
Ben Heavner, Kieran Smallbone, Brandon Barker, Pedro Mendes, and Larry P. Walker. Yeast 5 – an expanded reconstruction of the saccharomyces cerevisiae metabolic network. 6(1):55–55, 2012. [DOI].
Ines Thiele, Ronan M. T. Fleming, Richard Que, Aarash Bordbar, Dinh Diep, and Bernhard Ø. Palsson. Multiscale modeling of metabolism and macromolecular synthesis in e. coli and its application to the evolution of codon usage. 7(9):e45635–e45635, 2012. [DOI].
Wilbert B. Copeland, Bryan Bartley, Deepak Chandran, Michal Galdzicki, Kyung H. Kim, Sean C. Sleight, Costas D. Maranas, and Herbert M. Sauro. Computational tools for metabolic engineering. 14(3):270–280, 2012. [DOI].
Elhanan Borenstein. Computational systems biology and in silico modeling of the human microbiome. 13(6):769–780, 2012. [DOI].
Jennifer L. Reed. Shrinking the metabolic solution space using experimental datasets. 8(8):e1002662–e1002662, 2012. [DOI].
Joonhoon Kim and Jennifer L. Reed. Relatch: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. 13(9):R78–R78, 2012. [DOI].
Cristiana Gomes de Oliveira Dal’Molin and Lars K. Nielsen. Plant genome-scale metabolic reconstruction and modelling. 24(2):271–277, 2012. [DOI].
Ting Liu, Wei Zou, Li Liu, and Jian Chen. A constraint-based model of scheffersomyces stipitis for improved ethanol production. 5(1):72–72, 2012. [DOI].
Ines Thiele, Almut Heinken, and Ronan M. T. Fleming. A systems biology approach to studying the role of microbes in human health. 24(1):4–12, 2012. [DOI].
Joost Boele, Brett G. Olivier, and Bas Teusink. Fame, the flux analysis and modeling environment. 6(1):8–8, 2012. [DOI].
Swagatika Sahoo, Leifur Franzson, Jón J. Jónsson, and Ines Thiele. A compendium of inborn errors of metabolism mapped onto the human metabolic network. 8(10):2545–2545, 2012. [DOI].
Ali Navid and Eivind Almaas. Genome-level transcription data of yersinia pestis analyzed with a new metabolic constraint-based approach. 6(1):150–150, 2012. [DOI].
Steven Kelk, Brett G. Olivier, Leen Stougie, and Frank J. Bruggeman. Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks. 2(1):580–580, 2012. [DOI].
Abdelhalim Larhlimi, László Dávid, Joachim Selbig, and Alexander Bockmayr. F2c2: a fast tool for the computation of flux coupling in genome-scale metabolic networks. 13(1):57–57, 2012. [DOI].
Xin Fang, Anders Wallqvist, and Jaques Reifman. Modeling phenotypic metabolic adaptations of mycobacterium tuberculosis h37rv under hypoxia. 8(9):e1002688–e1002688, 2012. [DOI].
Guoqiang Xu, Wei Zou, Xiulai Chen, Nan Xu, Li Liu, and Jian Chen. Fumaric acid production in saccharomyces cerevisiae by in silico aided metabolic engineering. 7(12):e52086–e52086, 2012. [DOI].
Thomas Dandekar, Astrid Fieselmann, Jasmin Popp, and Michael Hensel. Salmonella enterica: a surprisingly well-adapted intracellular lifestyle. 3():164–164, 2012. [DOI].
Jeffrey D. Orth and Bernhard Ø. Palsson. Gap-filling analysis of the ijo1366 escherichia coli metabolic network reconstruction for discovery of metabolic functions. 6(1):30–30, 2012. [DOI].
Chuan Xu, Lili Liu, Zhao Zhang, Danfeng Jin, Juanping Qiu, and Ming Chen. Genome-scale metabolic model in guiding metabolic engineering of microbial improvement. 97(2):519–539, 2012. [DOI].
Hulda S. Haraldsdóttir, Ines Thiele, and Ronan M. T. Fleming. Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. 102(8):1703–1711, 2012. [DOI].
Jari Kämäräinen, Henning Knoop, Natalie Stanford, Fernando Guerrero, M. Akhtar, Eva–Mari Aro, Ralf Steuer, and Patrik R. Jones. Physiological tolerance and stoichiometric potential of cyanobacteria for hydrocarbon fuel production. 162(1):67–74, 2012. [DOI].
Brian J. Schmidt, Jason A. Papin, and Cynthia J. Musante. Mechanistic systems modeling to guide drug discovery and development. 18(3-4):116–127, 2012. [DOI].
Nan Xu, Li Liu, Wei Zou, Jie Liu, Qiang Hua, and Jian Chen. Reconstruction and analysis of the genome-scale metabolic network of candida glabrata. 9(2):205–216, 2012. [DOI].
Keiko Yonekura‐Sakakibara, Atsushi Fukushima, and Kazuki Saito. Transcriptome data modeling for targeted plant metabolic engineering. 24(2):285–290, 2012. [DOI].
Andrejs Kostromins and Egils Stalidzāns. Paint4net: cobra toolbox extension for visualization of stoichiometric models of metabolism. 109(2):233–239, 2012. [DOI].
Wei Zou, Li Liu, Jing Zhang, Haoru Yang, M. Zhou, Qiang Hua, and Jian Chen. Reconstruction and analysis of a genome-scale metabolic model of the vitamin c producing industrial strain ketogulonicigenium vulgare wsh-001. 161(1):42–48, 2012. [DOI].
Nuno Carinhas, Rui Oliveira, Paula M. Alves, Manuel J.T. Carrondo, and Ana P. Teixeira. Systems biotechnology of animal cells: the road to prediction. 30(7):377–385, 2012. [DOI].
Jan Schellenberger, Daniel C. Zielinski, Wing Choi, Sunthosh Madireddi, Vasiliy A. Portnoy, David A. Scott, Jennifer L. Reed, Andrei L. Osterman, and Bernhard Ø. Palsson. Predicting outcomes of steady-state 13c isotope tracing experiments using monte carlo sampling. 6(1):9–9, 2012. [DOI].
Miguel Ángel Medina. Systems biology for molecular life sciences and its impact in biomedicine. 70(6):1035–1053, 2012. [DOI].
Óttar Rolfsson, Giuseppe Paglia, Manuela Magnúsdóttir, Bernhard Ø. Palsson, and Ines Thiele. Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity. 449(2):427–435, 2012. [DOI].
Robert Dromms and Mark P. Styczynski. Systematic applications of metabolomics in metabolic engineering. 2(4):1090–1122, 2012. [DOI].
Carlos Eduardo García Sánchez, Cesar A. Vargas-García, and Rodrigo Torres. Predictive potential of flux balance analysis of saccharomyces cerevisiae using as optimization function combinations of cell compartmental objectives. 7(8):e43006–e43006, 2012. [DOI].
Nathan Crook and Hal S. Alper. Model-based design of synthetic, biological systems. 103():2–11, 2012. [DOI].
Paula Jouhten. Metabolic modelling in the development of cell factories by synthetic biology. 3(4):e201210009–e201210009, 2012. [DOI].
Santiago Comba, Ana Arabolaza, and Hugo Gramajo. Emerging engineering principles for yield improvement in microbial cell design. 3(4):e201210016–e201210016, 2012. [DOI].
Zeeshan Ahmed, Saman Majeed, and Thomas Dandekar. Computational feature performance and domain specific architecture evaluation of software applications towards metabolic flux analysis. 5(3):165–176, 2012. [DOI].
Andrejs Kostromins. Altfluxes: cobra toolbox extension for flux variability analysis of stoichiometric models of metabolism. ():, 2012. [DOI].
Ming‐Tsang Wu and Christina Chan. Human metabolic network: reconstruction, simulation, and applications in systems biology. 2(1):242–253, 2012. [DOI].
Maike K. Aurich and Ines Thiele. Contextualization procedure and modeling of monocyte specific tlr signaling. 7(12):e49978–e49978, 2012. [DOI].
Emily Gruber, Amy Ko, Michael MacGillvray, and Miranda Sawyer. Robust analysis of metabolic pathways. ():, 2012. [DOI].
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho‐Zhmayev, Supapon Cheevadhanarak, and Asawin Meechai. Analysis of metabolic network of synthetic escherichia coli producing linalool using constraint-based modeling. 11():24–35, 2012. [DOI].
Ben Heavner, Susan A. Henry, and Larry P. Walker. Evaluating sphingolipid biochemistry in the consensus reconstruction of yeast metabolism. 8(2):72–78, 2012. [DOI].
Xinyu Zhang. Enhanced biohydrogen production and substrate utilization by co-culture fermentation with reduced extracellular electron shuttles. ():, 2012. [DOI].
Maikel P. H. Verouden. Fusing prior knowledge with microbial metabolomics. ():, 2012. [DOI].
Ana Rita Brochado, Uffe Hasbro Mortensen, and Kiran Raosaheb Patil. Investigating genotype-phenotype relationships in saccharomyces cerevisiae metabolic network through stoichiometric modeling. ():, 2012. [DOI].
Agris Pentjuss, Ilona Odzina, and A. Gailums. Multi agent genome-scale metabolic reconstruction modeling software schema. 29(1):189–194, 2012. [DOI].
Peter Yan Li. In silico metabolic network reconstruction of scheffersomyces stipitis. ():, 2012. [DOI].
君王. Reconstruction of erwinia carotovora subsp. atroseptica scri1043 metabolic network and its application in screening potential targets. 02(01):1–9, 2012. [DOI].
Artículo De Investigación, César Augusto, Vargas García, Henry Argüello, Rodrigo Fernández Gonzalo, and Torres Sáez. Prediction of genome scale of saccharomyces cerevisiae by flux balance analysis. 14(1):93–107, 2012. [DOI].
Louise Mølgaard. Engineering the polyketide cell factory. ():, 2012. [DOI].
Olusegun Oshota. A systems biology approach to the production of biotechnological products through systematic in silico studies. ():, 2012. [DOI].

Jan Schellenberger, Richard Que, Ronan M. T. Fleming, Ines Thiele, Jeffrey D. Orth, Adam M. Feist, Daniel C. Zielinski, Aarash Bordbar, Nathan E. Lewis, Sorena Rahmanian, Joseph Kang, Daniel R. Hyduke, and Bernhard Ø. Palsson. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2.0. 6(9):1290–1307, 2011. [DOI].
Jeffrey D. Orth, Tom M Conrad, Jessica Na, Joshua A. Lerman, Hojung Nam, Adam M. Feist, and Bernhard Ø. Palsson. A comprehensive genome‐scale reconstruction of escherichia coli metabolism—2011. 7(1):535–535, 2011. [DOI].
Roger L. Chang, Lila Ghamsari, Ani Manichaikul, Erik Hom, Balaji Santhanam, Weiqi Fu, Yun Shen, Tong Hao, Bernhard Ø. Palsson, Kourosh Salehi‐Ashtiani, and Jason A. Papin. Metabolic network reconstruction of chlamydomonas offers insight into light‐driven algal metabolism. 7(1):518–518, 2011. [DOI].
Tom M Conrad, Nathan E. Lewis, and Bernhard Ø. Palsson. Microbial laboratory evolution in the era of genome‐scale science. 7(1):509–509, 2011. [DOI].
Zhi Wang and Jianzhi Zhang. Impact of gene expression noise on organismal fitness and the efficacy of natural selection. 108(16):E67–76, 2011. [DOI].
Colin Archer, Jihyun F. Kim, Haeyoung Jeong, Jin Hwan Park, Claudia E. Vickers, Sang Yup Lee, and Lars K. Nielsen. The genome sequence of e. coli w (atcc 9637): comparative genome analysis and an improved genome-scale reconstruction of e. coli. 12(1):9–9, 2011. [DOI].
Aarash Bordbar, Adam M. Feist, Renata Usaite-Black, Joseph Woodcock, Bernhard Ø. Palsson, and Iman Famili. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. 5(1):180–180, 2011. [DOI].
Cristiana Gomes de Oliveira Dal’Molin, Lake‐Ee Quek, Robin Palfreyman, and Lars K. Nielsen. Algagem – a genome-scale metabolic reconstruction of algae based on the chlamydomonas reinhardtii genome. 12(Suppl 4):S5–S5, 2011. [DOI].
Matthew Oberhardt, Jacek Puchałka, Vítor A. P. Martins dos Santos, and Jason A. Papin. Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. 7(3):e1001116–e1001116, 2011. [DOI].
Yu-Chieh Liao, Tzu‐Wen Huang, Feng-Chi Chen, Pep Charusanti, Jay Sung-Joong Hong, Hwan‐You Chang, Shih-Feng Tsai, Bernhard Ø. Palsson, and Chao A. Hsiung. An experimentally validated genome-scale metabolic reconstruction of klebsiella pneumoniae mgh 78578, i yl1228. 193(7):1710–1717, 2011. [DOI].
R. Lencastre Fernandes, Marta Nierychlo, Luisa Lundin, A.E. Pedersen, P.E. Puentes Tellez, Abhishek Dutta, Magnus Carlquist, Andrijana Bolic, Daniel Schäpper, A.C. Brunetti, Søren Helmark, A.-L. Heins, Anker Degn Jensen, Ingmar Nopens, Karsten Rottwitt, Nicolas Szita, J.D. van Elsas, Per Halkjær Nielsen, Jan Martinussen, Søren J. Sørensen, Anna Eliasson Lantz, and Krist V. Gernaey. Experimental methods and modeling techniques for description of cell population heterogeneity. 29(6):575–599, 2011. [DOI].
Paul A. Jensen, Kyla Lutz, and Jason A. Papin. Tiger: toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks. 5(1):147–147, 2011. [DOI].
Aarash Bordbar and Bernhard Ø. Palsson. Using the reconstructed genome‐scale human metabolic network to study physiology and pathology. 271(2):131–141, 2011. [DOI].
Suvi Santala, Elena Efimova, Virpi Kivinen, Antti Larjo, Tommi Aho, Matti Karp, and Ville Santala. Improved triacylglycerol production in acinetobacter baylyi adp1 by metabolic engineering. 10(1):36–36, 2011. [DOI].
Caroline B. Milne, James A. Eddy, Ravali Raju, Soroush Ardekani, Pan‐Jun Kim, Ryan S. Senger, Yong‐Su Jin, Hans P. Blaschek, and Nathan D. Price. Metabolic network reconstruction and genome-scale model of butanol-producing strain clostridium beijerinckii ncimb 8052. 5(1):130–130, 2011. [DOI].
Pablo Carbonell, Anne‐Gaëlle Planson, Davide Fichera, and Jean‐Loup Faulon. A retrosynthetic biology approach to metabolic pathway design for therapeutic production. 5(1):122–122, 2011. [DOI].
Il‐Kwon Kim, António Roldão, Verena Siewers, and Jens Nielsen. A systems-level approach for metabolic engineering of yeast cell factories. 12(2):228–248, 2011. [DOI].
Neil Swainston, Kieran Smallbone, Pedro Mendes, Douglas B. Kell, and Norman W. Paton. The subliminal toolbox: automating steps in the reconstruction of metabolic networks. 8(2):187–203, 2011. [DOI].
Jordan O. Hay and Jörg Schwender. Computational analysis of storage synthesis in developing brassica napus l. (oilseed rape) embryos: flux variability analysis in relation to 13c metabolic flux analysis. 67(3):513–525, 2011. [DOI].
Jordan O. Hay and Jörg Schwender. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (brassica napus l.) embryos. 67(3):526–541, 2011. [DOI].
Andreas Hoppe, Sabrina Hoffmann, Andreas Gerasch, Christoph Gille, and Hermann−Georg Holzhütter. Fasimu: flexible software for flux-balance computation series in large metabolic networks. 12(1):28–28, 2011. [DOI].
Steffen Klamt and Axel von Kamp. An application programming interface for cellnetanalyzer. 105(2):162–168, 2011. [DOI].
Siu Hung Joshua Chan and Ping Ji. Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks. 27(16):2256–2262, 2011. [DOI].
Daniel R. Hyduke, Daniel R. Hyduke, Jan Schellenberger, Richard Que, Ronan M. T. Fleming, Ines Thiele, Jeffery Orth, Adam M. Feist, Daniel C. Zielinski, Aarash Bordbar, Nathan E. Lewis, Sorena Rahmanian, Joseph Kang, and Bernhard Ø. Palsson. Cobra toolbox 2.0. ():, 2011. [DOI].
Aarash Bordbar, Neema Jamshidi, and Bernhard Ø. Palsson. Iab-rbc-283: a proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. 5(1):110–110, 2011. [DOI].
Wolfram Weckwerth. Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing. 400(7):1967–1978, 2011. [DOI].
Matthew N. Benedict, Matthew C. Gonnerman, William W. Metcalf, and Nathan D. Price. Genome-scale metabolic reconstruction and hypothesis testing in the methanogenic archaeon methanosarcina acetivorans c2a. 194(4):855–865, 2011. [DOI].
Óttar Rolfsson, Bernhard Ø. Palsson, and Ines Thiele. The human metabolic reconstruction recon 1 directs hypotheses of novel human metabolic functions. 5(1):155–155, 2011. [DOI].
Sandy MacDonald, Gavin H. Thomas, and Angela E. Douglas. Genetic and metabolic determinants of nutritional phenotype in an insect-bacterial symbiosis. 20(10):2073–2084, 2011. [DOI].
Anthony C. Smith and Alan J. Robinson. A metabolic model of the mitochondrion and its use in modelling diseases of the tricarboxylic acid cycle. 5(1):102–102, 2011. [DOI].
David J. Baumler, Roman G Peplinski, Jennifer L. Reed, Jeremy D. Glasner, and Nicole T. Perna. The evolution of metabolic networks of e. coli. 5(1):182–182, 2011. [DOI].
Amit Ghosh, Huimin Zhao, and Nathan D. Price. Genome-scale consequences of cofactor balancing in engineered pentose utilization pathways in saccharomyces cerevisiae. 6(11):e27316–e27316, 2011. [DOI].
Eleftherios Pilalis, Aristotelis Chatziioannou, Brigitte Thomasset, and Fragiskos N. Kolisis. An in silico compartmentalized metabolic model of brassica napus enables the systemic study of regulatory aspects of plant central metabolism. 108(7):1673–1682, 2011. [DOI].
Stefan Gretar Thorleifsson and Ines Thiele. Rbionet: a cobra toolbox extension for reconstructing high-quality biochemical networks. 27(14):2009–2010, 2011. [DOI].
Gino Baart and Dirk E. Martens. Genome-scale metabolic models: reconstruction and analysis. 799():107–126, 2011. [DOI].
Brett A. Boghigian, John Armando, Daniel Salas, and Blaine A. Pfeifer. Computational identification of gene over-expression targets for metabolic engineering of taxadiene production. 93(5):2063–2073, 2011. [DOI].
Guillaume Cogne, Marco Rügen, Alexander Bockmayr, Mariana Titica, Claude‐Gilles Dussap, Jean‐François Cornet, and Jack Legrand. A model‐based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae chlamydomonas reinhardtii. 27(3):631–640, 2011. [DOI].
Aline Métris, Mark Reuter, Duncan J. H. Gaskin, József Baranyi, and Arnoud H. M. van Vliet. In vivo and in silico determination of essential genes of campylobacter jejuni. 12(1):535–535, 2011. [DOI].
Aditya Vikram Pandit and Radhakrishnan Mahadevan. In silico characterization of microbial electrosynthesis for metabolic engineering of biochemicals. 10(1):76–76, 2011. [DOI].
Eleftheria Tzamali, Panayiota Poirazi, Ioannis G. Tollis, and Martin Reczko. A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities. 5(1):167–167, 2011. [DOI].
Osbaldo Reséndis-Antonio, Magdalena Hernández, Emmanuel Salazar, Sandra Contreras, G. Martínez Batallar, Yolanda Mora, and Sergio Encarnación‐Guevara. Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling. 5(1):120–120, 2011. [DOI].
Pep Charusanti, Sadhana Chauhan, Kathleen McAteer, Joshua A. Lerman, Daniel R. Hyduke, Vladimir L. Motin, Charles Ansong, Joshua Adkins, and Bernhard Ø. Palsson. An experimentally-supported genome-scale metabolic network reconstruction for yersinia pestis co92. 5(1):163–163, 2011. [DOI].
Charles R. Haggart, Jennifer Bartell, Jeffrey J. Saucerman, and Jason A. Papin. Whole-genome metabolic network reconstruction and constraint-based modeling⋆. 500():411–433, 2011. [DOI].
Stephan Pabinger, Robert Rader, Rasmus Ågren, Jens Nielsen, and Zlatko Trajanoski. Memosys: bioinformatics platform for genome-scale metabolic models. 5(1):20–20, 2011. [DOI].
Mona Yousofshahi, Kyongbum Lee, and Soha Hassoun. Probabilistic pathway construction. 13(4):435–444, 2011. [DOI].
Kathleen A. Curran, Nathan Crook, and Hal S. Alper. Using flux balance analysis to guide microbial metabolic engineering. 834():197–216, 2011. [DOI].
J. Bernadette Moore and Mark C. Willingham. Proteomics and systems biology: current and future applications in the nutritional sciences. 2(4):355–364, 2011. [DOI].
Hailin Meng, Yong Wang, Qiang Hua, Siliang Zhang, and Xiao‐Ning Wang. In silico analysis and experimental improvement of taxadiene heterologous biosynthesis in escherichia coli. 16(2):205–215, 2011. [DOI].
Oksana Sorokina, Florence Corellou, David Dauvillée, Anatoly Sorokin, Igor Goryanin, Steven Ball, François‐Yves Bouget, and Andrew J. Millar. Microarray data can predict diurnal changes of starch content in the picoalga ostreococcus. 5(1):36–36, 2011. [DOI].
Karoline Faust, Didier Croes, and Jacques van Helden. Prediction of metabolic pathways from genome-scale metabolic networks. 105(2):109–121, 2011. [DOI].
Mohammad Tauqeer Alam, Marnix H. Medema, Eriko Takano, and Rainer Breitling. Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism. 585(14):2389–2394, 2011. [DOI].
Tom A. Mendum, Jane Newcombe, Ahmad A. Mannan, Andrzej Kierzek, and Johnjoe McFadden. Interrogation of global mutagenesis data with a genome scale model of neisseria meningitidis to assess gene fitness in vitro and in sera. 12(12):R127–R127, 2011. [DOI].
Christopher S. Henry, Ross Overbeek, Fangfang Xia, Aaron A. Best, Elizabeth M. Glass, Jack A. Gilbert, Peter A. Larsen, Robert A. Edwards, Terry Disz, Folker Meyer, Veronika Vonstein, Matthew DeJongh, Daniela Bartels, Narayan Desai, Mark D’Souza, Scott Devoid, Kevin Keegan, Robert Olson, Andreas Wilke, Jared Wilkening, and Rick Stevens. Connecting genotype to phenotype in the era of high-throughput sequencing. 1810(10):967–977, 2011. [DOI].
Yoshihiro Toya, Nobuaki Kono, Kazuharu Arakawa, and Masaru Tomita. Metabolic flux analysis and visualization. 10(8):3313–3323, 2011. [DOI].
Yuqi Zhao and Jing‐Fei Huang. Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data. 415(3):450–454, 2011. [DOI].
Sean P. Cornelius, Joo Sang Lee, and Adilson E. Motter. Dispensability of escherichia coli ’s latent pathways. 108(8):3124–3129, 2011. [DOI].
Intawat Nookaew, Roberto Olivares‐Hernández, Sakarindr Bhumiratana, and Jens Nielsen. Genome-scale metabolic models of saccharomyces cerevisiae. 759():445–463, 2011. [DOI].
Juha Leppävuori, Michael M. Domach, and Lorenz T. Biegler. Parameter estimation in batch bioreactor simulation using metabolic models: sequential solution with direct sensitivities. 50(21):12080–12091, 2011. [DOI].
Hailin Meng, Zhiguo Lu, Yong Wang, Xiao‐Ning Wang, and Siliang Zhang. In silico improvement of heterologous biosynthesis of erythromycin precursor 6-deoxyerythronolide b in escherichia coli. 16(3):445–456, 2011. [DOI].
Andrew L. Hopkins, G. Richard Bickerton, Ian M. Carruthers, Stephen Boyer, Harvey Rubin, and John P. Overington. Rapid analysis of pharmacology for infectious diseases. 11(10):1292–1300, 2011. [DOI].
Ziomara P. Gerdtzen. Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools. 127():71–108, 2011. [DOI].
Reza Eslamloueyan and Payam Setoodeh. Optimization of fed-batch recombinant yeast fermentation for ethanol production using a reduced dynamic flux balance model based on artificial neural networks. 198(11):1309–1338, 2011. [DOI].
Neema Jamshidi, Franklin J. Miller, Jess Mandel, Timothy Evans, and Michael Kuo. Individualized therapy of hht driven by network analysis of metabolomic profiles. 5(1):200–200, 2011. [DOI].
Renato Umeton, Giovanni Stracquadanio, Anilkumar Sorathiya, Píetro Lió, Alessio Papini, and Giuseppe Nicosia. Design of robust metabolic pathways. ():, 2011. [DOI].
Aline Métris, Susan George, and József Baranyi. Modelling osmotic stress by flux balance analysis at the genomic scale. 152(3):123–128, 2011. [DOI].
Hannes Link and Dirk Weuster‐Botz. Medium formulation and development. ():119–134, 2011. [DOI].
Xin Fang, Anders Wallqvist, and Jaques Reifman. Modeling synergistic drug inhibition of mycobacterium tuberculosis growth in murine macrophages. 7(9):2622–2622, 2011. [DOI].
Daniela Xavier, Sara Vázquez, Clara Higuera, Federico Morán, and Francisco Montero. Tools-4-metatool (t4m): online suite of web-tools to process stoichiometric network analysis data from metatool. 105(2):169–172, 2011. [DOI].
Ali Navid. Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets. 10(6):354–364, 2011. [DOI].
Nicolas Turenne. Role of a web-based software platform for systems biology. 04(03):, 2011. [DOI].
Ehsan Motamedian and Fereshteh Naeimpoor. Prediction of proton exchange and bacterial growth on various substrates using constraint-based modeling approach. 16(5):875–884, 2011. [DOI].
Rajeev S. Assary and Linda J. Broadbelt. 2-keto acids to branched-chain alcohols as biofuels: application of reaction network analysis and high-level quantum chemical methods to understand thermodynamic landscapes. 978(1-3):160–165, 2011. [DOI].
Hannes Link and Dirk Weuster‐Botz. Medium formulation and development. ():199–213, 2011. [DOI].
Sarah Munro, Stephen H. Zinder, and Larry P. Walker. Original research: comparative constraint-based model development for thermophilic hydrogen production. 7(1):63–82, 2011. [DOI].
Rajeev S. Assary, Linda J. Broadbelt, and Larry A. Curtiss. Brønsted-evans-polanyi relationships for c–c bond forming and c–c bond breaking reactions in thiamine-catalyzed decarboxylation of 2-keto acids using density functional theory. 18(1):145–150, 2011. [DOI].
Aarash Bordbar and Bernhard Ø. Palsson. Moving toward genome-scale kinetic models: the mass action stoichiometric simulation approach. ():201–220, 2011. [DOI].
Ines Thiele, Ronan M. T. Fleming, Richard Que, Aarash Bordbar, and Bernhard Ø. Palsson. A systems biology approach to the evolution of codon use pattern. ():, 2011. [DOI].
Sarah Munro, Leila H. Choe, Stephen H. Zinder, Kelvin H. Lee, and Larry P. Walker. Proteomic and physiological experiments to test thermotoga neapolitana constraint‐based model hypotheses of carbon source utilization. 28(2):312–318, 2011. [DOI].
Ines Thiele, Ines Thiele, Ronan M. T. Fleming, Richard Que, Aarash Bordbar, and Bernhard Ø. Palsson. A systems biology approach to the evolution of codon use pattern. ():, 2011. [DOI].
Jan Schellenberger, Nathan E. Lewis, and Bernhard Ø. Palsson. Elimination of thermodynamically infeasible loops in steady-state metabolic models. 100(3):544–553, 2011. [DOI].
Ines Thiele, Daniel R. Hyduke, Benjamin Steeb, Guy Fankam, Doug K. Allen, Susanna Bazzani, Pep Charusanti, Feng‐Chi Chen, Ronan M. T. Fleming, Chao A. Hsiung, Sigrid C. J. De Keersmaecker, Yu-Chieh Liao, Kathleen Marchal, Monica L. Mo, Emre Özdemir, Anu Raghunathan, Jennifer L. Reed, Sookil Shin, Sara Sigurbjörnsdóttir, Jonas Steinmann, Suresh Sudarsan, Neil Swainston, Inge M Thijs, Karsten Zengler, Bernhard Ø. Palsson, Joshua Adkins, and Dirk Bumann. A community effort towards a knowledge-base and mathematical model of the human pathogen salmonella typhimurium lt2. 5(1):8–8, 2011. [DOI].
Bouke de Jong, Verena Siewers, and Jens Nielsen. Systems biology of yeast: enabling technology for development of cell factories for production of advanced biofuels. 23(4):624–630, 2011. [DOI].
Elias W. Krumholz, Hong Yang, Pamela Weisenhorn, Christopher S. Henry, and Igor G. L. Libourel. Genome-wide metabolic network reconstruction of the picoalga ostreococcus. 63(6):2353–2362, 2011. [DOI].
Neil Swainston, Kieran Smallbone, Pedro Mendes, Douglas B. Kell, and Norman W. Paton. The subliminal toolbox: automating steps in the reconstruction of metabolic networks. 8(2):186–186, 2011. [DOI].
Ronan M. T. Fleming, Christopher M. Maes, Michael A. Saunders, Y. Ye, and Bernhard Ø. Palsson. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks. 292():71–77, 2011. [DOI].
Srinubabu Gedela. Integration, warehousing, and analysis strategies of omics data. 719():399–414, 2011. [DOI].
Yu-Chieh Liao, Chih‐Yu Chen, Min-Hsin Tsai, Yueh-Hsia Tang, Feng‐Chi Chen, and Chao A. Hsiung. Mrbac: a web server for draft metabolic network reconstructions for bacteria. 2(5):284–287, 2011. [DOI].
Rafik Salama. Computational biology approaches for studying gene regulatory network discovery and modelling. ():, 2011. [DOI].
Wout Megchelenbrink, Martijn A. Huynen, and Elena Marchiori. Flux measurement selection in metabolic networks. ():214–224, 2011. [DOI].
Martin I. Sigurðsson. Bioinformatic and biological analysis of dna methylation in the human genome. ():, 2011. [DOI].
Ilona Odzina, Reinis Rutkis, and Uldis Kalnenieks. In silico engineering of biochemical network of zymomonas mobilis adaptation for glycerol conversion into bioethanol. ():272–276, 2011. [DOI].
William Parra Meneses. Análisis comparativo de la topología de redes metabólicas en una muestra metagenómica de aguas termales en el parque natural de los nevados en colombia. ():, 2011. [DOI].
Ronan M. T. Fleming, Christopher M. Maes, Michael A. Saunders, Yinyu Ye, and Bernhard Ø. Palsson. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks. ():, 2011. [DOI].
Christopher M. Gowen. Model-guided systems metabolic engineering of clostridium thermocellum. ():, 2011. [DOI].

Jeffrey D. Orth, Ines Thiele, and Bernhard Ø. Palsson. What is flux balance analysis? 28(3):245–248, 2010. [DOI].
Ines Thiele and Bernhard Ø. Palsson. A protocol for generating a high-quality genome-scale metabolic reconstruction. 5(1):93–121, 2010. [DOI].
Jan Schellenberger, Junyoung O. Park, Tom M Conrad, and Bernhard Ø. Palsson. Bigg: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. 11(1):213–213, 2010. [DOI].
Sriram Chandrasekaran and Nathan D. Price. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in escherichia coli and mycobacterium tuberculosis. 107(41):17845–17850, 2010. [DOI].
Isabel Rocha, Paulo Maia, Pedro Evangelista, Paulo Vilaça, Simão Soares, José P. Pinto, Jens Nielsen, Kiran Raosaheb Patil, Eugénio C. Ferreira, and Miguel Rocha. Optflux: an open-source software platform for in silico metabolic engineering. 4(1):45–45, 2010. [DOI].
Steinn Guðmundsson and Ines Thiele. Computationally efficient flux variability analysis. 11(1):, 2010. [DOI].
Niels Klitgord and Daniel Segrè. Environments that induce synthetic microbial ecosystems. 6(11):e1001002–e1001002, 2010. [DOI].
Jeffrey D. Orth, Ronan M. T. Fleming, and Bernhard Ø. Palsson. Reconstruction and use of microbial metabolic networks: the core escherichia coli metabolic model as an educational guide. 4(1):, 2010. [DOI].
Aarash Bordbar, Nathan E. Lewis, Jan Schellenberger, Bernhard Ø. Palsson, and Neema Jamshidi. Insight into human alveolar macrophage and m. tuberculosis interactions via metabolic reconstructions. 6(1):422–422, 2010. [DOI].
Keren Yizhak, Tomer Benyamini, Wolfram Liebermeister, Eytan Ruppin, and Tomer Shlomi. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. 26(12):i255–i260, 2010. [DOI].
Roger L. Chang, Li Xie, Lei Xie, Philip E. Bourne, and Bernhard Ø. Palsson. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. 6(9):e1000938–e1000938, 2010. [DOI].
Marnix H. Medema, Axel Trefzer, Andriy Kovalchuk, Marco van den Berg, Ulrike Müller, Wilbert H. M. Heijne, Liang Wu, Mohammad Tauqeer Alam, Catherine M. Ronning, William C. Nierman, Roel A. L. Bovenberg, Rainer Breitling, and Eriko Takano. The sequence of a 1.8-mb bacterial linear plasmid reveals a rich evolutionary reservoir of secondary metabolic pathways. 2():212–224, 2010. [DOI].
Sergio Bordel, Rasmus Ågren, and Jens Nielsen. Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. 6(7):e1000859–e1000859, 2010. [DOI].
Henning Knoop, Yvonne Zilliges, Wolfgang Lockau, and Ralf Steuer. The metabolic network of synechocystis sp. pcc 6803: systemic properties of autotrophic growth. 154(1):410–422, 2010. [DOI].
Martin I. Sigurðsson, Neema Jamshidi, Eirı́kur Steingrı́msson, Ines Thiele, and Bernhard Ø. Palsson. A detailed genome-wide reconstruction of mouse metabolism based on human recon 1. 4(1):140–140, 2010. [DOI].
Xuewen Chen, Ana Paula Alonso, Doug K. Allen, Jennifer L. Reed, and Yair Shachar‐Hill. Synergy between 13c-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in e. coli. 13(1):38–48, 2010. [DOI].
John Blazeck and Hal S. Alper. Systems metabolic engineering: genome‐scale models and beyond. 5(7):647–659, 2010. [DOI].
Germán Plata, Tzu‐Lin Hsiao, Kellen Olszewski, Manuel Llinás, and Dennis Vitkup. Reconstruction and flux‐balance analysis of the plasmodium falciparum metabolic network. 6(1):408–408, 2010. [DOI].
Fahimeh Salimi, Kai Zhuang, and Radhakrishnan Mahadevan. Genome‐scale metabolic modeling of a clostridial co‐culture for consolidated bioprocessing. 5(7):726–738, 2010. [DOI].
Paul D. Dobson, Kieran Smallbone, Daniel Jameson, Evangelos Simeonidis, Karin Lanthaler, Pınar Pir, Chuan Lü, Neil Swainston, Warwick B. Dunn, Paul G. Fisher, Duncan Hull, Marie Brown, Olusegun Oshota, Natalie Stanford, Douglas B. Kell, Ross D. King, Stephen G. Oliver, Robert Stevens, and Pedro Mendes. Further developments towards a genome-scale metabolic model of yeast. 4(1):145–145, 2010. [DOI].
Sarah Kinnings, Li Xie, Kingston H. Fung, Richard M. Jackson, Lei Xie, Philip E. Bourne, Lei Xie, and Philip E. Bourne. The mycobacterium tuberculosis drugome and its polypharmacological implications. 6(11):e1000976–e1000976, 2010. [DOI].
Marija Cvijović, Roberto Olivares‐Hernández, Rasmus Ågren, N. Dahr, Wanwipa Vongsangnak, Intawat Nookaew, Kiran Raosaheb Patil, and Jens Nielsen. Biomet toolbox: genome-wide analysis of metabolism. 38(Web Server):W144–W149, 2010. [DOI].
Mohammad Tauqeer Alam, Maria Elena Merlo, David A. Hodgson, Elizabeth M. H. Wellington, Eriko Takano, Rainer Breitling, and Rainer Breitling. Metabolic modeling and analysis of the metabolic switch in streptomyces coelicolor. 11(1):202–202, 2010. [DOI].
Matthew Oberhardt, Joanna B. Goldberg, Michael Hogardt, and Jason A. Papin. Metabolic network analysis of pseudomonas aeruginosa during chronic cystic fibrosis lung infection. 192(20):5534–5548, 2010. [DOI].
Li Liu, Rasmus Ågren, Sergio Bordel, and Jens Nielsen. Use of genome‐scale metabolic models for understanding microbial physiology. 584(12):2556–2564, 2010. [DOI].
M. Ahsanul Islam, Elizabeth A. Edwards, and Radhakrishnan Mahadevan. Characterizing the metabolism of dehalococcoides with a constraint-based model. 6(8):e1000887–e1000887, 2010. [DOI].
Osbaldo Reséndis-Antonio, Alberto Checa, and Sergio Encarnación‐Guevara. Modeling core metabolism in cancer cells: surveying the topology underlying the warburg effect. 5(8):e12383–e12383, 2010. [DOI].
Michael D. Barton, Daniela Delneri, Stephen G. Oliver, Magnus Rattray, and Casey Bergman. Evolutionary systems biology of amino acid biosynthetic cost in yeast. 5(8):e11935–e11935, 2010. [DOI].
Stephan Noack, Katharina Nöh, Matthias Moch, Marco Oldiges, and Wolfgang Wiechert. Stationary versus non-stationary 13c-mfa: a comparison using a consistent dataset. 154(2-3):179–190, 2010. [DOI].
Limin Li, Xiaobo Zhou, Wai Ki Ching, and Ping Wang. Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in nci-60 cell lines. 11(1):501–501, 2010. [DOI].
Albert Gevorgyan, Michael E. Bushell, Claudio Avignone–Rossa, and Andrzej Kierzek. Surreyfba: a command line tool and graphics user interface for constraint-based modeling of genome-scale metabolic reaction networks. 27(3):433–434, 2010. [DOI].
K. Srinivasan and Radhakrishnan Mahadevan. Characterization of proton production and consumption associated with microbial metabolism. 10(1):2–2, 2010. [DOI].
John S. Ramsey, Sandy MacDonald, Georg Jander, Atsushi Nakabachi, Gavin H. Thomas, and Angela E. Douglas. Genomic evidence for complementary purine metabolism in the pea aphid, acyrthosiphon pisum , and its symbiotic bacterium buchnera aphidicola. 19(s2):241–248, 2010. [DOI].
Ronan M. T. Fleming and Ines Thiele. Von bertalanffy 1.0: a cobra toolbox extension to thermodynamically constrain metabolic models. 27(1):142–143, 2010. [DOI].
Anu Raghunathan, Sookil Shin, and Simon Daefler. Systems approach to investigating host-pathogen interactions in infections with the biothreat agent francisella. constraints-based model of francisella tularensis. 4(1):118–118, 2010. [DOI].
Xin Fang, Anders Wallqvist, and Jaques Reifman. Development and analysis of an in vivo-compatible metabolic network of mycobacterium tuberculosis. 4(1):160–160, 2010. [DOI].
Xueyang Feng, Lawrence M. Page, Jacob R. Rubens, Lauren Chircus, Peter Colletti, Himadri B. Pakrasi, and Yinjie Tang. Bridging the gap between fluxomics and industrial biotechnology. 2010():1–13, 2010. [DOI].
Brian J. Schmidt, Xiefan Lin‐Schmidt, Austin Chamberlin, Kourosh Salehi‐Ashtiani, and Jason A. Papin. Metabolic systems analysis to advance algal biotechnology. 5(7):660–670, 2010. [DOI].
Sergey Kozhenkov, Yulia Dubinina, Mayya Sedova, Amarnath Gupta, Julia Ponomarenko, and Michael Baitaluk. Biologicalnetworks 2.0 - an integrative view of genome biology data. 11(1):610–610, 2010. [DOI].
Rainer Breitling, Robin Donaldson, David Gilbert, and Monika Heiner. Biomodel engineering – from structure to behavior. ():1–12, 2010. [DOI].
Han Min Woo, Stephan Noack, Gerd M. Seibold, Sabine Willbold, Bernhard J. Eikmanns, and Michael Bott. Link between phosphate starvation and glycogen metabolism in corynebacterium glutamicum , revealed by metabolomics. 76(20):6910–6919, 2010. [DOI].
Marija Cvijović, Sergio Bordel, and Jens Nielsen. Mathematical models of cell factories: moving towards the core of industrial biotechnology. 4(5):572–584, 2010. [DOI].
Paulo Vilaça, Isabel Rocha, and Miguel Rocha. A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information. 103(3):435–441, 2010. [DOI].
Thomas Forth, Glenn A. McConkey, and David R. Westhead. Metnetmaker: a free and open-source tool for the creation of novel metabolic networks in sbml format. 26(18):2352–2353, 2010. [DOI].
Michael D. Barton, Daniela Delneri, Stephen G. Oliver, Magnus Rattray, and Casey Bergman. Correction: evolutionary systems biology of amino acid biosynthetic cost in yeast. 5(10):, 2010. [DOI].
Dirk Koschützki, Björn H. Junker, Jörg Schwender, and Falk Schreiber. Structural analysis of metabolic networks based on flux centrality. 265(3):261–269, 2010. [DOI].
Yong Xi, Yi‐Ping Phoebe Chen, Qian Chen, and Fei Wang. Comparative study of computational methods to detect the correlated reaction sets in biochemical networks. 12(2):132–150, 2010. [DOI].
Sven Dietz and Sven Panke. Microbial systems engineering: first successes and the way ahead. 32(4):356–362, 2010. [DOI].
Patrick May, Nils Christian, Oliver Ebenhöh, Wolfram Weckwerth, and Dirk Walther. Integration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks. 694():341–363, 2010. [DOI].
Rajeev S. Assary and Linda J. Broadbelt. Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids. 34(3):375–388, 2010. [DOI].
Ashwini G. Bhat, Rohit Vashisht, and Nagasuma Chandra. Modeling metabolic adjustment in mycobacterium tuberculosis upon treatment with isoniazid. 4(4):299–309, 2010. [DOI].
Qi Chen, Zhuo Wang, and Dong‐Qing Wei. Progress in the applications of flux analysis of metabolic networks. 55(22):2315–2322, 2010. [DOI].
David Hala, Aneeqa Amin, Armin R. Mikler, and Duane B. Huggett. A constraint-based stoichiometric model of the steroidogenic network of zebrafish (danio rerio). 18(03):669–685, 2010. [DOI].
Maik Kschischo. A gentle introduction to the thermodynamics of biochemical stoichiometric networks in steady state. 187(1):255–274, 2010. [DOI].
Fahimeh Salimi, Rupasri Mandal, David S. Wishart, and Radhakrishnan Mahadevan. Understanding clostridium acetobutylicum atcc 824 metabolism using genome-scale thermodynamics and metabolomics-based modeling. 43(6):126–131, 2010. [DOI].
Nadeera Jayasinghe and Radhakrishnan Mahadevan. Metabolic modeling of spatial heterogeneity of biofilms in microbial fuel cells. 43(6):215–220, 2010. [DOI].
Balaji Veeramani and Joel S. Bader. Predicting functional associations from metabolism using bi-partite network algorithms. 4(1):95–95, 2010. [DOI].
Mano R. Maurya and Shankar Subramaniam. Computational challenges in systems biology. ():175–223, 2010. [DOI].
Joseph Bressler, Joel S. Bader, and Alan M. Goldberg. Alternatives to conventional toxicology testing. ():247–259, 2010. [DOI].
James M. Smith and Marc‐Thorsten Hütt. Network dynamics as an interface between modeling and experiment in systems biology. ():243–276, 2010. [DOI].
Ding De-wu and He Xiao-qing. Analyzing the core of e. coli metabolism with flux balance. 102():195–197, 2010. [DOI].
David Hala, Duane B. Huggett, and Dalma Martinović‐Weigelt. in silico predicted essential genes required for zebrafish ( danio rerio ) steroid hormone production. ():487–489, 2010. [DOI].
琦陈, 冬青魏, and 卓王. 代谢网络流分析进展及应用. 55(14):1302–1309, 2010. [DOI].
Kai Zhuang, Mounir Izallalen, Paula Mouser, Hanno Richter, Carla Risso, Radhakrishnan Mahadevan, and Derek R. Lovley. Genome-scale dynamic modeling of the competition between rhodoferax and geobacter in anoxic subsurface environments. 5(2):305–316, 2010. [DOI].
Masafumi Nozawa, Sayaka Miura, and Masatoshi Nei. Origins and evolution of microrna genes in drosophila species. 2():180–189, 2010. [DOI].
Daniel Machado, Rafael S. Costa, Miguel Rocha, Isabel Rocha, Bruce Tidor, and Eugénio C. Ferreira. Model transformation of metabolic networks using a petri net based framework. 827():101–115, 2010. [DOI].
Laurence Yang, Radhakrishnan Mahadevan, and W.R. Cluett. Designing experiments from noisy metabolomics data to refine constraint-based models. ():5143–5148, 2010. [DOI].
Hui Wang, Hongwu Ma, and Xueming Zhao. [progress in genome-scale metabolic network: a review]. 26(10):1340–8, 2010. [DOI].
Sarah Munro. Systems biology of thermotoga neapolitana for biological hydrogen production. ():, 2010. [DOI].
Adrián López García de Lomana. Computational approaches to the modelling of topological and dynamical aspects of biochemical networks. ():, 2010. [DOI].
C. D. Machado. Novel modeling formalisms and simulation tools in computational biosystems. ():18–18, 2010. [DOI].
Saeed Sheykhshoaieekhttiarabadi. An automated approach to assigning subcellular localization in genome-scale metabolic network reconstructions. ():, 2010. [DOI].
Zaldivar Comenges Jose and Fernanda Strozzi. Analysis and simulation of metabolic networks: application to hepg2. ():, 2010. [DOI].

Matthew Oberhardt, Bernhard Ø. Palsson, and Jason A. Papin. Applications of genome‐scale metabolic reconstructions. 5(1):320–320, 2009. [DOI].
Monica L. Mo, Bernhard Ø. Palsson, and Markus J. Herrgård. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. 3(1):37–37, 2009. [DOI].
Karthik Raman and Nagasuma Chandra. Flux balance analysis of biological systems: applications and challenges. 10(4):435–449, 2009. [DOI].
Adam M. Feist, Daniel C. Zielinski, Jeffrey D. Orth, Jan Schellenberger, Markus J. Herrgård, and Bernhard Ø. Palsson. Model-driven evaluation of the production potential for growth-coupled products of escherichia coli. 12(3):173–186, 2009. [DOI].
Kenji Nakahigashi, Yoshihiro Toya, Nobuyoshi Ishii, Tomoyoshi Soga, Miki Hasegawa, Hisami Watanabe, Yuki Takai, Masayuki Honma, Hirotada Mori, and Masaru Tomita. Systematic phenome analysis of escherichia coli multiple‐knockout mutants reveals hidden reactions in central carbon metabolism. 5(1):306–306, 2009. [DOI].
Bas Teusink, Anne Wiersma, Leo Jacobs, Richard A. Notebaart, and Eddy J. Smid. Understanding the adaptive growth strategy of lactobacillus plantarum by in silico optimisation. 5(6):e1000410–e1000410, 2009. [DOI].
Ani Manichaikul, Lila Ghamsari, Erik Hom, Chenwei Lin, Ryan R. Murray, Roger L. Chang, Balaji Santhanam, Tong Hao, Yun Shen, Arvind K. Chavali, Ines Thiele, Xinping Yang, Changyu Fan, Elizabeth Mello, David E. Hill, Marc Vidal, Kourosh Salehi‐Ashtiani, and Jason A. Papin. Metabolic network analysis integrated with transcript verification for sequenced genomes. 6(8):589–592, 2009. [DOI].
Bernhard Ø. Palsson. Metabolic systems biology. 583(24):3900–3904, 2009. [DOI].
Max Hoek and Paulien Hogeweg. Metabolic adaptation after whole genome duplication. 26(11):2441–2453, 2009. [DOI].
Xin Fang, Anders Wallqvist, and Jaques Reifman. A systems biology framework for modeling metabolic enzyme inhibition of mycobacterium tuberculosis. 3(1):92–92, 2009. [DOI].
Gavin H. Thomas, Jeremy Zucker, Sandy MacDonald, Anatoly Sorokin, Igor Goryanin, and Angela E. Douglas. A fragile metabolic network adapted for cooperation in the symbiotic bacterium buchnera aphidicola. 3(1):24–24, 2009. [DOI].
Zhi Wang and Jianzhi Zhang. Why is the correlation between gene importance and gene evolutionary rate so weak? 5(1):e1000329–e1000329, 2009. [DOI].
Zhi Wang and Jianzhi Zhang. Abundant indispensable redundancies in cellular metabolic networks. 1():23–33, 2009. [DOI].
Matthew Oberhardt, Arvind K. Chavali, and Jason A. Papin. Flux balance analysis: interrogating genome-scale metabolic networks. 500():61–80, 2009. [DOI].
Jacob Feala, Laurence Coquin, Dan Zhou, Gabriel G. Haddad, Giovanni Paternostro, and Andrew D. McCulloch. Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis. 3(1):91–91, 2009. [DOI].
Christian Barrett, Markus J. Herrgård, and Bernhard Ø. Palsson. Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation. 3(1):30–30, 2009. [DOI].
Fan Li, Ines Thiele, Neema Jamshidi, and Bernhard Ø. Palsson. Identification of potential pathway mediation targets in toll-like receptor signaling. 5(2):e1000292–e1000292, 2009. [DOI].
Eva Grafahrend‐Belau, Christian Klukas, Björn H. Junker, and Falk Schreiber. Fba-simvis: interactive visualization of constraint-based metabolic models. 25(20):2755–2757, 2009. [DOI].
Vicente Acuña, Alberto Marchetti-Spaccamela, Marie-France Sagot, and Leen Stougie. A note on the complexity of finding and enumerating elementary modes. 99(3):210–214, 2009. [DOI].
Osbaldo Reséndis-Antonio. Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking. 4(3):e4967–e4967, 2009. [DOI].
Alfonso Pérez‐Escudero, Marta Rivera-Alba, and Gonzalo G. de Polavieja. Structure of deviations from optimality in biological systems. 106(48):20544–20549, 2009. [DOI].
Karthik Raman, Rohit Vashisht, and Nagasuma Chandra. Strategies for efficient disruption of metabolism in mycobacterium tuberculosis from network analysis. 5(12):1740–1740, 2009. [DOI].
Hannes Link, Bernd Anselment, and Dirk Weuster‐Botz. Rapid media transition: an experimental approach for steady state analysis of metabolic pathways. 26(1):1–10, 2009. [DOI].
Douglas Densmore, Anne Van Devender, Matthew Johnson, and Nade Sritanyaratana. A platform-based design environment for synthetic biological systems. ():24–29, 2009. [DOI].
François Le Fèvre, Serge Smidtas, Cyril Combe, Maxime Durot, Florence d’Alché–Buc, and Vincent Schächter. Cycsim—an online tool for exploring and experimenting with genome-scale metabolic models. 25(15):1987–1988, 2009. [DOI].
Yanping Xi, Yi‐Ping Phoebe Chen, Ming Cao, Weirong Wang, and Fei Wang. Analysis on relationship between extreme pathways and correlated reaction sets. 10(S1):S58–S58, 2009. [DOI].
Brett A. Boghigian, Kyongbum Lee, and Blaine A. Pfeifer. Computational analysis of phenotypic space in heterologous polyketide biosynthesis—applications to escherichia coli, bacillus subtilis, and saccharomyces cerevisiae. 262(2):197–207, 2009. [DOI].
Jenni Heino, Daniela Calvetti, and Erkki Somersalo. Metabolica: a statistical research tool for analyzing metabolic networks. 97(2):151–167, 2009. [DOI].
Maikel P. H. Verouden, Richard A. Notebaart, Johan A. Westerhuis, Mariët J. van der Werf, Bas Teusink, and Age K. Smilde. Multi‐way analysis of flux distributions across multiple conditions. 23(7-8):406–420, 2009. [DOI].
Tae Sung Jung, Hock Chuan Yeo, Satty G. Reddy, Wan-Sup Cho, and Dong‐Yup Lee. Webcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale e.coli model. 25(21):2850–2852, 2009. [DOI].
Balaji Veeramani and Joel S. Bader. Metabolic flux correlations, genetic interactions, and disease. 16(2):291–302, 2009. [DOI].
Nathan E. Lewis, Neema Jamshidi, Ines Thiele, and Bernhard Ø. Palsson. Metabolic systems biology. ():5535–5552, 2009. [DOI].
Zixiang Xu, Xiao Sun, and Shihai Yu. Genome-scale analysis to the impact of gene deletion on the metabolism of e. coli: constraint-based simulation approach. 10(S1):S62–S62, 2009. [DOI].
Patrick B. Warren, Sı́lvio M. Duarte Queirós, and Janette L. Jones. Flux networks in metabolic graphs. 6(4):046006–046006, 2009. [DOI].
Adam M. Feist, Ines Thiele, and Bernhard Ø. Palsson. Genome-scale reconstruction, modeling, and simulation of e. coli℉s metabolic network. ():149–176, 2009. [DOI].
Scott Baker, Kenneth S. Bruno, Mark Butcher, James R. Collett, David Culley, Ziyu Dai, Jon Magnuson, and Ellen Panisko. Pnnl fungal biotechnology core doe-obp project. ():, 2009. [DOI].
Peter D. Karp, Suzanne Paley, Markus Krummenacker, Mario Latendresse, J. M. Dale, T. J. Lee, P. Kaipa, Fred Gilham, Aaron Spaulding, Liviu Popescu, Tomer Altman, Ian T. Paulsen, Ingrid M. Keseler, and Ron Caspi. Pathway tools version 13.0: integrated software for pathway/genome informatics and systems biology. 11(1):40–79, 2009. [DOI].
Brett A. Boghigian, Gargi Seth, Róbert Kiss, and Blaine A. Pfeifer. Metabolic flux analysis and pharmaceutical production. 12(2):81–95, 2009. [DOI].
Michael Y. Galperin. Sensory transduction network of e. coli. ():133–148, 2009. [DOI].
Richard A. Notebaart. Integrative bioinformatics of metabolic networks. ():, 2009. [DOI].
Alexander James Macdonald. Theoretical and in vitro analysis of iron acquisition in pasteurella multocida a:3. ():, 2009. [DOI].

Adam M. Feist, Markus J. Herrgård, Ines Thiele, Jennifer L. Reed, and Bernhard Ø. Palsson. Reconstruction of biochemical networks in microorganisms. 7(2):129–143, 2008. [DOI].
Scott A. Becker and Bernhard Ø. Palsson. Context-specific metabolic networks are consistent with experiments. 4(5):e1000082–e1000082, 2008. [DOI].
Adam M. Feist and Bernhard Ø. Palsson. The growing scope of applications of genome-scale metabolic reconstructions using escherichia coli. 26(6):659–667, 2008. [DOI].
Maxime Durot, Pierre-Yves Bourguignon, and Vincent Schächter. Genome-scale models of bacterial metabolism: reconstruction and applications. 33(1):164–190, 2008. [DOI].
Karthik Raman, Kalidas Yeturu, and Nagasuma Chandra. Targettb: a target identification pipeline for mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. 2(1):109–109, 2008. [DOI].
Juan Nogales, Bernhard Ø. Palsson, and Ines Thiele. A genome-scale metabolic reconstruction of pseudomonas putida kt2440: i jn746 as a cell factory. 2(1):79–79, 2008. [DOI].
Jan Schellenberger and Bernhard Ø. Palsson. Use of randomized sampling for analysis of metabolic networks. 284(9):5457–5461, 2008. [DOI].
Eva Grafahrend‐Belau, Falk Schreiber, Dirk Koschützki, and Björn H. Junker. Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism. 149(1):585–598, 2008. [DOI].
Chenggang Yu, Nela Zavaljevski, Valmik Desai, and Jaques Reifman. Genome‐wide enzyme annotation with precision control: catalytic families (catfam) databases. 74(2):449–460, 2008. [DOI].
Richard A. Notebaart, Bas Teusink, Roland J. Siezen, and Balázs Papp. Co-regulation of metabolic genes is better explained by flux coupling than by network distance. 4(1):e26–e26, 2008. [DOI].
Aneil Mallavarapu, Matthew Thomson, Benjamin Ullian, and Jeremy Gunawardena. Programming with models: modularity and abstraction provide powerful capabilities for systems biology. 6(32):257–270, 2008. [DOI].
Segun Fatumo, Kitiporn Plaimas, Jan‐Philipp Mallm, Gunnar Schramm, Ezekiel Adebiyi, Marcus Oswald, Roland Eils, and Rainer König. Estimating novel potential drug targets of plasmodium falciparum by analysing the metabolic network of knock-out strains in silico. 9(3):351–358, 2008. [DOI].
Laurence Coquin, Jacob Feala, Andrew D. McCulloch, and Giovanni Paternostro. Metabolomic and flux‐balance analysis of age‐related decline of hypoxia tolerance in drosophila muscle tissue. 4(1):233–233, 2008. [DOI].
Varun Mazumdar, Evan S. Snitkin, Salomon Amar, and Daniel Segrè. Metabolic network model of a human oral pathogen. 191(1):74–90, 2008. [DOI].
Vasiliy A. Portnoy, Markus J. Herrgård, and Bernhard Ø. Palsson. Aerobic fermentation of <scp>d</scp> -glucose by an evolved cytochrome oxidase-deficient escherichia coli strain. 74(24):7561–7569, 2008. [DOI].
Evan S. Snitkin, Aimée M. Dudley, Daniel M. Janse, K.K. Wong, George M. Church, and Daniel Segrè. Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. 9(9):R140–R140, 2008. [DOI].
Sakiko Okumoto, Hitomi Takanaga, and Wolf B. Frommer. Quantitative imaging for discovery and assembly of the metabo‐regulome. 180(2):271–295, 2008. [DOI].
Jeremiah Wright and Andreas Wagner. The systems biology research tool: evolvable open-source software. 2(1):55–55, 2008. [DOI].
Monica L. Mo and Bernhard Ø. Palsson. Understanding human metabolic physiology: a genome-to-systems approach. 27(1):37–44, 2008. [DOI].
Dirk Bumann. Has nature already identified all useful antibacterial targets? 11(5):387–392, 2008. [DOI].
Kitiporn Plaimas, Jan‐Philipp Mallm, Marcus Oswald, Fabian Svara, Victor Sourjik, Roland Eils, and Rainer König. Machine learning based analyses on metabolic networks supports high-throughput knockout screens. 2(1):67–67, 2008. [DOI].
Scott A. Becker and Bernhard Ø. Palsson. Three factors underlying incorrect in silico predictions of essential metabolic genes. 2(1):14–14, 2008. [DOI].
Pierre-Yves Bourguignon, Jacques van Helden, Christos Ouzounis, and Vincent Schächter. Computational analysis of metabolic networks. ():329–351, 2008. [DOI].
Eleftheria Tzamali and Martin Reczko. The benefit of cooperation: identifying growth-efficient interacting strains of escherichia coli using metabolic flux balance models. ():1–6, 2008. [DOI].
Xinghua Shi and Rick Stevens. Swarm. ():25–34, 2008. [DOI].
Ralf Steuer and Björn H. Junker. Computational models of metabolism: stability and regulation in metabolic networks. ():105–251, 2008. [DOI].
Zixiang Xu, Jianming Xie, Yang Xinan, and Xiao Sun. Genome-scale simulation analysis: the impact of gene deletion on the metabolic flux of e.coli and its flux-backbone. 2():695–699, 2008. [DOI].
Zixiang Xu, Jianming Xie, Xinan Yang, and Xiao Sun. Impact analysis of gene deletion on the metabolic flux distribution of e.coli and on its flux-backbone: genome-scale simulation approach. ():454–457, 2008. [DOI].
Patrik R. Jones. Improving fermentative biomass-derived h2-production by engineering microbial metabolism. 33(19):5122–5130, 2008. [DOI].
Zixiang Xu and Xiao Sun. Constrain-based analysis of gene deletion on the metabolic flux redistribution of saccharomyces cerevisiae. 01(02):121–126, 2008. [DOI].
Michael Bartoň, Balázs Papp, Daniela Delneri, Stephen G. Oliver, Magnus Rattray, and Casey Bergman. Systems biology of energetic and atomic costs in the yeast transcriptome, proteome, and metabolome. ():, 2008. [DOI].
Milan J. A. Hoek. Evolutionary dynamics of metabolic adaptation. ():, 2008. [DOI].
Itamar L. de Oliveira. Análise computacional de redes metabólicas com regulação. ():, 2008. [DOI].
Eva Grafahrend‐Belau, Björn H. Junker, Dirk Koschützki, Christian Klukas, Stéphan Weise, Uwe Scholz, and Falk Schreiber. Towards systems biology of developing barley grains : a framework for modeling metabolism. ():41–44, 2008. [DOI].