Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows.
In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models.
Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.
BMC Systems Biology 2018
Erol S Kavvas, Yara Seif, James T Yurkovich, Charles Norsigian, et. al.
The efficacy of antibiotics against M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M. tuberculosis growth in physiological conditions have described an environment that is different from common in vitro media. Thus, elucidating the interplay between available nutrient sources and antibiotic efficacy has clear medical relevance. While genome-scale reconstructions of M. tuberculosis have enabled the ability to interrogate media differences for the past 10 years, recent reconstructions have diverged from each other without standardization.
Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).
Bioinformatics and Biology Insights 2018
Irene Sui Lan Zeng and Thomas Lumley
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework.
Plasma and urinary metabolomic profiles of Down syndrome correlate with alteration of mitochondrial metabolism.
Scientific Reports 2018
Maria Caracausi, Veronica Ghini, Chiara Locatelli, Martina Mericio, et. al.
Down syndrome (DS) is caused by the presence of a supernumerary copy of the human chromosome 21 (Hsa21) and is the most frequent genetic cause of intellectual disability (ID). Key traits of DS are the distinctive facies and cognitive impairment. We conducted for the first time an analysis of the Nuclear Magnetic Resonance (NMR)-detectable part of the metabolome in plasma and urine samples, studying 67 subjects with DS and 29 normal subjects as controls selected among DS siblings.
Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP.
Nucleic Acids Research 2018
Donghyuk Kim, Sang Woo Seo, Ye Gao, Hojung Nam, et. al.
Two major transcriptional regulators of carbon metabolism in bacteria are Cra and CRP. CRP is considered to be the main mediator of catabolite repression. Unlike for CRP, in vivo DNA binding information of Cra is scarce. Here we generate and integrate ChIP-exo and RNA-seq data to identify 39 binding sites for Cra and 97 regulon genes that are regulated by Cra in Escherichia coli. An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction.
Prediction of reaction knockouts to maximize succinate production by Actinobacillus succinogenes.
PloS One 2018
Ambarish Nag, Peter C St John, Michael F Crowley and Yannick J Bomble
Succinate is a precursor of multiple commodity chemicals and bio-based succinate production is an active area of industrial bioengineering research. One of the most important microbial strains for bio-based production of succinate is the capnophilic gram-negative bacterium Actinobacillus succinogenes, which naturally produces succinate by a mixed-acid fermentative pathway. To engineer A. succinogenes to improve succinate yields during mixed acid fermentation, it is important to have a detailed understanding of the metabolic flux distribution in A.
Model-driven design of a minimal medium for Akkermansia muciniphila confirms mucus adaptation.
Microbial Biotechnology 2018
Kees C H van der Ark, Steven Aalvink, Maria Suarez-Diez, Peter J Schaap, et. al.
The abundance of the human intestinal symbiont Akkermansia muciniphila has found to be inversely correlated with several diseases, including metabolic syndrome and obesity. A. muciniphila is known to use mucin as sole carbon and nitrogen source. To study the physiology and the potential for therapeutic applications of this bacterium, we designed a defined minimal medium. The composition of the medium was based on the genome-scale metabolic model of A. muciniphila and the composition of mucin.
One-step fermentative production of aromatic polyesters from glucose by metabolically engineered Escherichia coli strains.
Nature Communications 2018
Jung Eun Yang, Si Jae Park, Won Jun Kim, Hyeong Jun Kim, et. al.
Aromatic polyesters are widely used plastics currently produced from petroleum. Here we engineer Escherichia coli strains for the production of aromatic polyesters from glucose by one-step fermentation. When the Clostridium difficile isocaprenoyl-CoA:2-hydroxyisocaproate CoA-transferase (HadA) and evolved polyhydroxyalkanoate (PHA) synthase genes are overexpressed in a D-phenyllactate-producing strain, poly(52.3 mol% 3-hydroxybutyrate (3HB)-co-47.7 mol% D-phenyllactate) can be produced from glucose and sodium 3HB. Also, various poly(3HB-co-D-phenyllactate) polymers having 11.0, 15.8, 20.0, 70.8, and 84.
Genome scale metabolic models as tools for drug design and personalized medicine.
PloS One 2018
Vytautas Raškevičius, Valeryia Mikalayeva, Ieva Antanavičiūtė, Ieva Ceslevičienė, et. al.
In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly.
Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for
Tyler W H Backman, David Ando, Jahnavi Singh, Jay D Keasling, et. al.
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism.
Total of 11 publications. 😄
Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium.
Frontiers in Microbiology 2017
Miguel Ponce-de-Leon, Daniel Tamarit, Jorge Calle-Espinosa, Matteo Mori, et. al.
Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins.
Characterizing steady states of genome-scale metabolic networks in continuous cell cultures.
PLoS Computational Biology 2017
Jorge Fernandez-de-Cossio-Diaz, Kalet Leon and Roberto Mulet
In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity.
Oxygen-limited metabolism in the methanotroph
Alexey Gilman, Yanfen Fu, Melissa Hendershott, Frances Chu, et. al.
The bacteria that grow on methane aerobically (methanotrophs) support populations of non-methanotrophs in the natural environment by excreting methane-derived carbon. One group of excreted compounds are short-chain organic acids, generated in highest abundance when cultures are grown under O
Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.
Julie Laniau, Clémence Frioux, Jacques Nicolas, Caroline Baroukh, et. al.
The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions.
Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity.
Cell Reports 2017
Ed Reznik, Dimitris Christodoulou, Joshua E Goldford, Emma Briars, et. al.
Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets.
Population FBA predicts metabolic phenotypes in yeast.
PLoS Computational Biology 2017
Piyush Labhsetwar, Marcelo C R Melo, John A Cole and Zaida Luthey-Schulten
Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media.
Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects.
PloS One 2017
Joseph R Peterson, John A Cole and Zaida Luthey-Schulten
Characterizing the complex spatial and temporal interactions among cells in a biological system (i.e. bacterial colony, microbiome, tissue, etc.) remains a challenge. Metabolic cooperativity in these systems can arise due to the subtle interplay between microenvironmental conditions and the cells’ regulatory machinery, often involving cascades of intra- and extracellular signalling molecules. In the simplest of cases, as demonstrated in a recent study of the model organism Escherichia coli, metabolic cross-feeding can arise in monoclonal colonies of bacteria driven merely by spatial heterogeneity in the availability of growth substrates; namely, acetate, glucose and oxygen.
Constraint-based modeling identifies new putative targets to fight colistin-resistant A. baumannii infections.
Scientific Reports 2017
Luana Presta, Emanuele Bosi, Leila Mansouri, Lenie Dijkshoorn, et. al.
Acinetobacter baumannii is a clinical threat to human health, causing major infection outbreaks worldwide. As new drugs against Gram-negative bacteria do not seem to be forthcoming, and due to the microbial capability of acquiring multi-resistance, there is an urgent need for novel therapeutic targets. Here we have derived a list of new potential targets by means of metabolic reconstruction and modelling of A. baumannii ATCC 19606. By integrating constraint-based modelling with gene expression data, we simulated microbial growth in normal and stressful conditions (i.
Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides.
Brian T Burger, Saheed Imam, Matthew J Scarborough, Daniel R Noguera, et. al.
Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosynthetic growth.
DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia.
Bioinformatics (Oxford, England) 2017
Laurent Heirendt, Ines Thiele and Ronan M T Fleming
Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.
The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.
BMC Bioinformatics 2017
Garrett W Birkel, Amit Ghosh, Vinay S Kumar, Daniel Weaver, et. al.
Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell.
Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133.
Scientific Reports 2017
Wonhee Hur, Jae Yong Ryu, Hyun Uk Kim, Sung Woo Hong, et. al.
Liver cancer stem cells (LCSCs) have attracted attention because they cause therapeutic resistance in hepatocellular carcinoma (HCC). Understanding the metabolism of LCSCs can be a key to developing therapeutic strategy, but metabolic characteristics have not yet been studied. Here, we systematically analyzed and compared the global metabolic phenotype between LCSCs and non-LCSCs using transcriptome and metabolome data. We also reconstructed genome-scale metabolic models (GEMs) for LCSC and non-LCSC to comparatively examine differences in their metabolism at genome-scale.
Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.
Michael Mundy, Helena Mendes-Soares and Nicholas Chia
Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED’s ability to automatically reconstruct metabolic models with COBRApy’s advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms.
Efficient estimation of the maximum metabolic productivity of batch systems.
Biotechnology for Biofuels 2017
Peter C St John, Michael F Crowley and Yannick J Bomble
Production of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield possible while maintaining growth viability. While these methods are computationally tractable, an optimum productivity could be achieved by a dynamic strategy in which the intracellular division of resources is permitted to change with time. New methods for the design and implementation of dynamic microbial processes, both computational and experimental, have therefore been explored to maximize productivity.
Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.
PLoS Computational Biology 2017
Sylvain Prigent, Clémence Frioux, Simon M Dittami, Sven Thiele, et. al.
Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors.
Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression.
Scientific Reports 2017
Ding Ma, Laurence Yang, Ronan M T Fleming, Ines Thiele, et. al.
Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger).
Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function.
Archaea (Vancouver, B.C.) 2017
ShengShee Thor, Joseph R Peterson and Zaida Luthey-Schulten
Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models.
Total of 17 publications. 😄
Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.
Frontiers in Physiology 2016
Christian Diener and Osbaldo Resendis-Antonio
Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas.
Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.
Proceedings of the National Academy of Sciences of the United States of America 2016
Jared T Broddrick, Benjamin E Rubin, David G Welkie, Niu Du, et. al.
The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism.
Modelling microbial metabolic rewiring during growth in a complex medium.
BMC Genomics 2016
Marco Fondi, Emanuele Bosi, Luana Presta, Diletta Natoli, et. al.
In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now.
Multi-omic data integration enables discovery of hidden biological regularities.
Nature Communications 2016
Ali Ebrahim, Elizabeth Brunk, Justin Tan, Edward J O'Brien, et. al.
Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge’ challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule.
solveME: fast and reliable solution of nonlinear ME models.
BMC Bioinformatics 2016
Laurence Yang, Ding Ma, Ali Ebrahim, Colton J Lloyd, et. al.
Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.
Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.
Cell Systems 2016
Jonathan M Monk, Anna Koza, Miguel A Campodonico, Daniel Machado, et. al.
Escherichia coli strains are widely used in academic research and biotechnology. New technologies for quantifying strain-specific differences and their underlying contributing factors promise greater understanding of how these differences significantly impact physiology, synthetic biology, metabolic engineering, and process design. Here, we quantified strain-specific differences in seven widely used strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics, and genome-scale modeling. Metabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titer.
MMinte: an application for predicting metabolic interactions among the microbial species in a community.
BMC Bioinformatics 2016
Helena Mendes-Soares, Michael Mundy, Luis Mendes Soares and Nicholas Chia
The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system.
Quantifying complexity in metabolic engineering using the LASER database.
Metabolic Engineering Communications 2016
James D Winkler, Andrea L Halweg-Edwards and Ryan T Gill
We previously introduced the LASER database (Learning Assisted Strain EngineeRing, https://bitbucket.org/jdwinkler/laser_release) (Winkler et al. 2015) to serve as a platform for understanding past and present metabolic engineering practices. Over the past year, LASER has been expanded by 50% to include over 600 engineered strains from 450 papers, including their growth conditions, genetic modifications, and other information in an easily searchable format. Here, we present the results of our efforts to use LASER as a means for defining the complexity of a metabolic engineering “design”.
Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.
Plant Physiology 2016
Cristal Zuñiga, Chien-Ting Li, Tyler Huelsman, Jennifer Levering, et. al.
The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C.
Integrated In Silico Analysis of Pathway Designs for Synthetic Photo-Electro-Autotrophy.
PloS One 2016
Michael Volpers, Nico J Claassens, Elad Noor, John van der Oost, et. al.
The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems.
From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.
Frontiers in Microbiology 2016
Daniel A Cuevas, Janaka Edirisinghe, Chris S Henry, Ross Overbeek, et. al.
Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico.
Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity.
Proceedings of the National Academy of Sciences of the United States of America 2016
Emanuele Bosi, Jonathan M Monk, Ramy K Aziz, Marco Fondi, et. al.
Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes.
Systems biology of the structural proteome.
BMC Systems Biology 2016
Elizabeth Brunk, Nathan Mih, Jonathan Monk, Zhen Zhang, et. al.
The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.
Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements.
Proceedings of the National Academy of Sciences of the United States of America 2016
Dan Davidi, Elad Noor, Wolfram Liebermeister, Arren Bar-Even, et. al.
Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.
What Makes a Bacterial Species Pathogenic?:Comparative Genomic Analysis of the Genus Leptospira.
PLoS Neglected Tropical Diseases 2016
Derrick E Fouts, Michael A Matthias, Haritha Adhikarla, Ben Adler, et. al.
Leptospirosis, caused by spirochetes of the genus Leptospira, is a globally widespread, neglected and emerging zoonotic disease. While whole genome analysis of individual pathogenic, intermediately pathogenic and saprophytic Leptospira species has been reported, comprehensive cross-species genomic comparison of all known species of infectious and non-infectious Leptospira, with the goal of identifying genes related to pathogenesis and mammalian host adaptation, remains a key gap in the field. Infectious Leptospira, comprised of pathogenic and intermediately pathogenic Leptospira, evolutionarily diverged from non-infectious, saprophytic Leptospira, as demonstrated by the following computational biology analyses: 1) the definitive taxonomy and evolutionary relatedness among all known Leptospira species; 2) genomically-predicted metabolic reconstructions that indicate novel adaptation of infectious Leptospira to mammals, including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of cobalamin (B12) autotrophy as a bacterial virulence factor; 3) CRISPR/Cas systems demonstrated only to be present in pathogenic Leptospira, suggesting a potential mechanism for this clade’s refractoriness to gene targeting; 4) finding Leptospira pathogen-specific specialized protein secretion systems; 5) novel virulence-related genes/gene families such as the Virulence Modifying (VM) (PF07598 paralogs) proteins and pathogen-specific adhesins; 6) discovery of novel, pathogen-specific protein modification and secretion mechanisms including unique lipoprotein signal peptide motifs, Sec-independent twin arginine protein secretion motifs, and the absence of certain canonical signal recognition particle proteins from all Leptospira; and 7) and demonstration of infectious Leptospira-specific signal-responsive gene expression, motility and chemotaxis systems.
PSAMM: A Portable System for the Analysis of Metabolic Models.
PLoS Computational Biology 2016
Jon Lund Steffensen, Keith Dufault-Thompson and Ying Zhang
The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models.
Total of 16 publications. 😄
FlexFlux: combining metabolic flux and regulatory network analyses.
BMC Systems Biology 2015
Lucas Marmiesse, Rémi Peyraud and Ludovic Cottret
Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network.
Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach.
PloS One 2015
Miguel Ponce-de-Leon, Jorge Calle-Espinosa, Juli Peretó and Francisco Montero
Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network.
Networks of energetic and metabolic interactions define dynamics in microbial communities.
Proceedings of the National Academy of Sciences of the United States of America 2015
Mallory Embree, Joanne K Liu, Mahmoud M Al-Bassam and Karsten Zengler
Microorganisms form diverse communities that have a profound impact on the environment and human health. Recent technological advances have enabled elucidation of community diversity at high resolution. Investigation of microbial communities has revealed that they often contain multiple members with complementing and seemingly redundant metabolic capabilities. An understanding of the communal impacts of redundant metabolic capabilities is currently lacking; specifically, it is not known whether metabolic redundancy will foster competition or motivate cooperation.
Systems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations.
Scientific Reports 2015
Ramy K Aziz, Jonathan M Monk, Robert M Lewis, Suh In Loh, et. al.
Mathematical models of metabolism from bacterial systems biology have proven their utility across multiple fields, for example metabolic engineering, growth phenotype simulation, and biological discovery. The usefulness of the models stems from their ability to compute a link between genotype and phenotype, but their ability to accurately simulate gene-gene interactions has not been investigated extensively. Here we assess how accurately a metabolic model for Escherichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the accuracy rate is between 25% and 43%.
The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases.
Nucleic Acids Research 2015
Ron Caspi, Richard Billington, Luciana Ferrer, Hartmut Foerster, et. al.
The MetaCyc database (MetaCyc.org) is a freely accessible comprehensive database describing metabolic pathways and enzymes from all domains of life. The majority of MetaCyc pathways are small-molecule metabolic pathways that have been experimentally determined. MetaCyc contains more than 2400 pathways derived from >46,000 publications, and is the largest curated collection of metabolic pathways. BioCyc (BioCyc.org) is a collection of 5700 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems, and pathway-hole fillers.
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.
Nucleic Acids Research 2015
Zachary A King, Justin Lu, Andreas Dräger, Philip Miller, et. al.
Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.
Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods.
Frontiers in Microbiology 2015
Neema Jamshidi and Anu Raghunathan
Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods.
Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC.
Frontiers in Microbiology 2015
Ramy K Aziz, Valerie L Khaw, Jonathan M Monk, Elizabeth Brunk, et. al.
Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA.
Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.
PLoS Computational Biology 2015
Zachary A King, Andreas Dräger, Ali Ebrahim, Nikolaus Sonnenschein, et. al.
Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction.
Tools for visualization and analysis of molecular networks, pathways, and -omics data.
Advances and Applications in Bioinformatics and Chemistry : AABC 2015
Jose M Villaveces, Prasanna Koti and Bianca H Habermann
Biological pathways have become the standard way to represent the coordinated reactions and actions of a series of molecules in a cell. A series of interconnected pathways is referred to as a biological network, which denotes a more holistic view on the entanglement of cellular reactions. Biological pathways and networks are not only an appropriate approach to visualize molecular reactions. They have also become one leading method in -omics data analysis and visualization.
Using Genome-scale Models to Predict Biological Capabilities.
Edward J O'Brien, Jonathan M Monk and Bernhard O Palsson
Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution.
Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain.
PloS One 2015
Anand K Gavai, Farahaniza Supandi, Hannes Hettling, Paul Murrell, et. al.
Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R.
Total of 12 publications. 😄
Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis.
Frontiers in Plant Science 2014
Jordan O Hay, Hai Shi, Nicolas Heinzel, Inga Hebbelmann, et. al.
The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis.
Synthetic biology outside the cell: linking computational tools to cell-free systems.
Frontiers in Bioengineering and Biotechnology 2014
Daniel D Lewis, Fernando D Villarreal, Fan Wu and Cheemeng Tan
As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems.
Improving collaboration by standardization efforts in systems biology.
Frontiers in Bioengineering and Biotechnology 2014
Andreas Dräger and Bernhard Ø Palsson
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments.
Current advances in systems and integrative biology.
Computational and Structural Biotechnology Journal 2014
Scott W Robinson, Marco Fernandes and Holger Husi
Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result.
Whole Cell Modeling: From Single Cells to Colonies.
Israel Journal of Chemistry 2014
John A Cole and Zaida Luthey-Schulten
A great deal of research over the last several years has focused on how the inherent randomness in movements and reactivity of biomolecules can give rise to unexpected large-scale differences in the behavior of otherwise identical cells. Our own research has approached this problem from two vantage points - a microscopic kinetic view of the individual molecules (nucleic acids, proteins, etc.) diffusing and interacting in a crowded cellular environment; and a broader systems-level view of how enzyme variability can give rise to well-defined metabolic phenotypes.
A genome-scale metabolic flux model of Escherichia coli K-12 derived from the EcoCyc database.
BMC Systems Biology 2014
Daniel S Weaver, Ingrid M Keseler, Amanda Mackie, Ian T Paulsen, et. al.
Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.
A data integration and visualization resource for the metabolic network of Synechocystis sp. PCC 6803.
Plant Physiology 2014
Timo R Maarleveld, Joost Boele, Frank J Bruggeman and Bas Teusink
Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight in addition to statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis sp. PCC 6803, an important model cyanobacterium for sustainable biofuel production.
Total of 7 publications. 😄
Sybil--efficient constraint-based modelling in R.
BMC Systems Biology 2013
Gabriel Gelius-Dietrich, Abdelmoneim Amer Desouki, Claus Jonathan Fritzemeier and Martin J Lercher
Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users.
Path2Models: large-scale generation of computational models from biochemical pathway maps.
BMC Systems Biology 2013
Finja Büchel, Nicolas Rodriguez, Neil Swainston, Clemens Wrzodek, et. al.
Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data.
Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti.
BMC Systems Biology 2013
Miguel Ponce-de-León, Francisco Montero and Juli Peretó
Metabolic reconstruction is the computational-based process that aims to elucidate the network of metabolites interconnected through reactions catalyzed by activities assigned to one or more genes. Reconstructed models may contain inconsistencies that appear as gap metabolites and blocked reactions. Although automatic methods for solving this problem have been previously developed, there are many situations where manual curation is still needed.
GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data.
Bioinformatics (Oxford, England) 2013
Brian J Schmidt, Ali Ebrahim, Thomas O Metz, Joshua N Adkins, et. al.
Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed.