Optlang is a Python package for solving mathematical optimization problems, i.e. maximizing or minimizing an objective function over a set of variables subject to a number of constraints. Optlang provides a common interface to a series of optimization tools, so different solver backends can be changed in a transparent way. Optlang’s object-oriented API takes advantage of the symbolic math library sympy to allow objective functions and constraints to be easily formulated from symbolic expressions of variables
Cameo is a high-level python library developed to aid the strain design process in metabolic engineering projects. The library provides a modular framework of simulation methods, strain design methods, access to models, that targets developers that want custom analysis workflows.
Computationally heavy methods have been parallelized and can be run on a clusters using the IPython parallelization framework (see example and documentation for more details). The default fallback is python’s multiprocessing library.
Furthermore, it exposes a high-level API to users that just want to compute promising strain designs.
Our goal in promoting this tool is to achieve two major shifts in the metabolic model building community:
- Models should be version-controlled such that changes can be tracked and if necessary reverted. Ideally, they should be available through a public repository such as GitHub that will allow other researchers to inspect, share, and contribute to the model.
- Models should, for the benefit of the community and for research gain, live up to certain standards and minimal functionality.
The memote tool therefore performs four subfunctions:
- Create a skeleton git repository for the model.
- Run the current model through a test suite that represents the community standard.
- Generate an informative report which details the results of the test suite in a visually appealing manner.
- (Re-)compute test statistics for an existing version controlled history of a metabolic model.
by Christian Diener and Osbaldo Resendis-Antonio
CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions developed by A. Schultz and A. Qutub. The package allows you to quickly reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements and is developed in the Human Systems Biology Group at the INMEGEN, Mexico.
Thermodynamics-based Flux Analysis, in Python.
Implements: Henry, Christopher S., Linda J. Broadbelt, and Vassily Hatzimanikatis. “Thermodynamics-based metabolic flux analysis.” Biophysical journal 92.5 (2007): 1792-1805. DOI: https://doi.org/10.1529/biophysj.106.093138
by Mike Mundy
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. Mackinac is 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.
by Christian Diener and Osbaldo Resendis-Antonio
micom is a Python package for metabolic modeling of microbial communities and is developed in the Human Systems Biology Group at the INMEGEN, Mexico.
micom allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community and can thus incorporate data from 16S rRNA sequencing or metagenomic shotgun experiments.
by Helena Mendes-Soares
MMinte (pronounced /‘minti/) is a set of widgets that allows you to explore the pairwise interactions (positive or negative) that occur in a microbial community. From an association network and 16S rDNA sequence data, MMinte identifies corresponding genomes, reconstructs metabolic models, estimates growth under specific metabolic conditions, analyzes pairwise interactions, assigns interaction types to network links, and generates the corresponding network of interactions. You can run the MMinte widgets as an integrated pipeline or run each widget independently.
by The Novo Nordisk Foundation Center for Biosustainability
DD-DeCaF is a Horizon 2020 project (grant agreement No 686070) bringing together leading academic partners from five European universities with five innovative European companies to address the challenge of building a comprehensive design tool. DD-DeCaF aims to develop cutting edge methods in order to use large scale data to design cell factories and communities for biotechnological applications. The project is built as a number of micro-services which can be used separately and via the web-based user-interface.