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choosealicense.com

A site to provide non-judgmental guidance on choosing a license for your open source project

cobradb

COBRAdb loads genome-scale metabolic models and genome annotations into a relational database. It already powers BiGG Models, and it is available under the MIT license.

cobratoolbox

The COnstraint-Based Reconstruction and Analysis Toolbox. Documentation:

etfl

ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraints

lcsb_docker

mattfa

A Matlab implementation of Thermodynamics-based Flux Analysis

memote

memote – the genome-scale metabolic model test suite

openbread

phenomapping

PhenoMapping is a computational framework that provides some workflows and methodologies for the understanding of mechanisms underlying phenotypes using genome-scale models (GEMs). PhenoMapping classifies the information in a GEM as organism-specific information and context-specific information. Organism-specific information includes the (i) biochemistry/metabolic functions annotated to the genes, (ii) the localization of enzymes, (iii) the intracellular transportability of metabolites, and (iv) the enzymatic irreversibilities defined/ad hoc pre-assigned directionalities. Context-specific information involves (i) the medium composition, (ii) the reaction directionalities given a set of metabolomics data, (iii) the reaction flux levels given a set of gene expression data, and (iv) the possibility of regulation of gene expression between isoenzymes given a set of gene expression data. PhenoMapping is modular and allows the independent study of these mechanisms. The PhenoMapping workflow suggests a sequence that one can follow for the study of these mechanisms and analysis and interpretation of the results. PhenoMapping was developed for the analysis of high-throughput fitness phenotypic data throughout the life cycle of the malaria parasite P. berghei, and served to curate the genome-scale model of this organism (iPbe) and generate context-specific models for the blood (iPbe-blood) and liver (iPbe-liver) stages - both of which show approximately 80% accuracy and 0.5 Matthew Correlation Coefficient (MCC) with the phenotypic data.

pytfa

A Python 3 implementation of Thermodynamics-based Flux Analysis

raven

The RAVEN toolbox. For the latest release go to https://github.com/SysBioChalmers/RAVEN/releases

yeast-gem

The consensus GEM for Saccharomyces cerevisiae