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pytfa
A Python 3 implementation of Thermodynamics-based Flux Analysisskimpy
Symbolic Kinetic Models with Pythonetfl
ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraintsRENAISSANCE
REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategiesNICEgame
BridgITplus
rekindle
REKINDLE is a python package for training the generative adversarial networks (GANs) to parametrize large-scale nonlinear kinetic models of cellular metabolismredhuman
redHUMAN: analyzing human metabolism and growth media through systematic reductions of thermodynamically curated genome-scale modelsnicepath
yetfl
ATLASxAnalyses
The data and scripts contained in this repository allow the user to reproduce the figures and analyses of the article "ATLASx: a computational map for the exploration of biochemical space", doi: https://doi.org/10.1101/2021.02.17.431583ARBRE
openbread
remind
matTFA
A Matlab implementation of Thermodynamics-based Flux Analysisremi
Relative Expression and Metabolite Integrationgeek
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.redgem
open
OPtimal ENzyme - Estimates catalytically optimal modes of operations of enzymatic reactionsecETFL
cromics
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