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pytfa
A Python 3 implementation of Thermodynamics-based Flux Analysisskimpy
Symbolic Kinetic Models with PythonRENAISSANCE
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
texfba
Integration of gene expression data with TFA constraintsphenomapping
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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