LCSB@EPFL

pytfa

A Python 3 implementation of Thermodynamics-based Flux Analysis

skimpy

Symbolic Kinetic Models with Python

etfl

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

BridgITplus

NICEgame

redhuman

redHUMAN: analyzing human metabolism and growth media through systematic reductions of thermodynamically curated genome-scale models

rekindle

REKINDLE is a python package for training the generative adversarial networks (GANs) to parametrize large-scale nonlinear kinetic models of cellular metabolism

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/10.1101/2021.02.17.431583

ARBRE

matTFA

A Matlab implementation of Thermodynamics-based Flux Analysis

nicepath

openbread

remi

Relative Expression and Metabolite Integration

yetfl

geek

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.

remind

RENAISSANCE

REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies

texfba

Integration of gene expression data with TFA constraints

cromics

CROMICS: CROwding-Modeling of In-silico Community Systems

ecETFL

open

OPtimal ENzyme - Estimates catalytically optimal modes of operations of enzymatic reactions

redgem

ci_ensemble_modeling

host_parasite_interactions

Generating host-parasite metabolic models to investigate the dependence of the parasite on the host's metabolic genes

iNTS_SL1344

Genome-scale metabolic model of Salmonella Typhimurium SL1344.

matMCP

NOMAD

A Python implementation of the NOMAD workflow for rational strain design using large-scale kinetic models.

NRA

Constraint-based metabolic control analysis for rational strain engineering

PASB-Lecture