LCSB@EPFL's repositories
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
RENAISSANCE
REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies
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.
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.
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