• Stars
    star
    28
  • Rank 882,216 (Top 18 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 4 years ago
  • Updated 2 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Symbolic Kinetic Models with Python

More Repositories

1

pytfa

A Python 3 implementation of Thermodynamics-based Flux Analysis
Python
38
star
2

etfl

ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraints
Python
16
star
3

RENAISSANCE

REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies
6
star
4

NICEgame

MATLAB
5
star
5

BridgITplus

Jupyter Notebook
5
star
6

rekindle

REKINDLE is a python package for training the generative adversarial networks (GANs) to parametrize large-scale nonlinear kinetic models of cellular metabolism
Python
5
star
7

redhuman

redHUMAN: analyzing human metabolism and growth media through systematic reductions of thermodynamically curated genome-scale models
MATLAB
5
star
8

nicepath

Python
4
star
9

yetfl

Python
4
star
10

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
Python
4
star
11

ARBRE

Python
3
star
12

openbread

C++
3
star
13

remind

Python
3
star
14

matTFA

A Matlab implementation of Thermodynamics-based Flux Analysis
HTML
3
star
15

remi

Relative Expression and Metabolite Integration
MATLAB
3
star
16

geek

Python
2
star
17

texfba

Integration of gene expression data with TFA constraints
MATLAB
2
star
18

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.
MATLAB
2
star
19

redgem

MATLAB
1
star
20

open

OPtimal ENzyme - Estimates catalytically optimal modes of operations of enzymatic reactions
Python
1
star
21

ecETFL

Python
1
star
22

cromics

CROMICS: CROwding-Modeling of In-silico Community Systems
MATLAB
1
star