Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 01 2023
01 01 2023
Historique:
received:
07
01
2022
revised:
22
11
2022
accepted:
09
12
2022
pubmed:
11
12
2022
medline:
11
1
2023
entrez:
10
12
2022
Statut:
ppublish
Résumé
Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently. We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 36495209
pii: 6887139
doi: 10.1093/bioinformatics/btac787
pmc: PMC9825757
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : European Union's Horizon 2020 Research and Innovation
ID : 722287
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press.
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