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Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models
MOTIVATION: 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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825757/ https://www.ncbi.nlm.nih.gov/pubmed/36495209 http://dx.doi.org/10.1093/bioinformatics/btac787 |
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author | Weilandt, Daniel R Salvy, Pierre Masid, Maria Fengos, Georgios Denhardt-Erikson, Robin Hosseini, Zhaleh Hatzimanikatis, Vassily |
author_facet | Weilandt, Daniel R Salvy, Pierre Masid, Maria Fengos, Georgios Denhardt-Erikson, Robin Hosseini, Zhaleh Hatzimanikatis, Vassily |
author_sort | Weilandt, Daniel R |
collection | PubMed |
description | MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9825757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98257572023-01-10 Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models Weilandt, Daniel R Salvy, Pierre Masid, Maria Fengos, Georgios Denhardt-Erikson, Robin Hosseini, Zhaleh Hatzimanikatis, Vassily Bioinformatics Applications Note MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-10 /pmc/articles/PMC9825757/ /pubmed/36495209 http://dx.doi.org/10.1093/bioinformatics/btac787 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Weilandt, Daniel R Salvy, Pierre Masid, Maria Fengos, Georgios Denhardt-Erikson, Robin Hosseini, Zhaleh Hatzimanikatis, Vassily Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title | Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title_full | Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title_fullStr | Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title_full_unstemmed | Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title_short | Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models |
title_sort | symbolic kinetic models in python (skimpy): intuitive modeling of large-scale biological kinetic models |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825757/ https://www.ncbi.nlm.nih.gov/pubmed/36495209 http://dx.doi.org/10.1093/bioinformatics/btac787 |
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