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Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models

View ORCID ProfileDaniel R. Weilandt, View ORCID ProfilePierre Salvy, View ORCID ProfileMaria Masid, View ORCID ProfileGeorgios Fengos, View ORCID ProfileRobin Denhardt-Erikson, View ORCID ProfileZhaleh Hosseini, View ORCID ProfileVassily Hatzimanikatis
doi: https://doi.org/10.1101/2022.01.17.476618
Daniel R. Weilandt
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, USA
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Pierre Salvy
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
3Cambrium GmBH, Germany
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Maria Masid
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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Georgios Fengos
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
4EMBION Technologies S.A., Switzerland
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Robin Denhardt-Erikson
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
5LYO-X GmbH, Switzerland
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Zhaleh Hosseini
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
6MRC Toxicology Unit, University of Cambridge, Cambridge, United-Kingdom
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Vassily Hatzimanikatis
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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  • For correspondence: vassily.hatzimanikatis@epfl.ch
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Abstract

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, parametrize 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 imple-ment multispecies bioreactor simulations to assess biotechnological processes.

Availability The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation.

Contact vassily.hatzimanikatis{at}epfl.ch

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/EPFL-LCSB/SKiMpy

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 20, 2022.
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Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models
Daniel R. Weilandt, Pierre Salvy, Maria Masid, Georgios Fengos, Robin Denhardt-Erikson, Zhaleh Hosseini, Vassily Hatzimanikatis
bioRxiv 2022.01.17.476618; doi: https://doi.org/10.1101/2022.01.17.476618
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Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models
Daniel R. Weilandt, Pierre Salvy, Maria Masid, Georgios Fengos, Robin Denhardt-Erikson, Zhaleh Hosseini, Vassily Hatzimanikatis
bioRxiv 2022.01.17.476618; doi: https://doi.org/10.1101/2022.01.17.476618

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