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bioRxiv · 10.1101/2022.01.17.476618

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

Weilandt, D. R.·Salvy, P.·Masid, M.·Fengos, G.·Denhardt Eriksson, R.·Hosseini, Z.·Hatzimanikatis, V.
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Abstract

MotivationLarge-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. ResultsWe 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. AvailabilityThe software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. Contactvassily.hatzimanikatis@epfl.ch

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Weilandt, D. R., Salvy, P., Masid, M., Fengos, G., Denhardt Eriksson, R., Hosseini, Z., Hatzimanikatis, V.. 2022-01-20. Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models. https://doi.org/10.1101/2022.01.17.476618

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