PT  - JOURNAL ARTICLE
AU  - Daniel R. Weilandt
AU  - Pierre Salvy
AU  - Maria Masid
AU  - Georgios Fengos
AU  - Robin Denhardt-Erikson
AU  - Zhaleh Hosseini
AU  - Vassily Hatzimanikatis
TI  - Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models
AID  - 10.1101/2022.01.17.476618
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.01.17.476618
4099  - http://biorxiv.org/content/early/2022/01/20/2022.01.17.476618.short
4100  - http://biorxiv.org/content/early/2022/01/20/2022.01.17.476618.full
AB  - 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.chCompeting Interest StatementThe authors have declared no competing interest.