RT Journal Article
SR Electronic
T1 Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.01.17.476618
DO 10.1101/2022.01.17.476618
A1 Weilandt, Daniel R.
A1 Salvy, Pierre
A1 Masid, Maria
A1 Fengos, Georgios
A1 Denhardt-Erikson, Robin
A1 Hosseini, Zhaleh
A1 Hatzimanikatis, Vassily
YR 2022
UL http://biorxiv.org/content/early/2022/01/20/2022.01.17.476618.abstract
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.