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 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.

Automated summary

Large-scale kinetic models are essential for understanding the dynamic and adaptive responses of biological systems, but the lack of computational tools for building and analyzing these models has been a limitation. This study presents a Python package (SKiMpy) that bridges this gap by providing an efficient toolbox for generating and analyzing large-scale kinetic models in various biological domains. The toolbox also allows for efficient parameterization of kinetic models and implementation of multispecies bioreactor simulations.