Speaker
Description
Understanding how the structure of biochemical reaction networks determines their long-term dynamical behavior remains one of the core problems in systems biology. Analysis of these models becomes increasingly challenging as network size and complexity grow. In this work, we present an enhanced framework for parameterizing the positive steady states of biochemical systems using the structural properties of the associated reaction network. The approach integrates network decomposition with a graph-theoretic network translation method based on elementary flux modes, enabling for an efficient derivation of positive steady states while significantly reducing computational overhead and addressing the various issues encountered in existing approaches. By benchmarking across a diverse set of biochemical models, we demonstrate the improved scalability of the resulting procedure. Finally, we apply the method to illustrate how steady state parameterizations can be used to investigate important biological properties of multistationarity and absolute concentration robustness in the EnvZ-OmpR signaling pathway and a large-scale CRISPRi toggle switch model. These results highlight how the newly developed framework enables a scalable analysis of larger, more complex biochemical systems and provides deeper insights into the long-term behavior of such models.
