A data-driven approach for timescale decomposition of biochemical reaction networks.

ABSTRACT: Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the con

SUBMITTER: Akbari A 

PROVIDER: S-EPMC10946255 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature