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

Amir Akbari, Zachary B. Haiman, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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 connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.

Original languageEnglish
Article numbere01001-23
Issue number2
Publication statusPublished - 2024


  • Coherent structures
  • Data-driven approach
  • Dynamic-mode decomposition
  • Kinetic models
  • Timescale decomposition


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