Graph-based, dynamics-preserving reduction of (bio)chemical systems
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Date
2024
Authors
Roussel, Marc R.
Soares, Talmon
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Abstract
Complex dynamical systems are often governed by equations containing many unknown parameters whose precise values may or may not be important for the system’s dynamics. In particular, for chemical and biochemical systems, there may be some reactions or subsystems that are inessential to understanding the bifurcation structure and consequent behavior of a model, such as oscillations, multistationarity and patterning. Due to the size, complexity and parametric uncertainties of many (bio)chemical models, a dynamics-preserving reduction scheme that is able to isolate the necessary contributors to particular dynamical behaviors would be useful. In this contribution, we describe model reduction methods for mass-action (bio)chemical models based on the preservation of instability-generating subnetworks known as critical fragments. These methods focus on structural conditions for instabilities and so are parameter-independent. We apply these results to an existing model for the control of the synthesis of the NO-detoxifying enzyme Hmp in Escherichia coli that displays bistability.
Description
Accepted author manuscript. Embargo in effect until September 14, 2025
Keywords
Model reduction , Chemical reaction networks , Mass-action modeling , Graph-theoretical methods , Control of gene expression , Nitric oxide metabolism
Citation
Roussel, M. R., & Soares, T. (2024). Graph-based, dynamics-preserving reduction of (bio)chemical systems. Journal of Mathematical Biology, 89, Article 24. https://doi.org/10.1007/s00285-024-02138-0