Graph-based, dynamics-preserving reduction of (bio)chemical systems

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dc.contributor.authorRoussel, Marc R.
dc.contributor.authorSoares, Talmon
dc.date.accessioned2025-02-21T22:53:48Z
dc.date.available2025-02-21T22:53:48Z
dc.date.issued2024
dc.descriptionAccepted author manuscript. Embargo in effect until September 14, 2025
dc.description.abstractComplex 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.
dc.description.peer-reviewYes
dc.identifier.citationRoussel, 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
dc.identifier.urihttps://hdl.handle.net/10133/6981
dc.language.isoen
dc.publisherSpringer Nature
dc.publisher.departmentDepartment of Chemistry and Biochemistry
dc.publisher.facultyArts and Science
dc.publisher.institutionUniversity of Lethbridge
dc.publisher.urlhttps://doi.org/10.1007/s00285-024-02138-0
dc.subjectModel reduction
dc.subjectChemical reaction networks
dc.subjectMass-action modeling
dc.subjectGraph-theoretical methods
dc.subjectControl of gene expression
dc.subjectNitric oxide metabolism
dc.subject.lcshGene expression
dc.subject.lcshNitric oxide--Metabolism
dc.subject.lcshChemical reactions
dc.titleGraph-based, dynamics-preserving reduction of (bio)chemical systems
dc.typeArticle
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