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  Reaction networks and kinetics of biochemical systems

Arceo, C. P. P., Jose, E. C., Lao, A. R., & Mendoza, E. R. (2017). Reaction networks and kinetics of biochemical systems. Mathematical biosciences, 283, 13-29. doi:10.1016/j.mbs.2016.10.004.

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Arceo, Carlene Perpetua P.1, Author
Jose, Editha C.1, Author
Lao, Angelyn R.1, Author
Mendoza, Eduardo R.2, Author              
Affiliations:
1external, ou_persistent22              
2Oesterhelt, Dieter / Membrane Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565164              

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Free keywords: MULTISTATIONARITY; SUBSPACESLife Sciences & Biomedicine - Other Topics; Mathematical & Computational Biology; Chemical reaction network; Kinetics set; Complex factorizable kinetics; Power law kinetics; Kinetic subspace; Span surjective kinetics; Total representation; Total kinetic system; Embedded representation; Embedded kinetic system;
 Abstract: This paper further develops the connection between Chemical Reaction Network Theory (CRNT) and Biochemical Systems Theory (BST) that we recently introduced [1]. We first use algebraic properties of kinetic sets to study the set of complex factorizable kinetics CFK(N) on a CRN, which shares many characteristics with its subset of mass action kinetics. In particular, we extend the Theorem of Feinberg-Horn [9] on the coincidence of the kinetic and stoichiometric subsets of a mass action system to CF kinetics, using the concept of span surjectivity. We also introduce the branching type of a network, which determines the availability of kinetics on it and allows us to characterize the networks for which all kinetics are complex factorizable: A "Kinetics Landscape" provides an overview of kinetics sets, their algebraic properties and containment relationships. We then apply our results and those (of other CRNT researchers) reviewed in [1] to fifteen BST models of complex biological systems and discover novel network and kinetic properties that so far have not been widely studied in CRNT. In our view, these findings show an important benefit of connecting CRNT and BST modeling efforts. (C) 2016 Elsevier Inc. All rights reserved.

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Language(s): eng - English
 Dates: 2016-11-042017-01
 Publication Status: Published in print
 Pages: 17
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000393007100003
DOI: 10.1016/j.mbs.2016.10.004
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Title: Mathematical biosciences
Source Genre: Journal
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Publ. Info: New York : Elsevier
Pages: - Volume / Issue: 283 Sequence Number: - Start / End Page: 13 - 29 Identifier: ISSN: 0025-5564
CoNE: https://pure.mpg.de/cone/journals/resource/991042744490246