A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-01

AUTHORS

Matthew D. Johnston, David Siegel, Gábor Szederkényi

ABSTRACT

A numerically effective procedure for determining weakly reversible chemical reaction networks that are linearly conjugate to a known reaction network is proposed in this paper. The method is based on translating the structural and algebraic characteristics of weak reversibility to logical statements and solving the obtained set of linear (in)equalities in the framework of mixed integer linear programming. The unknowns in the problem are the reaction rate coefficients and the parameters of the linear conjugacy transformation. The efficacy of the approach is shown through numerical examples. More... »

PAGES

274-288

References to SciGraph publications

  • 2011-06. Finding complex balanced and detailed balanced realizations of chemical reaction networks in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1972-01. Necessary and sufficient conditions for complex balancing in chemical kinetics in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1972-01. Complex balancing in general kinetic systems in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 2010-02. Computing sparse and dense realizations of reaction kinetic systems in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1970-01. The mathematical structure of chemical kinetics in homogeneous single-phase systems in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 2008-07. Identifiability of chemical reaction networks in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 2009-04. Comment on “identifiability of chemical reaction networks” by G. Craciun and C. Pantea in JOURNAL OF MATHEMATICAL CHEMISTRY
  • 1972-01. General mass action kinetics in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1990. Introduction to Applied Nonlinear Dynamical Systems and Chaos in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10910-011-9911-7

    DOI

    http://dx.doi.org/10.1007/s10910-011-9911-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030049136


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