Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

Rafael S. Costa, Daniel Machado, Isabel Rocha, Eugénio C. Ferreira

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The construction of dynamic metabolic models at reaction network level requires the use of mechanistic enzymatic rate equations that comprise a large number of parameters. The lack of knowledge on these equations and the difficulty in the experimental identification of their associated parameters, represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis–Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action, convenience kinetics, lin-log and power-law). Using the mechanistic model for Escherichia coli central carbon metabolism as a benchmark, we investigate the alternative modeling approaches through comparative simulations analyses. The good dynamic behavior and the powerful predictive capabilities obtained using the hybrid model composed of Michaelis–Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown.
Original languageEnglish
JournalBioSystems
Volume100
Issue number2
Pages (from-to)150-157
ISSN0303-2647
DOIs
Publication statusPublished - 2010

Keywords

  • Dynamic modeling
  • Escherichia coli metabolic network
  • Approximate rate equations
  • Parameter optimization

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