Simulating labeling to estimate kinetic parameters for flux control analysis

Amy Marshall-Colon, Neelanjan Sengupta, David Rhodes, John A. Morgan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

An important aspect of kinetic modeling is the ability to provide predictive information on network control and dynamic responses to genetic or environmental perturbations based on innate enzyme kinetics. In a top-down approach to model assembly, unknown kinetic parameters are calculated using experimental data such as metabolite pool concentrations and transient labeling patterns after supply of an isotopically labeled substrate. These kinetic parameters can then be used to calculate flux control coefficients for every reaction in a network, which aids in the identification of enzymatic reactions that exert the most control over the network as a whole. This chapter describes a modeling approach to estimate kinetic parameters which are then used to perform metabolic control analysis. An example is provided for the benzenoid network of Petunia hybrida; however, the methodologies can be applied to any small segment of metabolism.

Original languageEnglish (US)
Title of host publicationPlant Metabolic Flux Analysis
Subtitle of host publicationMethods and Protocols
PublisherHumana Press Inc.
Pages211-222
Number of pages12
ISBN (Print)9781627036870
DOIs
StatePublished - 2014
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume1090
ISSN (Print)1064-3745

Keywords

  • Computer simulation
  • Isotopic labeling
  • Kinetic modeling
  • Metabolic control analysis
  • Metabolic flux analysis
  • Network decomposition

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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  • Cite this

    Marshall-Colon, A., Sengupta, N., Rhodes, D., & Morgan, J. A. (2014). Simulating labeling to estimate kinetic parameters for flux control analysis. In Plant Metabolic Flux Analysis: Methods and Protocols (pp. 211-222). (Methods in Molecular Biology; Vol. 1090). Humana Press Inc.. https://doi.org/10.1007/978-1-62703-688-7_13