A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

Kieran Smallbone, Hanan L Messiha, Kathleen M Carroll, Catherine L Winder, Naglis Malys, Warwick B Dunn, Ettore Murabito, Neil Swainston, Joseph O Dada, Farid Khan, Pinar Pir, Evangelos Simeonidis, Irena Spasić, Jill Wishart, Dieter Weichart, Neil W. Hayes, Daniel Jameson, David S. Broomhead, Stephen G. Oliver, Simon J. GaskellJohn E G McCarthy, Norman Paton, Hans V Westerhoff, Douglas B. Kell, Pedro Mendes

Research output: Contribution to journalLetterpeer-review

136 Downloads (Pure)

Abstract

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)2832-2841
Number of pages10
JournalFEBS Letters
Volume587
Issue number17
Early online date3 Jul 2013
DOIs
Publication statusPublished - 2 Sept 2013

Keywords

  • enzyme kinetic
  • glycolysis
  • isoenzyme
  • modelling
  • systems biology

Fingerprint

Dive into the research topics of 'A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes'. Together they form a unique fingerprint.

Cite this