Dataset Information
Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 10
Ontology highlight
ABSTRACT: This model is described in the article: 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. This model is hosted on BioModels Database and identifiedby: MODEL1303260010 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models . To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.
SUBMITTER: Kieran Smallbone
PROVIDER: MODEL1303260010 | biostudies-other |
SECONDARY ACCESSION(S): 23831062
REPOSITORIES: biostudies-other
Publications
A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.
FEBS letters 20130704 17
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 es ...[more]