Dataset Information

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

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.

SUBMITTER: Smallbone K

PROVIDER: S-EPMC3764422 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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

Smallbone Kieran K Messiha Hanan L HL Carroll Kathleen M KM Winder Catherine L CL Malys Naglis N Dunn Warwick B WB Murabito Ettore E Swainston Neil N Dada Joseph O JO Khan Farid F Pir Pınar P Simeonidis Evangelos E Spasić Irena I Wishart Jill J Weichart Dieter D Hayes Neil W NW Jameson Daniel D Broomhead David S DS Oliver Stephen G SG Gaskell Simon J SJ McCarthy John E G JE Paton Norman W NW Westerhoff Hans V HV Kell Douglas B DB Mendes Pedro P

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]

PMID: 23831062

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Project description:BackgroundClostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes.ResultsIn this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances, and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters.ConclusionsThe k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.

Dataset Information

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

Publications

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

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