BioModels

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https://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.mhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.xpphttps://www.ebi.ac.uk/biomodels/model/download/MODEL1303260001?filename=MODEL1303260001.vcmlprimaryOK200Kieran SmallboneNon-curatedordinary differential equation modelL2V4https://www.ebi.ac.uk/biomodels/MODEL130326000123831062falseBioModelsSBMLModelsSmallbone2013 Glycolysis in S.cerevisiae Iteration 012013MODEL1303260001Kieran Smallbone, Hanan L Messiha, Kathleen M Carroll, Catherine L Winder, Naglis Malys, Warwick Dunn, Ettore Murabito, Neil Swainston, Joseph O Dada, Farid Khan, Pınar Pir, Evangelos Simeonidis, Irena Spasić, Jill Wishart, Dieter Weichart, Neil W Hayes, Daniel Jameson, David S Broomhead, Stephen G Oliver, Simon J Gaskell, John E G McCarthy, Norman W Paton, Hans V Westerhoff, D B Kell, Pedro MendesKieran Smallbone23831062, 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.. 17, 587. Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, UK.kieran.smallbone@ncl.ac.ukManchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.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.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 PEmbden-Meyerhof-Parnas Pathway, Pathway, glycolysis, Embden Meyerhof Pathway, modified Embden-Meyerhof pathway, anaerobic glycolysis, Embden-Meyerhof pathway., Embden-Meyerhof, Pathways, Embden-Meyerhof Pathway, Embden-Meyerhof-Parnas, Embden-Meyerhof Pathways, Embden Meyerhof Parnas Pathway, Embden-Meyerhof-Parnas pathwaySaccharomyces oviformis, Yeast, Pathway, Embden Meyerhof Pathway, enzymes, Biocatalysts, anaerobic glycolysis, Biocatalyst., Saccharomyes cerevisiae, baker's yeast, Embden-Meyerhof, Pathways, enzyme activity, Saccharomyces uvarum var. melibiosus, Embden-Meyerhof-Parnas, Embden Meyerhof Parnas Pathway, Embden-Meyerhof-Parnas pathway, Saccharomyces italicus, Saccaromyces cerevisiae, Embden-Meyerhof pathway, Sccharomyces cerevisiae, Embden-Meyerhof-Parnas Pathway, Enzyme, Candida robusta, glycolysis, Saccharomyces capensis, yeast, modified Embden-Meyerhof pathway, Embden-Meyerhof Pathway, brewer's yeast, Embden-Meyerhof Pathways, lager beer yeastbiochemical pathways, IPP2A2, Metabolic Process, cycline, Experimental Models, Isozymes, Processes, Theoretical Model, DmcyclinE, fond, Metabolic Concepts, Brewer's, secondary metabolites, Embden-Meyerhof, baker's yeast, cycE, Metabolic Processes, CycEI, Embden Meyerhof Parnas Pathway, body system, Saccharomyces italicus, l(2)br37, Theoretical, prevention, PHAPII, Mathematical Models, CYCLE, cdi7, 5730420M11Rik, primary metabolites, Ccne, cyclinE, Metabolism, modified Embden-Meyerhof pathway, yeast, Studies, Cdi7, CDI7, Concepts, Experimental Model, system, Embden-Meyerhof Pathway, Alloenzyme, Metabolism Concept, Allozymes, Phenomenon, Metabolism Phenomena, Models, prevention and control, lager beer yeast, study, SET, anatomical systems, Theoretical Study, CYCE, reference sample, TAF-I, catabolism, DmelCG3938, CyclE, ipp2a2, 3938, Metabolic Concept, Pathways, Saccharomyes cerevisiae, 2pp2a, metabolic process resulting in cell growth, Baker, Theoretic, DmcycE, Saccharomyces uvarum var. melibiosus, CG10574, S. cerevisiae, DmelCG4299, Study, l(2)k05007, preventive measures, IGAAD, set, dm-cycE, S cerevisiae, Candida robusta, 2PP2A, DmelCG10574, Saccharomyces capensis, Mathematical, taf-ibeta, Alloenzymes, dSET, dSet, Baker's, biotransformation, Mycoderma cerevisiae, 1883, brewer's yeast, Model, Catabolism, Controlled, phapii, Controlling, data, Pathway, preventive therapy, Embden Meyerhof Pathway, degradation, Process, Saccharomyces cerevisiae 'var. diastaticus', metabolism resulting in cell growth, igaad, anaerobic glycolysis, StF-IT-1, Model (Theoretical), Embden-Meyerhof-Parnas, Cyc E, br37, Saccaromyces cerevisiae, group, Concept, Metabolic Phenomena, Embden-Meyerhof-Parnas Pathway, Baker's Yeasts, Sccharomyces cerevisiae, Metabolism Concepts, Experimental, l(2)k02514, DmCycE, BG:DS07108.3, Saccharomyces diastaticus, I-2PP2A, Theoretical Studies, Dm I-2, Phenomena, I2PP2A, Embden-Meyerhof pathway., secretion, CyeE, connected anatomical system, l(2)05206, metabolism, Metabolic Phenomenon, Saccharomyces oviformis, Yeast, multicellular organism metabolic process, Epistemology, Isozyme, l(2)k02602, HLA-DR-associated protein II, biodegradation, Metabolic, ensemble, DI-2, Allozyme, prophylaxis, I-2Dm, l(2)35Dd, metabolite, CG4299, Embden-Meyerhof-Parnas pathway, Embden-Meyerhof pathway, Theoretical Models, I-2PP1, organ system, D-CycE, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, dSET/TAF-Ibeta, Models (Theoretical), single-organism metabolic process, 2610030F17Rik, glycolysis, l35Dd, TAF-IBETA, control, metabolites, TAF-Ibeta, Baker's Yeast, AA407739, CG3938, Isoenzyme, i2pp2a, Embden-Meyerhof Pathways, Brewer's Yeast, Mathematical Model, Baker Yeast, Anabolismbiochemical pathways, extent, IPP2A2, Metabolic Process, Sectors, Public Sectors, experimental, KLK, DmelCG1810, Isozymes, Biocatalysts, Processes, Metabolic Concepts, number, Brewer's, secondary metabolites, Embden-Meyerhof, baker's yeast, Copyrights, xpir, Metabolic Processes, Embden Meyerhof Parnas Pathway, body system, presence, Saccharomyces italicus, prevention, PHAPII, 5730420M11Rik, primary metabolites, Metabolism, modified Embden-Meyerhof pathway, yeast, Concepts, system, Embden-Meyerhof Pathway, Alloenzyme, Metabolism Concept, Allozymes, Public Enterprise, IPEX, Phenomenon, F20L16_130, XPID, Metabolism Phenomena, prevention and control, Enterprises, lager beer yeast, study, SET, methods, anatomical systems, enzymes, reference sample, PIR-A1, Ly89, TAF-I, catabolism, experimental section, 6M21, ipp2a2, Public Domains, Pathways, Saccharomyes cerevisiae, Metabolic Concept, 2pp2a, metabolic process resulting in cell growth, Baker, Saccharomyces uvarum var. melibiosus, Public Enterprises, AIID, CG10574, S. cerevisiae, DmelCG4299, preventive measures, IGAAD, set, Enzyme, S cerevisiae, Abstract, Candida robusta, 2PP2A, PIDX, DmelCG10574, Saccharomyces capensis, taf-ibeta, Alloenzymes, Pirnl, dSET, dSet, Baker's, biotransformation, Mycoderma cerevisiae, 1883, brewer's yeast, Pir, PIR, Enterprise, Catabolism, F20L16.130, Controlled, 2310042L19Rik, phapii, Controlling, data, Pathway, preventive therapy, Embden Meyerhof Pathway, SRA1, degradation, Process, Saccharomyces cerevisiae 'var. diastaticus', completeness, DIETER, metabolism resulting in cell growth, igaad, anaerobic glycolysis, StF-IT-1, enzyme activity, Embden-Meyerhof-Parnas, Saccaromyces cerevisiae, group, Concept, Metabolic Phenomena, Embden-Meyerhof-Parnas Pathway, Sccharomyces cerevisiae, Baker's Yeasts, Metabolism Concepts, count in organism, Saccharomyces diastaticus, I-2PP2A, Public, Public Domain, Dm I-2, Phenomena, I2PP2A, Domains, secretion, PIROGI 121, connected anatomical system, metabolism, Domain, Data Base, CG1810, Metabolic Phenomenon, Saccharomyces oviformis, Yeast, multicellular organism metabolic process, Isozyme, KLUNKER, HLA-DR-associated protein II, biodegradation, Metabolic, ensemble, DI-2, Allozyme, prophylaxis, I-2Dm, PIRP, ATSRA1, metabolite, CG4299, Embden-Meyerhof-Parnas pathway, Embden-Meyerhof pathway, experimental procedures, I-2PP1, organ system, Saccharomyces cerevisiae (Desm.) Meyen ex E.C. Hansen, dSET/TAF-Ibeta, single-organism metabolic process, 2610030F17Rik, Sector, glycolysis, TAF-IBETA, control, metabolites, Biocatalyst, TAF-Ibeta, Public., PIROGI, Baker's Yeast, AA407739, Isoenzyme, i2pp2a, Embden-Meyerhof Pathways, Brewer's Yeast, Baker Yeast, AnabolismfalseSmallbone2013 - Glycolysis in S.cerevisiae - Iteration 01 Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 01 This model is described in the article: A model of yeast glycolysis based on a consistent kinetic characterization 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, Pınar Pir, Evangelos Simeonidis, Irena Spasić, Jill Wishart, Dieter Weichart, Neil W. Hayes, Daniel Jameson, David S. Broomhead, Stephen G. Oliver, Simon J. Gaskell, John E.G. McCarthy, Norman W. Paton, Hans V. Westerhoff, Douglas B. Kell, Pedro Mendes FEBS Letters (in press) 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 identified by: MODEL1303260001 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. 2013-06-252005-01-012013-03-26MODEL1303260001R00754R00127R00658R01068R01518R02848R00636R00756R00771R01512R00200R01061R01015R00842R07298

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