Dataset Information

Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 06

ABSTRACT: Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 06 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: MODEL1303260006 . 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.

SUBMITTER: Kieran Smallbone

PROVIDER: MODEL1303260006 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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	MODEL1303260006?filename=MODEL1303260006-biopax2.owl	Owl
	MODEL1303260006?filename=MODEL1303260006-biopax3.owl	Owl
	MODEL1303260006?filename=MODEL1303260006.m	Other
	MODEL1303260006?filename=MODEL1303260006.pdf	Pdf
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Publications

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:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 02 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: MODEL1303260002 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 03 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: MODEL1303260003 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 04 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: MODEL1303260004 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 05 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: MODEL1303260005 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 07 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: MODEL1303260007 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 08 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: MODEL1303260008 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 09 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: MODEL1303260009 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 10 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: MODEL1303260010 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 11 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: MODEL1303260011 . 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.

Project description:Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 13 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: MODEL1303260013 . 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.