{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL1006230049?filename=MODEL1006230049.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Camille Laibe"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1006230049"],"publication_pubmed":["16617075"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Vinnakota2006 MuscleGlycogenolysis ModelC"],"publication_year":["2006"],"submissionId":["MODEL1006230049"],"publication_authors":["Kalyan C Vinnakota, Melissa L Kemp, Martin J Kushmerick"],"first_author":["Kalyan C Vinnakota"],"publication":["16617075,\n                            Cellular metabolites are moieties defined by their specific binding constants to H+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical reaction in the cytoplasm has an associated proton stoichiometry that is generally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is altered in a medium with finite buffer capacity. Apparent equilibrium constants and maximum enzyme velocities, which are functions of pH, are also altered. We augmented an earlier mathematical model of skeletal muscle glycogenolysis with pH-dependent enzyme kinetics and reaction equilibria to compute the time course of pH changes. Analysis shows that kinetics and final equilibrium states of the closed system are highly constrained by the pH-dependent parameters. This kinetic model of glycogenolysis, coupled to creatine kinase and adenylate kinase, simulated published experiments made with a cell-free enzyme mixture to reconstitute the network and to synthesize PCr and lactate in vitro. Using the enzyme kinetic and thermodynamic data in the literature, the simulations required minimal adjustments of parameters to describe the data. These results show that incorporation of appropriate physical chemistry of the reactions with accurate kinetic modeling gives a reasonable simulation of experimental data and is necessary for a physically correct representation of the metabolic network. The approach is general for modeling metabolic networks beyond the specific pathway and conditions presented here.. 4, 91.\n                            Department of Bioengineering, University of Washington, Seattle, Washington, USA."],"submitter_mail":["laibe@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"pubmed_abstract":["Cellular metabolites are moieties defined by their specific binding constants to H+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical reaction in the cytoplasm has an associated proton stoichiometry that is generally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is altered in a medium with finite buffer capacity. Apparent equilibrium constants and maximum enzyme velocities, which are functions of pH, are also altered. We augmented an earlier mathematical model of skeletal muscle glycogenolysis with pH-dependent enzyme kinetics and reaction equilibria to compute the time course of pH changes. Analysis shows that kinetics and final equilibrium states of the closed system are highly constrained by the pH-dependent parameters. This kinetic model of glycogenolysis, coupled to creatine kinase and adenylate kinase, simulated published experiments made with a cell-free enzyme mixture to reconstitute the network and to synthesize PCr and lactate in vitro. Using the enzyme kinetic and thermodynamic data in the literature, the simulations required minimal adjustments of parameters to describe the data. These results show that incorporation of appropriate physical chemistry of the reactions with accurate kinetic modeling gives a reasonable simulation of experimental data and is necessary for a physically correct representation of the metabolic network. The approach is general for modeling metabolic networks beyond the specific pathway and conditions presented here."],"pubmed_title":["Dynamics of muscle glycogenolysis modeled with pH time course computation and pH-dependent reaction equilibria and enzyme kinetics."],"pubmed_authors":["Vinnakota Kalyan K, Kemp Melissa L ML, Kushmerick Martin J MJ"],"additional_accession":[]},"is_claimable":false,"name":"Vinnakota2006_MuscleGlycogenolysis_ModelC","description":"\n      \n        This a model from the article:      \n        Dynamics of muscle glycogenolysis modeled with pH time course computation and\npH-dependent reaction equilibria and enzyme kinetics.\n        \n          Vinnakota K, Kemp ML, Kushmerick MJ.      Biophys J\n          2006 Aug 15;91(4):1264-87      16617075\n          ,      \n        Abstract:\n        \n          Cellular metabolites are moieties defined by their specific binding constants to\nH+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical\nreaction in the cytoplasm has an associated proton stoichiometry that is\ngenerally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is\naltered in a medium with finite buffer capacity. Apparent equilibrium constants\nand maximum enzyme velocities, which are functions of pH, are also altered. We\naugmented an earlier mathematical model of skeletal muscle glycogenolysis with\npH-dependent enzyme kinetics and reaction equilibria to compute the time course\nof pH changes. Analysis shows that kinetics and final equilibrium states of the\nclosed system are highly constrained by the pH-dependent parameters. This\nkinetic model of glycogenolysis, coupled to creatine kinase and adenylate\nkinase, simulated published experiments made with a cell-free enzyme mixture to\nreconstitute the network and to synthesize PCr and lactate in vitro. Using the\nenzyme kinetic and thermodynamic data in the literature, the simulations\nrequired minimal adjustments of parameters to describe the data. These results\nshow that incorporation of appropriate physical chemistry of the reactions with\naccurate kinetic modeling gives a reasonable simulation of experimental data and\nis necessary for a physically correct representation of the metabolic network.\nThe approach is general for modeling metabolic networks beyond the specific\npathway and conditions presented here.      \n      This model was taken from the      CellML repository\n          and automatically converted to SBML.      \n          The original model was:      \n        Vinnakota K, Kemp ML, Kushmerick MJ. (2006) - version=1.0\n      \n      \n          The original CellML model was created by:      \n      Geoffrey Nunns\n      \n          gnunns1@jhu.edu      \n          The University of Auckland      \n    This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.      \n          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\n          for more information.      \n  In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..      \n  \n          To cite BioModels Database, please use:      Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.\n\n\n","dates":{"last_modification":"2010-06-25","publication":"2005-01-01","submission":"2010-06-23"},"accession":"MODEL1006230049","cross_references":{"pubmed":["16617075"],"biomodels__db":["MODEL1006230049"],"go":["GO:0005980"],"taxonomy":["9606"],"bto":["BTO:0001103"]}}