{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL5952308332?filename=MODEL5952308332.vcml"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"scores":null,"additional":{"submitter":["Lukas Endler"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL5952308332"],"publication_pubmed":["17010168"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Danø2006 Glycolysis Reduction"],"publication_year":["2006"],"submissionId":["MODEL5952308332"],"publication_authors":["Sune Danø, Mads F Madsen, Henning Schmidt, Gunnar Cedersund"],"first_author":["Sune Danø"],"publication":["17010168,\n                            The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models.. 21, 273.\n                            Department of Medical Biochemistry and Genetics, University of Copenhagen, Denmark."],"submitter_mail":["lukas@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"pubmed_abstract":["The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models."],"pubmed_title":["Reduction of a biochemical model with preservation of its basic dynamic properties."],"pubmed_authors":["Danø Sune S, Madsen Mads F MF, Schmidt Henning H, Cedersund Gunnar G"],"additional_accession":[]},"is_claimable":false,"name":"Danø2006_Glycolysis_Reduction","description":"\n      \n        This model originates from BioModels Database: A Database of Annotated Published Models. 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":"2009-09-23","publication":"2005-01-01","submission":"2009-06-25"},"accession":"MODEL5952308332","cross_references":{"pubmed":["17010168"],"biomodels__db":["MODEL5952308332"],"taxonomy":["4932"]}}