{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=manifest.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.vcml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000085?filename=BIOMD0000000085_url.sedml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Sharat Vayttaden"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000085"],"publication_pubmed":["16986265"],"isPrivate":["false"],"repository":["BioModels"],"non_derived_xrefs":["BIOMD0000000086 biomodels.db"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Maurya2005 GTPaseCycle reducedOrder"],"publication_year":["2005"],"submissionId":["MODEL5317679037"],"first_author":["M R Maurya"],"publication_authors":["M R Maurya, S J Bornheimer, V Venkatasubramanian, S Subramaniam"],"publication":["16986265,\n                            Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.. 4, 152.\n                            San Diego Supercomputer Center, La Jolla, CA 92093, USA."],"submitter_mail":["doqcs@ncbs.res.in"],"submitter_affiliation":["DOQCS"],"publicationId":["BIOMD0000000085"],"pubmed_abstract":["Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems."],"pubmed_title":["Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module."],"pubmed_authors":["Maurya M R MR, Bornheimer S J SJ, Venkatasubramanian V V, Subramaniam S S"],"additional_accession":[]},"is_claimable":false,"name":"Maurya2005_GTPaseCycle_reducedOrder","description":"\n      \n        This model is according to the paper      Reduced-order modeling of biochemical networks: application to the GTPase-cycle signalling module\n          by Maurya et al 2006.The figure 4c is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z at various concentrations of R and GAP.      \n            \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      \n    ","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2006-12-11"},"accession":"BIOMD0000000085","cross_references":{"reactome":["REACT_348"],"pubmed":["16986265"],"chebi":["CHEBI:15996","CHEBI:18367","CHEBI:17552"],"biomodels__db":["MODEL5317679037","BIOMD0000000085"],"go":["GO:0005623","GO:0005834","GO:0008277","GO:0043235","GO:0005515","GO:0005525","GO:0003924","GO:0043241","GO:0032403","GO:0005102","GO:0001664"],"kegg__compound":["C00044","C00035"],"taxonomy":["131567"],"interpro":["IPR000342","IPR000337"]}}