{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.vcml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1002160000?filename=MODEL1002160000.m"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["David Gomez-Cabrero"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1002160000"],"publication_pubmed":["22670212"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Gomez Cabrero2011 Atherogenesis"],"publication_year":["2011"],"submissionId":["MODEL1002160000"],"publication_authors":["David Gomez-Cabrero, Albert Compte, Jesper Tegner"],"first_author":["David Gomez-Cabrero"],"publication":["22670212,\n                            Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.. 3, 1.\n                            Department of Medicine, Karolinska Institutet ,  Unit of Computational Medicine, Centre for Molecular Medicine ,  Solna, Stockholm ,  Sweden."],"submitter_mail":["david.gomezcabrero@ki.se"],"submitter_affiliation":["Karolinska Institute"],"pubmed_abstract":["Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters."],"pubmed_title":["Workflow for generating competing hypothesis from models with parameter uncertainty."],"pubmed_authors":["Gomez-Cabrero David D, Compte Albert A, Tegner Jesper J"],"name_synonyms":["Atherogenesis, Atheroscleroses, Atherogeneses, atherosclerosis artery."],"pubmed_abstract_synonyms":["SCARB3, lifespan, BODYFAT, CT11259, experimental, determination, taxonomy, acetylglucosaminyltransferase-like protein, developmental stage, number, Mbp1, GPIIIB, l(2)ft, fat, hFat1, BDPLT10, Dm Fat, presence, LARGE1, froggy, Gyltl1a, protrusion, Classifications, Readability, hierarchies, hierarchy, CDHF7, systematics, PAS-4, Scarb3, CG3352, Cph1, Work Flow, GP3B, myd, wall of artery, fat tissue, Phenomenography, Taxonomy, Atherogeneses, methods, like-acetylglucosaminyltransferase, adipose system, entire lifespan, entire life cycle, experimental section, MDDGB6, Atheroscleroses, Mbp-1, Sciences, 79/18, LARGE, procedures, CG1945, Fatty acid translocase, BPFD#36, GP4, Glycoprotein IIIb, bodyfat, stage, CPH, HHT1, DmelCG3352, anatomical protrusion, l(2)gd-1, Edg, BcDNA:LD22582, R74677, gyltl1b-b, Systematics, BcDNA.LD22582, l(2)gd2, CD36, ME5, Taxonomies, count in organism, Workflows, Body Fat, Platelet glycoprotein IV, PASIV, MDDGA6, Algorithm, mKIAA0609, chemical analysis, sequence, core, spine., techniques, PAS IV, KIAA0609, END, Cph-1, acetylglucosaminyltransferase-like 1A, l(2)k07918, CDHR8, Platelet collagen receptor, Atherogenesis, fg, Epistemology, atherosclerosis artery, Leukocyte differentiation antigen CD36, gyltl1b, distinct, life, mdc1d, CHDS7, l(2)gd-l, arterial wall, Understanding, LARGE_HUMAN, primary structure of sequence macromolecule, experimental procedures, MDC1D, l(2)fd, like-glycosyltransferase, enr, fatty depot, adipose, Faf, DmelCG1945, Ft, cardinality, Thrombospondin receptor, l(2)fat, l(2)24Da, assay, GPIV, ORW1, Fat, FAT, Work Flows, l(2)79/18, hypothesis, methodology, glycosyltransferase-like protein LARGE1"],"description_synonyms":["extent, BODYFAT, CT11259, Public Sectors, determination, acetylglucosaminyltransferase-like protein, AUTSX5, Mbp1, l(2)ft, NOVH, CCN3, QM, Classifications, Readability, hierarchies, hierarchy, systematics, Cph1, Public Enterprise, Work Flow, myd, fat tissue, fs(1)M104, Atherogeneses, like-acetylglucosaminyltransferase, adipose system, entire life cycle, Public Domains, Mbp-1, procedures, Fatty acid translocase, DmelCG4063, IBP-9, GP4, stage, Tbl1, TBL1, NOVh, anatomical protrusion, l(2)gd-1, BcDNA:LD22582, gyltl1b-b, completeness, Systematics, CD36, Taxonomies, Workflows, Body Fat, Public Domain, MDDGA6, Algorithm, mKIAA0609, Domains, NOV, KIAA0609, END, PlexA1, acetylglucosaminyltransferase-like 1A, Domain, l(2)k07918, fg, Epistemology, Plxn1, gyltl1b, CG4063, life, mdc1d, CHDS7, l(2)gd-l, arterial wall, nov, LARGE_HUMAN, E-2f, mKIAA4053, E-2g, fs(1)Y[b], experimental procedures, MDC1D, Sector, l(2)fd, enr, Faf, spine, C130088N23Rik, Ft, Thrombospondin receptor, PLXN1, DXS648E, Fat, FAT, Work Flows, SCARB3, Sectors, lifespan, Tb11, YB, experimental, taxonomy, developmental stage, number, GPIIIB, Copyrights, fat, hFat1, BDPLT10, Dm Fat, presence, LARGE1, froggy, DOI, Gyltl1a, protrusion, FBXW4, CDHF7, Yb, PAS-4, Scarb3, CG3352, GP3B, Enterprises, wall of artery, CG2706, Phenomenography, doi, Taxonomy, methods, FOCUS, entire lifespan, experimental section, MDDGB6, Ebi, EBI, Atheroscleroses, Sciences, 79/18, LARGE, IGFBP9, CG1945, Public Enterprises, BPFD#36, Kiaa4053, Abstract, L10, Glycoprotein IIIb, bodyfat, Enterprise, CPH, HHT1, DmelCG3352, Edg, R74677, DmelCG2706, BcDNA.LD22582, DXS648, l(2)gd2, SMAP55, ME5, presence., count in organism, Platelet glycoprotein IV, PASIV, IGFBP-9, Public, chemical analysis, sequence, core, techniques, PAS IV, Cph-1, CDHR8, Data Base, Platelet collagen receptor, Atherogenesis, atherosclerosis artery, Leukocyte differentiation antigen CD36, distinct, Understanding, primary structure of sequence macromolecule, l(2)k16213, like-glycosyltransferase, fatty depot, adipose, DmelCG1945, cardinality, EG:95B7.8, l(2)fat, l(2)24Da, 2600013D04Rik, assay, GPIV, ORW1, l(2)79/18, hypothesis, methodology, glycosyltransferase-like protein LARGE1"],"pubmed_title_synonyms":["hypothesis., Workflows, Work Flow, Work Flows"],"additional_accession":[]},"is_claimable":false,"name":"Gomez-Cabrero2011_Atherogenesis","description":"\n      \n        This model is from the article:      \n        Workflow for generating competing hypothesis from models with parameter uncertainty.\n        \n          David Gomez-Cabrero, Albert Compte and Jesper Tegner      Interface Focus\n          6 June 2011 vol. 1 no. 3 438-449;  \n      doi:      10.1098/rsfs.2011.0015\n        \n        Abstract:\n        \n          Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.      \n      This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/).      \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":"2012-02-16","publication":"2005-01-01","submission":"2010-02-16"},"accession":"MODEL1002160000","cross_references":{"pubmed":["22670212"],"biomodels__db":["MODEL1002160000"],"go":["GO:0070723"],"taxonomy":["9606"],"efo":["0003914"],"doi":["doi:10.1098/rsfs.2011.0015"]}}