{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.vcml","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL0568648427?filename=MODEL0568648427.xpp"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"scores":null,"additional":{"submitter":["Gwenael Kervizic"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V3"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL0568648427"],"publication_pubmed":["19025648"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Kervizic2008 Cholesterol SREBP"],"publication_year":["2008"],"submissionId":["MODEL0568648427"],"modelFlag":["Non Kinetic","Sbml Extended"],"first_author":["Gwenael Kervizic"],"publication_authors":["Gwenael Kervizic, Laurent Corcos"],"publication":["19025648,\n                            <h4>Background</h4>Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now.<h4>Results</h4>We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs), as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway.<h4>Conclusion</h4>We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico experiments and confront the resulting properties with published and experimental data. The model of the cholesterol pathway and its regulation, along with Boolean formulae used for simulation are available on our web site http://Bioinformaticsu613.free.fr. Graphical results of the simulation are also shown online. The SBML model is available in the BioModels database http://www.ebi.ac.uk/biomodels/ with submission ID: MODEL0568648427.. null, 2.\n                            Inserm U613, Faculté de Médecine, Université de Bretagne Occidentale, Brest, FRANCE. gwenael.kervizic@univ-brest.fr"],"submitter_mail":["Gwenael.Kervizic@univ-brest.fr"],"submitter_affiliation":["University of Brest"],"pubmed_abstract":["<h4>Background</h4>Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now.<h4>Results</h4>We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs), as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway.<h4>Conclusion</h4>We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico experiments and confront the resulting properties with published and experimental data. The model of the cholesterol pathway and its regulation, along with Boolean formulae used for simulation are available on our web site http://Bioinformaticsu613.free.fr. Graphical results of the simulation are also shown online. The SBML model is available in the BioModels database http://www.ebi.ac.uk/biomodels/ with submission ID: MODEL0568648427."],"pubmed_title":["Dynamical modeling of the cholesterol regulatory pathway with Boolean networks."],"pubmed_authors":["Kervizic Gwenael G, Corcos Laurent L"],"pubmed_title_synonyms":["17alpha)-cholest-5-en-3-ol, Cholest-5-en-3beta-ol, (3beta, Cholesterin, Epicholesterol., Cholest-5-en-3-ol (3beta)-, 14beta"],"description_synonyms":["extent, dmBest1, Public Sectors, Product, NetrinA, AUTSX5, D430049E23Rik, NOVH, Tumor, Hydroxymethylglutaryl CoA Reductase Inhibitors, DmelCG6264, Social Controls, prevention, 5730420M11Rik, Method, 17alpha)-cholest-5-en-3-ol, 3, Public Enterprise, myd, 14beta, Transcriptional, SET, Elkh, anabolism, (Z)-isomer, EK6, Cholest-5-en-3beta-ol, Mbp-1, procedures, SAP-2, Sap-2, Epicholesterol, racemic mevalonate, DmelCG4063, Social, DmelCG4299, neuroendocrine tumour, set, Solute carrier family 6 member 2, medicine, Gene Network, cholesterol anabolism, Role Concepts, SLC6A5, Tbl1, TBL1, Editorial Comment, NOVh, Tumors, Elk, ELK, preventive therapy, completeness, Neuronally-expressed EPH-related tyrosine kinase, SAP2, Procedure, neuroendocrine neoplasm, DmIKKgamma, Role Concept, Benign, Norepinephrine transporter, dIKK, Public Domain, mKIAA0609, Role, Domains, atomo, atome, TU15B, fg, Acid, Plxn1, Hydroxymethylglutaryl CoA, Gene Circuit, HLA-DR-associated protein II, DI-2, I-2Dm, atoms, dbest1, IKK-gamma, expanded, Benign Neoplasms, Methodological, Controls, E-2f, mKIAA4053, RS-mevalonate, human, E-2g, Malignant Neoplasms, experimental procedures, I-2PP1, MDC1D, DmelCG18657, Sector, TAF-IBETA, enr, Regulatory Networks, HMG-CoA, TAF-Ibeta, PLXN1, HMG-CoA Reductase Inhibitor, humans, Regulations, big, AW488255, Sectors, (+-)-mevalonate, Tb11, Processes, Neoplasms, number, Copyrights, biosynthesis, HMG CoA Reductase, (E, dIKK-gamma, DOI, Human, FBXW4, large, Cholest-5-en-3-ol (3beta)-, DmIKK-gamma, Gene Products, 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determination, Mbp1, CCN3, QM, element, Productivity, Techniques, Circuit, Roles, Pharmaceutical Product, Transcriptional Networks, Concepts, ARB, Inhibitors, prevention and control, Formal Social Controls, IKKg, fs(1)M104, multicellular organismal biosynthetic process, KEY, Key, single-organism biosynthetic process, (3beta, Man (Taxonomy), reference sample, Biology, TAF-I, Public Domains, hypoplasia, free, (E)-isomer, IBP-9, preventive measures, IGAAD, lithium salt, Methodological Studies, DmelCG10574, malignant neoplasm, HMG CoA Reductase Inhibitor, Pharmaceutical, CT27014, NET1, Malignancies, netA, phapii, CG6264, Process, gyltl1b-b, Modern, StF-IT-1, 9330129L11, results, Hydroxymethylglutaryl CoA Reductase Inhibitor, Pentanoic acid, Statin, MDDGA6, Kenny, EPH-like kinase 6, Pharmaceutic, NOV, Markov Chain, geranyl pyrophosphate, KIAA0609, PlexA1, region, Domain, farnesyl pyrophosphate, dBest1, Hydroxymethylglutaryl-Coenzyme A Inhibitors, Epistemology, gyltl1b, Element, CG4063, 5-dihydroxy-3-methyl-, mdc1d, Control, nov, CG4299, hEK6, Methodological Study, fs(1)Y[b], Statins, Hydroxymethylglutaryl-CoA Inhibitors, DmelCG16910, Gene Modules, enlarged, Markov Processes, C130088N23Rik, rac-mevalonate, DXS648E, Regulation, i2pp2a, HMG-CoA Reductase Inhibitors, HMG-CoA Statins, Z)-isomer, Modules, Gene Regulatory, human being, bioformation, Procedures, YB, experimental, Benign Neoplasm, Gene, Mevalonic, Network, anon-WO0118547.380, Malignant, froggy, PHAPII, Gyltl1a, netrin, method, reduced, Homo sapiens, Yb, Hek6, (RS)-mevalonate, 14C-labeled, method used in an experiment, E)-isomer, Studies, Hydroxymethylglutaryl-CoA, Mevalonate, neryl pyrophosphate, Gene Regulatory Network, tiny, dmIKKgamma, Hydroxymethylglutaryl-CoA Reductase Inhibitor, IKK[[gamma]], CG2706, Module, ENSMUSG00000074119, Drugs, mevalonic acid anion, Regulatory Network, Circuits, cholesterol synthesis, methods, Malignancy, formation, experimental section, Ebi, EBI, ipp2a2, Tyrosine-protein kinase 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14beta"],"pubmed_abstract_synonyms":["Networks, IPP2A2, dmBest1, Product, determination, Mbp1, Tumor, Hydroxymethylglutaryl CoA Reductase Inhibitors, DmelCG6264, Social Controls, prevention, element, Productivity, 5730420M11Rik, Techniques, Circuit, Roles, Method, Pharmaceutical Product, Transcriptional Networks, Concepts, 17alpha)-cholest-5-en-3-ol, ARB, 3, Inhibitors, myd, prevention and control, Formal Social Controls, 14beta, multicellular organismal biosynthetic process, Transcriptional, DmelCG4063., SET, single-organism biosynthetic process, (3beta, Man (Taxonomy), reference sample, Biology, TAF-I, anabolism, (Z)-isomer, hypoplasia, Cholest-5-en-3beta-ol, Mbp-1, procedures, free, racemic mevalonate, Epicholesterol, (E)-isomer, DmelCG4299, Social, preventive measures, IGAAD, set, lithium salt, Methodological Studies, DmelCG10574, malignant neoplasm, HMG CoA Reductase Inhibitor, medicine, Pharmaceutical, Gene Network, cholesterol anabolism, Role Concepts, Malignancies, Tbl1, TBL1, Tumors, phapii, preventive therapy, CG6264, Process, gyltl1b-b, Modern, StF-IT-1, Procedure, results, Hydroxymethylglutaryl CoA Reductase Inhibitor, Pentanoic acid, Role Concept, Benign, Statin, MDDGA6, mKIAA0609, Role, atomo, Pharmaceutic, Markov Chain, geranyl pyrophosphate, KIAA0609, atome, TU15B, region, farnesyl pyrophosphate, dBest1, Hydroxymethylglutaryl-Coenzyme A Inhibitors, fg, Acid, Epistemology, Hydroxymethylglutaryl CoA, gyltl1b, Element, Gene Circuit, HLA-DR-associated protein II, CG4063, DI-2, 5-dihydroxy-3-methyl-, I-2Dm, atoms, dbest1, mdc1d, expanded, Control, Benign Neoplasms, Methodological, CG4299, Controls, Methodological Study, E-2f, RS-mevalonate, human, E-2g, Statins, Malignant Neoplasms, experimental procedures, I-2PP1, Hydroxymethylglutaryl-CoA Inhibitors, MDC1D, TAF-IBETA, enr, Gene Modules, Regulatory Networks, enlarged, HMG-CoA, Markov Processes, TAF-Ibeta, rac-mevalonate, HMG-CoA Reductase Inhibitor, i2pp2a, Regulation, HMG-CoA Reductase Inhibitors, HMG-CoA Statins, humans, Z)-isomer, big, Regulations, Modules, Gene Regulatory, (+-)-mevalonate, human being, bioformation, Procedures, Tb11, experimental, Processes, Neoplasms, Benign Neoplasm, Gene, Mevalonic, Network, biosynthesis, HMG CoA Reductase, anon-WO0118547.380, Malignant, (E, froggy, PHAPII, Gyltl1a, Human, FBXW4, large, method, reduced, Homo sapiens, Cholest-5-en-3-ol (3beta)-, (RS)-mevalonate, 14C-labeled, method used in an experiment, E)-isomer, Studies, Gene Products, Hydroxymethylglutaryl-CoA, Transcriptional Network, (Z, Mevalonate, neryl pyrophosphate, Gene Regulatory Network, tiny, Hydroxymethylglutaryl-CoA Reductase Inhibitor, Technique, Man, Module, Drugs, mevalonic acid anion, Chain, Regulatory Network, Circuits, cholesterol synthesis, methods, Malignancy, formation, MDDGB6, VMD2, experimental section, Ebi, EBI, ipp2a2, cholesterol formation, inhibiteur, 2pp2a, LARGE, BMD, CG10574, synthesis, Study, Neoplasias, BPFD#36, Reductase Inhibitor, Reductase Inhibitors, Markov, drugs, 2PP2A, Hydroxymethylglutaryl-CoA Reductase, inhibidor, taf-ibeta, Hydroxymethylglutaryl-Coenzyme A, great, dSET, dSet, site, farnesylpyrophosphate, Preparation, elements, HMG CoA Reductase Inhibitors, atom, Controlled, Cancer, Pharmaceuticals, RP50, small, inhibitors, Products, Controlling, farnesyl diphosphate, Malignant Neoplasm, Formal Social Control, synthesize, Proteins, igaad, inhibitor, SMAP55, Markov Process, Medications, Gene Circuits, group, Concept, HMG-CoA reductase inhibitors, MT, I-2PP2A, Social Control, chemical analysis, Dm I-2, Protein, Systems, I2PP2A, Neoplasm, atomus, cholesterol biosynthesis, background, techniques, Cholesterin, Data Base, Dbest, primary cancer, Chains, underdeveloped, Gene Module, ensemble, Pharmaceutic Preparations, best, prophylaxis, Cancers, malignant tumor, introduction, l(2)k16213, plan specification, Protein Gene Products, Drug, Gene Proteins, dSET/TAF-Ibeta, Preparations, 2610030F17Rik, HMG-CoA Reductase, HMG CoA, control, Gene Networks, Modern Man, regulation, assay, AA407739, Hydroxymethylglutaryl Coenzyme A, Pharmaceutical Products, SRE binding, BEST, Neoplasia, methodology, Pharmaceutical Preparation"],"additional_accession":[]},"is_claimable":false,"name":"Kervizic2008_Cholesterol_SREBP","description":"\n      \n        Model of cholesterol regulation (with Boolean Formulae) (2008)\n        This model is described in      \n        Dynamical modeling of the cholesterol regulatory pathway with Boolean networks\n          ; \nGwenael Kervizic and Laurent Corcos, BMC Systems Biology 2008, 2:99 doi:      10.1186/1752-0509-2-99\n        \n        Abstract:\n        \n        Background:\n          Qualitative dynamics of small gene regulatory networks have been studied in quite some details both with synchronous and asynchronous analysis. However, both methods have their drawbacks: synchronous analysis leads to spurious attractors and asynchronous analysis lacks computational efficiency, which is a problem to simulate large networks. We addressed this question through the analysis of a major biosynthesis pathway. Indeed the cholesterol synthesis pathway plays a pivotal role in dislypidemia and, ultimately, in cancer through intermediates such as mevalonate, farnesyl pyrophosphate and geranyl geranyl pyrophosphate, but no dynamic model of this pathway has been proposed until now.      \n        Results:\n          We set up a computational framework to dynamically analyze large biological networks. This framework associates a classical and computationally efficient synchronous Boolean analysis with a newly introduced method based on Markov chains, which identifies spurious cycles among the results of the synchronous simulation. Based on this method, we present here the results of the analysis of the cholesterol biosynthesis pathway and its physiological regulation by the Sterol Response Element Binding Proteins (SREBPs), as well as the modeling of the action of statins, inhibitor drugs, on this pathway. The in silico experiments show the blockade of the cholesterol endogenous synthesis by statins and its regulation by SREPBs, in full agreement with the known biochemical features of the pathway.      \n        Conclusion:\n          We believe that the method described here to identify spurious cycles opens new routes to compute large and biologically relevant models, thanks to the computational efficiency of synchronous simulation. Furthermore, to the best of our knowledge, we present here the first dynamic systems biology model of the human cholesterol pathway and several of its key regulatory control elements, hoping it would provide a good basis to perform in silico experiments and confront the resulting properties with published and experimental data. The model of the cholesterol pathway and its regulation, along with Boolean formulae used for simulation are available on our web site      http://Bioinformaticsu613.free.fr\n          . Graphical results of the simulation are also shown online. The SBML model is available in the BioModels database (http://www.ebi.ac.uk/biomodels/).      \n      Curators comment:\n        To make this model valid SBML, we had to put the    BooleanLaws\n        tags in the reactions' annotations into an ancillary namespace called    http://kervizic/BooleanLaws\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":"2009-02-26","publication":"2005-01-01","submission":"2008-12-02"},"accession":"MODEL0568648427","cross_references":{"pubmed":["19025648"],"biomodels__db":["MODEL0568648427"],"go":["GO:0008203"],"taxonomy":["9606"]}}