{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=manifest.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227_url.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000227?filename=BIOMD0000000227.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Andrei Zinovyev"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000227"],"publication_pubmed":["18854041"],"isPrivate":["false"],"repository":["BioModels"],"non_derived_xrefs":["BIOMD0000000139 biomodels.db BIOMD0000000140 biomodels.db"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Radulescu2008 NFkB hierarchy M 39 65 90"],"publication_year":["2008"],"submissionId":["MODEL7743656488"],"first_author":["Ovidiu Radulescu"],"publication_authors":["Ovidiu Radulescu, Alexander N Gorban, Andrei Zinovyev, Alain Lilienbaum"],"publication":["18854041,\n                            <h4>Background</h4>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.<h4>Results</h4>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway.<h4>Conclusion</h4>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.. null, 2.\n                            IRMAR (CNRS UMR 6025), Université de Rennes 1, Rennes, France. ovidiu.radulescu@univ-rennes1.fr"],"submitter_mail":["andrei.zinovyev@curie.fr"],"submitter_affiliation":["Institut Curie"],"publicationId":["BIOMD0000000227"],"pubmed_abstract":["<h4>Background</h4>Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.<h4>Results</h4>We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway.<h4>Conclusion</h4>Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.","The two-feedback-loop regulatory module of nuclear factor kappaB (NF-kappaB) signaling pathway is modeled by means of ordinary differential equations. The constructed model involves two-compartment kinetics of the activators IkappaB (IKK) and NF-kappaB, the inhibitors A20 and IkappaBalpha, and their complexes. In resting cells, the unphosphorylated IkappaBalpha binds to NF-kappaB and sequesters it in an inactive form in the cytoplasm. In response to extracellular signals such as tumor necrosis factor or interleukin-1, IKK is transformed from its neutral form (IKKn) into its active form (IKKa), a form capable of phosphorylating IkappaBalpha, leading to IkappaBalpha degradation. Degradation of IkappaBalpha releases the main activator NF-kappaB, which then enters the nucleus and triggers transcription of the inhibitors and numerous other genes. The newly synthesized IkappaBalpha leads NF-kappaB out of the nucleus and sequesters it in the cytoplasm, while A20 inhibits IKK converting IKKa into the inactive form (IKKi), a form different from IKKn, no longer capable of phosphorylating IkappaBalpha. After parameter fitting, the proposed model is able to properly reproduce time behavior of all variables for which the data are available: NF-kappaB, cytoplasmic IkappaBalpha, A20 and IkappaBalpha mRNA transcripts, IKK and IKK catalytic activity in both wild-type and A20-deficient cells. The model allows detailed analysis of kinetics of the involved proteins and their complexes and gives the predictions of the possible responses of whole kinetics to the change in the level of a given activator or inhibitor."],"pubmed_title":["Robust simplifications of multiscale biochemical networks.","Mathematical model of NF-kappaB regulatory module."],"pubmed_authors":["Radulescu Ovidiu O, Gorban Alexander N AN, Zinovyev Andrei A, Lilienbaum Alain A","Lipniacki Tomasz T, Paszek Pawel P, Brasier A R Allan R AR, Luxon Bruce B, Kimmel Marek M"],"pubmed_abstract_synonyms":["biochemical pathways, scale tissue, Metabolic Process, kappa B Enhancer Binding Protein, single-organism developmental process, acetylglucosaminyltransferase-like protein, NFKB-p50, postnatal development, Metabolic Concepts, Mbp1, growth and development, Measure, Long Term, prevention, Techniques, Enhancer-Binding Protein, hierarchies, hierarchy, Method, systematics, GRP1, Grp1, Concepts, Metabolism Concept, 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Protein, Controlled, TP53I7, Controlling, degradation, Regressions, Log Linear Models, stepk, Rel-p110, Concept, Metabolic Phenomena, near to, development, Metabolism Concepts, count in organism, Linear Regressions, Factor NF-kB, Period, Systems, Long Term Effects, Phenomena, Nuclear, p105, background, techniques, scales, metabolism, l(2)SH0323, Metabolic Phenomenon, NFkappaB, CYH1, multicellular organism metabolic process, Log-Linear Model, DmelCG11992, biodegradation, Metabolic, Scales, prophylaxis, Credit Assignments, postnatal growth, Credit, NF-kappa B Complex, EBP-1, introduction, signalling, DmelCG11628, Longterm Effects, signalling process, like-glycosyltransferase, Regression, Therapeutic, control, approaches, vicinity of, Treatment, growth, Rel/NF-kappaB, glycosyltransferase-like protein LARGE1, methodology, Anabolism"],"description_synonyms":["biochemical pathways, scale tissue, IPP2A2, Metabolic Process, kappa B Enhancer Binding Protein, single-organism developmental process, acetylglucosaminyltransferase-like protein, NFKB-p50, postnatal development, AUTSX5, Metabolic Concepts, Mbp1, growth and development, NOVH, Measure, CCN3, QM, Long Term, prevention, 5730420M11Rik, Techniques, Enhancer-Binding Protein, hierarchies, hierarchy, Method, systematics, GRP1, Grp1, SEC, Concepts, Metabolism Concept, Phenomenon, Credit Assignment, myd, Effect, prevention and control, fs(1)M104, treatment, strong, Log-Linear Models, SET, like-acetylglucosaminyltransferase, reference sample, Biology, TAF-I, catabolism, PTPSTEP, sec, NF-kappa B, Mbp-1, Weights, metabolic process resulting in cell growth, procedures, generalised, ird, Nuclear Factor Kappab, free, Nuclear Factor-Kappab, DmelCG4299, IBP-9, KBF1, preventive measures, reaction, IGAAD, set, Ig-EBP-1, Methodological Studies, acidos nucleicos, DmelCG10574, signaling process, l(2)k08110, NF-kappaB1, disease management, Therapies, biotransformation, s, NOVh, REL, SIMPLE, Catabolism, Long-Term Effects, single 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biological signaling, NF-kB, GRP1/cytohesin 1, ird4, Procedures, YB, NF-kappaB, p50, Immunoglobulin, Effects, taxonomy, Processes, relish, Assignment, number, Copyrights, Metabolic Processes, Transcription Factor, LARGE1, Ig EBP 1, presence, froggy, PHAPII, Gyltl1a, protrusion, CG11633, cytohesin/GRP1, method, acides nucleiques, resilient, Publication, Yb, Metabolism, tough, method used in an experiment, NF-KB1, Studies, Nuclear Factor kappa B, Log-Linear, Metabolism Phenomena, Models, Technique, CG2706, CG11992, Transcription Factor NF-kB, T6K12_14, Transcription, Longterm, MDDGB6, NF-kB1, Complex, Selb, ipp2a2, l(2)SH2 0323, Metabolic Concept, Measures, 2pp2a, LARGE, Long-Term, IGFBP9, CG10574, Solution, Study, BPFD#36, Kiaa4053, Abstract, Neural-specific protein-tyrosine phosphatase, 2PP2A, taf-ibeta, L10, dSET, dSet, Long-Term Effect, species, Model, Factor-Kappab, Immunoglobulin Enhancer Binding Protein, Controlled, TP53I7, Controlling, degradation, acide nucleique, DmelCG2706, 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glycosyltransferase-like protein LARGE1, Anabolism"],"additional_accession":[]},"is_claimable":false,"name":"Radulescu2008_NFkB_hierarchy_M_39_65_90","description":"\n      \n        \n          NFkB model M(39,65,90) - most complex model\n          This is a model of NFkB pathway functioning \nfrom hierarchy of models of decreasing complexity,\ncreated to demonstrate application of model reduction methods \nproposed in\n          \n            Robust simplifications of multiscale biochemical networks.\n\n            \nRadulescu O, Gorban A., Zinovyev A., Lilienbaum. A.\n      BMC Syst Biol2008:2:86\n      18854041,\n      \n            Abstract:\n            \nBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology withpotential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.\n   \n            The models are provided in CellDesigner v3.5\nformat. The name of the model M(x,y,z) should be\ndeciphered as following:\n            x - number of species\ny - number of reactions\nz - number of parameters\n            Simulation protocol:\nThe model can be simulated in CellDesigner\ndirectly, or in any simulator supporting\nevents. The simulation period should be\nset up in 40 hours (t=144000 sec).\nThe 'signal' event applies signal to the\npathway at the moment t=20 hours=72000 sec. This model reproduces Figure 7c (M(39,65,90)) of the publication. \n            For additional information please contact\nAndrei.Zinovyev at curie.fr \n            This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.\n          \n        \n      \n    ","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2009-02-27"},"accession":"BIOMD0000000227","cross_references":{"ensembl":["ENSMUST00000029812","ENSMUST00000025867","ENSMUST00000019997","ENSMUST00000021413"],"kegg__pathway":["mmu04660"],"pubmed":["18854041","15094015"],"biomodels__db":["MODEL7743656488","BIOMD0000000227"],"go":["GO:0007249","GO:0005829","GO:0005634"],"taxonomy":["10090"],"uniprot":["Q04863","Q9Z1E3","P25799","Q04207","Q60769","O88351","Q60680","O88522"]}}