BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.mhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.xpphttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743576806?filename=MODEL7743576806.pngprimaryOK200Andrei ZinovyevNon-curatedordinary differential equation modelL2V1https://www.ebi.ac.uk/biomodels/MODEL774357680618854041falseBioModelsSBMLModelsRadulescu2008 NF κB hierarchy ℳ(16,34,46)2008MODEL7743576806Ovidiu Radulescu, Alexander N Gorban, Andrei Zinovyev, Alain LilienbaumOvidiu Radulescu18854041, <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. IRMAR (CNRS UMR 6025), Université de Rennes 1, Rennes, France. ovidiu.radulescu@univ-rennes1.frandrei.zinovyev@curie.frInstitut Curie<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.Robust simplifications of multiscale biochemical networks.Radulescu Ovidiu O, Gorban Alexander N AN, Zinovyev Andrei A, Lilienbaum Alain Ahierarchies, hierarchy, systematics., taxonomyprojections, biochemical pathways, extent, IPP2A2, scale tissue, Metabolic Process, Public Sectors, kappa B Enhancer Binding Protein, PLXN2, NFKB-p50, postnatal development, Metabolic Concepts, Mbp1, growth and development, Measure, Long Term, prevention, 5730420M11Rik, Techniques, Enhancer-Binding Protein, hierarchies, hierarchy, Method, systematics, GRP1, Grp1, SEC, Concepts, Metabolism Concept, Public Enterprise, Phenomenon, Credit Assignment, myd, Effect, prevention and control, treatment, strong, Log-Linear Models, SET, reference sample, Biology, TAF-I, catabolism, PTPSTEP, sec, Public Domains, NF-kappa B, Mbp-1, Weights, metabolic process resulting in cell growth, procedures, generalised, ird, Nuclear Factor Kappab, Nuclear Factor-Kappab, DmelCG4299, KBF1, preventive measures, IGAAD, set, reaction, Ig-EBP-1, Methodological Studies, DmelCG10574, papilla, signaling process, l(2)k08110, NF-kappaB1, disease management, Therapies, biotransformation, s, associated, REL, SIMPLE, Catabolism, mKIAA0463, Long-Term Effects, single organism signaling, GPH, phapii, Therapy, close to, PlexA2, preventive therapy, Assignments, l(3)neo36, Process, gyltl1b-b, completeness, metabolism resulting in cell growth, lamina, Longterm Effect, StF-IT-1, flanges, Linear Model, rel, Procedure, results, Menstruation, AW457381, NFKB-p105, Public Domain, MDDGA6, PIG7, mKIAA0609, Algorithm, shelf, 3.1.3.48, Domains, secretion, Transcription Factor NF kB, KIAA0609, Domain, fg, gyltl1b, HLA-DR-associated protein II, DI-2, p50|p105, Step, CG11628, I-2Dm, shelves, mdc1d, expanded, common, Striatum-enriched protein-tyrosine phosphatase, RELI, Methodological, CG4299, study protocol, Methodological Study, projection, ridge, Treatments, NF kappa B Complex, I-2PP1, NF-kappa-B, MDC1D, Sector, TAF-IBETA, enr, enlarged, STEP, NF kappaB, p50/p105, Linear, TAF-Ibeta, Measures and Weights, NF kB, Linear Regression, i2pp2a, Plxn2, Immunoglobulin Enhancer-Binding Protein, OCT, big, biological signaling, Sectors, NF-kB, GRP1/cytohesin 1, ird4, Procedures, NF-kappaB, p50, Immunoglobulin, Effects, taxonomy, lamellae, Processes, relish, Assignment, number, Copyrights, Metabolic Processes, Transcription Factor, process of organ, Ig EBP 1, presence, PHAPII, froggy, Gyltl1a, CG11633, lamella, cytohesin/GRP1, method, large, Publication, resilient, Metabolism, method used in an experiment, tough, NF-KB1, Studies, Nuclear Factor kappa B, Log-Linear, Metabolism Phenomena, Models, Enterprises, Technique, CG11992, Transcription Factor NF-kB, T6K12_14, Transcription, Longterm, MDDGB6, Selb, NF-kB1, Complex, ipp2a2, l(2)SH2 0323, 2pp2a, Metabolic Concept, Measures, 2810428A13Rik, LARGE, ridges, Long-Term, Public Enterprises, CG10574, Solution, Study, BPFD#36, Abstract, 2PP2A, Neural-specific protein-tyrosine phosphatase, taf-ibeta, great, dSET, dSet, Long-Term Effect, species, Model, Factor-Kappab, Enterprise, laminae, Immunoglobulin Enhancer Binding Protein, Controlled, TP53I7, Controlling, degradation, Regressions, igaad, Log Linear Models, stepk, Rel-p110, AA589422, group, Concept, Metabolic Phenomena, near to, development, Metabolism Concepts, count in organism, Linear Regressions, secret agent, Factor NF-kB, Period, I-2PP2A, Public, Dm I-2, Systems, Long Term Effects, I2PP2A, Phenomena, Nuclear, p105, techniques, background, scales, metabolism, l(2)SH0323, flange, organ process, Data Base, Metabolic Phenomenon, NFkappaB, T6K12.14, CYH1, multicellular organism metabolic process, Log-Linear Model, DmelCG11992, biodegradation, ensemble, Metabolic, Scales, prophylaxis, Credit Assignments, postnatal growth, Credit, NF-kappa B Complex, EBP-1, introduction, signalling, DmelCG11628, Longterm Effects, plan specification, processes, dSET/TAF-Ibeta, 2610030F17Rik, signalling process, Regression, Therapeutic, control, approaches, cardinality, vicinity of, Treatment, Public., AA407739, growth, Rel/NF-kappaB, methodology, Anabolismprojections, biochemical pathways, scale tissue, Metabolic Process, kappa B Enhancer Binding 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, Phenomenon, Credit Assignment, myd, Effect, prevention and control, treatment, strong, Log-Linear Models, reference sample, Biology, catabolism, PTPSTEP, NF-kappa B, Mbp-1, Weights, metabolic process resulting in cell growth, procedures, generalised, ird, Nuclear Factor Kappab, Nuclear Factor-Kappab, KBF1, preventive measures, reaction, Ig-EBP-1, Methodological Studies, papilla, signaling process, l(2)k08110, NF-kappaB1, disease management, Therapies, biotransformation, s, REL, SIMPLE, Catabolism, Long-Term Effects, single organism signaling, GPH, Therapy, close to, preventive therapy, Assignments, l(3)neo36, Process, gyltl1b-b, metabolism resulting in cell growth, lamina, Longterm Effect, flanges, Linear Model, rel, Procedure, results, Menstruation, NFKB-p105, MDDGA6, PIG7, mKIAA0609, Algorithm, shelf, 3.1.3.48, secretion, Transcription Factor NF kB, KIAA0609, fg, gyltl1b, p50|p105, Step, CG11628, shelves, mdc1d, expanded, common, Striatum-enriched protein-tyrosine phosphatase, RELI, Methodological, projection, Methodological Study, ridge, Treatments, NF kappa B Complex, NF-kappa-B, MDC1D, enr, enlarged, STEP, NF kappaB, p50/p105, Linear, Measures and Weights, NF kB, Linear Regression, Immunoglobulin Enhancer-Binding Protein, big, biological signaling, NF-kB, GRP1/cytohesin 1, ird4, Procedures, Systems., NF-kappaB, p50, Immunoglobulin, Effects, taxonomy, lamellae, Processes, relish, Assignment, number, Metabolic Processes, Transcription Factor, process of organ, Ig EBP 1, presence, froggy, Gyltl1a, CG11633, lamella, cytohesin/GRP1, large, resilient, Metabolism, tough, NF-KB1, Studies, Nuclear Factor kappa B, Log-Linear, Metabolism Phenomena, Models, Technique, CG11992, Transcription Factor NF-kB, Transcription, Longterm, MDDGB6, NF-kB1, Complex, l(2)SH2 0323, Metabolic Concept, Measures, LARGE, ridges, Long-Term, Solution, Study, BPFD#36, Neural-specific protein-tyrosine phosphatase, great, Long-Term Effect, Model, Factor-Kappab, laminae, Immunoglobulin Enhancer Binding 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, flange, organ process, 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, processes, signalling process, Regression, Therapeutic, control, approaches, vicinity of, Treatment, growth, Rel/NF-kappaB, methodology, AnabolismfalseRadulescu2008 - NF-κB hierarchy ℳ(16,34,46) Radulescu2008 - NF-κB hierarchy ℳ(16,34,46) This is a model of NF-κB pathway functioning from hierarchy of models of decreasing complexity, created to demonstrate application of model reduction methods proposed in the associated publication. The name of the model M(x,y,z) should be deciphered as following: x: number of species y: number of reactions z: number of parameters Simulation protocol: the model can be simulated in CellDesigner directly, or in any simulator supporting events. The simulation period should be set up in 40 hours (t=144000 sec). The 'signal' event applies signal to the pathway at the moment t=20 hours=72000 sec. This model is described in the article: Robust simplifications of multiscale biochemical networks. Radulescu O, Gorban AN, Zinovyev A, Lilienbaum A. BMC Syst Biol 2008 Oct; 2(1):86 Abstract: BACKGROUND: 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 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. This model is hosted on BioModels Database and identified by: MODEL7743576806 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . 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 for more information. 2013-03-272005-01-012009-02-27MODEL774357680618854041MODEL7743576806