BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.xpphttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743631122?filename=MODEL7743631122.mprimaryOK200Andrei ZinovyevNon-curatedordinary differential equation modelL2V1https://www.ebi.ac.uk/biomodels/MODEL774363112218854041falseBioModelsSBMLModelsRadulescu2008 NFkB hierarchy M 34 60 822008MODEL7743631122Ovidiu 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 ANFkB.extent, AW488255, IPP2A2, Sectors, Public Sectors, Procedures, YB, NetrinA, AUTSX5, number, D430049E23Rik, Copyrights, NOVH, CCN3, QM, PHAPII, netrin, 5730420M11Rik, Techniques, method, Yb, Hek6, Method, method used in an experiment, Studies, SEC, Cek6, Public Enterprise, Enterprises, Technique, CG2706, fs(1)M104, ENSMUSG00000074119, T6K12_14, ERP, APUDoma, Erp, SET, Elkh, Biology, TAF-I, Selb, ipp2a2, sec, Public Domains, Tyrosine-protein kinase receptor EPH-2, EK6, 2pp2a, procedures, SAP-2, Sap-2, IGFBP9, Public Enterprises, CG10574, DmelCG4299, IBP-9, Study, neuroendocrine tumour, IGAAD, set, Kiaa4053, 2.7.10.1, 2PP2A, Methodological Studies, Solute carrier family 6 member 2, DmelCG10574, taf-ibeta, L10, Etrp, CT27014, dSET, dSet, NET1, SLC6A5, NAT1, species, netA, NOVh, Enterprise, NET, Net, phapii, Elk, ELK, Ci, C130099E04Rik, completeness, Neuronally-expressed EPH-related tyrosine kinase, DmelCG2706, igaad, EPH tyrosine kinase 2, StF-IT-1, DXS648, SAP2, 9330129L11, Procedure, neuroendocrine tumor, net, group, neuroendocrine neoplasm, presence., Menstruation, count in organism, secret agent, Period, I-2PP2A, Norepinephrine transporter, IGFBP-9, Public, Public Domain, Systems, Dm I-2, I2PP2A, Domains, EPH-like kinase 6, NOV, techniques, PlexA1, Domain, Data Base, T6K12.14, Plxn1, HLA-DR-associated protein II, ensemble, DI-2, NFkB, I-2Dm, nov, Methodological, CG4299, hEK6, study protocol, Methodological Study, Curie, CG18657, mKIAA4053, fs(1)Y[b], plan specification, I-2PP1, dSET/TAF-Ibeta, CURIE, DmelCG18657, 2610030F17Rik, Sector, TAF-IBETA, EPHT2, C130088N23Rik, Manuscripts, cardinality, EG:95B7.8, 2600013D04Rik, TAF-Ibeta, PLXN1, DXS648E, AA407739, i2pp2a, netrin A, methodologyprojections, 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_NFkB_hierarchy_M_34_60_82
NFkB model M(34,60,82)
This is a model of NFkB pathway functioning
from hierarchy of models of decreasing complexity,
created to demonstrate application of model reduction methods
proposed in
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A.
Robust simplifications of multiscale models in
systems biology. Manuscript submitted.
The models are provided in CellDesigner v3.5
format. 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.
For additional information please contact
Andrei.Zinovyev at curie.fr
This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
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for more information.
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..
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.
2009-10-082005-01-012009-02-27MODEL774363112218854041MODEL7743631122GO:003806140674