BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.mhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL7743315447?filename=MODEL7743315447.xppprimaryOK200Andrei ZinovyevNon-curatedordinary differential equation modelL2V1https://www.ebi.ac.uk/biomodels/MODEL774331544718854041falseBioModelsSBMLModelsRadulescu2008 NFkB hierarchy M 6 10 152008MODEL7743315447Ovidiu 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 AfalseRadulescu2008_NFkB_hierarchy_M_6_10_15
NFkB model M(6,10,15)
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
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2009-10-082005-01-012009-02-27MODEL774331544718854041MODEL7743315447GO:003806140674