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Linking Discrete and Stochastic Models: The Chemical Master Equation as a Bridge between Process Hitting and Proper Generalized Decomposition

hal.structure.identifierInstitut de Recherche en Communications et en Cybernétique de Nantes [IRCCyN]
hal.structure.identifierInstitut de Recherche en Génie Civil et Mécanique [GeM]
dc.contributor.authorCHACELLOR, Courtney
hal.structure.identifierLaboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
dc.contributor.authorAMMAR, Amine
hal.structure.identifierInstitut de Recherche en Génie Civil et Mécanique [GeM]
dc.contributor.authorCHINESTA, Francisco
hal.structure.identifierInstitut de Recherche en Communications et en Cybernétique de Nantes [IRCCyN]
hal.structure.identifierNational Institute of Informatics [NII]
dc.contributor.authorMAGNIN, Morgan
hal.structure.identifierInstitut de Recherche en Communications et en Cybernétique de Nantes [IRCCyN]
dc.contributor.authorROUX, Olivier
dc.date.accessioned2021-05-14T09:55:25Z
dc.date.available2021-05-14T09:55:25Z
dc.date.issued2013
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/77691
dc.descriptionModeling frameworks bring structure and analysis tools to large and non-intuitive systems but come with certain inherent assumptions and limitations, sometimes to an inhibitive extent. By building bridges in existing models, we can exploit the advantages of each, widening the range of analysis possible for larger, more detailed models of gene regulatory networks. In this paper, we create just such a link between Process Hitting [6,7,8], a recently introduced discrete framework, and the Chemical Master Equation in such a way that allows the application of powerful numerical techniques, namely Proper Generalized Decomposition [1,2,3], to overcome the curse of dimensionality. With these tools in hand, one can exploit the formal analysis of discrete models without sacrificing the ability to obtain a full space state solution, widening the scope of analysis and interpretation possible. As a demonstration of the utility of this methodology, we have applied it here to the p53-mdm2 network [4,5], a widely studied biological regulatory network.
dc.description.abstractEnModeling frameworks bring structure and analysis tools to large and non-intuitive systems but come with certain inherent assumptions and limitations, sometimes to an inhibitive extent. By building bridges in existing models, we can exploit the advantages of each, widening the range of analysis possible for larger, more detailed models of gene regulatory networks. In this paper, we create just such a link between Process Hitting [6,7,8], a recently introduced discrete framework, and the Chemical Master Equation in such a way that allows the application of powerful numerical techniques, namely Proper Generalized Decomposition [1,2,3], to overcome the curse of dimensionality. With these tools in hand, one can exploit the formal analysis of discrete models without sacrificing the ability to obtain a full space state solution, widening the scope of analysis and interpretation possible. As a demonstration of the utility of this methodology, we have applied it here to the p53-mdm2 network [4,5], a widely studied biological regulatory network.
dc.language.isoen
dc.title.enLinking Discrete and Stochastic Models: The Chemical Master Equation as a Bridge between Process Hitting and Proper Generalized Decomposition
dc.typeArticle de revue
dc.identifier.doi10.1007/978-3-642-40708-6_5
dc.subject.halInformatique [cs]/Ingénierie assistée par ordinateur
bordeaux.journalComputational Methods in Systems Biology
bordeaux.page50-63
bordeaux.volume8130
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-01207078
hal.version1
dc.subject.itStochastic models
dc.subject.itChemical master equation
dc.subject.itProcess hitting
dc.subject.itProper generalized decomposition
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01207078v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational%20Methods%20in%20Systems%20Biology&rft.date=2013&rft.volume=8130&rft.spage=50-63&rft.epage=50-63&rft.au=CHACELLOR,%20Courtney&AMMAR,%20Amine&CHINESTA,%20Francisco&MAGNIN,%20Morgan&ROUX,%20Olivier&rft.genre=article


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