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dc.contributor.authorLiepe, Julianeen
dc.contributor.authorKirk, Paulen
dc.contributor.authorFilippi, Sarahen
dc.contributor.authorToni, Tinaen
dc.contributor.authorBarnes, Chris Pen
dc.contributor.authorStumpf, Michael PHen
dc.date.accessioned2019-03-18T12:16:25Z
dc.date.available2019-03-18T12:16:25Z
dc.date.issued2014-02en
dc.identifier.issn1754-2189
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/290615
dc.description.abstractAs modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.subjectBayes Theoremen
dc.subjectSystems Biologyen
dc.subjectModels, Biologicalen
dc.subjectSoftwareen
dc.titleA framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.en
dc.typeArticle
prism.endingPage456
prism.issueIdentifier2en
prism.publicationDate2014en
prism.publicationNameNature protocolsen
prism.startingPage439
prism.volume9en
dc.identifier.doi10.17863/CAM.25496
dcterms.dateAccepted2013-11-05en
rioxxterms.versionofrecord10.1038/nprot.2014.025en
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-02en
dc.contributor.orcidLiepe, Juliane [0000-0003-2515-9707]
dc.contributor.orcidKirk, Paul [0000-0002-5931-7489]
dc.contributor.orcidBarnes, Chris P [0000-0002-9459-1395]
dc.contributor.orcidStumpf, Michael PH [0000-0002-3577-1222]
dc.identifier.eissn1750-2799
rioxxterms.typeJournal Article/Reviewen


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