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dc.contributor.authorZurauskienė, Justinaen
dc.contributor.authorKirk, Paulen
dc.contributor.authorThorne, Thomasen
dc.contributor.authorPinney, Johnen
dc.contributor.authorStumpf, Michaelen
dc.date.accessioned2018-07-17T08:25:02Z
dc.date.available2018-07-17T08:25:02Z
dc.date.issued2014-07en
dc.identifier.issn1367-4803
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/278151
dc.description.abstractMOTIVATION: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. RESULTS: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. AVAILABILITY AND IMPLEMENTATION: R code is available from the authors upon request.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEscherichia colien
dc.subjectNitrogenen
dc.subjectBayes Theoremen
dc.subjectModels, Biologicalen
dc.subjectMetabolic Networks and Pathwaysen
dc.titleDerivative processes for modelling metabolic fluxes.en
dc.typeArticle
prism.endingPage1898
prism.issueIdentifier13en
prism.publicationDate2014en
prism.publicationNameBioinformatics (Oxford, England)en
prism.startingPage1892
prism.volume30en
dc.identifier.doi10.17863/CAM.25495
dcterms.dateAccepted2014-01-26en
rioxxterms.versionofrecord10.1093/bioinformatics/btu069en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-07en
dc.contributor.orcidKirk, Paul [0000-0002-5931-7489]
dc.contributor.orcidThorne, Thomas [0000-0002-7396-5116]
dc.contributor.orcidStumpf, Michael [0000-0002-3577-1222]
dc.identifier.eissn1367-4811
rioxxterms.typeJournal Article/Reviewen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International