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dc.contributor.authorLawson, B.A.J.
dc.contributor.authorDrovandi, C.C.
dc.contributor.authorCusimano, N. 
dc.contributor.authorBurrage, P.
dc.contributor.authorRodriguez, B.
dc.contributor.authorBurrage, K.
dc.date.accessioned2018-02-07T10:23:24Z
dc.date.available2018-02-07T10:23:24Z
dc.date.issued2018-01-10
dc.identifier.urihttp://hdl.handle.net/20.500.11824/766
dc.description.abstractThe understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a singlemathematicalmodel butwith different parameter values, in response to experimental data. To date, this calibration has been largely limited to selectingmodels that produce outputs that fallwithin the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set.We demonstrate our technique using a data set from a cardiac electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Not only does our approach accurately capture the variability inherent in the experimental population, but we also demonstrate how the POMs that it produces may be used to extract additional information from the data used for calibration, including improved identification of the differences underlying stratified data.We also show how our approach allows different hypotheses regarding the variability in complex systems to be quantitatively compared.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectpopulation of modelsen_US
dc.subjectatrial electrophysiologyen_US
dc.subjectmathematical modellingen_US
dc.subjectvariabilityen_US
dc.subjectcalibration against data distributionen_US
dc.titleUnlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiologyen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1126/sciadv.1701676
dc.relation.publisherversionhttp://advances.sciencemag.org/content/4/1/e1701676en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDES/1PE/MTM2015-69992-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2014-2017en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleScience Advancesen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España