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      Considering discrepancy when calibrating a mechanistic electrophysiology model

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          Abstract

          Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions—that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.

          This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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          Most cited references 36

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          Bayesian calibration of computer models

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            Bayesian Forecasting for Complex Systems Using Computer Simulators

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              Learning about physical parameters: the importance of model discrepancy

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                Author and article information

                Journal
                Philos Trans A Math Phys Eng Sci
                Philos Trans A Math Phys Eng Sci
                RSTA
                roypta
                Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
                The Royal Society Publishing
                1364-503X
                1471-2962
                12 June 2020
                25 May 2020
                25 May 2020
                : 378
                : 2173 , Theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’ compiled and edited by Gary R. Mirams, Steven A. Niederer and Richard H. Clayton
                Affiliations
                [1 ]Computational Biology and Health Informatics, Department of Computer Science, University of Oxford , Oxford, UK
                [2 ]MRC Biostatistics Unit, University of Cambridge , Cambridge, UK
                [3 ]Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham , Nottingham, UK
                [4 ]Department of Bioengineering, University of California San Diego , La Jolla, CA, USA
                [5 ]Systems Modeling and Translational Biology , GlaxoSmithKline R&D, Stevenage, UK
                [6 ]ElectroCardioMaths Programme, Centre for Cardiac Engineering, Imperial College London , London, UK
                [7 ]CARIM School for Cardiovascular Diseases, Maastricht University , Maastricht, The Netherlands
                [8 ]Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora , Juiz de Fora, Brazil
                [9 ]Department of Physics and Astronomy, Ghent University , Ghent, Belgium
                [10 ]Laboratory of Computational Biology and Medicine, Ural Federal University , Ekaterinburg, Russia
                [11 ]US Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories , Silver Spring, MD, USA
                [12 ]Department of Biomedical Engineering King’s College London and Alan Turing Institute , London, UK
                [13 ]James T. Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin , Austin, TX, USA
                [14 ]Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield , Sheffield, UK
                [15 ]School of Mathematics and Statistics, University of Sheffield , Sheffield, UK
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4978052.

                Article
                rsta20190349
                10.1098/rsta.2019.0349
                7287333
                32448065
                © 2020 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                Product
                Funding
                Funded by: Wellcome Trust, http://dx.doi.org/10.13039/100004440;
                Award ID: 101222/Z/13/Z
                Award ID: 212203/Z/18/Z
                Funded by: British Heart Foundation, http://dx.doi.org/10.13039/501100000274;
                Award ID: PG/15/59/31621
                Award ID: RE/13/4/30184
                Award ID: SP/18/6/33805
                Funded by: Russian Foundation for Basic Research, http://dx.doi.org/10.13039/501100002261;
                Award ID: 18-29-13008
                Funded by: Engineering and Physical Sciences Research Council, http://dx.doi.org/10.13039/501100000266;
                Award ID: EP/L016044/1
                Award ID: EP/P010741/1
                Award ID: EP/R003645/1
                Award ID: EP/R006768/1
                Award ID: EP/R014604/1
                Categories
                1003
                44
                1008
                6
                119
                175
                1009
                34
                65
                Articles
                Review Article
                Custom metadata
                June 12, 2020

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