Chang, E.T.Y., Coveney, S. and Clayton, R.H. orcid.org/0000-0002-8438-7518 (2017) Variance Based Sensitivity Analysis of IKrIKr in a Model of the Human Atrial Action Potential Using Gaussian Process Emulators. In: Pop, M. and Wright, G., (eds.) Functional Imaging and Modelling of the Heart 2017, Proceedings. 9th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2017 , June 11-13, 2017, Toronto, ON, Canada. Lecture Notes in Computer Science, 10263 (10263). Springer, Cham , pp. 249-259. ISBN 9783319594477
Abstract
Cardiac cell models have become valuable research tools, but biophysically detailed models embed large numbers of parameters, which must be fitted from experimental data. The provenance of these parameters can be difficult to establish, and so it is important to understand how parameter values influence model behaviour. In this study we examined how model parameters influence the repolarising current I Kr in the Courtemenache-Ramirez-Nattel model of the human atrial action potential. We used a statistical approach in which Gaussian processes (GP) are used to emulate the model outputs. A GP emulator can treat model inputs and outputs as uncertain, and so can be used to directly calculate sensitivity indices. We found that 3 of the 10 parameters influencing I Kr had a strong influence on AP D 70 , AP D 90 , and Dome V m . These three parameters scale the magnitude of the I Kr gating variable time constant and the voltage dependence of the steady state activation curve, and these mechanisms act to modify the amplitude of I Kr during repolarisation. This study highlights the potential value of statistical approaches for investigating cardiac models, and that uncertainties or errors in parameters resulting from attempts to fit experimental data during model development can ultimately affect model behaviour.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper subsequently published in LNCS. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/K037145/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jul 2017 15:22 |
Last Modified: | 27 Nov 2017 14:20 |
Published Version: | https://doi.org/10.1007/978-3-319-59448-4_24 |
Status: | Published |
Publisher: | Springer, Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-59448-4_24 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118258 |