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Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?

Johnstone, Ross H.; Chang, Eugene T.Y.; Bardenet, R�mi; de Boer, Teun P.; Gavaghan, David J.; Pathmanathan, Pras; Clayton, Richard H.; Mirams, Gary R.

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Authors

Ross H. Johnstone

Eugene T.Y. Chang

R�mi Bardenet

Teun P. de Boer

David J. Gavaghan

Pras Pathmanathan

Richard H. Clayton



Abstract

Cardiac electrophysiology models have been developed for over 50 years, and now include detailed descriptions of individual ion currents and sub-cellular calcium handling. It is commonly accepted that there are many uncertainties in these systems, with quantities such as ion channel kinetics or expression levels being difficult to measure or variable between samples. Until recently, the original approach of describing model parameters using single values has been retained, and consequently the majority of mathematical models in use today provide point predictions, with no associated uncertainty.

In recent years, statistical techniques have been developed and applied in many scientific areas to capture uncertainties in the quantities that determine model behaviour, and to provide a distribution of predictions which accounts for this uncertainty. In this paper we discuss this concept, which is termed uncertainty quantification, and consider how it might be applied to cardiac electrophysiology models.

We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology.

Journal Article Type Article
Acceptance Date Nov 17, 2015
Online Publication Date Dec 2, 2015
Publication Date Jul 1, 2016
Deposit Date May 17, 2018
Publicly Available Date Dec 5, 2018
Electronic ISSN 1095-8584
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 96
Pages 49-62
DOI https://doi.org/10.1016/j.yjmcc.2015.11.018
Keywords Uncertainty quantification; Cardiac electrophysiology; Mathematical model; Probability
Public URL https://nottingham-repository.worktribe.com/output/1117025
Publisher URL https://www.sciencedirect.com/science/article/pii/S0022282815301231?via%3Dihub
PMID 26611884

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