Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?. (July 2016)

• Record Type: Journal Article
• Authors: Johnstone, Ross H.; Chang, Eugene T.Y.; Bardenet, Rémi; de Boer, Teun P.; Gavaghan, David J.; Pathmanathan, Pras; Clayton, Richard H.; Mirams, Gary R.
• Is Part Of: Journal of molecular and cellular cardiology. Volume 96(2016:Jul.)
• Page Start: 49
• Page End: 62
• Publication Date: 2016-07
• Subjects: AP[D] Action Potential [Duration] -- CMA–ES Covariance Matrix Adaptation–Evolution Strategy -- GP Gaussian Process -- MCMC Markov Chain Monte Carlo -- NLME Non-Linear Mixed Effects -- TP06 the ten Tusscher et al. (2006) [45] action potential model -- UQ Uncertainty Quantification -- Vm trans-membrane Voltage -- VVUQ Verification, Validation & Uncertainty Quantification; Uncertainty quantification -- Cardiac electrophysiology -- Mathematical model -- Probability; Cardiology -- Periodicals; Heart Diseases -- Periodicals; Molecular Biology -- Periodicals; Cardiologie -- Périodiques; Cardiology; Electronic journals; Periodicals; 616.12
• DOI: 10.1016/j.yjmcc.2015.11.018
• Languages: English
• ISSNs: 0022-2828

Abstract

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. Highlights: Uncertainty and variability in action potential models can be quantified. A probabilistic method for inferring maximal current densities is developed and applied. We use this to infer the currents responsible for canine beat-to-beat variability. Emulation of mathematical models provides rich information at low computational cost. The importance of considering uncertainty and variability in future is discussed.