Evaluating the predictive accuracy of ion-channel models using data from multiple experimental designs.

Joseph G Shuttleworth, Chon Lok Lei, Monique J Windley, Adam P Hill, Simon P Preston, Gary R Mirams
Author Information
  1. Joseph G Shuttleworth: Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK. ORCID
  2. Chon Lok Lei: Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, People's Republic of China. ORCID
  3. Monique J Windley: Computational Cardiology Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia. ORCID
  4. Adam P Hill: Computational Cardiology Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia. ORCID
  5. Simon P Preston: Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK. ORCID
  6. Gary R Mirams: Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK. ORCID

Abstract

Mathematical models are increasingly being relied upon to provide quantitatively accurate predictions of cardiac electrophysiology. Many such models concern the behaviour of particular subcellular components (namely, ion channels) which, together, allow the propagation of electrical signals through heart-muscle tissue; that is, the firing of action potentials. In particular, I, a voltage-sensitive potassium ion-channel current, is of interest owing to the central pore of its primary protein having a propensity to blockage by various small molecules. We use newly collected data obtained from an ensemble of voltage-clamp experiment designs (protocols) to validate the predictive accuracy of various dynamical models of I. To do this, we fit models to each protocol individually and quantify the error in the resultant model predictions for other protocols. This allows the comparison of predictive accuracy for I models under a diverse collection of previously unexplored dynamics. Our results highlight heterogeneity between parameter estimates obtained from different cells, suggesting the presence of latent effects not yet accounted for in our models. This heterogeneity has a significant effect on our parameter estimates and suggests routes for model improvement.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Keywords

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Grants

  1. /Australian Research Council
  2. /EPSRC
  3. /Fundo para o Desenvolvimento das Ci��ncias e da Tecnologia
  4. /Wellcome Trust
  5. /Universidade de Macau

MeSH Term

Action Potentials
Ion Channels
Animals
Humans
Patch-Clamp Techniques

Chemicals

Ion Channels

Word Cloud

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