title: Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.
abstract: Between-subject and within-subject variability is ubiquitous in biology and physiology, and understanding and dealing with this is one of the biggest challenges in medicine. At the same time, it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency for SMC that produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block. Finally, we show the performance of our approach on a complex atrial electrophysiological model, namely the Courtemanche-Ramirez-Nattel model.
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia c.drovandi@qut.edu.au.
authors: C C
authors: 0000-0001-9222-8763
authors: CC
authors: Drovandi
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia.
authors: N
authors: N
authors: Cusimano
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia.
authors: S
authors: 0000-0001-6893-5994
authors: S
authors: Psaltis
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia.
authors: B A J
authors: BA
authors: Lawson
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia.
authors: A N
authors: AN
authors: Pettitt
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia.
authors: P
authors: 0000-0002-6612-3084
authors: P
authors: Burrage
authors: ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Queensland 4000, Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, Victoria 3010, Australia Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
authors: K
authors: K
authors: Burrage
mesh_terms: D003198:Computer Simulation; D022062:Electrophysiologic Techniques, Cardiac; D006801:Humans; D008955:Models, Cardiovascular; D009010:Monte Carlo Method; D015588:Observer Variation; D015983:Selection Bias
keywords: Beeler–Reuter cell model; Latin hypercube sampling; approximate Bayesian computation; cardiac electrophysiology; population of models; sequential Monte Carlo