Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments
Drovandi, Christopher C., Cusimano, Nicole, Psaltis, Steven, Lawson, Brodie A. J., Pettitt, Anthony N., Burrage, Pamela, & Burrage, Kevin (2016) Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments. Journal of the Royal Society Interface, 13(121), Article no. 20160214.
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.
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|Item Type:||Journal Article|
|Keywords:||cardiac electroyphysiology, population of models, Beeler-Reuter cell model, sequential Monte Carlo, Latin hypercube sampling, Bayesian inference, approximate Bayesian computation|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
|Divisions:||Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2016 The Author(s)|
|Deposited On:||03 Dec 2015 23:43|
|Last Modified:||12 Aug 2016 23:15|
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