ArticleJournal of the Royal Society, Interface2016
Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An organic artificial cardiomyocyte.Nature communications · 2026Article
- Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modelling.Royal Society open science · 2023Article
- Cellular heterogeneity and repolarisation across the atria: an in silico study.Medical & biological engineering & computing · 2022Article
- Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.Science advances · 2022Article
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
- Calibration of ionic and cellular cardiac electrophysiology models.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- Mathematical Models of Cancer Cell Plasticity.Journal of oncology · 2019Review
- Bayesian, Likelihood-Free Modelling of Phenotypic Plasticity and Variability in Individuals and Populations.Frontiers in genetics · 2019Article
- From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study.American journal of physiology. Heart and circulatory physiology · 2018Article
- Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.Science advances · 2018Article
- Modelling variability in cardiac electrophysiology: a moment-matching approach.Journal of the Royal Society, Interface · 2017Article
- Humans Vary, So Cardiac Models Should Account for That Too!Frontiers in physiology · 2017Review
- Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia.Progress in biophysics and molecular biology · 2016Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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