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Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.

C C Drovandi¹, N Cusimano², S Psaltis²

¹ARC 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.

Journal of the Royal Society, Interface

|August 12, 2016

Summary

This summary is machine-generated.

A new sequential Monte Carlo (SMC) algorithm efficiently explores biological variability in computational models. This method improves upon traditional sampling techniques for population of models (POM) in electrophysiology.

Keywords:

Beeler–Reuter cell model Latin hypercube sampling approximate Bayesian computation cardiac electrophysiology population of models sequential Monte Carlo

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Area of Science:

Computational Biology and Physiology
Mathematical Modeling in Medicine
Electrophysiology Research

Background:

Biological and physiological variability presents significant challenges in medical research and understanding.
Investigating this variability experimentally is often insufficient.
Population of models (POM) approaches use mathematical models with multiple parameter sets to explore variability but face computational challenges in high-dimensional spaces.

Purpose of the Study:

To develop a novel and efficient algorithm for exploring parameter spaces in complex electrophysiological models.
To address the computational challenges associated with the population of models (POM) approach.
To compare the efficiency and output variability of the new algorithm against existing methods.

Main Methods:

Developed a new algorithm based on sequential Monte Carlo (SMC) within a statistical framework for the POM approach.
Compared the SMC algorithm with Latin hypercube sampling (LHS), a common method for POM generation.
Investigated performance using the Beeler-Reuter cardiac electrophysiological model and a complex atrial electrophysiological model (Courtemanche-Ramirez-Nattel).

Main Results:

The SMC algorithm demonstrated improved computational efficiency compared to LHS.
SMC produced comparable results to LHS for out-of-sample predictions under simulated drug block conditions.
The approach was successfully validated on a complex atrial electrophysiological model.

Conclusions:

The novel SMC-based algorithm offers an efficient and effective method for building populations of models in electrophysiology.
This approach facilitates the exploration of biological variability in complex physiological systems.
The validated algorithm holds promise for advancing computational medicine and drug effect modeling.