Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator

Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult...

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Bibliographic Details
Published in:PloS one 2015-06, Vol.10 (6), p.e0130252-e0130252
Main Authors: Chang, Eugene T Y, Strong, Mark, Clayton, Richard H
Format: Article
Language:English
Subjects:
Action potential
Action Potentials - physiology
Analysis
Animals
Bayes Theorem
Bayesian analysis
Cardiology
Cell culture
Computer science
Emulators
Experiments
Gaussian process
Gaussian processes
Heart
Heart Conduction System - cytology
Heart Conduction System - physiology
Heart diseases
Humans
Mathematical models
Models, Cardiovascular
Mutation
Myocardium - cytology
Myocardium - metabolism
Normal distribution
Ordinary differential equations
Parameter uncertainty
Physiological aspects
Physiology
Restitution
Sensitivity analysis
Star & galaxy formation
Statistical models
Uncertainty analysis
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Summary:Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model runs. In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators. Using this approach we examined how eight outputs describing the action potential shape and action potential duration restitution depend on six inputs, which we selected to be the maximum conductances in the Luo-Rudy 1991 model. We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents. We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0130252