期刊
JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY
卷 96, 期 -, 页码 49-62出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.yjmcc.2015.11.018
关键词
Uncertainty quantification; Cardiac electrophysiology; Mathematical model; Probability
资金
- Systems Approaches to Biomedical Science Industrial Doctorate Centre studentship [EP/G037280/1]
- UK Engineering and Physical Sciences Research Council [EP/K037145/1]
- Wellcome Trust [101222/Z/13/Z]
- Royal Society [101222/Z/13/Z]
- F. Hoffmann-La Roche AG
- Engineering and Physical Sciences Research Council [1366641, EP/K037145/1, EP/I017909/1] Funding Source: researchfish
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) [NC/K001337/1] Funding Source: researchfish
- Wellcome Trust [101222/A/13/Z] Funding Source: researchfish
- EPSRC [EP/K037145/1, EP/I017909/1] Funding Source: UKRI
Cardiac electrophysiology models have been developed for over 50 years, and now include detailed descriptions of individual ion currents and sub-cellular calcium handling. It is commonly accepted that there are many uncertainties in these systems, with quantities such as ion channel kinetics or expression levels being difficult to measure or variable between samples. Until recently, the original approach of describing model parameters using single values has been retained, and consequently the majority of mathematical models in use today provide point predictions, with no associated uncertainty. In recent years, statistical techniques have been developed and applied in many scientific areas to capture uncertainties in the quantities that determine model behaviour, and to provide a distribution of predictions which accounts for this uncertainty. In this paper we discuss this concept, which is termed uncertainty quantification, and consider how it might be applied to cardiac electrophysiology models. We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology. (C) 2015 The Authors. Published by Elsevier Ltd.
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