The electrophysiology of cardiomyocytes is traditionally described with ordinary differential equations, the parameters of which are fitted to experimental data using the least-squares method, however, some physiological processes cannot be described by one model and require a statistical analysis of a population of models. This population and Bayes’ theorem can provide a probability density function for the model parameter values under certain experimental observations. However, the results of Bayesian approaches are fully determined by the model of the studied process. In this preliminary study, we analyze the sensitivity of Majumder2016 model, which describes the neonatal rat atrial cardiomyocyte, to variation of its parameters. In addition, we test several functions translating distance between action potential shapes into probability, that may be useful for Bayes’ approaches that fitting of model parameters to experimental observations.
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