Hierarchical Bayesian Modelling of Variability and Uncertainty in Synthetic Action Potential Traces

There are many sources of uncertainty in the recording and modelling of membrane action potentials (APs) from cardiomyocytes. For example, there are measurement, parameter, and model uncertainties. There is also extrinsic variability between cells, and intrinsic beat-to-beat variability within a single cell. These combined uncertainties and variability make it very difficult to extrapolate predictions from these models, since current AP models have single parameter values and thus produce a single AP trace. We aim to re-parameterise existing AP models to fit experimental data, and to quantify uncertainty associated with ion current densities when measuring and modelling these APs. We then wish to propagate this uncertainty into model predictions, such as ion channel block effected by a pharmaceutical compound. We perform an in silico study using synthetic data generated from different sets of parameters. We then 'forget' these parameter values and re-infer their distributions using hierarchical Markov chain Monte Carlo methods. We find that we can successfully infer the 'correct' distributions for most ion current densities for each AP trace, along with an approximation to the higher-level distribution from which these parameter values were sampled. There is, however, some level of unidentifiability amongst some of the current densities.