A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models - Barral, Yann-Stanislas H. M.; Shuttleworth, Joseph G.; Clerx, Michael; Whittaker, Dominic G.; Wang, Ken; Polonchuk, Liudmila; Gavaghan, David J.; Mirams, Gary R. (Frontiers in Physiology, 2022)
Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine membrane voltage and form action potentials (APs). Although this relationship is usually expressed as a differential equation, previous studies have shown it can be rewritten in an algebraic form, allowing direct calculation of membrane voltage. Rewriting in this form requires the introduction of a new parameter, called Gamma_0 in this manuscript, that represents the sum of all charges that influence membrane voltage but are not considered separately in the model. Although several studies have examined the impact of Gamma_0 on long-term stability and drift in model predictions, there has been little examination of its effects on model predictions, particularly when a model is refit to new data. In this study, we illustrate how Gamma_0 affects important physiological properties such as action potential duration restitution, and examine the effects of (in)correctly specifying Gamma_0 during model calibration. We show that, although physiologically plausible, the range of concentrations used in popular models leads to orders of magnitude differences in Gamma_0, which can lead to very different model predictions. In model calibration, we find that using an incorrect value of Gamma_0 can lead to biased estimates of the inferred parameters, but that the predictive power of these models can be restored by fitting Gamma_0 as a separate parameter. These results show the value of making Gamma_0 explicit in model formulations, as it forces modellers and experimenters to consider the effects of uncertainty and potential discrepancy in the intracellular concentrations upon model predictions.