Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia

Abstract

Computational modelling, combined with experimental investigations, is a powerful method for investigating complex cardiac electrophysiological behaviour. The use of rabbit-specific models, due to the similarities of cardiac electrophysiology in this species with human, is especially prevalent. In this paper, we first briefly review rabbit-specific computational modelling of ventricular cell electrophysiology, multi-cellular simulations including cellular heterogeneity, and acute ischemia. This mini-review is followed by an original computational investigation of variability in the electrophysiological response of two experimentally-calibrated populations of rabbit-specific ventricular myocyte action potential models to acute ischemia. We performed a systematic exploration of the response of the model populations to varying degrees of ischemia and individual ischemic parameters, to investigate their individual and combined effects on action potential duration and refractoriness. This revealed complex interactions between model population variability and ischemic factors, which combined to enhance variability during ischemia. This represents an important step towards an improved understanding of the role that physiological variability may play in electrophysiological alterations during acute ischemia.

Keywords: Cardiac cell electrophysiology; Computational modelling; Ischemia; Populations of models; Rabbit; Variability.

Fig. 1

Effect of increasing ischemic severity on action potential variability. Action potentials from the Shannon (A) and Mahajan (A) model populations at different points during the first 10 min of ischemia, as well as histograms showing the distribution of action potential duration (APD₉₀) in the Shannon (C) and Mahajan (D) populations. Arrows indicate mean values of APD₉₀, with bars presenting standard deviation.

Fig. 2

Effect of change in individual ischemic parameters on action potential duration. Histograms showing the percentage decrease of action potential duration (APD₉₀) in the Shannon (A) and Mahajan (B) populations resulting from a change in individual ischemic parameters to their 10 min value. For each, the other ischemic parameters are set to all combinations of tested values. Arrows indicate mean values of APD₉₀, with bars presenting standard deviation.

Fig. 3

Effect of increasing ATP-inactivated potassium current (I_K,ATP) conductance on action potential variability during control. Action potentials from the Shannon (A) and Mahajan (B) model populations with different levels of I_K,ATP activation (f_K-ATP) in control conditions, as well as histograms showing the distribution of action potential duration (APD₉₀) in the Shannon (C) and Mahajan (D) populations. Arrows indicate mean values of APD₉₀, with bars presenting standard deviation.

Fig. 4

Effect of increasing extracellular potassium concentration ([K⁺]_o) on action potential variability during control. Action potentials from the Shannon (A) and Mahajan (B) model populations with different levels of I_K,ATP activation (f_K-ATP) in control conditions, as well as histograms showing the distribution of action potential duration (APD₉₀) in the Shannon (C) and Mahajan (D) populations. Arrows indicate mean values of APD₉₀, with bars presenting standard deviation.

Fig. 5

Relationship between action potential duration (APD₉₀) and effective refractory period (ERP) with increasing ischemic severity. Scatter plots of APD₉₀versus ERP at various stages of ischemia, and with increasing ATP-inactivated K⁺ current conductance (f_K-ATP), for the Shannon (A) and Mahajan (B) model populations, along with histograms showing the distributions of APD₉₀ and ERP.

Fig. 6

Evolution of action potential duration (APD₉₀) and effective refractory period (ERP) with increasing ischemic severity for each combination of currents’ conductance in the model populations. Column plots of normalised APD₉₀ and ERP (divided into multiple bars of 100 rows each) with all combinations of currents’ conductance (each row represents a single combination) in the Shannon (A,C) and Mahajan (B,D) model populations at different points during the first 10 min of ischemia (each column represents a different time point). The models are arranged in sequence of APD₉₀ value under control conditions.

Fig. 7

Sensitivity of action potential duration (APD₉₀) and effective refractory period (ERP) to changes in currents’ conductance during control and ischemia in the Shannon population. Clutter-based dimension reordering images of APD₉₀ and ERP with all viable combinations of currents’ conductance in the Shannon model population in control conditions (A,C) and at 10 min of ischemia (B,D).