Reducing complexity and unidentifiability when modelling human atrial cells
This addresses uncertainty in cardiac models for personalized medicine, but it is incremental as it applies existing methods to reduce complexity in specific models.
The study tackled the problem of uncertainty and unidentifiability in complex human atrial cell models by re-calibrating gating kinetics using approximate Bayesian computation, finding that a less complex model for the fast sodium current improved fit to data, reduced parameter uncertainty by providing a measure of it, and increased computational speed.
Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the action potential. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed.