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 from the parameter posterior distributions. Two approaches are investigated to reduce the uncertainty present: firstly to re-calibrate the models to a more complete ‘unified’ dataset and, secondly, the use of a standardised formulation with fewer parameters to constrain. The study shows that the use of more complete datasets does not eliminate uncertainty present in parameter estimates. The standardised model, particularly for the fast sodium current, shows reduced residuals from experimental data alongside lower parameter uncertainty and improved performance.

All database files are the raw output from a pyABC calibration using the ion-channel-ABC Python library, as described in the corresponding research paper. Jupyter notebooks are available at the ion-channel-ABC (https://github.com/charleshouston/ion-channel-ABC) within the human-atrial folder describing how each database file was generated and how to analyse the results within.

Jupyter notebooks in the open code repository (https://github.com/charleshouston/ion-channel-ABC) within the human-atrial sub folder facilitate loading the databases and analysis of the results. For more details on how to handle/convert the database files, the reader is directed to documentation for the pyABC Python library (https://pyabc.readthedocs.io/en/latest/).