Data from: Reducing complexity and unidentifiability when modelling human atrial cells

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.

心脏建模领域中，心肌细胞动作电位的数学模型日趋复杂，其中调控单个离子通道电流启闭的门控动力学（gating kinetics）更是如此。随着心脏模型朝着服务于精准医疗、辅助临床决策的方向发展，理解模型参数经实验数据校准后得到的估计值所隐含的不确定性，已成为关键课题。本研究采用近似贝叶斯计算（approximate Bayesian computation）方法，针对两类已有的人心房细胞模型中的四种离子通道的门控动力学参数，基于其原始数据集进行重新校准，并通过参数后验分布（posterior distributions）量化不确定性水平。本研究探索了两种降低现有不确定性的路径：其一，将模型重新校准至更为完整的「统一化」数据集；其二，采用参数更少的标准化公式以实现更严格的约束。研究结果显示，使用更完整的数据集并不能消除参数估计中存在的不确定性。而标准化模型——尤其是针对快速钠电流的模型——不仅展现出与实验数据拟合的残差更低的特性，同时伴随参数不确定性的降低与性能的提升。