Yang Zhechao, Gao Hao, Smith Godfrey L, Simitev Radostin D
School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom.
School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
PLoS One. 2025 Jul 30;20(7):e0328261. doi: 10.1371/journal.pone.0328261. eCollection 2025.
Mathematical models of cardiac cell electrical activity include numerous parameters, making calibration to experimental data and individual-specific modeling challenging. This study applies Sobol sensitivity analysis, a global variance-decomposition method, to identify the most influential parameters in the Shannon model of rabbit ventricular myocyte action potential (AP). The analysis highlights the background chloride current ([Formula: see text]) as the dominant determinant of AP variability. Additionally, the inward rectifier potassium current ([Formula: see text]), fast/slow delayed rectifier potassium currents (IKr, [Formula: see text]), sodium-calcium exchanger current ([Formula: see text]), the slow component of the transient outward potassium current ([Formula: see text]), and L-type calcium current ([Formula: see text]) significantly affect AP biomarkers, including duration, plateau potential, and resting potential. Exploiting these results, a hierarchical reduction of the model is performed and demonstrates that retaining only six key parameters can capture sufficiently well individual biomarkers, with a coefficient of determination exceeding 0.9 for selected cases. These findings improve the utility of the Shannon model for personalized simulations, aiding applications like digital twins and drug response predictions in biomedical research.
心脏细胞电活动的数学模型包含众多参数,这使得根据实验数据进行校准以及进行个体特异性建模具有挑战性。本研究应用Sobol灵敏度分析(一种全局方差分解方法)来确定兔心室肌细胞动作电位(AP)的香农模型中最具影响力的参数。分析突出了背景氯离子电流([公式:见原文])是AP变异性的主要决定因素。此外,内向整流钾电流([公式:见原文])、快速/慢速延迟整流钾电流(IKr,[公式:见原文])、钠钙交换电流([公式:见原文])、瞬时外向钾电流的慢成分([公式:见原文])和L型钙电流([公式:见原文])对AP生物标志物有显著影响,包括持续时间、平台电位和静息电位。利用这些结果,对模型进行了分层简化,结果表明仅保留六个关键参数就能充分捕捉个体生物标志物,在选定案例中决定系数超过0.9。这些发现提高了香农模型在个性化模拟中的实用性,有助于生物医学研究中的数字孪生和药物反应预测等应用。