Cardiac ventricular myocyte action potential dynamics are regulated by intricate and nonlinear interactions between the cell transmembrane potential and ionic currents and concentrations. Present technology limits the ability to measure transmembrane potential and multiple ionic currents simultaneously, which narrows the scope of experiments to provide a complete snapshot of the cardiac myocyte state. This limitation presents an obstacle for understanding how perturbations can trigger arrhythmias and more broadly how the myocyte responds to different conditions, such as changes in pacing rate or responses to drug treatment. In this study, we demonstrate that a data-assimilation approach can successfully reconstruct and predict the dynamics of a heterogeneous virtual cardiac ventricular myocyte population in the presence of parameter uncertainty. A population of heterogeneous cardiac ventricular myocytes is generated by varying ionic current conductance parameters, and additional observational uncertainty is mimicked by the addition of Gaussian noise to the transmembrane potential. We demonstrate that the data-assimilation approach accurately reconstructs transmembrane potential, with error less than the magnitude of the noise. Further, the data-assimilation approach successfully estimates the conductances of ionic currents generally with high accuracy and requiring low computational time. As a proof of concept, we apply the data-assimilation approach to reconstruct action potential dynamics from optical mapping experiments in an ex vivo isolated guinea pig heart. Critically, we demonstrate that the ionic conductance parameters estimated from a recording at one pacing frequency can accurately predict action potential dynamics at different rates.

中文翻译：

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使用数据同化重建心室心肌细胞动力学和参数估计


心肌室肌细胞动作电位动力学受细胞跨膜电位与离子电流和浓度之间错综复杂且非线性的相互作用的调节。目前的技术限制了同时测量跨膜电位和多个离子电流的能力，这缩小了提供心肌细胞状态完整快照的实验范围。这种局限性为理解扰动如何引发心律失常以及更广泛地理解肌细胞如何对不同情况做出反应（例如起搏率的变化或对药物治疗的反应）构成了障碍。在这项研究中，我们证明了数据同化方法可以在参数不确定性存在的情况下成功重建和预测异质虚拟心室肌细胞群的动力学。异质性心室肌细胞群是通过改变离子电流电导参数产生的，并且通过向跨膜电位添加高斯噪声来模拟额外的观察不确定性。我们证明数据同化方法准确地重建了跨膜电位，误差小于噪声的大小。此外，数据同化方法成功地估计了离子电流的电导率，通常精度高，计算时间短。作为概念验证，我们应用数据同化方法从离体离体豚鼠心脏的光学映射实验中重建动作电位动力学。至关重要的是，我们证明了从一个起搏频率的记录中估计的离子电导参数可以准确预测不同速率下的动作电位动力学。