Efficient parameterization of cardiac action potential models using a genetic algorithm
Efficient parameterization of cardiac action potential models using a genetic algorithm
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DOI：
10.1063/1.5000354
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发表时间：
2017-09-01
期刊：
影响因子：
2.9
通讯作者：
Cherry, E. M.
中科院分区：
文献类型：
--
作者：
Cairns, Darby I.;Fenton, Flavio H.;Cherry, E. M.
在心肌细胞的数学模型中找到合适的参数值是一项具有挑战性的任务。在这里，我们表明，它是可能的，以获得良好的参数化，在短短的30-40秒时，多达27个参数同时使用遗传算法和两个灵活的现象学模型的心脏动作电位。我们演示了我们的实现工作原理，考虑的情况下，“模型恢复”，我们试图找到参数值，匹配模型衍生的动作电位数据从几个周期长度。我们通过评估获得的参数值、拟合和非拟合周期长度下的动作电位、以及对真实情况的保真度的分叉图以及算法不同运行的一致性来评估性能。我们还将模型拟合到使用微电极实验记录的动作电位并分析性能。我们发现，我们的实现可以有效地获得模型参数化，与拟合过程中包含的底层系统所表现出的动态一致。然而，在良好的参数化中获得的参数值可以表现出显著的可变性，从而引起参数可识别性和敏感性的问题。沿着类似的路线，我们还发现，这两个模型不同的容易获得参数化，再现模型动态准确，最有可能反映了两个模型的参数可识别性的不同水平。出版社：AIP Publishing
Finding appropriate values for parameters in mathematical models of cardiac cells is a challenging task. Here, we show that it is possible to obtain good parameterizations in as little as 30-40 s when as many as 27 parameters are fit simultaneously using a genetic algorithm and two flexible phenomenological models of cardiac action potentials. We demonstrate how our implementation works by considering cases of "model recovery" in which we attempt to find parameter values that match model-derived action potential data from several cycle lengths. We assess performance by evaluating the parameter values obtained, action potentials at fit and non-fit cycle lengths, and bifurcation plots for fidelity to the truth as well as consistency across different runs of the algorithm. We also fit the models to action potentials recorded experimentally using microelectrodes and analyze performance. We find that our implementation can efficiently obtain model parameterizations that are in good agreement with the dynamics exhibited by the underlying systems that are included in the fitting process. However, the parameter values obtained in good parameterizations can exhibit a significant amount of variability, raising issues of parameter identifiability and sensitivity. Along similar lines, we also find that the two models differ in terms of the ease of obtaining parameterizations that reproduce model dynamics accurately, most likely reflecting different levels of parameter identifiability for the two models. Published by AIP Publishing.