Fitting membrane resistance along with action potential shape in cardiac myocytes improves convergence: application of a multi-objective parallel genetic algorithm.
Fitting membrane resistance along with action potential shape in cardiac myocytes improves convergence: application of a multi-objective parallel genetic algorithm.
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DOI：
10.1371/journal.pone.0107984
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Vigmond EJ
中科院分区：
文献类型：
--
作者：
Kaur J;Nygren A;Vigmond EJ
在心脏单细胞离子模型中拟合非线性方程的参数集以再现实验行为是一个耗时的过程。标准程序是调整离子模型中的最大通道电导，以重现在分离细胞中记录的动作电位（AP）。然而，完全不同的参数集可以产生相似的AP。此外，即使在单细胞的情况下具有优异的AP匹配，组织行为也可能非常不同。我们假设这种不确定性可以通过额外拟合膜电阻（Rm）来降低。为了研究Rm的重要性，我们开发了一种遗传算法的方法，它将Rm数据计算在几个点的周期，除了AP形态。性能进行了比较，仅使用AP形态数据的遗传算法。比较了不同方法计算的最优参数集和拟合优度。首先，我们从一个随机参数集开始拟合一个离子模型。接下来，我们将一个离子模型的AP拟合到另一个离子模型的AP。最后，我们将离子模型拟合到实验记录的兔动作电位。将额外的目标（Rm，在几个电压下）添加到AP拟合，导致更好的收敛。通常，与仅使用AP数据相比，在五分之一的世代数中实现了较小的MSE（均方误差，定义为目标AP和待拟合的AP之间的平方误差的平均值）。重要的是，拟合参数的变异性也大大降低，许多参数显示变异性降低了一个数量级。将Rm添加到目标函数提高了拟合的鲁棒性，更好地保留了组织水平行为，并且应该被并入。
Fitting parameter sets of non-linear equations in cardiac single cell ionic models to reproduce experimental behavior is a time consuming process. The standard procedure is to adjust maximum channel conductances in ionic models to reproduce action potentials (APs) recorded in isolated cells. However, vastly different sets of parameters can produce similar APs. Furthermore, even with an excellent AP match in case of single cell, tissue behaviour may be very different. We hypothesize that this uncertainty can be reduced by additionally fitting membrane resistance (Rm). To investigate the importance of Rm, we developed a genetic algorithm approach which incorporated Rm data calculated at a few points in the cycle, in addition to AP morphology. Performance was compared to a genetic algorithm using only AP morphology data. The optimal parameter sets and goodness of fit as computed by the different methods were compared. First, we fit an ionic model to itself, starting from a random parameter set. Next, we fit the AP of one ionic model to that of another. Finally, we fit an ionic model to experimentally recorded rabbit action potentials. Adding the extra objective (Rm, at a few voltages) to the AP fit, lead to much better convergence. Typically, a smaller MSE (mean square error, defined as the average of the squared error between the target AP and AP that is to be fitted) was achieved in one fifth of the number of generations compared to using only AP data. Importantly, the variability in fit parameters was also greatly reduced, with many parameters showing an order of magnitude decrease in variability. Adding Rm to the objective function improves the robustness of fitting, better preserving tissue level behavior, and should be incorporated.
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影响因子：
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作者：
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DOI：
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