Parameter Sensitivity Analysis in Electrophysiological Models Using Multivariable Regression
Parameter Sensitivity Analysis in Electrophysiological Models Using Multivariable Regression
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DOI：
10.1016/j.bpj.2008.10.056
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发表时间：
2009-02-18
影响因子：
3.4
通讯作者：
Sobie, Eric A.
中科院分区：
文献类型：
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作者：
Sobie, Eric A.
心肌细胞电活动和钙信号的计算模型是理解生理学的重要工具。然而，这些模型对参数变化的敏感性往往没有得到很好的理解，因为参数评估可能是一个耗时、乏味的过程。我在这里展示了一种我认为是一种新的方法，可以快速确定参数的变化如何影响产出。在三个心室动作电位模型中，对参数进行随机化，进行重复模拟，计算重要输出，并对收集的结果进行多变量回归。随机参数包括最大离子传输速率和门控可变特性。该程序生成了简化的经验模型，这些模型预测了新的输入参数集所产生的产出。线性回归模型非常准确，尽管机械模型中存在非线性。此外，代表参数敏感性的回归系数是稳健的，即使当参数在很大范围内变化时也是如此。最重要的是，对两个类似模型的并列比较发现了模型行为的根本差异，并揭示了与实验数据既一致又不一致的模型预测。因此，这种新方法显示出作为描述和评估计算模型的工具的前景。总体战略还可能提出将传统量化模型与使用高通量技术获得的大规模数据集相结合的方法。
Computational models of electrical activity and calcium signaling in cardiac myocytes are important tools for understanding physiology. The sensitivity of these models to changes in parameters is often not well-understood, however, because parameter evaluation can be a time-consuming, tedious process. I demonstrate here what I believe is a novel method for rapidly determining how changes in parameters affect outputs. In three models of the ventricular action potential, parameters were randomized, repeated simulations were run, important outputs were calculated, and multivariable regression was performed on the collected results. Random parameters included both maximal rates of ion transport and gating variable characteristics. The procedure generated simplified, empirical models that predicted outputs resulting from new sets of input parameters. The linear regression models were quite accurate, despite nonlinearities in the mechanistic models. Moreover, the regression coefficients, which represent parameter sensitivities, were robust, even when parameters were varied over a wide range. Most importantly, a side-by-side comparison of two similar models identified fundamental differences in model behavior, and revealed model predictions that were both consistent with, and inconsistent with, experimental data. This new method therefore shows promise as a tool for the characterization and assessment of computational models. The general strategy may also suggest methods for integrating traditional quantitative models with large-scale data sets obtained using high-throughput technologies.