Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells

• 成果类型: Article
• 署名作者: Plumlee, Matthew; Joseph, V. Roshan; Yang, Hui
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1119695
• 发表日期: 2016
• 页码: 500-509
• 关键词: computer-model; modulation; EQUATIONS; DESIGN

摘要

Computational modeling is a popular tool to understand a diverse set of complex systems. The output from a computational model depends on a set of parameters that are unknown to the designer, but a modeler can estimate them by collecting physical data. In the described study of the ion channels of ventricular myocytes, the parameter of interest is a function as opposed to a scalar or a set of scalars. This article develops a new modeling strategy to nonparametrically study the functional parameter using Bayesian inference with Gaussian process prior distributions. A new sampling scheme is devised to address this unique problem.