Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells

ABSTRACT

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.

Keywords:

• Calibration
• Computer experiment
• Functional response
• High-dimensional parameters
• Simulation experiment

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Acknowledgment

We thank two anonymous reviewers and an anonymous associate editor for their very helpful comments in improving this work. We also thank Dr. Andrew R. Ednie and Dr. Eric S. Bennett for sharing their datasets.

Funding

The authors acknowledge the support from the National Science Foundation (CMMI-1266331, CMMI-1266025).

Funding

The authors acknowledge the support from the National Science Foundation (CMMI-1266331, CMMI-1266025).