Statistical Metamodeling and Computer Experiments of Large-scale Cardiac Models
نویسندگان
چکیده
In-vitro experiments encountered many limitations in investigating the detailed mechanisms of pathological variations among cardiac ion channels and cells. It is essential to integrate in-vitro experiments with in-silico studies. However, computer model of ion channels and cardiac myocytes involves greater levels of complexity. Traditional linear and nonlinear optimization methods have encountered many difficulties for model optimization. This paper presents a new statistical metamodeling approach for efficient computer experiments and optimization of Nav channel models. First, we utilize a fractional factorial design to reduce the dimensionality of parametric space. Further, we develop the Gaussian Process (GP) model as a surrogate of expensive and time-consuming computer models and then identify the next best design point that yields the maximal probability of improvement. This process iterates until convergence, and the performance is evaluated and validated with real-world experimental data. Experimental results show the proposed algorithm achieves superior performance in modeling diseased and controlled kinetics of Nav channels. The proposed approach of statistical design of computer experiments is generally extensible to many other disciplines that involve large-scale and computationally expensive models.
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تاریخ انتشار 2015