Visualization-Based Analysis of Multiparameter Models Using Environment for N-Dimensional Model Analysis

Abstract

We present a methodology, based on N-dimensional computer visualization, for analyzing multiparameter models. This approach originally consisted of three steps: behavior analysis, sensitivity analysis, identifiability analysis. We have now developed a new way of calculating sensitivity based on the statistical measure of the coefficient of variation. Furthermore, we extended the methodology through the addition of an extra step, visual regression. Visual regression allows the user to visualize the process of actual parameter identification and presents a combined, empirical view of the first three steps in a single image. Next we applied this methodology to pulmonary capillary-transport models. Finally, we implemented the model analysis process as a stand-alone program. EN-DIMAN, the resulting software, allows researchers to carry out model analysis in a graphical user interface (GUI)-based environment.