Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells
Figure 2
Schematic of input, output and regression matrix structures.
Randomly-varied model parameters are collected in an input matrix X with dimensions n, corresponding to the number of trials, by p, corresponding to the number of parameters. Simulation results define m outputs that are collected in the output matrix Y, with dimensions an n×m. Regression matrix B, with dimensions p×m, can be used to predict Y from X, the so-called “forward problem.” If m = n, and the outputs are linearly independent, then B can be inverted, and YB−1 should be a good approximation of X. This is our strategy for addressing the “reverse problem.”