Computational prediction of drug response in short QT syndrome type 1 based on measurements of compound effect in stem cell-derived cardiomyocytes
Fig 4
Illustration of the continuation algorithm used for optimization in the inversion method.
A) The problem is defined by a default model and some data we are trying to invert by finding an optimal model parameterization fitting the data. B) In the continuation algorithm, we seek temporary optimal parameters in M iterations (θ-steps). The objective for each θ-step is gradually changed from the default model to the data we are trying to invert. C) In each θ-step, we look for optimal parameters for the temporary objective by drawing NG random guesses in the vicinity of the optimal parameters from the previous θ-step. For each random guess, we run NNM Nelder-Mead iterations, and from the result, we select the best fit as the new optimal parameters. D) The final parameterization is given by the optimal parameters found in the last θ-step.