Fig. 8
From: Quantifying the relative importance of experimental data points in parameter estimation
Sampling algorithm for evaluating the MAPK module. The black curves show the noisy experimental data. The gray belts show the model predictions based on the acceptable parameters obtained by the sampling algorithm. By comparing the belt width of the second variable XE between the two cost functions, we can see the benefit of the weighted cost function. a The equal-weight cost function generates imbalanced belt width between dynamic regions and flat regions. b The weighted cost function produces a thin belt, meaning that it is able to better constrain the model parameters to reproduce the experimental data