Parameter estimation and model selection results for conversion process with rather homogeneous subpopulations.

For both scenarios (homogeneous subpopulations and heterogeneous subpopulations) four different model hypothesis (H1: no subpopulations; H2: different levels of activatability, ; H3: different basal activation rates, ; and H4: different deactivation rates, ) were tested using three models each, differing in the distribution assumption (normal vs. log-normal) and the ODE constrained properties (subpopulation mean vs. subpopulation median). The resulting 12 ODE-MMs were fitted to the experimental data using multi-start local optimisation (accuracy: 10 digits). The plausibility of models has been evaluated using the Bayesian information criterion (BIC) and models were rejected if [85]. For both scenarios, ODE-MM unraveled the true underlying population structure (different values in the subpopulations) with high significance.