A comparable ranking of all models was obtained utilizing the Akaike Information Criterion ( AIC ) (S6 Fig).

First, the Akaike Information Criterion ( AIC ) is defined as

In this work, we present all rankings with unprocessed -210g(L) values, AIC ranking and LRT for all model structures against the complete model.

While the AIC allows the creation of a complete ranking and therefore a comparison of different candidate models against each other, the LRT provides us with more detailed information regarding the pairwise comparison of a nested model against the null model.

ODE model selection according to the Akaike Information Criterion ( AIC ).

The Akaike Information Criterion ( AIC ) has been utilized to penalize the likelihood.

To find the model version that would best approximate reality given the data and the number of parameters we employed the Akaike Information Criterion ( AIC ) to rank the models [46].

The AIC establishes a relationship between the maximum likelihood and the Kullback-Leibler information, which is a measure for the information lost when approximating reality with a model [62].

The AIC was computed as

To test which model is more suitable to describe the given data, we ranked them using the Akaike Information Criterion ( AIC ) [46].

The values of the best fit and the average over 100 fits are given for the objective value (WRSS), the log-likelihood (ln(L( p ) )) and the Akaike Information Criterion ( AIC ), as defined in Materials and Methods.