The discrimination between the rival models was based on physical criteria, statistical tests and the closeness of fit.
A new procedure to be used in the sequential design of experiments for discriminating among rival models is presented.
If the rival models are not nested then, depending on which model is true, two different Kullback Leibler distances may be defined.
Because new types of data can be obtained in this way a deeper discrimination between rival kinetic models is possible.
There are cases when it is difficult or even impossible to distinguish between rival models due to the incomplete observability of their internal states.
Specifically, we present a framework for defining and designing optimally discriminating experiments, that is, experiments that are the best (in some mathematically defined but practically meaningful way) at discriminating between rival models.
Discrimination among rival mechanistic models is not possible though the rate data seem highly reliable.
Two rival kinetic models were proposed that both describe the kinetics well.
Several rival path models are tested by comparing the goodness-of-fit of each.
When several rival mathematical models are proposed for one and the same process, experimental design techniques are available to design optimal discriminatory experiments.
