Optimal Experimental Design for Discriminating between Microbial Growth Models as a Function of Suboptimal Temperature

In the field of predictive microbiology, mathematical models play an important role for describing microbial growth, survival and inactivation. Microbial dynamics are often described likewise by different models. However, the model that describes the system in the best way is desired. Optimal experimental design for model discrimination (OED/MD) is an efficient tool for discriminating among rival models.

This dissertation focuses on the use of methods for optimal experiment design for model discrimination between microbial kinetic models, with particular emphasis to secondary models describing microbial kinetics in the suboptimal temperature range.

On the one hand, different methods have been considered and tested for their applicability on the current application domain, i.e., secondary models describing microbial kinetics in the suboptimal temperature range. It appears that the method proposed by Schwaab et al. (2008) and Donckels et al. (2009) would behave better in a real life application. On the other hand, a thorough study has been performed as to define the possibilities of this particular selected method and get an indication of the expected experimental burden, when applied on the discrimination between the models under interest. Given the above aspects the last step includes the smooth and efficient transition from the in silico to the in vivo environment.