On the trade-off between experimental effort and information content in optimal experimental design for calibrating a predictive microbiology model

In predictive microbiology, dynamic mathematical models are developed to describe microbial evolution under time-varying environmental conditions. Next to an acceptable model structure, reliable parameter values are necessary to obtain valid model predictions. To obtain these accurate estimates of the model parameters, labor-and cost-intensive experiments have to be performed. Optimal experimental design techniques for parameter estimation are beneficial to limit the experimental burden. An important issue in optimal experimental design, included in this work, is the sampling scheme. Recent work illustrates that identifying sampling decisions results in bang-bang control of the weighting function in the Fisher information matrix. A second point addressed in this work is the trade-off between the amount of time an experimenter has available for measurements on the one hand, and information content on the other hand. Recently, multi-objective optimization is applied to several different optimal experimental design criteria, whereas in this paper the workload expressed as when to sample, is considered. The procedure is illustrated through simulations with a case study for the Cardinal Temperature Model with Inflection. The viability of the obtained experiments is assessed by calculating the confidence regions with two different methods: the Fisher information matrix approach and the Monte-Carlo method approach.