Cybernetic models of bioreactors are appealing due to their capacity to account for regulatory mechanisms in cell metabolism by modeling the synthesis of enzymes and their activities. For a given objective of interest, experimental data used to fit the cybernetic model parameters should be maximally informative. To excite purposefully the most relevant metabolic pathways, a dynamic experiment is designed by accounting for the sensitivity of the chosen objective to time-varying operating conditions. In this work, the bioreactor feeding profile and sampling times are designed to maximize the information content. A Bayesian optimization approach is proposed to solve the resulting mathematical program. As a case study, biomass production is used as the objective to be maximized in fed-batch cultivation of Saccharomyces cerevisiae growing on glucose as a carbon source. Experimental results demonstrate that the proposed approach helps to iteratively improve a cybernetic model by designing experiments that maximize the information content. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.