An experimental verification and validation of the neural network parameter function approach to modeling dynamic systems is provided. The neural-network parameter-function modeling scheme utilizes some a priori process knowledge (usually material balances) and experimental data to develop a dynamic neural-network model. Other models based on fundamental principles are also developed. The experimental system under consideration is the host-vector system Escherichia coli D1210 and plasmid pSD8, which produces the foreign protein beta-galactosidase under the effect of the inducer IPTG. Optimal operational conditions are derived and the neural-network-based model is shown to better predict the dynamics and optimum for protein production than the proposed fundamental kinetic models.