Although fed‐batch suspension culture of animal cells continues to be of industrial importance for the large scale production of pharmaceutical products, existing control concepts are still insufficient. Changes in cell metabolism during cultivation and between similar cultivations, the complexity of the cell metabolism, and the lack of on‐line state variables restrict the transfer of available control strategies established in bioprocess engineering. A process control strategy designed to achieve optimized process control must account for all these difficulties and fit sophisticated requirements toward adaptability and flexibility. The combination of a fed‐batch process and an Open‐Loop‐Feedback‐Optimal (OLFO) control provides a new approach for cell culture process control that couples an efficient cultivation concept to a capable process control strategy. The application of an adaptive, model‐based OLFO controller to a hybridoma cultivation and experimental results are presented.

For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model‐assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high‐quality data is available, and description of inference uncertainty, providing, apart from a “best fit”‐prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch‐to‐batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.