Model-Driven Monitoring and Parameter-Based Development of an Escherichia coli Fed-Batch Process for Production of a...

Maintaining consistent cellular physiology during bioprocess scale-up or scale-down is key to developing robust processes. However, the reliability of scale-down bioreactor systems remains debated. Although the calibration of physiological parameters in mechanistic models across scales has been proposed as a scale-down strategy, few studies have critically evaluated this approach. In this study, model parameter behavior was investigated across five scales using an Escherichia coli fed-batch process for extracellular Fab fragment production. A macro-kinetic model of aerobic growth and overflow metabolism was extended with equations for product formation and release. Model-based cross-scale analysis demonstrated that scale-down from a 30 L pilot scale to 15 mL mini-bioreactors was feasible, as indicated by comparable growth-related parameters during the batch phase. However, the 15 mL system was limited to pulse-based feeding, whereas continuous feeding was used at the reference scales and could also be applied at the remaining cultivation scales. Pulse-based feeding was identified as the main factor preventing full alignment across scales, as it induced physiological changes that particularly reduced cell lysis and improved productivity. These effects were confirmed at the 150 mL scale. Furthermore, adaptations of milliliter-scale process conditions, particularly reduced IPTG concentrations used to maintain a constant IPTG-to-biomass ratio, substantially affected production performance by doubling the specific product yield and enabling more sustained product formation. These findings emphasize that consistent scale transfer also requires a detailed understanding of how process input parameters affect process performance. Overall, this study demonstrates that model-based evaluation enables physiology-informed comparison of bioprocesses across scales and supports identification of process variables critical for knowledge-driven scale-down and robust bioprocess development.