Automated high-throughput screening combined with model-assisted analysis enables quantitative identification of process drivers and trade-offs during extracellular Fab production in Escherichia coli.

Production of Fab antibody fragments with Escherichia coli strains engineered for extracellular product release can reduce downstream processing requirements but often compromises cellular integrity, resulting in increased cell lysis. To investigate the balance between product formation, product release, and cellular robustness, 32 fed-batch cultivation conditions spanning six process parameters were systematically evaluated in automated high-throughput bioreactors. In total, 100 cultivations were performed, and mechanistic modeling was applied to estimate cell lysis, product formation, and product release kinetics. The combined experimental and model-based analysis revealed a pronounced trade-off between productivity and robustness. Conditions yielding high Fab titers showed minimal cell lysis rates but also slow product release, whereas elevated release kinetics were consistently associated with substantial lysis and reduced Fab titers. Induction strength emerged as the primary driver for maximizing Fab titer, while production temperature governed the balance between product release and cell lysis. Compared to the reference condition, Fab titers increased approximately twofold to 730 mg L[Formula: see text] while reducing the cell lysis rate by up to eightfold to near-negligible levels, albeit at the expense of a 2.2-fold lower product release rate. Robustness studies under large-scale-relevant conditions identified moderate substrate heterogeneities as an additional factor enhancing productivity while reducing cell lysis. Furthermore, the extensive dataset enabled refinement of a previously developed product model by introducing an induction-strength-dependent production load and provided mechanistic insight into the relationship between product release and cell lysis. Overall, the study demonstrates how mechanistic modeling enhances the interpretation of automated high-throughput screening experiments by linking observable process responses to underlying physiological behavior and enabling quantitative analysis of complex production trade-offs.