Integrates strain engineering with bioprocess development to enhance efficiency and prevent scale-up failures.
The Design-Build-Test-Learn (DBTL) cycle forms the basis of modern strain engineering and has accelerated the development of microbial cell factories through increasing automation. Despite significant advances in design and build capabilities, physiology-aware testing and predictive learning remain limited. High-throughput screenings often generate large but shallow datasets that cannot identify mechanistic bottlenecks or ensure robustness under industrial conditions. Furthermore, strain engineering and bioprocess development are frequently treated as sequential rather than integrated activities, leading to scale-up failures and costly late-stage corrections. We propose extending the DBTL framework by treating cell physiology and process constraints as key design variables and integrating automated strain construction, production-relevant phenotyping, and computational models linking genotype, phenotype, and process parameters. • Automated strain engineering advances rapidly, but physiological understanding lags. • Decoupling DBTL from bioprocess development creates a scale-up blind spot. • An integrated genotype-phenotype-process framework is needed.
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Stoltmann et al. (2026) studied this question.
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