Rethinking the loop: toward integrated strain and bioprocess development.

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Biotechnology

Background:

• The Design-Build-Test-Learn (DBTL) cycle accelerates microbial cell factory development via automation.
• Current limitations exist in physiology-aware testing and predictive learning within the DBTL framework.
• High-throughput screening yields shallow datasets, hindering identification of bottlenecks and robustness.

Purpose of the Study:

• To extend the DBTL framework by integrating cell physiology and process constraints.
• To address limitations in current strain engineering and bioprocess development integration.
• To improve the development of robust microbial cell factories for industrial applications.

Main Methods:

• Integrating automated strain construction with production-relevant phenotyping.
• Developing computational models linking genotype, phenotype, and process parameters.
• Treating cell physiology and process constraints as key design variables within the DBTL cycle.

Main Results:

• The proposed framework aims to overcome limitations of shallow datasets from high-throughput screening.
• It seeks to prevent scale-up failures by integrating strain engineering and bioprocess development.
• Enhanced predictive learning and identification of mechanistic bottlenecks are anticipated.

Conclusions:

• Extending the DBTL cycle with integrated physiology and process data is crucial for robust microbial cell factory development.
• This approach promises to accelerate the creation of efficient and industrially viable microbial production systems.
• Bridging the gap between lab-scale engineering and industrial bioprocesses is a key outcome.