Optimizing the strain engineering process for industrial-scale production of bio-based molecules

Area of Science:

• Biotechnology
• Synthetic Biology
• Metabolic Engineering

Background:

• The growing bioeconomy relies on efficient biomanufacturing of high-performing microbial strains.
• The Design-Build-Test-Learn (DBTL) framework is a standard approach for strain engineering.
• Current DBTL cycles face limitations in time and cost-effectiveness despite throughput improvements.

Purpose of the Study:

• To propose an integrated approach for optimizing the entire DBTL cycle in strain engineering.
• To address challenges in industrial strain development for cost and time reduction.
• To leverage advances in computational design, high-throughput methods, and machine learning.

Main Methods:

• Integrating all four stages of the DBTL cycle.
• Utilizing computational design tools for strain development.
• Employing high-throughput genome engineering and phenotyping.
• Applying machine learning for predicting strain scale-up performance.

Main Results:

• Demonstrated successful strain engineering for producing heterologous proteins, amino acids, and small molecules.
• Showcased improvements in strain tolerance and fitness.
• Highlighted methods for de-risking the scale-up of industrial strains.

Conclusions:

• An integrated DBTL approach is crucial for radical reductions in strain development time and cost.
• Leveraging computational and machine learning tools enhances the efficiency of biomanufacturing.
• Optimizing the whole cycle is essential for the success of the future bioeconomy.