Intelligent host engineering for metabolic flux optimisation in biotechnology

Area of Science:

• Biotechnology
• Synthetic Biology
• Metabolic Engineering

Background:

• Directed protein evolution and host engineering address similar combinatorial challenges in optimizing biological functions.
• Metabolic networks possess spare capacity, allowing for significant flux increases through host engineering.

Purpose of the Study:

• To review genome-wide host engineering tools and strategies for optimizing product fluxes in biotechnological processes.
• To provide insights for making biological systems more predictable through engineering.

Main Methods:

• Exploration of sequence space for protein function optimization.
• Analysis of host engineering strategies targeting transcription and translation.
• Consideration of cellular protein production limits.

Main Results:

• Host engineering can substantially increase metabolic flux due to inherent spare capacity.
• Strategies targeting regulatory processes affecting multiple targets have been developed.
• Increasing catalytic rates (kcat) can be superior to boosting protein expression for optimal outcomes.

Conclusions:

• Host engineering and directed protein evolution are comparable in complexity but differ in practical implementation.
• Predictability in biological systems is a key goal of modern genome-wide engineering.
• For optimal host engineering, focusing on enhancing enzyme efficiency (kcat) is crucial, given cellular constraints on protein production.