Machine Learning for Microbial Cell Factories: Pathway Design, Enzyme Engineering, and Metabolic Regulation

Area of Science:

• Synthetic biology
• Biotechnology
• Metabolic engineering

Background:

• Microbial cell factories offer sustainable routes for producing fuels, chemicals, and therapeutics.
• Development is hindered by challenges in pathway discovery, enzyme optimization, and metabolic regulation.

Purpose of the Study:

• To review recent advancements in microbial engineering driven by artificial intelligence (AI) and machine learning (ML).
• To discuss the shift towards AI-driven frameworks in biomanufacturing.

Main Methods:

• Utilizing AI and ML for predictive pathway design.
• Employing graph neural networks, generative models, and reinforcement learning (RL) for systematic exploration of design spaces.
• Leveraging AI for enhanced protein engineering and dynamic metabolic network regulation.

Main Results:

• AI and ML enable predictive pathway design, improved protein engineering, and dynamic metabolic regulation.
• AI-driven frameworks facilitate systematic exploration of vast biological design spaces with high accuracy and scalability.
• The field is transitioning from experience-guided to data-driven, model-assisted, and autonomous biomanufacturing workflows.

Conclusions:

• AI and ML are critical for overcoming limitations in microbial cell factory development.
• These technologies are paving the way for more efficient and scalable biomanufacturing processes.
• The integration of AI promises to unlock the full potential of microbial platforms for sustainable production.