Machine learning for metabolic engineering: A review.

Area of Science:

• Biotechnology and Synthetic Biology
• Computational Biology and Bioinformatics

Background:

• Metabolic engineering aims to optimize cellular functions for desired products.
• Predicting and controlling metabolic pathways remains a significant challenge.
• The integration of machine learning offers novel solutions for complex biological systems.

Purpose of the Study:

• To introduce machine learning concepts to metabolic engineers.
• To provide practical guidance on implementing machine learning in metabolic engineering.
• To review current and future applications of machine learning in the field.

Main Methods:

• Review of existing literature on machine learning in metabolic engineering.
• Illustrative examples using omics data for pathway optimization and production improvement.
• Discussion of computational resources, data management, and non-technical considerations.

Main Results:

• Machine learning applications span pathway construction, genetic editing, cell factory testing, and production scale-up.
• The synergy between machine learning and mechanistic models shows significant promise.
• Practical advice is provided for data handling, algorithm selection, and resource management.

Conclusions:

• Machine learning significantly increases the predictability of metabolic engineering.
• The combination of machine learning with mechanistic models is a key future direction.
• This review equips metabolic engineers with the knowledge to leverage machine learning effectively.