Machine learning for metabolic pathway optimization: A review.

Area of Science:

• Biotechnology
• Synthetic Biology
• Metabolic Engineering

Background:

• Optimizing microbial cell factories is crucial for biotechnology but is hindered by complex cellular machinery.
• Current methods for engineering microbial cell factories are often tedious and time-consuming.

Purpose of the Study:

• To review recent applications of machine learning (ML) in advancing microbial cell factory development.
• To highlight ML's role in constructing genome-scale metabolic models and optimizing bioprocesses.

Main Methods:

• Review of recent literature on machine learning applications in metabolic engineering.
• Analysis of ML integration with high-throughput technologies and Design-Build-Test-Learn cycles.
• Focus on ML for metabolic model construction, pathway optimization, enzyme engineering, and gene regulatory element design.

Main Results:

• Machine learning effectively analyzes large biological datasets to build data-driven models for complex bioprocesses.
• ML accelerates the development of microbial cell factories through applications in metabolic model construction and pathway optimization.
• Recent ML advancements aid in rate-limiting enzyme engineering and gene regulatory element design.

Conclusions:

• Machine learning offers powerful solutions for overcoming challenges in microbial cell factory optimization.
• Integrating ML with experimental workflows significantly speeds up the development of efficient biotechnological production processes.
• Further research into ML limitations and solutions will enhance its utility in synthetic biology.