Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Area of Science:

• Biotechnology
• Metabolic Engineering
• Synthetic Biology

Background:

• Growing demand for biopharmaceuticals drives innovation in natural product biomanufacturing.
• Metabolically engineered microbes are key hosts for producing diverse bioactive compounds like terpenes, flavonoids, alkaloids, and cannabinoids.
• Discovery and expression of plant biosynthetic genes are crucial for microbial synthesis of complex natural products.

Purpose of the Study:

• To review recent advancements in omics platforms and machine learning (ML) for microbial strain optimization.
• To highlight the role of omics-enabled gene discovery in producing plant-based natural products.
• To discuss strategies for improving biomanufacturing capacity of engineered microbes.

Main Methods:

• Utilizing omics technologies (genomics, transcriptomics, metabolomics) for high-throughput analysis.
• Employing machine learning (ML) platforms for data-guided optimization of biosynthetic pathways.
• Constructing and expressing plant biosynthetic genes in microbial hosts.

Main Results:

• Omics and ML tools accelerate the optimization of microbial production strains.
• Successful synthesis of various natural products, including terpenes, flavonoids, alkaloids, and cannabinoids, in engineered microbes.
• Streamlined discovery and implementation of plant biosynthetic pathways for enhanced biomanufacturing.

Conclusions:

• Omics platforms and ML are critical for debottlenecking and improving natural product biomanufacturing.
• Engineered microbes offer a viable platform for sustainable production of complex plant-derived bioactive compounds.
• Integration of omics-based gene discovery and ML-driven optimization is key to advancing bioproduction.