Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Area of Science:

• Synthetic biology
• Biotechnology
• Artificial Intelligence

Background:

• High-performance cell factories are crucial for industrial biotechnology success.
• Creating efficient cell factories remains a significant challenge in synthetic biology.
• Metabolic network models and AI-powered strain engineering are current approaches.

Purpose of the Study:

• To review the latest applications of metabolic models and AI in computational strain design.
• To discuss the deep integration of AI with metabolic models for superior cell factory construction.
• To highlight the potential of AI-powered metabolic models for industrial biotechnology.

Main Methods:

• Review of advanced metabolic models and AI applications in computational strain design.
• Discussion of approaches for integrating AI with metabolic models.
• Analysis of current challenges and future directions in cell factory development.

Main Results:

• Both metabolic models and AI have advantages and disadvantages in strain engineering.
• Deep integration of AI and metabolic models is crucial for enhanced cell factories.
• AI-powered mechanistic metabolic models show promise for efficient industrial chassis strain construction.

Conclusions:

• The synergy between AI and metabolic models is essential for advancing industrial biotechnology.
• AI-driven computational strain design will accelerate the development of high-performance cell factories.
• This integrated approach is anticipated to lead to efficient construction of powerful industrial chassis strains.