[Intelligent design of nucleic acid elements in biomanufacturing]

Area of Science:

• Biotechnology and Synthetic Biology
• Bioinformatics and Computational Biology

Background:

• Nucleic acid elements are vital for gene regulation, pathway optimization, and gene editing in biomanufacturing.
• Efficient cell factory construction relies on the design and optimization of these functional sequences.
• Artificial intelligence (AI) offers powerful tools for predicting, designing, and understanding nucleic acid elements.

Purpose of the Study:

• To review nucleic acid elements used in biomanufacturing.
• To summarize AI-driven tools for predicting and designing these elements.
• To highlight case studies of AI applications in biomanufacturing.

Main Methods:

• Literature review of nucleic acid elements in biomanufacturing.
• Analysis of AI algorithms and tools for sequence design and prediction.
• Compilation of case studies demonstrating AI's impact on biomanufacturing processes.

Main Results:

• AI significantly reduces experimental workload in designing promoters, ribosome-binding sites, and terminators.
• AI accurately predicts functional nucleic acid elements and elucidates their mechanisms.
• AI accelerates biomanufacturing progress through optimized sequence design.

Conclusions:

• AI is crucial for advancing biomanufacturing by enabling efficient design of nucleic acid elements.
• Challenges remain due to biological complexity and data limitations.
• Integrating AI with synthetic biology and high-throughput techniques promises more efficient tools and wider applications.