Designing prokaryotic gene expression regulatory elements: From genomic mining to artificial intelligence-driven...

Area of Science:

• Synthetic biology
• Molecular biology
• Biotechnology

Background:

• Gene expression regulatory elements (GEREs) are essential for controlling gene transcription and translation.
• Designing GEREs with precise and tunable activity is a significant challenge in synthetic biology.
• Engineering strategies have advanced from empirical methods to rational approaches using biophysical models and AI.

Purpose of the Study:

• To systematically review design principles and strategies for various GERE classes.
• To highlight the role of sequence mining, modular recombination, mutagenesis, and deep generative modeling in GERE development.
• To provide guidance for optimizing microbial cell factory bioproduction via gene expression fine-tuning.

Main Methods:

• Systematic literature review of GERE design strategies.
• Examination of empirical sequence mining and random mutagenesis.
• Analysis of rational design approaches including biophysical models and artificial intelligence (AI).

Main Results:

• Engineering strategies have evolved towards more rational and AI-guided approaches.
• Specific methods like modular recombination and deep generative modeling enhance functional GERE development.
• Fine-tuning gene expression is key for optimizing microbial bioproduction.

Conclusions:

• Advanced computational and engineering methods are crucial for designing effective GEREs.
• The development of tunable GEREs will significantly impact synthetic biology applications.
• Optimized GEREs offer practical solutions for enhancing microbial cell factory efficiency.