[Intelligent design of transcription factor-based biosensors]

Area of Science:

• Synthetic biology
• Metabolic engineering
• Biosensor development

Background:

• Transcription factor (TF)-based biosensors offer high orthogonality, modularity, and operability.
• Natural TFs often exhibit weak responses and low specificity, necessitating optimization for practical applications.

Purpose of the Study:

• To review recent advances in engineering and optimizing TF-based biosensors.
• To highlight the role of computational simulation and artificial intelligence in biosensor development.

Main Methods:

• Protein structure prediction and ligand binding simulations for regulatory protein engineering.
• Machine learning models trained on mutagenesis data to predict regulatory protein responses.
• Integration of computational tools for accelerated biosensor design and construction.

Main Results:

• Computational simulation and AI enable more accurate and rapid design of TF-based biosensors.
• Optimized biosensors show enhanced performance for various applications.
• AI-driven approaches facilitate the prediction of protein responses.

Conclusions:

• Computational simulation and AI are transformative technologies for TF-based biosensor development.
• These advanced methods significantly improve biosensor accuracy, specificity, and speed.
• The integration of AI and simulation promises novel biosensors for diverse applications.