Engineering synthetic biology sensors with artificial intelligence: From programmable circuits to next-generation...

Area of Science:

• Synthetic biology
• Artificial intelligence
• Biosensors

Background:

• Synthetic biology biosensors (SBBs) traditionally rely on rational design.
• Advancements in artificial intelligence (AI) offer new predictive capabilities for SBB development.
• Bridging the gap between computational prediction and real-world biosensor performance is crucial.

Purpose of the Study:

• To establish a systematic framework linking AI algorithms to the Design-Build-Test-Learn (DBTL) cycle for SBBs.
• To analyze AI's role in both cell-based and cell-free SBB engineering paradigms.
• To synthesize AI-driven workflows into key frontiers for SBB advancement.

Main Methods:

• Systematic review of AI applications in SBBs.
• Analysis of AI-driven engineering paradigms for cell-based and cell-free SBBs.
• Synthesis of AI workflows into sensor design, signal processing, and closed-loop optimization.

Main Results:

• AI integration shifts SBB development from rational design to AI-driven prediction.
• AI addresses platform-specific bottlenecks in cell-based and cell-free SBBs.
• Key AI frontiers include sensor element design, signal processing, and autonomous evolution.

Conclusions:

• AI significantly accelerates the development and optimization of SBBs.
• Overcoming challenges like the 'reality gap' and 'small-data dilemma' is essential.
• Future roadmaps involve bio-digital interfaces, explainable AI, and data standardization for robust, field-deployable SBBs.