Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Area of Science:

• Synthetic biology
• Biosensor engineering
• Molecular biology

Background:

• Transcription factor (TF)-based biosensors (TFBs) convert biological signals into measurable outputs, offering alternatives to traditional detection methods.
• Native TFBs often display suboptimal performance (low specificity, sensitivity, narrow dynamic range) due to evolutionary optimization for microbial survival, not laboratory use.

Purpose of the Study:

• To analyze regulatory mechanisms of TFBs and present strategies for constructing efficient sensing systems.
• To review recent advances, challenges in commercialization, and systematic improvements of TFBs.
• To propose future directions, including AI-driven programming for enhanced TFB applications.

Main Methods:

• Analysis of four regulatory mechanisms governing TFBs.
• Review of recent advancements and commercialization challenges in TFB technology.
• Discussion of element modification strategies for TFB performance enhancement.

Main Results:

• Identified limitations of native TFBs and outlined engineering strategies for improved performance.
• Highlighted progress in TFB applications across various fields.
• Proposed future research avenues focusing on rapid response, application isolation, and AI integration.

Conclusions:

• Engineering TFBs is crucial for overcoming limitations and expanding their utility.
• AI and advanced genetic circuit programming hold significant potential for next-generation TFBs.
• Optimized TFBs can drive advancements in Industry 4.0 applications like biomanufacturing and diagnostics.