Biosensors have been widely used as cost-effective, rapid, in situ, and real-time analytical tools for monitoring environments. The development of synthetic biology has enabled emergence of genetically engineered whole-cell microbial biosensors. This review updates the design and optimization principles for a diverse array of whole-cell biosensors based on transcription factors (TF) including activators or repressors derived from heavy metal resistance systems, alkanes, and aromatics metabolic pathways of bacteria. By designing genetic circuits, the whole-cell biosensors could be engineered to intelligently sense heavy metals (Hg²⁺, Zn²⁺, Pb²⁺, Au³⁺, Cd²⁺, As³⁺, Ni²⁺, Cu²⁺, and UO₂²⁺) or organic compounds (alcohols, alkanes, phenols, and benzenes) through one-component or two-component system-based TFs, transduce signals through genetic amplifiers, and response as various outputs such as cell fluorescence and bioelectricity for monitoring heavy metals and organic pollutants in real conditions, synthetic curli and surface metal-binding peptides for in situ bio-sorption of heavy metals. We further review strategies that have been implemented to optimize the selectivity and correlation between ligand concentration and output signal of the TF-based biosensors, so as to meet requirements of practical applications. The optimization strategies include protein engineering to change specificities, promoter engineering to improve sensitivities, and genetic circuit-based amplification to enhance dynamic ranges via designing transcriptional amplifiers, logic gates, and feedback loops. At last, we outlook future trends in developing novel forms of biosensors.

生物传感器已被广泛用作用于监测环境的具有成本效益、快速、原位和实时的分析工具。合成生物学的发展使基因工程全细胞微生物生物传感器的出现成为可能。本综述更新了基于转录因子 (TF) 的各种全细胞生物传感器的设计和优化原则，包括源自细菌的重金属抗性系统、烷烃和芳烃代谢途径的激活剂或阻遏剂。通过设计基因电路，全细胞生物传感器可以智能感知重金属（Hg ²⁺、Zn ²⁺、Pb ²⁺、Au ³⁺、Cd ²⁺、As ³⁺、Ni ²⁺、Cu ²⁺和 UO ₂ ²⁺) 或有机化合物（醇、烷烃、酚和苯）通过基于单组分或双组分系统的 TFs，通过基因放大器转导信号，并作为各种输出（如细胞荧光和生物电）响应，用于监测重金属和有机物真实条件下的污染物，合成的卷曲和表面金属结合肽，用于重金属的原位生物吸附。我们进一步回顾了已实施的优化基于TF的生物传感器的配体浓度和输出信号之间的选择性和相关性的策略，以满足实际应用的要求。优化策略包括改变特异性的蛋白质工程、提高敏感性的启动子工程、和基于遗传电路的放大，通过设计转录放大器、逻辑门和反馈回路来增强动态范围。最后，我们展望了开发新型生物传感器的未来趋势。