Genetic circuit-enabled synthetic biosensors： design and applications Synthetic Biology Journal    DOI: 10.12211/2096-8280.2025-031

Fig. 7 Synthetic biosensing systems with multimodal outputs for integration with various detection platforms
(a) A agarose hydrogel-encapsulated arsenic ion sensor for visual array display on a smartphone platform^[32]; (b) A sensor that visualizes biosensing signals through the formation of bacterial spatial motility patterns^[36]; (c) A sensing system coupling a fluorescent sensor with a photoelectric chip to convert optical signals into wireless signals^[48].

Extracts from the Article

传统合成生物传感器多依赖荧光报告基因作为信号输出模块，但其现场应用常受限于专业荧光检测设备的配置要求。为突破这一应用瓶颈，合成生物学界已开发出多种可替代的标准化信号输出系统，主要技术路径可分为以下四类：（1）生物-电信号转换系统：Atkinson等［6］通过合成生物学与材料工程交叉创新，构建了基于电化学信号输出的生物传感器。该团队在大肠杆菌中编程构建了模块化的八组分合成电子传递链，使其在接触硫代硫酸盐等环境污染物后快速产生电极可检测的电流响应，为环境监测提供了快速响应的实时监测方案；（2）比色传感系统：基于显色反应的可视化传感系统显著提升了现场检测便捷性，例如通过显色蛋白或酶催化显色反应实现肉眼可辨的信号输出［44，164-165］；（3）空间图案传感系统：Wan等［32］通过琼脂糖水凝胶包埋和微流控封装技术设计了便于解读的砷离子传感阵列，其图案化的显色模式可通过便携设备快速解读（图7a）；Doshi等［36］开发的细菌群体运动传感系统，通过环境信号调控鞭毛基因表达，诱导细菌形成特征性空间运动图案，为污染物检测提供了新型可视化策略（图7b）。（4）光-电信号转换系统：在光信号转换领域，Mimee等［166］开发的无线胶囊系统可将血红素诱导的荧光信号转换为射频信号，成功应用于猪模型消化道出血的精准诊断；进一步的，Inda-Webb等［48］将工程化益生菌传感器与光电芯片耦合成小于1.4 cm3的微型胶囊，该胶囊可以将细菌的生物光信号转化为无线电信号，从而实现了胃肠道炎症分子的原位监测（图7c）。这些多模态输出技术通过降低设备依赖性与操作复杂度，显著推动了生物传感器的现场化应用进程。

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• Table 1  Typical genetic circuit-enabled synthetic biosensors and applications
• Fig. 1  Mechanisms and applications of transcriptional regulatory protein-enabled synthetic biosensors(a-c) Mechanisms of allosteric transcription factors, transmembrane transcription factors, and two-component systems-enabled biosensors; (d) Response curves of biosensors based on activation-type and repression-type transcription factor; (e) Gene circuit of a thiosulfate-sensing sensor based on the two-component system ThsRS^[54]; (f) Gene circuit of a transcription factor-enabled biosensor for monitoring glucose uptake^[14]; (g) Gene circuit of a target-dependent RNA polymerase-enabled biosensor^[95].
• Fig. 2  Mechanisms of riboswitches-enabled synthetic biosensors(a) Riboswitch-enabled sensors based on transcriptional activation and transcriptional repression.(b) Riboswitch-enabled sensors based on translational activation and translational repression..
• Fig. 3  Mechanisms and applications of riboregulator-enabled synthetic biosensors(a) Mechanism of the toehold switch^[124]; (b) A ribozyme-based sensing platform RENDR^[125]; (c) Gene circuit of a sensor for reporting bacterial DNA transfer in soil using split ribozymes^[133]; (d) Mechanism of the group I intron-enabled RNA trans-splicing system SENTR and its application for intracellular mRNA detection ^[122].
• Fig. 4  Mechanisms of CRISPR-enabled synthetic biosensors(a) RNA sensing enabled by reprogrammed tracrRNA in type II CRISPR system^[17]; (b) RNA sensing enabled by RNA-triggered RNA processing^[142]; (c) RNA sensing enabled by strand displacement reactions^[143].
• Table 2  Advantages and limitations of genetic circuit-enabled synthetic biosensors based on different signal recognition mechanisms
• Fig. 5  Typical biomemory modules for recording biosensing signals(a) Gene circuit for a biomemory module based on toggle switch; (b) Gene circuit of a zinc ion memory module based on the recombinase flip mechanism^[153]; (c) Principle of the CAMERA recording system based on CRISPR/Cas^[158]; (d) Gene circuit of the DNA sensing and recording module (CATCH) based on CRISPR/Cas^[159].
• Fig. 6  Circuit designs and applications of multilayered biocomputing modules(a) Gene circuit of a dual-parameter biosensor based on an AND logic gate and its response heatmap^[104]; (b) Gene circuit of a metabolic control sensor integrating a multi-input genetic logic circuit ^[163];
• Fig. 8  Strategies for increasing the sensitivity of genetic circuit-enabled synthetic biosensors(a) Typical response curves and characteristics of genetic circuit-enabled synthetic biosensors.; (b) Dynamic range optimization based on promoter engineering; (c) Limit of detection optimization by gradient regulation of biorecognition components concentration and target enrichment technology; (d) Dynamic range optimization using sensing signal amplifiers based on activator transcription factors, positive feedback loops and cascade repressors ^[7]; (e) Leakage optimization utilizing antisense RNA-mediated translation blocking, decoy DNA binding site competitive inhibition, and output protein degradation tags^[1].
• Fig. 9  Strategies for increasing the specificity of genetic circuit-enabled synthetic biosensors(a) Semi-rational design and saturation mutagenesis of the transcription factor LysG to prevent its binding to L-lysine (L-Lys) while retaining its ability to bind L-arginine (L-Arg) and L-histidine (L-His)^[191]; (b) Semi-rational design and saturation mutagenesis of the TynA-FeaR sensing system for specific detection of phenylethylamine. (DA: dopamine; PEA: phenylethylamine; Tyra: tyramine; Trypta: tryptamine)^[192]; (c) Coupling transcription factors ZraR and ZntR through an AND gate for specific respond to Zn²⁺^[7,21].
• Fig. 10  Examples of improving the response speed of genetic circuit-enabled synthetic biosensors(a) An E. coli-based sensor with a synthetic electron transfer chain that express an electrical protein switch enable rapid detection of an endocrine disruptor 4-hydroxytamoxifen (4-HT)^[6]; (b) Reversible response kinetics curve and sensing mechanism of a protein phosphorylation signaling network with rapid activation and deactivation^[197].
• Fig. 11  Examples of stability and biosafety optimization for genetic circuit-enabled synthetic biosensors(a) A plasmid copy number control platform in Escherichia coli based on dynamic regulation of translation initiation factors^[204]; (b) A biocontaining CRISPR-Cas9 dual-modal kill switch (CRISPRks) for E. coli under the dual control of chemical inducer anhydrotetracycline (aTc) and temperature changes^[212].