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1.浙江大学化学工程与生物工程学院,浙江 杭州 310058
2.浙江大学杭州国际科创中心,浙江 杭州 311215
Received:02 April 2025,
Revised:2025-05-28,
Published:28 February 2026
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邓稼轩, 陈升言, 王宝俊. 基于基因线路的合成生物传感器设计与应用[J]. 合成生物学, 2026, 7(1): 32-65
DENG Jiaxuan, CHEN Shengyan, WANG Baojun. Genetic circuit-enabled synthetic biosensors: designs and applications[J]. Synthetic Biology Journal, 2026, 7(1): 32-65
邓稼轩, 陈升言, 王宝俊. 基于基因线路的合成生物传感器设计与应用[J]. 合成生物学, 2026, 7(1): 32-65 DOI: 10.12211/2096-8280.2025-031.
DENG Jiaxuan, CHEN Shengyan, WANG Baojun. Genetic circuit-enabled synthetic biosensors: designs and applications[J]. Synthetic Biology Journal, 2026, 7(1): 32-65 DOI: 10.12211/2096-8280.2025-031.
合成生物传感器利用基因编码的生物识别元件特异性识别靶标并将其转换成可量化的生物信号,然后通过基因线路介导的功能器件实现生物信号的定制化处理与多模态信号输出,具有生物相容性高、成本低、环境友好等优势,已在环境监测、生物制造过程监控、精准医学诊疗等领域展现出重要应用潜力。合成生物学方法和前沿技术的突破性进展,特别是基因线路的模块化工程设计原理、可编程动态调控策略和人工智能辅助的生物元件挖掘与从头设计技术,为合成生物传感器的开发提供了前所未有的助力。然而,当前合成生物传感器的产业化应用仍在多个性能指标方面面临制约。即:敏感性(Sensitivity)、特异性(Specificity)、响应速度(Speed)、稳定性(Stability)和安全性(BioSafety)——简称5S挑战。本文系统梳理了基于基因线路的合成生物传感器信号识别机制与设计范式,深入剖析了各类型合成生物传感器的技术优势与应用瓶颈,并归纳了代表性的基因线路传感功能扩展模块与应用案例。本文还介绍了合成生物传感器的关键特征以及典型优化方法,探讨了未来需要交叉融合新机制与新技术以实现合成生物传感器的范式突破与可预测定制,从而加速推动合成生物传感器广泛实际应用。
Synthetic biosensors leverage genetically encoded biorecognition components for specifically identified targets and convert them into quantifiable biological signals. These signals can then be processed through genetic circuits to achieve customized signal processing and produce multi-modular signal outputs. Synthetic biosensors offer several compelling advantages including high biocompatibility
low cost
and sustainability
which have shown significant application potentials in environmental monitoring
biomanufacturing
and precision diagnostics. Advances in synthetic biology methods and technological developments
particularly the adoption of various engineering design principles
genetic circuit-enabled programmable signal processing
and AI-assisted mining
and
de novo
design of biological components
have provided unprecedented momentum for developing new generation synthetic biosensors. However
the practical applications of synthetic biosensors have been restricted by certain technical limitations in sensing performance including sensitivity
specificity
speed
stabilit
y
and biosafety
termed as the “5S” challenges. Here we summarize the signal recognition mechanisms and design principles of representative genetic circuit-enabled synthetic biosensors
and provide an in-depth analysis of their technical advantages and remaining challenges. Furthermore
we highlight typical genetic circuit-enabled functional expansion modules for myriad application scenarios. Finally
we introduce the key features of synthetic biosensors and the latest optimization methods for improving sensing performance. We conclude by discussing the challenges and opportunities for accelerating the development of new generation synthetic biosensors for achieving sustainable
affordable
and reliable detection in diverse settings.
2
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