基于合成生物学的生物传感器：环境检测新进展

姜瑞琴; 田微; 刘鸿雁; 吴家骏; 杨舒; 李久兴; 刘猛; 张子杰

doi:10.12211/2096-8280.2026-032

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基于合成生物学的生物传感器：环境检测新进展

特约评述 | 更新时间：2026-06-23

- 基于合成生物学的生物传感器：环境检测新进展
- Synthetic biology-based biosensors： advances in environmental detection
- 合成生物学 2026年页码：1-31
- 作者机构：
  
  1.大连理工大学，环境学院，工业生态与环境工程教育部重点实验室，辽宁大连116024
  2.大连理工大学附属中心医院（大连市中心医院），辽宁大连116033
- 作者简介：
  
  姜瑞琴（2002— ），女，2025年毕业于中山大学生物医学工程专业，获学士学位。现就读于大连理工大学环境科学与技术学院、工业生态学与环境工程重点实验室（教育部）在读硕士研究生。主要研究方向包括功能核酸分子体外筛选、病原微生物检测及纳米诊疗。
  刘猛（1985— ），男，大连理工大学环境学院教授、博士研究生导师、党委副书记、院长。2012年于大连理工大学获环境工程博士学位，2013年-2016年于加拿大McMaster大学进行博士后研究。长期从事环境分析化学研究领域，相关成果发表在Accounts of Chemical Research，Nature Communications，Journal of the American Chemical Society，Angewandte Chemie International Edition等期刊上，申请发明专利30余项，主持及参与科技部重点研发计划、国家自然科学基金及省部级科研项目10余项。
  张子杰（1990— ），男，大连理工大学环境学院教授、博士生导师。2019年在加拿大滑铁卢大学化学系获得博士学位，2019-2023年在加拿大麦克马斯特大学生物化学与生物医学系从事研究工作。主要从事功能核酸筛选、传感器、生物分析，致力于病原微生物即时检测POCT、污染监测与控制、医工交叉等前沿领域研究。在Journal of the American Chemical Society， Angewandte Chemie International Edition， Nano Letters， Nucleic Acids Research等期刊发表论文50余篇，申请多项发明专利，主持和参与国家自然科学基金、国家重点研发计划项目等。
- 基金信息：
  
  国家重点研发计划“新污染物的环境过程与溯源技术”(2024YFA0918900);国家自然科学基金(22476014;22425602)
- DOI：10.12211/2096-8280.2026-032
  中图分类号： Q816
- 收稿：2026-04-30，
  
  修回：2026-06-18，
  
  网络首发：2026-06-23，
- 稿件说明：
移动端阅览
姜瑞琴，田微，刘鸿雁，吴家骏，杨舒，李久兴，刘猛，张子杰. 基于合成生物学的生物传感器：环境检测新进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-032

JIANG Ruiqin， TIAN Wei， LIU Hongyan， WU Jiajun， YANG Shu， LI Jiuxing， LIU Meng， ZHANG Zijie. Synthetic biology-based biosensors： advances in environmental detection［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-032
姜瑞琴，田微，刘鸿雁，吴家骏，杨舒，李久兴，刘猛，张子杰. 基于合成生物学的生物传感器：环境检测新进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-032 DOI：

JIANG Ruiqin， TIAN Wei， LIU Hongyan， WU Jiajun， YANG Shu， LI Jiuxing， LIU Meng， ZHANG Zijie. Synthetic biology-based biosensors： advances in environmental detection［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-032 DOI：

摘要

环境污染日趋复杂，传统检测技术存在操作繁琐、检测周期长、灵敏度低且难以实现现场快速检测的局限，常规生物传感器则受限于特异性不足、响应范围窄、稳定性差、抗干扰能力弱等问题，难以满足复杂环境基质中低浓度、多类型污染物的精准检测需求。合成生物学通过对生物元件开展基因回路与逻辑调控设计，组装生物功能器件与模块，实现生物传感器从天然被动响应到工程化、可编程人工构建的转变，定向调控传感器的灵敏度、特异性与响应动态范围，拓展待测污染物靶点种类，为高性能生物传感器的开发提供变革性技术工具。本文系统阐述合成生物传感器“感知—转导—输出”的模块化原理与核心组成，介绍识别元件、信号放大、输出方式及全细胞、无细胞、基因回路三类系统形式。重点综述其在重金属、有机污染物、抗生素、内分泌干扰物、微塑料等环境污染物检测中的应用进展，分析多污染物协同检测与智能传感集成趋势，最后提出该技术当前面临基质干扰、元件标准化不足、生物安全及成本等挑战，并对未来发展方向进行展望。

Abstract

Environmental pollution is becoming increasingly complex

driven by the coexistence of conventional pollutants and emerging contaminants

posing significant risks to ecosystems and human health. Accurate

real-time

and in situ detection of these pollutants is essential for effective environmental governance and early risk warning. Traditional analytical techniques

represented by high-performance liquid chromatography and mass spectrometry

deliver high sensitivity and accuracy but are inherently limited by exorbitant instrumentation costs

labor-intensive and time-consuming sample pretreatment protocols

dependence on highly skilled personnel

and inability to perform continuous in-field monitoring

making them unsuitable for large-scale environmental surveillance. Conventional biosensors are portable but often suffer from limited specificity

poor resistance to matrix interference

narrow detection ranges

and low sensitivity

which restrict their performance in complex environmental settings. Synthetic biology provides a transformative framework for biosensor design

enabling engineered

modular

and programmable systems that go beyond naturally occurring sensing mechanisms. Specifically

rationally engineered recognition elements (e.g.

transcription factors

aptamers

riboswitches

and receptor proteins) exhibit ultra-high target specificity and minimal cross-reactivity

significantly mitigating non-specific binding and matrix interference effects. Diverse tunable signal amplification strategies

including transcriptional cascades

protease cascades

and CRISPR-Cas-based isothermal amplification

have enhanced detection sensitivity by 3–6 orders of magnitude

achieving pM to fM level detection limits for trace contaminants and extending dynamic detection ranges to cover environmentally relevant concentration gradients. Standardized modular component design enables rapid assembly and reconfiguration of biosensors for diverse targets

while genetic logic circuits (AND

NOT gates) enable simultaneous quantitative detection and logical discrimination of multiple pollutants

breaking through the single-analyte limitation of conventional systems. The integration of synthetic biosensors with artificial intelligence (AI) and Internet of Things (IoT) technologies enables intelligent sensing platforms for automated signal acquisition

real-time data analysis

and remote early warning

supporting continuous environmental monitoring. This review summarizes the three-module architecture of synthetic biosensors

including target sensing

signal transduction

and signal output

along with their key functional components. Three major system formats are also discussed: whole-cell

cell-free

and synthetic gene circuit–based biosensors. Recent advances and representative applications in the detection of heavy metals

organic pollutants

antibiotics

endocrine-disrupting chemicals

and microplastics are comprehensively summarized. Emerging trends

including multi-analyte detection enabled by genetic logic circuits and the integration of biosensors with artificial intelligence and the Internet of Things for intelligent sensing

are highlighted. Key challenges—such as matrix interference in complex samples

insufficient standardization of biological components

biosafety concerns

and limitations in stability and cost—are critically discussed. Finally

future perspectives are proposed

focusing on component engineering

system integration

intelligent detection

application-oriented design

and enhanced safety control

aiming to facilitate the practical deployment of synthetic biosensors in environmental monitoring and early warning systems.

关键词

Keywords

references

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