生物传感器及其用于功能糖生物合成的研究进展

刘丹; 彭云柯; 江正强

doi:10.12211/2096-8280.2026-047

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生物传感器及其用于功能糖生物合成的研究进展

特约评述 | 更新时间：2026-07-24

- 生物传感器及其用于功能糖生物合成的研究进展
- Biosensors and their advances in the biosynthesis of functional saccharides
- 合成生物学 2026年页码：1-20
- 作者机构：
  
  1.中国农业大学食品科学与营养工程学院，中国轻工业食品生物工程重点实验室，北京 100083
  2.中原食品实验室，河南漯河 462300
- 作者简介：
  
  刘丹（1995—），女，博士研究生。研究方向为食品生物技术。
  江正强（1971—），男，教授，博士生导师，研究方向为食品酶工程等。
- 基金信息：
  
  国家自然科学基金重点项目(22338013)
- DOI：10.12211/2096-8280.2026-047
  中图分类号： Q815
- 收稿：2025-05-26，
  
  修回：2026-07-09，
  
  网络首发：2026-07-24，
- 稿件说明：
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刘丹，彭云柯，江正强. 生物传感器及其用于功能糖生物合成的研究进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-047

LIU Dan， PENG Yunke， JIANG Zhengqiang. Biosensors and their advances in the biosynthesis of functional saccharides［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-047
刘丹，彭云柯，江正强. 生物传感器及其用于功能糖生物合成的研究进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-047 DOI：

LIU Dan， PENG Yunke， JIANG Zhengqiang. Biosensors and their advances in the biosynthesis of functional saccharides［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-047 DOI：

摘要

随着合成生物学与代谢工程的飞速发展，功能糖的生物合成取得显著突破。生物传感器在微生物细胞工厂合成过程动态调控及合成所需关键酶高通量筛选中作用突出，正逐渐成为提高功能糖生物合成效率的前沿技术。本文系统概述了生物传感器的核心组成模块，阐述了转录因子型生物传感器、RNA型生物传感器、双组分系统型生物传感器及荧光蛋白型生物传感器等的基本工作原理与性能评估指标，并总结了提升上述传感器特异性、灵敏度、动态响应范围等重要参数的主要方法。在此基础上，系统归纳了生物传感器在母乳寡糖、D-阿洛酮糖等功能糖高通量筛选与底盘动态调控中的最新研究进展。重点指出了生物传感器在代谢网络动态调控中的显著成效，部分目标产物（如2′-岩藻糖基乳糖、N-乙酰氨基葡萄糖）的发酵水平提升至百克级以上。最后，本文总结了生物传感器在功能糖生物合成中的核心作用，并展望了AI辅助智能化元件创制、真核底盘拓展、多信号逻辑门集成及自动化平台驱动等下一代智能化细胞工厂的生物传感器发展趋势。

Abstract

Rapid development in synthetic biology and metabolic engineering has driven significant progress in the microbial biosynthesis of functional saccharides

such as human milk oligosaccharides (HMOs)

D-allulose

N-acetylglucosamine (GlcNAc)

and N-acetylneuraminic acid (NeuAc). Nevertheless

conventional static metabolic engineering strategies frequently suffer from metabolic imbalances and resource allocation conflicts within chassis cells

and the bottleneck in high-throughput screening for key enzymes severely hinders further enhancements in biosynthetic efficiency. Furthermore

the bottleneck in high-throughput screening for key enzymes severely hinders further enhancements in biosynthetic efficiency. To address these challenges

biosensors have emerged as powerful tools

playing a prominent role in the dynamic regulation of microbial cell factories and the high-throughput screening of key enzymes and high-yield strains. This review provides a systematic overview of the core modules of genetically encoded biosensors and elucidates the fundamental working principles of various types

including transcription factor-based

RNA-based

two-component system-based

and fluorescent protein-based biosensors. Additionally

essential performance metrics of genetically encoded biosensors—such as specificity

dynamic range

sensitivity

basal leakage

response time

and orthogonality—are systematically discussed

and major strategies for optimizing these key parameters are comprehensively summarized. Furthermore

major strategies for optimizing key parameters such as specificity

sensitivity

and dynamic range are comprehensively summarized. Subsequently

we systematically review the latest studies of these biosensors in the biosynthesis of functional saccharides

including HMOs

D-allulose

GlcNAc

and NeuAc. Various biosensors (e.g.

lactose-

temperature-

quorum-sensing-

and metabolite-responsive) are employed to achieve precise dynamic regulation of metabolic pathways

successfully balancing precursor supply and pushing fermentation titers to more than hundred-gram scales. Through coupling biosensors with droplet microfluidics

fluorescence-activated cell sorting (FACS)

and toxin-antitoxin systems

the directed evolution of key enzymes and high-throughput screening of overproducing strains are rapidly driven. Future biosensor engineering in functional saccharide biosynthesis centers on AI-assisted de novo design

diversified non-transcription-factor mechanisms

and host expansion into eukaryotic chassis. Crucially

integrating multi-signal logic gates will facilitate autonomous phase switching and metabolic balance. Concurrently

automation-platform-driven integration with biofoundries

droplet microfluidics

and machine learning will accelerate high-throughput iterative optimization. Together

these technological advancements will seamlessly propel functional saccharide production from empirical static optimization toward a new paradigm of highly intelligent

autonomous metabolic regulation. These advances will propel biosynthesis of functional saccharides from static optimization towards intelligent

autonomous regulation.

关键词

Keywords

references

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