Evidence map›Paper›PMID 41655251›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Programming Next-Generation Synthetic Biosensors by Genetic Circuit Design.

Yuanli Gao, Cheng Huang, Jiaxuan Deng, Lei Wang, Baojun Wang

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuanli GaoKey Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-0535-249X
Cheng HuangKey Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Jiaxuan DengKey Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Lei WangCenter of Synthetic Biology and Integrated Bioengineering & School of Engineering, Westlake University, Hangzhou, China.
Baojun WangKey Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-4858-8937

Funding

National Key Research and Development Program of China 2025YFA0921900National Natural Science Foundation of China 32271475National Natural Science Foundation of China 32320103001National Natural Science Foundation of China 32501287"Pioneer" and "Leading Goose" R&D Program of Zhejiang 2024C03011"Pioneer" and "Leading Goose" R&D Program of Zhejiang 2025C01097
6 · The paper itself

Abstract

Synthetic biology employs engineering principles to construct genetic circuits with customized functionality, empowering unprecedented control over biological systems. By harnessing this capability to precisely manipulate biological systems, synthetic biosensors are being developed as promising biosensing platforms for on-site, sustainable, affordable, and easy-to-use detection across diverse scenarios, such as environmental monitoring, disease diagnosis, food safety control, and bioproduction optimization. However, the field deployment and real-world application of synthetic biosensors face considerable challenges in biosensing sensitivity, specificity, speed, stability, and biosafety. This review summarizes recent advancements of genetic circuit-enabled synthetic biosensors, focusing on their sensory mechanisms, designs, and applications. Moreover, the design principles, enabling tools, and engineering strategies for creating a high-performing synthetic biosensor are analyzed. In particular, methods for tuning various characteristics of the dose-response curve, including detection limit, detection threshold, operating range, dynamic range, and leakiness, are thoroughly examined. Finally, this review discusses the functional extension of biosensors by customizing signal-processing and output modules, and outlines future directions to expedite the transition of synthetic biosensors from laboratory settings to field applications. Genetic circuit-enabled synthetic biosensors, in collaboration with materials science, electronic engineering, and artificial intelligence, will tremendously expand the application space of synthetic biology.

Indexed as

Biosensing TechniquesGene Regulatory NetworksGenetic EngineeringSynthetic BiologyHumanscell‐free biosensorgenetic circuit designsynthetic biologysynthetic biosensorwhole‐cell biosensor

Identifiers

PMID41655251
PMCPMC12970293

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.