Evidence map›Paper›PMID 29089935›Full record

ReviewFrontiers in microbiology2017

Design, Optimization and Application of Small Molecule Biosensor in Metabolic Engineering.

Yang Liu, Ye Liu, Meng Wang

Abstract readReview
In one paragraph

Review in Frontiers in microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Live-cell omics with Raman spectroscopy.Microscopy (Oxford, England) · 2025
    Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Plant Life with and without Oxygen: A Metabolomics Approach.International journal of molecular sciences · 2023
    Review
  11. Article
  12. Transcription factor-based biosensors for screening and dynamic regulation.Frontiers in bioengineering and biotechnology · 2023
    Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
  18. Review
  19. Article
  20. 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

3 authors.

Yang LiuTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
Ye LiuTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
Meng WangTianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of synthetic biology and metabolic engineering has painted a great future for the bio-based economy, including fuels, chemicals, and drugs produced from renewable feedstocks. With the rapid advance of genome-scale modeling, pathway assembling and genome engineering/editing, our ability to design and generate microbial cell factories with various phenotype becomes almost limitless. However, our lack of ability to measure and exert precise control over metabolite concentration related phenotypes becomes a bottleneck in metabolic engineering. Genetically encoded small molecule biosensors, which provide the means to couple metabolite concentration to measurable or actionable outputs, are highly promising solutions to the bottleneck. Here we review recent advances in the design, optimization and application of small molecule biosensor in metabolic engineering, with particular focus on optimization strategies for transcription factor (TF) based biosensors.

Indexed as

industrial applicationmetabolic engineeringoptimization strategysmall molecule biosensorsynthetic biologytranscription factor

Identifiers

PMID29089935
PMCPMC5651080

What OpenQuestion 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 OpenQuestion graph.