Evidence map›Paper›PMID 27106061›Full record

ArticleNucleic acids research2016

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

Baudoin Delépine, Vincent Libis, Pablo Carbonell, Jean-Loup Faulon

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
  2. Programming Next-Generation Synthetic Biosensors by Genetic Circuit Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Synthetic biology (Oxford, England) · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Transcription factor-based biosensors for screening and dynamic regulation.Frontiers in bioengineering and biotechnology · 2023
    Review
  13. Article
  14. Research progress of pathway and genome evolution in microbes.Synthetic and systems biotechnology · 2022
    Article
  15. Rhea, the reaction knowledgebase in 2022.Nucleic acids research · 2022
    Article
  16. Cell-Free Biosensors and AI Integration.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  17. Article
  18. Recent trends in biocatalysis.Chemical Society reviews · 2021
    Review
  19. Article
  20. Article
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

4 authors.

Baudoin DelépineiSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France.
Vincent LibisiSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France.
Pablo CarbonellSYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, M1 7DN Manchester, UK.
Jean-Loup FauloniSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, M1 7DN Manchester, UK jlfaulon@jouy.inra.fr.ORCID 0000-0003-4274-2953

Funding

Biotechnology and Biological Sciences Research Council BB/M017702/1
6 · The paper itself

Abstract

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.

Indexed as

AlgorithmsBiosensing TechniquesMetabolic EngineeringMetabolic Networks and PathwaysSoftwareBenzoic AcidCocaineComputer-Aided DesignComputer GraphicsComputer SimulationDatabases, FactualDatabases, GeneticEscherichia coliInternetModels, ChemicalParathionBenzoic AcidCocaineParathionTranscription Factors

Identifiers

PMID27106061
PMCPMC5741204

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.