Evidence map›Paper›PMID 29223465›Full record

ReviewCurrent opinion in biotechnology2018

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens.

Laura J Dunphy, Jason A Papin

Open access · greenAbstract readReview
In one paragraph

Review in Current opinion in biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

23 citing papers in PubMed, 43 citations in OpenAlex.

  1. Review
  2. Article
  3. Reconstruction and Analysis of a Genome-Scale Metabolic Model ofInternational journal of molecular sciences · 2024
    Article
  4. Article
  5. Article
  6. Review
  7. Machine Learning Study of Metabolic NetworksMolecular pharmaceutics · 2022
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
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  17. Article
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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

2 authors at 1 institution in 1 country.

Laura J DunphyDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA.
Jason A PapinDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22903, USA. Electronic address: papin@virginia.edu.
University of Virginia · US

Funding

Mapping and predicting metabolic fluxes between the ileal microbiome and hostR01GM108501 · NIGMS · UNIVERSITY OF VIRGINIA · PI PAPIN, JASON · 2014 to 2017
$1.4M
NIGMS NIH HHS R01 GM108501
6 · The paper itself

Abstract

The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.

Indexed as

GenomeHost-Pathogen InteractionsMetabolic Networks and PathwaysAnimalsHumansInfections

Identifiers

PMID29223465
PMCPMC5991985
OpenAlexW2772256670

What OpenQuestion holds

Textmetadata
LicenceTDM
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.