Evidence map›Paper›PMID 25477869›Full record

ArticleFrontiers in microbiology2014

The habits of highly effective phages: population dynamics as a framework for identifying therapeutic phages.

James J Bull, Jason J Gill

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
5.3field-weighted citation impact, top 5% 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

48 citing papers in PubMed, 79 citations in OpenAlex.

  1. Article
  2. Whole-Genome Sequencing and Functional Characterization of a NovelInternational journal of molecular sciences · 2025
    Article
  3. Article
  4. JAC-antimicrobial resistance · 2025
    Article
  5. Isolation and Genomic Analysis ofAntibiotics (Basel, Switzerland) · 2025
    Article
  6. Isolation and Characterization of a NovelAntibiotics (Basel, Switzerland) · 2024
    Article
  7. Article
  8. Article
  9. Characterization and genomic analysis of a lyticFrontiers in microbiology · 2024
    Article
  10. Automating Predictive Phage Therapy Pharmacology.Antibiotics (Basel, Switzerland) · 2023
    Review
  11. Article
  12. Characterization and biocontrol efficacy of lytic phage (KPP-1) that infects multidrug resistant Klebsiella variicola.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023
    Article
  13. New Bacteriophages Members of thePHAGE (New Rochelle, N.Y.) · 2023
    Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Review
  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

2 authors at 2 institutions in 1 country.

James J BullInstitute for Cellular and Molecular Biology, University of Texas, Austin, TX USA ; Center for Computational Biology and Bioinformatics, University of Texas, Austin, TX USA ; Department of Integrative Biology, University of Texas, Austin, TX USA.
Jason J GillDepartment of Animal Science, Texas A&M University, College Station, TX USA ; Center for Phage Technology, Texas A&M University, College Station, TX USA.
Texas A&M University · USThe University of Texas at Austin · US

Funding

Experimental viral evolution at high mutation rateR01GM057756 · NIGMS · UNIVERSITY OF TEXAS AUSTIN · PI BULL, JAMES J · 1998 to 2013
$2.9M
NIGMS NIH HHS R01 GM057756
6 · The paper itself

Abstract

The use of bacteriophages as antibacterial agents is being actively researched on a global scale. Typically, the phages used are isolated from the wild by plating on the bacteria of interest, and a far larger set of candidate phages is often available than can be used in any application. When an excess of phages is available, how should the best phages be identified? Here we consider phage-bacterial population dynamics as a basis for evaluating and predicting phage success. A central question is whether the innate dynamical properties of phages are the determinants of success, or instead, whether extrinsic, indirect effects can be responsible. We address the dynamical perspective, motivated in part by the absence of dynamics in previously suggested principles of phage therapy. Current mathematical models of bacterial-phage dynamics do not capture the realities of in vivo dynamics, nor is this likely to change, but they do give insight to qualitative properties that may be generalizable. In particular, phage adsorption rate may be critical to treatment success, so understanding the effects of the in vivo environment on host availability may allow prediction of useful phages prior to in vivo experimentation. Principles for predicting efficacy may be derived by developing a greater understanding of the in vivo system, or such principles could be determined empirically by comparing phages with known differences in their dynamic properties. The comparative approach promises to be a powerful method of discovering the key to phage success. We offer five recommendations for future study: (i) compare phages differing in treatment efficacy to identify the phage properties associated with success, (ii) assay dynamics in vivo, (iii) understand mechanisms of bacterial escape from phages, (iv) test phages in model infections that are relevant to the intended clinical applications, and (v) develop new classes of models for phage growth in spatially heterogeneous environments.

Indexed as

bacterial infectionsbacteriophagemathematical modelphage therapypopulation dynamics

Identifiers

PMID25477869
PMCPMC4235362
OpenAlexW2087220763

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.