Evidence map›Paper›PMID 31525321›Full record

ArticleJournal of theoretical biology2019

Optimality of the spontaneous prophage induction rate.

Michael G Cortes, Jonathan Krog, Gábor Balázsi

Open access · hybridAbstract read
In one paragraph

Article in Journal of theoretical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 33 citations in OpenAlex.

  1. Review
  2. Review
  3. Human gut strains ofApplied and environmental microbiology · 2025
    Article
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  7. Article
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  10. Characterization of a New TemperatePathogens (Basel, Switzerland) · 2022
    Article
  11. Article
  12. Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  13. Review
  14. Review
  15. 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

3 authors at 1 institution in 1 country.

Michael G CortesDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
Jonathan KrogThe Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.
Gábor BalázsiDepartment of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu.
Stony Brook University · US

Funding

Dynamics and evolution of synthetic and natural gene regulatory networksR35GM122561 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI BALAZSI, GABOR · 2017 to 2025
$4.2M
Integration of Diverse Inputs Determines Developmental OutcomesR01GM107597 · NIGMS · TEXAS A&M AGRILIFE RESEARCH · PI BALAZSI, GABOR, ZENG, LANYING · 2015 to 2018
$1.7M
NIGMS NIH HHS R01 GM107597NIGMS NIH HHS R35 GM122561
6 · The paper itself

Abstract

Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.

Indexed as

Computer SimulationLysogenyModels, BiologicalProbabilityProphagesStochastic Processes

Identifiers

PMID31525321
PMCPMC6948929
OpenAlexW2972491753

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.