Evidence map›Paper›PMID 29117533›Full record

ArticleBiophysical journal2017

Late-Arriving Signals Contribute Less to Cell-Fate Decisions.

Michael G Cortes, Jimmy T Trinh, Lanying Zeng, Gábor Balázsi

Abstract read
In one paragraph

Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Revisiting the life cycle of temperate phages.Nature reviews. Microbiology · 2026
    Review
  2. Counting to two: how phages decide between lysis and lysogeny.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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
  8. Bacteriophage self-counting in the presence of viral replication.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  9. Review
  10. Article
  11. Article
  12. Optimality of the spontaneous prophage induction rate.Journal of theoretical biology · 2019
    Article
  13. Review
  14. Article
  15. Modeling cell population dynamics.In silico biology · 2019
    Article
  16. Article
  17. Article
  18. 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.

Michael G CortesThe Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.
Jimmy T TrinhDepartment of Biochemistry and Biophysics, Center for Phage Technology, Texas A&M University, College Station, Texas.
Lanying ZengDepartment of Biochemistry and Biophysics, Center for Phage Technology, Texas A&M University, College Station, Texas.
Gábor BalázsiThe Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York; Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York. Electronic address: gabor.balazsi@stonybrook.edu.

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

Gene regulatory networks are largely responsible for cellular decision-making. These networks sense diverse external signals and respond by adjusting gene expression, enabling cells to reach environment-dependent decisions crucial for their survival or reproduction. However, information-carrying signals may arrive at variable times. Besides the intrinsic strength of these signals, their arrival time (timing) may also carry information about the environment and can influence cellular decision-making in ways that are poorly understood. For example, it is unclear how the timing of individual phage infections affects the lysis-lysogeny decision of bacteriophage λ despite variable infection times being likely in the wild and even in laboratory conditions. In this work, we combine mathematical modeling with experimentation to address this question. We develop an experimentally testable theory, which reveals that late-infecting phages contribute less to cellular decision-making. This implies that infection delays lower the probability of lysogeny compared to simultaneous infections. Furthermore, we show that infection delays reduce lysogenization by providing insufficient CII for threshold crossing during the critical decision-making period. We find evidence for a cutoff time after which subsequent infections cannot influence the cellular decision. We derive an intuitive formula that approximates the probability of lysogeny for variable infection times by a time-weighted average of probabilities for simultaneous infections. We validate these theoretical predictions experimentally. Similar concepts and simplifying modeling approaches may help elucidate the mechanisms underlying other cellular decisions.

Indexed as

LysogenyModels, BiologicalSignal TransductionBacteriophage lambdaEscherichia coliGene Regulatory NetworksStochastic Processes

Identifiers

PMID29117533
PMCPMC5685783

What OpenQuestion holds

Textmetadata
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.