Evidence map›Paper›PMID 33957052›Full record

ReviewAnnual review of biophysics2021

From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling.

Michael G Cortes, Yiruo Lin, Lanying Zeng, Gábor Balázsi

Abstract readReview
In one paragraph

Review in Annual review of biophysics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Discovery and characterization of a novelPHAGE (New Rochelle, N.Y.) · 2026
    Article
  3. Article
  4. Article
  5. Bacterial defense and phage counterdefense lead to coexistence in a modeled ecosystem.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Bacteriophage therapy for drug-resistantFrontiers in cellular and infection microbiology · 2024
    Review
  7. Article
  8. Disarm The Bacteria: What Temperate Phages Can Do.Current issues in molecular biology · 2023
    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

4 authors.

Michael G CortesThe Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; email: gabor.balazsi@stonybrook.edu.
Yiruo LinDepartment of Computer Science and Engineering, Texas A&M University, College Station, Texas 77843, USA.
Lanying ZengDepartment of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, USA; email: lzeng@tamu.edu.
Gábor BalázsiThe Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; email: 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

Cellular decision making is the process whereby cells choose one developmental pathway from multiple possible ones, either spontaneously or due to environmental stimuli. Examples in various cell types suggest an almost inexhaustible plethora of underlying molecular mechanisms. In general, cellular decisions rely on the gene regulatory network, which integrates external signals to drive cell fate choice. The search for general principles of such a process benefits from appropriate biological model systems that reveal how and why certain gene regulatory mechanisms drive specific cellular decisions according to ecological context and evolutionary outcomes. In this article, we review the historical and ongoing development of the phage lambda lysis-lysogeny decision as a model system to investigate all aspects of cellular decision making. The unique generality, simplicity, and richness of phage lambda decision making render it a constant source ofmathematical modeling-aided inspiration across all of biology. We discuss the origins and progress of quantitative phage lambda modeling from the 1950s until today, as well as its possible future directions. We provide examples of how modeling enabled methods and theory development, leading to new biological insights by revealing gaps in the theory and pinpointing areas requiring further experimental investigation. Overall, we highlight the utility of theoretical approaches both as predictive tools, to forecast the outcome of novel experiments, and as explanatory tools, to elucidate the natural processes underlying experimental data.

Indexed as

Models, BiologicalBacteriophage lambdaGene Regulatory NetworksLysogenybistablecellular decision makingevolutionphage lambdastochasticthreshold

Identifiers

PMID33957052
PMCPMC8590857

What OpenQuestion holds

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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.