Evidence map›Paper›PMID 33465069›Full record

ArticlePLoS computational biology2021

Reconciling kinetic and thermodynamic models of bacterial transcription.

Muir Morrison, Manuel Razo-Mejia, Rob Phillips

Abstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Exchange dynamics and kinetic control of gene regulation complexes.Nature reviews. Molecular cell biology · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Flexibility and sensitivity in gene regulation out of equilibrium.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  8. Article
  9. Energy Aware Technology Mapping of Genetic Logic Circuits.bioRxiv : the preprint server for biology · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Eukaryotic gene regulation at equilibrium, or non?Current opinion in systems biology · 2022
    Article
  14. Frontiers in genetics · 2021
    Review
  15. Review
  16. 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

3 authors.

Muir MorrisonDepartment of Physics, California Institute of Technology, Pasadena, California, USA.ORCID 0000-0002-0768-7234
Manuel Razo-MejiaDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.ORCID 0000-0002-9510-0527
Rob PhillipsDepartment of Physics, California Institute of Technology, Pasadena, California, USA.ORCID 0000-0003-3082-2809

Funding

The Principles of Regulatory, Conformational and Evolutionary AdaptationR35GM118043 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI ROB PHILLIPS · 2016 to 2026
$7.9M
NIGMS NIH HHS R35 GM118043
6 · The paper itself

Abstract

The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

Indexed as

Models, GeneticBacterial ProteinsComputational BiologyGene Expression Regulation, BacterialKineticsPromoter Regions, GeneticRepressor ProteinsRNA, BacterialRNA, MessengerThermodynamicsTranscription FactorsTranscription, GeneticBacterial ProteinsRepressor ProteinsRNA, BacterialRNA, MessengerTranscription Factors

Identifiers

PMID33465069
PMCPMC7845990

What OpenQuestion holds

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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.