Evidence map›Paper›PMID 36199250›Full record

ArticleBiophysical journal2023

Stochastic dynamics and ribosome-RNAP interactions in transcription-translation coupling.

Xiangting Li, Tom Chou

Abstract read
In one paragraph

Article in Biophysical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Xiangting LiDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California.
Tom ChouDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California; Department of Mathematics, University of California, Los Angeles, Los Angeles, California. Electronic address: tomchou@ucla.edu.

Funding

Tracking clonal dynamics during hematopoiesis: mechanistic insight via modeling and data analysisR01HL146552 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHOU, TOM · 2019 to 2022
$907k
NHLBI NIH HHS R01 HL146552
6 · The paper itself

Abstract

Under certain cellular conditions, transcription and mRNA translation in prokaryotes appear to be "coupled," in which the formation of mRNA transcript and production of its associated protein are temporally correlated. Such transcription-translation coupling (TTC) has been evoked as a mechanism that speeds up the overall process, provides protection against premature termination, and/or regulates the timing of transcript and protein formation. What molecular mechanisms underlie ribosome-RNAP coupling and how they can perform these functions have not been explicitly modeled. We develop and analyze a continuous-time stochastic model that incorporates ribosome and RNAP elongation rates, initiation and termination rates, RNAP pausing, and direct ribosome and RNAP interactions (exclusion and binding). Our model predicts how distributions of delay times depend on these molecular features of transcription and translation. We also propose additional measures for TTC: a direct ribosome-RNAP binding probability and the fraction of time the translation-transcription process is "protected" from attack by transcription-terminating proteins. These metrics quantify different aspects of TTC and differentially depend on parameters of known molecular processes. We use our metrics to reveal how and when our model can exhibit either acceleration or deceleration of transcription, as well as protection from termination. Our detailed mechanistic model provides a basis for designing new experimental assays that can better elucidate the mechanisms of TTC.

Indexed as

Protein BiosynthesisTranscription, GeneticDNA-Directed RNA PolymerasesEscherichia coliRibosomesDNA-Directed RNA Polymerases

Identifiers

PMID36199250
PMCPMC9822797

What OpenQuestion holds

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Registered trials

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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.