Evidence map›Paper›PMID 31034470›Full record

ArticlePLoS computational biology2019

The nonlinear dynamics and fluctuations of mRNA levels in cell cycle coupled transcription.

Qiwen Sun, Feng Jiao, Genghong Lin, Jianshe Yu, Moxun Tang

Abstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Qiwen SunCenter for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.
Feng JiaoCenter for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.
Genghong LinCenter for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.
Jianshe YuCenter for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China.
Moxun TangDepartment of Mathematics, Michigan State University, East Lansing, Michigan, United States of America.ORCID 0000-0001-7589-8052

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene transcription is a noisy process, and cell division cycle is an important source of gene transcription noise. In this work, we develop a mathematical approach by coupling transcription kinetics with cell division cycles to delineate how they are combined to regulate transcription output and noise. In view of gene dosage, a cell cycle is divided into an early stage [Formula: see text] and a late stage [Formula: see text]. The analytical forms for the mean and the noise of mRNA numbers are given in each stage. The analysis based on these formulas predicts precisely the fold change r* of mRNA numbers from [Formula: see text] to [Formula: see text] measured in a mouse embryonic stem cell line. When transcription follows similar kinetics in both stages, r* buffers against DNA dosage variation and r* ∈ (1, 2). Numerical simulations suggest that increasing cell cycle durations up-regulates transcription with less noise, whereas rapid stage transitions induce highly noisy transcription. A minimization of the transcription noise is observed when transcription homeostasis is attained by varying a single kinetic rate. When the transcription level scales with cellular volume, either by reducing the transcription burst frequency or by increasing the burst size in [Formula: see text], the noise shows only a minor variation over a wide range of cell cycle stage durations. The reduction level in the burst frequency is nearly a constant, whereas the increase in the burst size is conceivably sensitive, when responding to a large random variation of the cell cycle durations and the gene duplication time.

Indexed as

Cell CycleModels, BiologicalRNA, MessengerTranscription, GeneticAnimalsCell LineComputational BiologyMiceNonlinear DynamicsRNA, Messenger

Identifiers

PMID31034470
PMCPMC6508750

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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.