Evidence map›Paper›PMID 28289838›Full record

ArticleJournal of mathematical biology2017

A stochastic analysis of autoregulation of gene expression.

Renaud Dessalles, Vincent Fromion, Philippe Robert

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Journal of mathematical biology, 2017. 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
1.6field-weighted citation impact, top 18% of its field
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, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Limits of noise for autoregulated gene expression.Journal of mathematical biology · 2018
    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 at 2 institutions in 2 countries.

Renaud DessallesMaIAGE, INRA, Domaine de Vilvert, 78350, Jouy-en-Josas, France.
Vincent FromionMaIAGE, INRA, Domaine de Vilvert, 78350, Jouy-en-Josas, France.
Philippe RobertINRIA Paris, 2 rue Simone Iff, CS 42112, 75589, Paris Cedex 12, France. Philippe.Robert@inria.fr.ORCID 0000-0001-8122-8880
Institut national de recherche en sciences et technologies du numérique · FRMathématiques et Informatique Appliquées du Génome à l'Environnement · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper analyzes, in the context of a prokaryotic cell, the stochastic variability of the number of proteins when there is a control of gene expression by an autoregulation scheme. The goal of this work is to estimate the efficiency of the regulation to limit the fluctuations of the number of copies of a given protein. The autoregulation considered in this paper relies mainly on a negative feedback: the proteins are repressors of their own gene expression. The efficiency of a production process without feedback control is compared to a production process with an autoregulation of the gene expression assuming that both of them produce the same average number of proteins. The main characteristic used for the comparison is the standard deviation of the number of proteins at equilibrium. With a Markovian representation and a simple model of repression, we prove that, under a scaling regime, the repression mechanism follows a Hill repression scheme with an hyperbolic control. An explicit asymptotic expression of the variance of the number of proteins under this regulation mechanism is obtained. Simulations are used to study other aspects of autoregulation such as the rate of convergence to equilibrium of the production process and the case where the control of the production process of proteins is achieved via the inhibition of mRNAs.

Indexed as

Gene Expression RegulationModels, GeneticFeedback, PhysiologicalHomeostasisMarkov ChainsMathematical ConceptsProkaryotic CellsProtein BiosynthesisRNA, MessengerStochastic ProcessesRNA, Messenger60G5592C3792D20

Identifiers

PMID28289838
OpenAlexW2479390832

What OpenQuestion holds

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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.