Evidence map›Paper›PMID 22365642›Full record

ReviewTrends in genetics : TIG2012

Interplay between gene expression noise and regulatory network architecture.

Guilhem Chalancon, Charles N J Ravarani, S Balaji, Alfonso Martinez-Arias, L Aravind, Raja Jothi, M Madan Babu

Abstract readReview
In one paragraph

Review in Trends in genetics : TIG, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
130citing papers in PubMed, 1 pooled it
–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

130 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  15. Joint disruption ofProceedings of the National Academy of Sciences of the United States of America · 2025
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  17. Computational and structural biotechnology journal · 2025
    Article
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70 more citing papers are in PubMed but not listed here.

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

7 authors.

Guilhem ChalanconMRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK. guilhem@mrc-lmb.cam.ac.uk
Charles N J Ravarani
S Balaji
Alfonso Martinez-Arias
L Aravind
Raja Jothi
M Madan Babu

Funding

Gene regulatory elements, networks, and mechanismsZIAES102625 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI JOTHI, RAJA · 2009 to 2025
$27.4M
Intramural NIH HHS ZIA ES102625Medical Research Council MC_U105185859PHS HHS 1ZIAES102625-03Wellcome Trust 079249
6 · The paper itself

Abstract

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.

Indexed as

AnimalsBasal MetabolismCell Physiological PhenomenaGene ExpressionGene Regulatory NetworksHumansModels, Biological

Identifiers

PMID22365642
PMCPMC3340541

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.