Evidence map›Paper›PMID 21832809›Full record

ArticlePhysical biology2011

Quantifying negative feedback regulation by micro-RNAs.

Shangying Wang, Sridhar Raghavachari

Open access · greenAbstract read
In one paragraph

Article in Physical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
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  8. Bistable switch in let-7 miRNA biogenesis pathway involving Lin28.International journal of molecular sciences · 2014
    Article
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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

2 authors at 1 institution in 1 country.

Shangying WangDepartment of Physics, Duke University, Durham, NC 27708, USA. shangying.wang@duke.edu
Sridhar Raghavachari
Duke University · US

Funding

Contributions of Areas LIP and VIP to Numerical BehaviorR01EY019303 · NEI · DUKE UNIVERSITY · PI PLATT, MICHAEL L · 2009 to 2012
$1.8M
CRCNS: Quantitative description of initial biochemical steps in LTPR01DA027807 · NIDA · BRANDEIS UNIVERSITY · PI LISMAN, JOHN E · 2009 to 2013
$1.6M
NEI NIH HHS EY019303NEI NIH HHS R01 EY019303NIDA NIH HHS DA027807NIDA NIH HHS R01 DA027807
6 · The paper itself

Abstract

Micro-RNAs (miRNAs) play a crucial role in post-transcriptional gene regulation by pairing with target mRNAs to repress protein production. It has been shown that over one-third of human genes are targeted by miRNA. Although hundreds of miRNAs have been identified in mammalian genomes, the function of miRNA-based repression in the context of gene regulation networks still remains unclear. In this study, we explore the functional roles of feedback regulation by miRNAs. In a model where repression of translation occurs by sequestration of mRNA by miRNA, we find that miRNA and mRNA levels are anti-correlated, resulting in larger fluctuation in protein levels than theoretically expected assuming no correlation between miRNA and mRNA levels. If miRNA repression is due to a catalytic suppression of translation rates, we analytically show that the protein fluctuations can be strongly repressed with miRNA regulation. We also discuss how either of these modes may be relevant for cell function.

Indexed as

Gene Expression RegulationAnimalsDown-RegulationFeedback, PhysiologicalGene Regulatory NetworksGenomeHumansMicroRNAsRNA, MessengerMicroRNAsRNA, Messenger

Identifiers

PMID21832809
PMCPMC3184398
OpenAlexW2043169862

What OpenQuestion holds

Textmetadata
LicenceTDM
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.