Evidence map›Paper›PMID 26812364›Full record

ArticlePLoS computational biology2016

Probing the Limits to MicroRNA-Mediated Control of Gene Expression.

Araks Martirosyan, Matteo Figliuzzi, Enzo Marinari, Andrea De Martino

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Review
  3. Out-of-Equilibrium ceRNA Crosstalk.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  4. Article
  5. microRNA-mediated noise processing in cells: A fight or a game?Computational and structural biotechnology journal · 2020
    Review
  6. Competing endogenous RNA crosstalk at system level.PLoS computational biology · 2019
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

4 authors at 4 institutions in 2 countries.

Araks MartirosyanDipartimento di Fisica, Sapienza Università di Roma, Rome, Italy.
Matteo FigliuzziSorbonne Universités, UPMC, Institut de Calcul et de la Simulation, Paris, France.
Enzo MarinariDipartimento di Fisica, Sapienza Università di Roma, Rome, Italy.
Andrea De MartinoDipartimento di Fisica, Sapienza Università di Roma, Rome, Italy.
Centre National de la Recherche Scientifique · FRIstituto di Nanotecnologia · ITIstituto Nazionale di Fisica Nucleare, Sezione di Roma I · ITItalian Institute of Technology · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to the 'ceRNA hypothesis', microRNAs (miRNAs) may act as mediators of an effective positive interaction between long coding or non-coding RNA molecules, carrying significant potential implications for a variety of biological processes. Here, inspired by recent work providing a quantitative description of small regulatory elements as information-conveying channels, we characterize the effectiveness of miRNA-mediated regulation in terms of the optimal information flow achievable between modulator (transcription factors) and target nodes (long RNAs). Our findings show that, while a sufficiently large degree of target derepression is needed to activate miRNA-mediated transmission, (a) in case of differential mechanisms of complex processing and/or transcriptional capabilities, regulation by a post-transcriptional miRNA-channel can outperform that achieved through direct transcriptional control; moreover, (b) in the presence of large populations of weakly interacting miRNA molecules the extra noise coming from titration disappears, allowing the miRNA-channel to process information as effectively as the direct channel. These observations establish the limits of miRNA-mediated post-transcriptional cross-talk and suggest that, besides providing a degree of noise buffering, this type of control may be effectively employed in cells both as a failsafe mechanism and as a preferential fine tuner of gene expression, pointing to the specific situations in which each of these functionalities is maximized.

Indexed as

Models, GeneticAlgorithmsComputational BiologyGene Expression RegulationMicroRNAsTranscription FactorsMicroRNAsTranscription Factors

Identifiers

PMID26812364
PMCPMC4727922
OpenAlexW2267017199

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.