Evidence map›Paper›PMID 24350286›Full record

ArticleBioMed research international2013

A systems' biology approach to study microRNA-mediated gene regulatory networks.

Xin Lai, Animesh Bhattacharya, Ulf Schmitz, Manfred Kunz, Julio Vera, Olaf Wolkenhauer

Abstract read
In one paragraph

Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Guideline
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  6. iScience · 2021
    Article
  7. Biomarkers in traumatic brain injury: new concepts.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2020
    Review
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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

6 authors.

Xin LaiDepartment of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany ; Laboratory of Systems Tumor Immunology, Department of Dermatology, Faculty of Medicine, University of Erlangen-Nuremberg, Ulmenweg 18, 91054 Erlangen, Germany.
Animesh BhattacharyaDepartment of Dermatology, Venereology and Allergology, University of Leipzig, 04155 Leipzig, Germany.
Ulf SchmitzDepartment of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
Manfred KunzDepartment of Dermatology, Venereology and Allergology, University of Leipzig, 04155 Leipzig, Germany.
Julio VeraLaboratory of Systems Tumor Immunology, Department of Dermatology, Faculty of Medicine, University of Erlangen-Nuremberg, Ulmenweg 18, 91054 Erlangen, Germany.
Olaf WolkenhauerDepartment of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany ; Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch 7600, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

Indexed as

Computational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsModels, GeneticNeoplasmsp21-Activated KinasesMicroRNAsp21-Activated Kinases

Identifiers

PMID24350286
PMCPMC3848080

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.