Evidence map›Paper›PMID 23804438›Full record

ArticleJournal of the Royal Society, Interface2013

Computational modelling of Smad-mediated negative feedback and crosstalk in the TGF-β superfamily network.

Daniel Nicklas, Leonor Saiz

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

17 citing papers in PubMed, 30 citations in OpenAlex.

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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.

Daniel NicklasModeling of Biological Networks Laboratory, Department of Biomedical Engineering, University of California, 451 East Health Sciences Drive, Davis, CA 95616, USA.
Leonor Saiz
West Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transforming growth factor-β (TGF-β) signal transduction pathway controls many cellular processes, including differentiation, proliferation and apoptosis. It plays a fundamental role during development and it is dysregulated in many diseases. The factors that control the dynamics of the pathway, however, are not fully elucidated yet and so far computational approaches have been very limited in capturing the distinct types of behaviour observed under different cellular backgrounds and conditions into a single-model description. Here, we develop a detailed computational model for TGF-β signalling that incorporates elements of previous models together with crosstalking between Smad1/5/8 and Smad2/3 channels through a negative feedback loop dependent on Smad7. The resulting model accurately reproduces the diverse behaviour of experimental datasets for human keratinocytes, bovine aortic endothelial cells and mouse mesenchymal cells, capturing the dynamics of activation and nucleocytoplasmic shuttling of both R-Smad channels. The analysis of the model dynamics and its system properties revealed Smad7-mediated crosstalking between Smad1/5/8 and Smad2/3 channels as a major determinant in shaping the distinct responses to single and multiple ligand stimulation for different cell types.

Indexed as

Computer SimulationModels, BiologicalActive Transport, Cell NucleusAnimalsAortaCattleCell NucleusEndothelial CellsHumansKeratinocytesMiceSignal TransductionSmad Proteins, Receptor-RegulatedTransforming Growth Factor betaSmad Proteins, Receptor-RegulatedTransforming Growth Factor betacomputational modelling of signalling pathwayscoupled signalling and crosstalknegative feedback loopsignal transductiontransforming growth factor-β signal transduction pathwaytransforming growth factor-β superfamily network

Identifiers

PMID23804438
PMCPMC3730684
OpenAlexW2128942175

What OpenQuestion holds

Textmetadata
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.