Evidence map›Paper›PMID 28407804›Full record

ArticleBMC systems biology2017

Mathematical model of TGF-βsignalling: feedback coupling is consistent with signal switching.

Shabnam Khatibi, Hong-Jian Zhu, John Wagner, Chin Wee Tan, Jonathan H Manton, Antony W Burgess

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC systems biology, 2017. 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
0.6field-weighted citation impact, top 34% 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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  11. Review
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 at 3 institutions in 1 country.

Shabnam KhatibiElectrical and Electronic Engineering Department, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Hong-Jian ZhuDepartment of Surgery (RMH), The University of Melbourne, Parkville, Victoria, 3050, Australia.
John WagnerIBM Research Collaboratory for Life Sciences-Melbourne, Victorian Life Sciences Computation Initiative, 87 Grattan Street, Victoria, 3010, Australia.
Chin Wee TanThe Walter and Eliza Hall Institute of Medical Research (WEHI), 1G Royal Parade, Parkville, Victoria, 3052, Australia.
Jonathan H MantonElectrical and Electronic Engineering Department, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Antony W BurgessDepartment of Surgery (RMH), The University of Melbourne, Parkville, Victoria, 3050, Australia. Tony.Burgess@wehi.edu.au.
Walter and Eliza Hall Institute of Medical Research · AUUniversity of Melbourne · AUMelbourne Bioinformatics · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTransforming growth factor β (TGF-β) signalling regulates the development of embryos and tissue homeostasis in adults. In conjunction with other oncogenic changes, long-term perturbation of TGF-β signalling is associated with cancer metastasis. Although TGF-β signalling can be complex, many of the signalling components are well defined, so it is possible to develop mathematical models of TGF-β signalling using reduction and scaling methods. The parameterization of our TGF-β signalling model is consistent with experimental data.

resultsWe developed our mathematical model for the TGF-β signalling pathway, i.e. the RF- model of TGF-β signalling, using the "rapid equilibrium assumption" to reduce the network of TGF-β signalling reactions based on the time scales of the individual reactions. By adding time-delayed positive feedback to the inherent time-delayed negative feedback for TGF-β signalling. We were able to simulate the sigmoidal, switch-like behaviour observed for the concentration dependence of long-term (> 3 hours) TGF-β stimulation. Computer simulations revealed the vital role of the coupling of the positive and negative feedback loops on the regulation of the TGF-β signalling system. The incorporation of time-delays for the negative feedback loop improved the accuracy, stability and robustness of the model. This model reproduces both the short-term and long-term switching responses for the intracellular signalling pathways at different TGF-β concentrations. We have tested the model against experimental data from MEF (mouse embryonic fibroblasts) WT, SV40-immortalized MEFs and Gp130

conclusionsSignalling feedback loops are required to model TGF-β signal transduction and its effects on normal and cancer cells. We focus on the effects of time-delayed feedback loops and their coupling to ligand stimulation in this system. The model was simplified and reduced to its key components using standard methods and the rapid equilibrium assumption. We detected differences in short-term and long-term signal switching. The results from the RF- model compare well with experimental data and predict the dynamics of TGF-β signalling in cancer cells with different mutations.

Indexed as

Feedback, PhysiologicalModels, BiologicalSignal TransductionAnimalsKineticsMiceNeoplasmsTransforming Growth Factor betaTransforming Growth Factor betaCancerFeedback couplingMathematical modellingRapid equilibrium assumptionReductionSignal switchingTGF-β signallingTime-delay

Identifiers

PMID28407804
PMCPMC5390422
OpenAlexW2607363868

What OpenQuestion holds

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LicenceCC BY
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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.