Evidence map›Paper›PMID 22737250›Full record

ArticlePloS one2012

Nonparametric simulation of signal transduction networks with semi-synchronized update.

Isar Nassiri, Ali Masoudi-Nejad, Mahdi Jalili, Ali Moeini

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Article
  2. 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.

Isar NassiriLaboratory of System Biology and Bioinformatics-LBB, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Ali Masoudi-Nejad
Mahdi Jalili
Ali Moeini

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.

Indexed as

Signal TransductionAlgorithmsCell Line, TumorCluster AnalysisComputer SimulationEnzyme-Linked Immunosorbent AssayErbB ReceptorsGene Expression Regulation, NeoplasticHepatocytesHumansInflammationKineticsLigandsModels, StatisticalStatistics, NonparametricErbB ReceptorsLigands

Identifiers

PMID22737250
PMCPMC3380921

What OpenQuestion holds

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Registered trials

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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.