Evidence map›Paper›PMID 21167222›Full record

ArticleJournal of biotechnology2011

Monte Carlo simulations of plasma membrane corral-induced EGFR clustering.

Michelle N Costa, Krishnan Radhakrishnan, Jeremy S Edwards

Abstract read
In one paragraph

Article in Journal of biotechnology, 2011. 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
1.4field-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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Single-molecule labeling for studying trafficking of renal transporters.American journal of physiology. Renal physiology · 2018
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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

3 authors at 2 institutions in 1 country.

Michelle N CostaChemical and Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131, United States.
Krishnan Radhakrishnan
Jeremy S Edwards
University of New Mexico · USNew Mexico Cancer Center · US

Funding

UNM TRAININGP50GM085273 · NIGMS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2009 to 2018
$26.1M
A Spatially coarse-grained, rule-based frame work for modeling large molecularR01GM104973 · NIGMS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2012 to 2015
$1.3M
NIGMS NIH HHS P50 GM085273NIGMS NIH HHS P50GM085273NIGMS NIH HHS R01 GM104973
6 · The paper itself

Abstract

Experimental evidence suggests that the cell membrane is a highly organized structure that is compartmentalized by the underlying membrane cytoskeleton (MSK). The interaction between the cell membrane and the cytoskeleton led to the "picket-fence" model, which was proposed to explain certain aspects of membrane compartmentalization. This model assumes that the MSK hinders and confines the motion of receptors and lipids to compartments in the membrane. However, the impact of the MSK on receptor clustering, aggregation, and downstream signaling remains unclear. For example, some evidence suggests that the MSK enhances dimerization, while other evidence suggests decreased dimerization and signaling. Herein, we use computational Monte Carlo simulations to examine the effects of MSK density and receptor concentration on receptor dimerization and clustering. Preliminary results suggest that the MSK may have the potential to induce receptor clustering, which is a function of both picket-fence density and receptor concentration.

Indexed as

Models, BiologicalMonte Carlo MethodAlgorithmsCell MembraneCluster AnalysisComputational BiologyErbB ReceptorsFuzzy LogicHumansMembrane MicrodomainsErbB Receptors

Identifiers

PMID21167222
PMCPMC3822008
OpenAlexW1997276487

What OpenQuestion holds

Textmetadata
LicenceTDM
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.