Evidence map›Paper›PMID 20224792›Full record

ArticlePloS one2010

Human mammary epithelial cells exhibit a bimodal correlated random walk pattern.

Alka A Potdar, Junhwan Jeon, Alissa M Weaver, Vito Quaranta, Peter T Cummings

Abstract read
In one paragraph

Article in PloS one, 2010. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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.

  1. Article
  2. pERK transition-induced directional mode switching promotes epithelial tumor cell migration.Proceedings of the National Academy of Sciences of the United States of America · 2024
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  4. Review
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  11. Unimodal and bimodal random motions of independent exponential steps.The European physical journal. E, Soft matter · 2014
    Article
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  13. Article
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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

5 authors.

Alka A PotdarDepartment of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.
Junhwan Jeon
Alissa M Weaver
Vito Quaranta
Peter T Cummings

Funding

TrainingU54CA113007 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO · 2004 to 2014
$19.2M
Cortactin function in lamellipodial protrusionR01GM075126 · NIGMS · VANDERBILT UNIVERSITY · PI WEAVER, ALISSA M · 2007 to 2011
$1.4M
NCI NIH HHS 5U54CA113007-02NCI NIH HHS U54 CA113007NIGMS NIH HHS R01 GM075126
6 · The paper itself

Abstract

backgroundOrganisms, at scales ranging from unicellular to mammals, have been known to exhibit foraging behavior described by random walks whose segments confirm to Lévy or exponential distributions. For the first time, we present evidence that single cells (mammary epithelial cells) that exist in multi-cellular organisms (humans) follow a bimodal correlated random walk (BCRW). METHODOLOGY/PRINCIPAL

findingsCellular tracks of MCF-10A pBabe, neuN and neuT random migration on 2-D plastic substrates, analyzed using bimodal analysis, were found to reveal the BCRW pattern. We find two types of exponentially distributed correlated flights (corresponding to what we refer to as the directional and re-orientation phases) each having its own correlation between move step-lengths within flights. The exponential distribution of flight lengths was confirmed using different analysis methods (logarithmic binning with normalization, survival frequency plots and maximum likelihood estimation). CONCLUSIONS/SIGNIFICANCE: Because of the presence of non-uniform turn angle distribution of move step-lengths within a flight and two different types of flights, we propose that the epithelial random walk is a BCRW comprising of two alternating modes with varying degree of correlations, rather than a simple persistent random walk. A BCRW model rather than a simple persistent random walk correctly matches the super-diffusivity in the cell migration paths as indicated by simulations based on the BCRW model.

Indexed as

Cell MovementAlgorithmsComputer SimulationDiffusionEpithelial CellsHumansLikelihood FunctionsMammary Glands, HumanModels, BiologicalModels, StatisticalModels, TheoreticalNormal Distribution

Identifiers

PMID20224792
PMCPMC2835765

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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.