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Collective Cell Motion in an Epithelial Sheet Can Be Quantitatively Described by a Stochastic Interacting Particle Model

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Collective Cell Motion in an Epithelial Sheet Can Be Quantitatively Described by a Stochastic Interacting Particle Model
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002944
Pubmed ID
Authors

Néstor Sepúlveda, Laurence Petitjean, Olivier Cochet, Erwan Grasland-Mongrain, Pascal Silberzan, Vincent Hakim

Abstract

Modelling the displacement of thousands of cells that move in a collective way is required for the simulation and the theoretical analysis of various biological processes. Here, we tackle this question in the controlled setting where the motion of Madin-Darby Canine Kidney (MDCK) cells in a confluent epithelium is triggered by the unmasking of free surface. We develop a simple model in which cells are described as point particles with a dynamic based on the two premises that, first, cells move in a stochastic manner and, second, tend to adapt their motion to that of their neighbors. Detailed comparison to experimental data show that the model provides a quantitatively accurate description of cell motion in the epithelium bulk at early times. In addition, inclusion of model "leader" cells with modified characteristics, accounts for the digitated shape of the interface which develops over the subsequent hours, providing that leader cells invade free surface more easily than other cells and coordinate their motion with their followers. The previously-described progression of the epithelium border is reproduced by the model and quantitatively explained.

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Mendeley readers

The data shown below were compiled from readership statistics for 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
France 3 1%
Germany 1 <1%
United Kingdom 1 <1%
India 1 <1%
Japan 1 <1%
Denmark 1 <1%
Unknown 242 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 85 33%
Researcher 42 16%
Student > Master 25 10%
Professor > Associate Professor 18 7%
Professor 14 5%
Other 41 16%
Unknown 32 12%
Readers by discipline Count As %
Physics and Astronomy 78 30%
Agricultural and Biological Sciences 52 20%
Engineering 31 12%
Biochemistry, Genetics and Molecular Biology 21 8%
Mathematics 10 4%
Other 25 10%
Unknown 40 16%