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A Comparison of Computational Models for Eukaryotic Cell Shape and Motility

Overview of attention for article published in PLoS Computational Biology, December 2012
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Title
A Comparison of Computational Models for Eukaryotic Cell Shape and Motility
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002793
Pubmed ID
Authors

William R. Holmes, Leah Edelstein-Keshet

Abstract

Eukaryotic cell motility involves complex interactions of signalling molecules, cytoskeleton, cell membrane, and mechanics interacting in space and time. Collectively, these components are used by the cell to interpret and respond to external stimuli, leading to polarization, protrusion, adhesion formation, and myosin-facilitated retraction. When these processes are choreographed correctly, shape change and motility results. A wealth of experimental data have identified numerous molecular constituents involved in these processes, but the complexity of their interactions and spatial organization make this a challenging problem to understand. This has motivated theoretical and computational approaches with simplified caricatures of cell structure and behaviour, each aiming to gain better understanding of certain kinds of cells and/or repertoire of behaviour. Reaction-diffusion (RD) equations as well as equations of viscoelastic flows have been used to describe the motility machinery. In this review, we describe some of the recent computational models for cell motility, concentrating on simulations of cell shape changes (mainly in two but also three dimensions). The problem is challenging not only due to the difficulty of abstracting and simplifying biological complexity but also because computing RD or fluid flow equations in deforming regions, known as a "free-boundary" problem, is an extremely challenging problem in applied mathematics. Here we describe the distinct approaches, comparing their strengths and weaknesses, and the kinds of biological questions that they have been able to address.

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Geographical breakdown

Country Count As %
United States 4 2%
Germany 3 2%
United Kingdom 3 2%
Japan 2 1%
Brazil 1 <1%
Denmark 1 <1%
Czechia 1 <1%
France 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 172 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 29%
Researcher 49 26%
Student > Master 18 10%
Professor 11 6%
Student > Doctoral Student 9 5%
Other 28 15%
Unknown 19 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 23%
Physics and Astronomy 30 16%
Engineering 26 14%
Biochemistry, Genetics and Molecular Biology 21 11%
Mathematics 19 10%
Other 24 13%
Unknown 25 13%