A comparison of computational models for eukaryotic cell shape and motility.
AuthorHolmes, WR; Edelstein-Keshet, L
Source TitlePLoS Computational Biology
PublisherPublic Library of Science (PLoS)
University of Melbourne Author/sHOLMES, WILLIAM
AffiliationSchool of Mathematics and Statistics
Document TypeJournal Article
CitationsHolmes, W. R. & Edelstein-Keshet, L. (2012). A comparison of computational models for eukaryotic cell shape and motility.. PLoS Comput Biol, 8 (12), pp.e1002793-. https://doi.org/10.1371/journal.pcbi.1002793.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531321
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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