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Analysis of initial cell spreading using mechanistic contact formulations for a deformable cell model.

Abstract : Adhesion governs to a large extent the mechanical interaction between a cell and its microenvironment. As initial cell spreading is purely adhesion driven, understanding this phenomenon leads to profound insight in both cell adhesion and cell-substrate interaction. It has been found that across a wide variety of cell types, initial spreading behavior universally follows the same power laws. The simplest cell type providing this scaling of the radius of the spreading area with time are modified red blood cells (RBCs), whose elastic responses are well characterized. Using a mechanistic description of the contact interaction between a cell and its substrate in combination with a deformable RBC model, we are now able to investigate in detail the mechanisms behind this universal power law. The presented model suggests that the initial slope of the spreading curve with time results from a purely geometrical effect facilitated mainly by dissipation upon contact. Later on, the spreading rate decreases due to increasing tension and dissipation in the cell's cortex as the cell spreads more and more. To reproduce this observed initial spreading, no irreversible deformations are required. Since the model created in this effort is extensible to more complex cell types and can cope with arbitrarily shaped, smooth mechanical microenvironments of the cells, it can be useful for a wide range of investigations where forces at the cell boundary play a decisive role.
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Contributor : Paul Van Liedekerke Connect in order to contact the contributor
Submitted on : Tuesday, November 26, 2013 - 2:27:18 PM
Last modification on : Thursday, February 3, 2022 - 11:15:48 AM
Long-term archiving on: : Thursday, February 27, 2014 - 9:36:17 AM


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Tim Odenthal, Bart Smeets, Paul van Liedekerke, Engelbert Tijskens, Herman Ramon, et al.. Analysis of initial cell spreading using mechanistic contact formulations for a deformable cell model.. PLoS Computational Biology, Public Library of Science, 2013, 9 (10), ⟨10.1371/journal.pcbi.1003267⟩. ⟨hal-00909485⟩



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