Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10237-012-0374-y
Title: A power-law rheology-based finite element model for single cell deformation
Authors: Zhou, E.H.
Xu, F.
Quek, S.T. 
Lim, C.T. 
Keywords: Cell injury
Cell mechanics
Finite element analysis
Mechanotransduction
Soft glassy rheology
Issue Date: Sep-2012
Source: Zhou, E.H., Xu, F., Quek, S.T., Lim, C.T. (2012-09). A power-law rheology-based finite element model for single cell deformation. Biomechanics and Modeling in Mechanobiology 11 (7) : 1075-1084. ScholarBank@NUS Repository. https://doi.org/10.1007/s10237-012-0374-y
Abstract: Physical forces can elicit complex time- and space-dependent deformations in living cells. These deformations at the subcellular level are difficult to measure but can be estimated using computational approaches such as finite element (FE) simulation. Existing FE models predominantly treat cells as spring-dashpot viscoelastic materials, while broad experimental data are now lending support to the power-law rheology (PLR) model. Here, we developed a large deformation FE model that incorporated PLR and experimentally verified this model by performing micropipette aspiration on fibroblasts under various mechanical loadings. With a single set of rheological properties, this model recapitulated the diverse micropipette aspiration data obtained using three protocols and with a range of micropipette sizes. More intriguingly, our analysis revealed that decreased pipette size leads to increased pressure gradient, potentially explaining our previous counterintuitive finding that decreased pipette size leads to increased incidence of cell blebbing and injury. Taken together, our work leads to more accurate rheological interpretation of micropipette aspiration experiments than previous models and suggests pressure gradient as a potential determinant of cell injury. © 2012 Springer-Verlag.
Source Title: Biomechanics and Modeling in Mechanobiology
URI: http://scholarbank.nus.edu.sg/handle/10635/50817
ISSN: 16177959
DOI: 10.1007/s10237-012-0374-y
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