Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1005726
Title: Probing eukaryotic cell mechanics via mesoscopic simulations
Authors: Lykov K.
Nematbakhsh Y.
Shang M.
Lim C.T. 
Pivkin I.V.
Keywords: Article
biomechanics
calibration
cell density
cell membrane
cell nucleus
cell size
controlled study
cytoskeleton
eukaryotic cell
human
human cell
microfluidic analysis
micropipette
simulation
velocity
viscoelasticity
Young modulus
biological model
biology
biomechanics
cell line
cytology
elasticity
epithelium cell
microfluidics
micromanipulation
physiology
viscosity
Biomechanical Phenomena
Cell Line
Cell Membrane
Cell Nucleus
Computational Biology
Cytoskeleton
Elasticity
Epithelial Cells
Humans
Microfluidics
Micromanipulation
Models, Biological
Viscosity
Issue Date: 2017
Publisher: Public Library of Science
Citation: Lykov K., Nematbakhsh Y., Shang M., Lim C.T., Pivkin I.V. (2017). Probing eukaryotic cell mechanics via mesoscopic simulations. PLoS Computational Biology 13 (9) : e1005726. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1005726
Abstract: Cell mechanics has proven to be important in many biological processes. Although there is a number of experimental techniques which allow us to study mechanical properties of cell, there is still a lack of understanding of the role each sub-cellular component plays during cell deformations. We present a new mesoscopic particle-based eukaryotic cell model which explicitly describes cell membrane, nucleus and cytoskeleton. We employ Dissipative Particle Dynamics (DPD) method that provides us with the unified framework for modeling of a cell and its interactions in the flow. Data from micropipette aspiration experiments were used to define model parameters. The model was validated using data from microfluidic experiments. The validated model was then applied to study the impact of the sub-cellular components on the cell viscoelastic response in micropipette aspiration and microfluidic experiments. © 2017 Lykov et al.
Source Title: PLoS Computational Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/165370
ISSN: 1553734X
DOI: 10.1371/journal.pcbi.1005726
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