A phenomenological model for cell and nucleus deformation during cancer metastasis

Jiao Chen*, Daphne Weihs, Marcel van Dijk, Fred J. Vermolen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)
68 Downloads (Pure)

Abstract

Cell migration plays an essential role in cancer metastasis. In cancer invasion through confined spaces, cells must undergo extensive deformation, which is a capability related to their metastatic potentials. Here, we simulate the deformation of the cell and nucleus during invasion through a dense, physiological microenvironment by developing a phenomenological computational model. In our work, cells are attracted by a generic emitting source (e.g., a chemokine or stiffness signal), which is treated by using Green’s Fundamental solutions. We use an IMEX integration method where the linear parts and the nonlinear parts are treated by using an Euler backward scheme and an Euler forward method, respectively. We develop the numerical model for an obstacle-induced deformation in 2D or/and 3D. Considering the uncertainty in cell mobility, stochastic processes are incorporated and uncertainties in the input variables are evaluated using Monte Carlo simulations. This quantitative study aims at estimating the likelihood for invasion and the length of the time interval in which the cell invades the tissue through an obstacle. Subsequently, the two-dimensional cell deformation model is applied to simplified cancer metastasis processes to serve as a model for in vivo or in vitro biomedical experiments.

Original languageEnglish
Pages (from-to)1429-1450
Number of pages22
JournalBiomechanics and Modeling in Mechanobiology
Volume17
Issue number5
DOIs
Publication statusPublished - 2018

Keywords

  • Cancer metastasis
  • Cell deformation
  • Cell-based model
  • Monte Carlo simulations
  • Nucleus deformation

Fingerprint

Dive into the research topics of 'A phenomenological model for cell and nucleus deformation during cancer metastasis'. Together they form a unique fingerprint.

Cite this