Literature DB >> 18068728

Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion.

A Gerisch1, M A J Chaplain.   

Abstract

The ability to invade tissue is one of the hallmarks of cancer. Cancer cells achieve this through the secretion of matrix degrading enzymes, cell proliferation, loss of cell-cell adhesion, enhanced cell-matrix adhesion and active migration. Invasion of tissue by the cancer cells is one of the key components in the metastatic cascade, whereby cancer cells spread to distant parts of the host and initiate the growth of secondary tumours (metastases). A better understanding of the complex processes involved in cancer invasion may ultimately lead to treatments being developed which can localise cancer and prevent metastasis. In this paper we formulate a novel continuum model of cancer cell invasion of tissue which explicitly incorporates the important biological processes of cell-cell and cell-matrix adhesion. This is achieved using non-local (integral) terms in a system of partial differential equations where the cells use a so-called "sensing radius"R to detect their environment. We show that in the limit as R-->0 the non-local model converges to a related system of reaction-diffusion-taxis equations. A numerical exploration of this model using computational simulations shows that it can form the basis for future models incorporating more details of the invasion process.

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Year:  2008        PMID: 18068728     DOI: 10.1016/j.jtbi.2007.10.026

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  46 in total

1.  Improving the time-machine: estimating date of birth of grade II gliomas.

Authors:  C Gerin; J Pallud; B Grammaticos; E Mandonnet; C Deroulers; P Varlet; L Capelle; L Taillandier; L Bauchet; H Duffau; M Badoual
Journal:  Cell Prolif       Date:  2011-12-14       Impact factor: 6.831

2.  Cellular automata and integrodifferential equation models for cell renewal in mosaic tissues.

Authors:  J M Bloomfield; J A Sherratt; K J Painter; G Landini
Journal:  J R Soc Interface       Date:  2010-04-07       Impact factor: 4.118

3.  An Adaptive Multigrid Algorithm for Simulating Solid Tumor Growth Using Mixture Models.

Authors:  S M Wise; J S Lowengrub; V Cristini
Journal:  Math Comput Model       Date:  2011-01-01

4.  An investigation of the influence of extracellular matrix anisotropy and cell-matrix interactions on tissue architecture.

Authors:  R J Dyson; J E F Green; J P Whiteley; H M Byrne
Journal:  J Math Biol       Date:  2015-09-02       Impact factor: 2.259

5.  Ground states in the diffusion-dominated regime.

Authors:  José A Carrillo; Franca Hoffmann; Edoardo Mainini; Bruno Volzone
Journal:  Calc Var Partial Differ Equ       Date:  2018-08-11       Impact factor: 1.945

6.  Mathematical modeling of cancer cell invasion of tissue: biological insight from mathematical analysis and computational simulation.

Authors:  Vivi Andasari; Alf Gerisch; Georgios Lolas; Andrew P South; Mark A J Chaplain
Journal:  J Math Biol       Date:  2010-09-26       Impact factor: 2.259

7.  Mathematical modelling of the influence of heat shock proteins on cancer invasion of tissue.

Authors:  Zuzanna Szymańska; Jakub Urbański; Anna Marciniak-Czochra
Journal:  J Math Biol       Date:  2008-09-20       Impact factor: 2.259

8.  A mathematical model for mesenchymal and chemosensitive cell dynamics.

Authors:  Anita Häcker
Journal:  J Math Biol       Date:  2011-03-25       Impact factor: 2.259

Review 9.  Mathematical models for cell migration: a non-local perspective.

Authors:  Li Chen; Kevin Painter; Christina Surulescu; Anna Zhigun
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

10.  Computational modeling of epithelial-mesenchymal transformations.

Authors:  Adrian Neagu; Vladimir Mironov; Ioan Kosztin; Bogdan Barz; Monica Neagu; Ricardo A Moreno-Rodriguez; Roger R Markwald; Gabor Forgacs
Journal:  Biosystems       Date:  2009-12-31       Impact factor: 1.973

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