Literature DB >> 14575659

A cellular automaton model for tumour growth in inhomogeneous environment.

T Alarcón1, H M Byrne, P K Maini.   

Abstract

Most of the existing mathematical models for tumour growth and tumour-induced angiogenesis neglect blood flow. This is an important factor on which both nutrient and metabolite supply depend. In this paper we aim to address this shortcoming by developing a mathematical model which shows how blood flow and red blood cell heterogeneity influence the growth of systems of normal and cancerous cells. The model is developed in two stages. First we determine the distribution of oxygen in a native vascular network, incorporating into our model features of blood flow and vascular dynamics such as structural adaptation, complex rheology and red blood cell circulation. Once we have calculated the oxygen distribution, we then study the dynamics of a colony of normal and cancerous cells, placed in such a heterogeneous environment. During this second stage, we assume that the vascular network does not evolve and is independent of the dynamics of the surrounding tissue. The cells are considered as elements of a cellular automaton, whose evolution rules are inspired by the different behaviour of normal and cancer cells. Our aim is to show that blood flow and red blood cell heterogeneity play major roles in the development of such colonies, even when the red blood cells are flowing through the vasculature of normal, healthy tissue.

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Year:  2003        PMID: 14575659     DOI: 10.1016/s0022-5193(03)00244-3

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


  73 in total

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Journal:  Interface Focus       Date:  2010-11-24       Impact factor: 3.906

2.  Models of collective cell behaviour with crowding effects: comparing lattice-based and lattice-free approaches.

Authors:  Michael J Plank; Matthew J Simpson
Journal:  J R Soc Interface       Date:  2012-06-13       Impact factor: 4.118

3.  Physical determinants of vascular network remodeling during tumor growth.

Authors:  M Welter; H Rieger
Journal:  Eur Phys J E Soft Matter       Date:  2010-07-06       Impact factor: 1.890

4.  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

5.  The dynamics of tumour-vasculature interaction suggests low-dose, time-dense anti-angiogenic schedulings.

Authors:  A d'Onofrio; A Gandolfi; A Rocca
Journal:  Cell Prolif       Date:  2009-03-31       Impact factor: 6.831

6.  Spatio-temporal tumour model for analysis and mechanism of action of intracellular drug accumulation.

Authors:  Somna Mishra; V K Katiyar
Journal:  J Biosci       Date:  2008-09       Impact factor: 1.826

7.  An integrative computational model for intestinal tissue renewal.

Authors:  I M M van Leeuwen; G R Mirams; A Walter; A Fletcher; P Murray; J Osborne; S Varma; S J Young; J Cooper; B Doyle; J Pitt-Francis; L Momtahan; P Pathmanathan; J P Whiteley; S J Chapman; D J Gavaghan; O E Jensen; J R King; P K Maini; S L Waters; H M Byrne
Journal:  Cell Prolif       Date:  2009-07-20       Impact factor: 6.831

8.  A 2D mechanistic model of breast ductal carcinoma in situ (DCIS) morphology and progression.

Authors:  Kerri-Ann Norton; Michael Wininger; Gyan Bhanot; Shridar Ganesan; Nicola Barnard; Troy Shinbrot
Journal:  J Theor Biol       Date:  2009-12-16       Impact factor: 2.691

9.  Individual cell-based models of tumor-environment interactions: Multiple effects of CD97 on tumor invasion.

Authors:  Joerg Galle; Doreen Sittig; Isabelle Hanisch; Manja Wobus; Elke Wandel; Markus Loeffler; Gabriela Aust
Journal:  Am J Pathol       Date:  2006-11       Impact factor: 4.307

Review 10.  In silico cancer modeling: is it ready for prime time?

Authors:  Thomas S Deisboeck; Le Zhang; Jeongah Yoon; Jose Costa
Journal:  Nat Clin Pract Oncol       Date:  2008-10-14
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