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Analytical Cellular Pathology
Volume 14 (1997), Issue 2, Pages 75-86
http://dx.doi.org/10.1155/1997/183028

Topographical Analysis of Spatial Patterns Generated by a Cellular Automaton Model of the Proliferation of a Cancer Cell Line In Vitro

Jacqueline Palmari,1 Bruno Lafon,2 Pierre Marie Martin,2 and Christophe Dussert2

1Laboratoire des Interactions Photons Matière, Université Aix‐Marseille III, case EC1, Avenue Escadrille Normandie‐Niemen, 13397 Marseille cedex 20, France
2CJF INSERM 9311, Université Aix‐Marseille II, Boulevard P. Dramard, 13326 Marseille cedex 20, France

Received 17 September 1996; Revised 15 April 1997

Copyright © 1997 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A well‐suited model to simulate cellular population dynamics is the two‐dimensional cellular automaton model, which consists of a lattice of sites, the value ai,j of each site being updated in discrete time steps according to an identical deterministic rule depending on a neighbourhood of sites around it. A cellular automaton is described which mimics cell population proliferation by replacing the site values by the age and the cycle phase of cells. The model takes into account the size of the cells. It is used to simulate the proliferation of the human breast cancer cell line MCF‐7 and the results of the simulation are compared with experimental data obtained from a light microscopic image analysis of the proliferation process. The initial configuration of the cellular automaton is obtained from the discretization of the results of the initial stage of the image processing. After each day of proliferation the pattern obtained from the simulation is compared to the experimental result of the corresponding image analysis. The comparison is made from a topographical point of view through the concept of the minimal spanning tree graph. The agreement between experiment and model is a good starting point to complex models such as cell proliferation under growth effectors or drugs.