An evolutionary hybrid cellular automaton model of solid tumour growth
We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behav...
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| Published in | Journal of theoretical biology Vol. 246; no. 4; pp. 583 - 603 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
21.06.2007
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-5193 1095-8541 |
| DOI | 10.1016/j.jtbi.2007.01.027 |
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| Abstract | We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level. |
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| AbstractList | We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level. We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level.We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level. |
| Author | Gerlee, P. Anderson, A.R.A. |
| Author_xml | – sequence: 1 givenname: P. surname: Gerlee fullname: Gerlee, P. email: gerlee@maths.dundee.ac.uk – sequence: 2 givenname: A.R.A. surname: Anderson fullname: Anderson, A.R.A. email: anderson@maths.dundee.ac.uk |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17374383$$D View this record in MEDLINE/PubMed |
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| Keywords | Clonal evolution Cancer development Response network Cellular automaton Evolutionary dynamics Artificial neural networks Hybrid Mathematical model Tumourigenesis Micro-environment |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Corresponding author. Email addresses: gerlee@maths.dundee.ac.uk (P. Gerlee), anderson@maths.dundee.ac.uk (A.R.A. Anderson). |
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| Snippet | We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is... |
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| SubjectTerms | Apoptosis - physiology Artificial neural networks Cancer development Cell Division - genetics Cell Division - physiology Cell Physiological Phenomena Cells - metabolism Cellular automaton Chimera - growth & development Clonal evolution Evolution, Molecular Evolutionary dynamics Humans Hybrid Mathematical model Micro-environment Models, Biological Mutation - genetics Neoplasms - genetics Neoplasms - pathology Neoplasms - physiopathology Neural Networks, Computer Oxygen - physiology Phenotype Response network Tumourigenesis |
| Title | An evolutionary hybrid cellular automaton model of solid tumour growth |
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