Limits...
Emergent properties of a computational model of tumour growth.

Pantziarka P - PeerJ (2016)

Bottom Line: The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity.One strategy in dealing with such complexity is to develop models to replicate salient features of the system and therefore to generate hypotheses which reflect on the real system.Analysis of model data suggests that the processes of cell competition and apoptosis are key drivers of these emergent behaviours.

View Article: PubMed Central - HTML - PubMed

Affiliation: The George Pantziarka TP53 Trust , London , United Kingdom.

ABSTRACT
While there have been enormous advances in our understanding of the genetic drivers and molecular pathways involved in cancer in recent decades, there also remain key areas of dispute with respect to fundamental theories of cancer. The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity. One strategy in dealing with such complexity is to develop models to replicate salient features of the system and therefore to generate hypotheses which reflect on the real system. A simple tumour growth model is outlined which displays emergent behaviours that correspond to a number of clinically relevant phenomena including tumour growth, intra-tumour heterogeneity, growth arrest and accelerated repopulation following cytotoxic insult. Analysis of model data suggests that the processes of cell competition and apoptosis are key drivers of these emergent behaviours. Questions are raised as to the role of cell competition and cell death in physical cancer growth and the relevance that these have to cancer research in general is discussed.

No MeSH data available.


Related in: MedlinePlus

Moore neighbourhood of radius 2.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4933089&req=5

fig-1: Moore neighbourhood of radius 2.

Mentions: The tissue-level is represented as a rectangular grid, with each grid element containing a set of modelled cells, which may be Malignant or Normal. The relative proportion of Normal and Malignant cells in a grid element determines the state of that grid element. These states are: \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*}E=\{\text{Normal, Majority Normal, Majority Malignant, Tumour, Necrotic}\}. \end{eqnarray*}\end{document}E=Normal, Majority Normal, Majority Malignant, Tumour, Necrotic.Transition of a grid element from one state to another takes place at every clock tick (generation) and is determined by the proportions of different cell populations within that element, but also by the state of neighbouring grid elements. Grid elements which are in the Tumour state (that is, they do not have any Normal cells within them) can transition to a Necrotic state if they are surrounded by an extended neighbourhood which consists exclusively of other Tumour grid elements. By default this is a Moore neighbourhood of radius 2 (see Fig. 1), though this is a configurable model parameter. This Necrotic state is designed to model cellular compartments within solid tumours in which a high rate of hypoxia and a low level of nutrient availability lead to high levels of cellular necrosis.


Emergent properties of a computational model of tumour growth.

Pantziarka P - PeerJ (2016)

Moore neighbourhood of radius 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4933089&req=5

fig-1: Moore neighbourhood of radius 2.
Mentions: The tissue-level is represented as a rectangular grid, with each grid element containing a set of modelled cells, which may be Malignant or Normal. The relative proportion of Normal and Malignant cells in a grid element determines the state of that grid element. These states are: \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{upgreek}\usepackage{mathrsfs}\setlength{\oddsidemargin}{-69pt}\begin{document}}{}\begin{eqnarray*}E=\{\text{Normal, Majority Normal, Majority Malignant, Tumour, Necrotic}\}. \end{eqnarray*}\end{document}E=Normal, Majority Normal, Majority Malignant, Tumour, Necrotic.Transition of a grid element from one state to another takes place at every clock tick (generation) and is determined by the proportions of different cell populations within that element, but also by the state of neighbouring grid elements. Grid elements which are in the Tumour state (that is, they do not have any Normal cells within them) can transition to a Necrotic state if they are surrounded by an extended neighbourhood which consists exclusively of other Tumour grid elements. By default this is a Moore neighbourhood of radius 2 (see Fig. 1), though this is a configurable model parameter. This Necrotic state is designed to model cellular compartments within solid tumours in which a high rate of hypoxia and a low level of nutrient availability lead to high levels of cellular necrosis.

Bottom Line: The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity.One strategy in dealing with such complexity is to develop models to replicate salient features of the system and therefore to generate hypotheses which reflect on the real system.Analysis of model data suggests that the processes of cell competition and apoptosis are key drivers of these emergent behaviours.

View Article: PubMed Central - HTML - PubMed

Affiliation: The George Pantziarka TP53 Trust , London , United Kingdom.

ABSTRACT
While there have been enormous advances in our understanding of the genetic drivers and molecular pathways involved in cancer in recent decades, there also remain key areas of dispute with respect to fundamental theories of cancer. The accumulation of vast new datasets from genomics and other fields, in addition to detailed descriptions of molecular pathways, cloud the issues and lead to ever greater complexity. One strategy in dealing with such complexity is to develop models to replicate salient features of the system and therefore to generate hypotheses which reflect on the real system. A simple tumour growth model is outlined which displays emergent behaviours that correspond to a number of clinically relevant phenomena including tumour growth, intra-tumour heterogeneity, growth arrest and accelerated repopulation following cytotoxic insult. Analysis of model data suggests that the processes of cell competition and apoptosis are key drivers of these emergent behaviours. Questions are raised as to the role of cell competition and cell death in physical cancer growth and the relevance that these have to cancer research in general is discussed.

No MeSH data available.


Related in: MedlinePlus