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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.


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Spatial distribution of tumour growth.Evolving tumour mass at (A) 2,000 generations. (B) 4,000 generations. (C) 6,000 generations. Note that black areas are necrotic grid elements.
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fig-7: Spatial distribution of tumour growth.Evolving tumour mass at (A) 2,000 generations. (B) 4,000 generations. (C) 6,000 generations. Note that black areas are necrotic grid elements.

Mentions: Finally, while we have explored the rates of change at the cellular and grid element levels, we have not explored the spatial distribution of the spread of Malignant cells. A representative example of the ‘no treatment’ scenario is shown in Fig. 7, an extended run of 6,000 generations and a grid size of 45 × 45 has been used to illustrate more fully the development of the tumour over time. Figure 7A shows ‘tendrils’ of cancer cells infiltrating into healthy tissue (light coloured background representing Normal cells) from the centre of the dark blue tumour mass, in Fig. 7B the tumour mass has expanded considerably and shows a black inner necrotic core and a perimeter of tumour cells with tendrils extending into the healthy cells. Finally Fig. 7C shows continued expansion, including an expanding area of necrosis. If allowed to continue expanding, the tumour eventually dominates the grid completely until further growth is impossible and the mass becomes mainly necrotic.


Emergent properties of a computational model of tumour growth.

Pantziarka P - PeerJ (2016)

Spatial distribution of tumour growth.Evolving tumour mass at (A) 2,000 generations. (B) 4,000 generations. (C) 6,000 generations. Note that black areas are necrotic grid elements.
© Copyright Policy
Related In: Results  -  Collection

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

fig-7: Spatial distribution of tumour growth.Evolving tumour mass at (A) 2,000 generations. (B) 4,000 generations. (C) 6,000 generations. Note that black areas are necrotic grid elements.
Mentions: Finally, while we have explored the rates of change at the cellular and grid element levels, we have not explored the spatial distribution of the spread of Malignant cells. A representative example of the ‘no treatment’ scenario is shown in Fig. 7, an extended run of 6,000 generations and a grid size of 45 × 45 has been used to illustrate more fully the development of the tumour over time. Figure 7A shows ‘tendrils’ of cancer cells infiltrating into healthy tissue (light coloured background representing Normal cells) from the centre of the dark blue tumour mass, in Fig. 7B the tumour mass has expanded considerably and shows a black inner necrotic core and a perimeter of tumour cells with tendrils extending into the healthy cells. Finally Fig. 7C shows continued expansion, including an expanding area of necrosis. If allowed to continue expanding, the tumour eventually dominates the grid completely until further growth is impossible and the mass becomes mainly necrotic.

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