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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.


Related in: MedlinePlus

Growth of tumour mass over time.Treatment is initiated at 3,000 generations (E). (F) and (G) show tumour mass shrinkage. (H–J) show the accelerated growth following treatment. (K) shows the corresponding graph of malignant cell counts over time.
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fig-16: Growth of tumour mass over time.Treatment is initiated at 3,000 generations (E). (F) and (G) show tumour mass shrinkage. (H–J) show the accelerated growth following treatment. (K) shows the corresponding graph of malignant cell counts over time.

Mentions: The response to this treatment, which we have varied in intensity and duration, is consistent in our experiments. There is an initial response marked by massive tumour kill followed by a resumption of tumour growth, which is often characterised by an accelerated and aggressive tumour expansion, as shown in Fig. 16.


Emergent properties of a computational model of tumour growth.

Pantziarka P - PeerJ (2016)

Growth of tumour mass over time.Treatment is initiated at 3,000 generations (E). (F) and (G) show tumour mass shrinkage. (H–J) show the accelerated growth following treatment. (K) shows the corresponding graph of malignant cell counts over time.
© Copyright Policy
Related In: Results  -  Collection

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

fig-16: Growth of tumour mass over time.Treatment is initiated at 3,000 generations (E). (F) and (G) show tumour mass shrinkage. (H–J) show the accelerated growth following treatment. (K) shows the corresponding graph of malignant cell counts over time.
Mentions: The response to this treatment, which we have varied in intensity and duration, is consistent in our experiments. There is an initial response marked by massive tumour kill followed by a resumption of tumour growth, which is often characterised by an accelerated and aggressive tumour expansion, as shown in Fig. 16.

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