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A simple model of a growing tumour
This paper presents the CellCycler, a model of a growing tumour which aims to simulate and predict the effect of treatment on xenograft studies or in the clinic. The model, which is freely available as a web application, uses ordinary differential equations (ODEs) to simulate cells as they pass thro...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546075/ https://www.ncbi.nlm.nih.gov/pubmed/31183252 http://dx.doi.org/10.7717/peerj.6983 |
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author | Orrell, David Mistry, Hitesh B. |
author_facet | Orrell, David Mistry, Hitesh B. |
author_sort | Orrell, David |
collection | PubMed |
description | This paper presents the CellCycler, a model of a growing tumour which aims to simulate and predict the effect of treatment on xenograft studies or in the clinic. The model, which is freely available as a web application, uses ordinary differential equations (ODEs) to simulate cells as they pass through the phases of the cell cycle. However the guiding philosophy of the model is that it should only use parameters that can be observed or reasonably well approximated. There is no representation of the complex internal dynamics of each cell; instead the level of analysis is limited to cell state observables such as cell phase, apoptosis, and damage. We show that this approach, while limited in many respects, still naturally accounts for a heteregenous cell population with varying doubling time, and closely captures the dynamics of a growing tumour as it is exposed to treatment. The program is demonstrated using three case studies. |
format | Online Article Text |
id | pubmed-6546075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65460752019-06-10 A simple model of a growing tumour Orrell, David Mistry, Hitesh B. PeerJ Computational Biology This paper presents the CellCycler, a model of a growing tumour which aims to simulate and predict the effect of treatment on xenograft studies or in the clinic. The model, which is freely available as a web application, uses ordinary differential equations (ODEs) to simulate cells as they pass through the phases of the cell cycle. However the guiding philosophy of the model is that it should only use parameters that can be observed or reasonably well approximated. There is no representation of the complex internal dynamics of each cell; instead the level of analysis is limited to cell state observables such as cell phase, apoptosis, and damage. We show that this approach, while limited in many respects, still naturally accounts for a heteregenous cell population with varying doubling time, and closely captures the dynamics of a growing tumour as it is exposed to treatment. The program is demonstrated using three case studies. PeerJ Inc. 2019-05-31 /pmc/articles/PMC6546075/ /pubmed/31183252 http://dx.doi.org/10.7717/peerj.6983 Text en ©2019 Orrell and Mistry http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Computational Biology Orrell, David Mistry, Hitesh B. A simple model of a growing tumour |
title | A simple model of a growing tumour |
title_full | A simple model of a growing tumour |
title_fullStr | A simple model of a growing tumour |
title_full_unstemmed | A simple model of a growing tumour |
title_short | A simple model of a growing tumour |
title_sort | simple model of a growing tumour |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546075/ https://www.ncbi.nlm.nih.gov/pubmed/31183252 http://dx.doi.org/10.7717/peerj.6983 |
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