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Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study
BACKGROUND: Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational model...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712764/ https://www.ncbi.nlm.nih.gov/pubmed/31455206 http://dx.doi.org/10.1186/s12859-019-2997-9 |
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author | Antonopoulos, Markos Dionysiou, Dimitra Stamatakos, Georgios Uzunoglu, Nikolaos |
author_facet | Antonopoulos, Markos Dionysiou, Dimitra Stamatakos, Georgios Uzunoglu, Nikolaos |
author_sort | Antonopoulos, Markos |
collection | PubMed |
description | BACKGROUND: Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. RESULTS: This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. CONCLUSION: In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations. |
format | Online Article Text |
id | pubmed-6712764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-67127642019-08-29 Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study Antonopoulos, Markos Dionysiou, Dimitra Stamatakos, Georgios Uzunoglu, Nikolaos BMC Bioinformatics Research Article BACKGROUND: Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. RESULTS: This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. CONCLUSION: In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations. BioMed Central 2019-08-27 /pmc/articles/PMC6712764/ /pubmed/31455206 http://dx.doi.org/10.1186/s12859-019-2997-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Antonopoulos, Markos Dionysiou, Dimitra Stamatakos, Georgios Uzunoglu, Nikolaos Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title | Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title_full | Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title_fullStr | Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title_full_unstemmed | Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title_short | Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
title_sort | three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712764/ https://www.ncbi.nlm.nih.gov/pubmed/31455206 http://dx.doi.org/10.1186/s12859-019-2997-9 |
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