Cargando…

Hybrid multiscale modeling and prediction of cancer cell behavior

BACKGROUND: Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine t...

Descripción completa

Detalles Bibliográficos
Autores principales: Zangooei, Mohammad Hossein, Habibi, Jafar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5573302/
https://www.ncbi.nlm.nih.gov/pubmed/28846712
http://dx.doi.org/10.1371/journal.pone.0183810
_version_ 1783259631633563648
author Zangooei, Mohammad Hossein
Habibi, Jafar
author_facet Zangooei, Mohammad Hossein
Habibi, Jafar
author_sort Zangooei, Mohammad Hossein
collection PubMed
description BACKGROUND: Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. METHODS: In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. RESULTS: Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. CONCLUSION: Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.
format Online
Article
Text
id pubmed-5573302
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-55733022017-09-09 Hybrid multiscale modeling and prediction of cancer cell behavior Zangooei, Mohammad Hossein Habibi, Jafar PLoS One Research Article BACKGROUND: Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. METHODS: In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. RESULTS: Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. CONCLUSION: Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. Public Library of Science 2017-08-28 /pmc/articles/PMC5573302/ /pubmed/28846712 http://dx.doi.org/10.1371/journal.pone.0183810 Text en © 2017 Zangooei, Habibi 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zangooei, Mohammad Hossein
Habibi, Jafar
Hybrid multiscale modeling and prediction of cancer cell behavior
title Hybrid multiscale modeling and prediction of cancer cell behavior
title_full Hybrid multiscale modeling and prediction of cancer cell behavior
title_fullStr Hybrid multiscale modeling and prediction of cancer cell behavior
title_full_unstemmed Hybrid multiscale modeling and prediction of cancer cell behavior
title_short Hybrid multiscale modeling and prediction of cancer cell behavior
title_sort hybrid multiscale modeling and prediction of cancer cell behavior
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5573302/
https://www.ncbi.nlm.nih.gov/pubmed/28846712
http://dx.doi.org/10.1371/journal.pone.0183810
work_keys_str_mv AT zangooeimohammadhossein hybridmultiscalemodelingandpredictionofcancercellbehavior
AT habibijafar hybridmultiscalemodelingandpredictionofcancercellbehavior