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In silico characterization of cell–cell interactions using a cellular automata model of cell culture
BACKGROUND: Cell proliferation is a key characteristic of eukaryotic cells. During cell proliferation, cells interact with each other. In this study, we developed a cellular automata model to estimate cell–cell interactions using experimentally obtained images of cultured cells. RESULTS: We used fou...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5513360/ https://www.ncbi.nlm.nih.gov/pubmed/28705234 http://dx.doi.org/10.1186/s13104-017-2613-x |
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author | Kihara, Takanori Kashitani, Kosuke Miyake, Jun |
author_facet | Kihara, Takanori Kashitani, Kosuke Miyake, Jun |
author_sort | Kihara, Takanori |
collection | PubMed |
description | BACKGROUND: Cell proliferation is a key characteristic of eukaryotic cells. During cell proliferation, cells interact with each other. In this study, we developed a cellular automata model to estimate cell–cell interactions using experimentally obtained images of cultured cells. RESULTS: We used four types of cells; HeLa cells, human osteosarcoma (HOS) cells, rat mesenchymal stem cells (MSCs), and rat smooth muscle A7r5 cells. These cells were cultured and stained daily. The obtained cell images were binarized and clipped into squares containing about 10(4) cells. These cells showed characteristic cell proliferation patterns. The growth curves of these cells were generated from the cell proliferation images and we determined the doubling time of these cells from the growth curves. We developed a simple cellular automata system with an easily accessible graphical user interface. This system has five variable parameters, namely, initial cell number, doubling time, motility, cell–cell adhesion, and cell–cell contact inhibition (of proliferation). Within these parameters, we obtained initial cell numbers and doubling times experimentally. We set the motility at a constant value because the effect of the parameter for our simulation was restricted. Therefore, we simulated cell proliferation behavior with cell–cell adhesion and cell–cell contact inhibition as variables. By comparing growth curves and proliferation cell images, we succeeded in determining the cell–cell interaction properties of each cell. Simulated HeLa and HOS cells exhibited low cell–cell adhesion and weak cell–cell contact inhibition. Simulated MSCs exhibited high cell–cell adhesion and positive cell–cell contact inhibition. Simulated A7r5 cells exhibited low cell–cell adhesion and strong cell–cell contact inhibition. These simulated results correlated with the experimental growth curves and proliferation images. CONCLUSIONS: Our simulation approach is an easy method for evaluating the cell–cell interaction properties of cells. |
format | Online Article Text |
id | pubmed-5513360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55133602017-07-19 In silico characterization of cell–cell interactions using a cellular automata model of cell culture Kihara, Takanori Kashitani, Kosuke Miyake, Jun BMC Res Notes Research Article BACKGROUND: Cell proliferation is a key characteristic of eukaryotic cells. During cell proliferation, cells interact with each other. In this study, we developed a cellular automata model to estimate cell–cell interactions using experimentally obtained images of cultured cells. RESULTS: We used four types of cells; HeLa cells, human osteosarcoma (HOS) cells, rat mesenchymal stem cells (MSCs), and rat smooth muscle A7r5 cells. These cells were cultured and stained daily. The obtained cell images were binarized and clipped into squares containing about 10(4) cells. These cells showed characteristic cell proliferation patterns. The growth curves of these cells were generated from the cell proliferation images and we determined the doubling time of these cells from the growth curves. We developed a simple cellular automata system with an easily accessible graphical user interface. This system has five variable parameters, namely, initial cell number, doubling time, motility, cell–cell adhesion, and cell–cell contact inhibition (of proliferation). Within these parameters, we obtained initial cell numbers and doubling times experimentally. We set the motility at a constant value because the effect of the parameter for our simulation was restricted. Therefore, we simulated cell proliferation behavior with cell–cell adhesion and cell–cell contact inhibition as variables. By comparing growth curves and proliferation cell images, we succeeded in determining the cell–cell interaction properties of each cell. Simulated HeLa and HOS cells exhibited low cell–cell adhesion and weak cell–cell contact inhibition. Simulated MSCs exhibited high cell–cell adhesion and positive cell–cell contact inhibition. Simulated A7r5 cells exhibited low cell–cell adhesion and strong cell–cell contact inhibition. These simulated results correlated with the experimental growth curves and proliferation images. CONCLUSIONS: Our simulation approach is an easy method for evaluating the cell–cell interaction properties of cells. BioMed Central 2017-07-14 /pmc/articles/PMC5513360/ /pubmed/28705234 http://dx.doi.org/10.1186/s13104-017-2613-x Text en © The Author(s) 2017 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 Kihara, Takanori Kashitani, Kosuke Miyake, Jun In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title | In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title_full | In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title_fullStr | In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title_full_unstemmed | In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title_short | In silico characterization of cell–cell interactions using a cellular automata model of cell culture |
title_sort | in silico characterization of cell–cell interactions using a cellular automata model of cell culture |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5513360/ https://www.ncbi.nlm.nih.gov/pubmed/28705234 http://dx.doi.org/10.1186/s13104-017-2613-x |
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