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In silico characterization of cell–cell interactions using a cellular automata model of cell culture

Overview of attention for article published in BMC Research Notes, July 2017
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  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
In silico characterization of cell–cell interactions using a cellular automata model of cell culture
Published in
BMC Research Notes, July 2017
DOI 10.1186/s13104-017-2613-x
Pubmed ID
Authors

Takanori Kihara, Kosuke Kashitani, Jun Miyake

Abstract

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. 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. Our simulation approach is an easy method for evaluating the cell-cell interaction properties of cells.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 43%
Student > Doctoral Student 3 21%
Lecturer 2 14%
Student > Master 1 7%
Student > Postgraduate 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 21%
Agricultural and Biological Sciences 2 14%
Engineering 2 14%
Computer Science 2 14%
Unspecified 1 7%
Other 2 14%
Unknown 2 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 July 2017.
All research outputs
#14,945,861
of 22,988,380 outputs
Outputs from BMC Research Notes
#2,144
of 4,284 outputs
Outputs of similar age
#186,049
of 312,506 outputs
Outputs of similar age from BMC Research Notes
#59
of 142 outputs
Altmetric has tracked 22,988,380 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,506 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.