Zhang, Zhen, Bedder, Matthew, Smith, Stephen Leslie orcid.org/0000-0002-6885-2643 et al. (3 more authors) (2016) Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms. Biosystems. pp. 110-121. ISSN 0303-2647
Abstract
This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24 hour period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviors, but can be extracted as mathematical formulae for the parameterization of computational models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Published by Elsevier Ireland Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Biology (York) > Jack Birch Unit for Molecular Carcinogenesis (York) |
Depositing User: | Pure (York) |
Date Deposited: | 13 Jun 2016 09:18 |
Last Modified: | 07 Feb 2025 00:14 |
Published Version: | https://doi.org/10.1016/j.biosystems.2016.05.009 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.biosystems.2016.05.009 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100819 |
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