Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms

Zhen Zhang, Matthew Bedder, Stephen Leslie Smith, Dawn Walker, Saqib Shabir, Jennifer Southgate

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)110-121
Number of pages22
Early online date3 Jun 2016
Publication statusPublished - Aug 2016

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© 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.

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