Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms. (August 2016)

• Record Type: Journal Article
• Authors: Zhang, Zhen; Bedder, Matthew; Smith, Stephen L.; Walker, Dawn; Shabir, Saqib; Southgate, Jennifer
• Is Part Of: Bio systems. Volume 146(2016:Aug.)
• Page Start: 110
• Page End: 121
• Publication Date: 2016-08
• Subjects: Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2016.05.009
• Languages: English
• ISSNs: 0303-2647

Abstract

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 h 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 behaviours, but can be extracted as mathematical formulae for the parameterization of computational models.