Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms

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@Article{ZHANG:2016:Biosystems,

• author = "Zhen Zhang and Matthew Bedder and Stephen L. Smith and Dawn Walker and Saqib Shabir and Jennifer Southgate",
• title = "Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms",
• journal = "Biosystems",
• volume = "146",
• pages = "110--121",
• year = "2016",
• note = "Information Processing in Cells and Tissues",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "0303-2647",
• DOI = "doi:10.1016/j.biosystems.2016.05.009",
• URL = "http://www.sciencedirect.com/science/article/pii/S0303264716300727",
• 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 24h 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",

}

Genetic Programming entries for Zhen Zhang Matthew Bedder Stephen L Smith Dawn Walker Saqib Shabir Jennifer Southgate

Citations