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

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

A Novel Method for Classification and Characterization of Urothelium Cell Culture Exposed to the Different PPARg Agonists

The main purpose of the thesis is to classify and characterize urothelium cell cultures under PPARg activator/inhibitor. In this project, the raw data are obtained from videos of three different cell cultures: TZ (PPARg activator), T0070907 (PPARg inhibitor) and a control culture. A cell tracking program based on the OpenCV computer vision library is applied to the videos to generate a dataset consisting of x,y coordinates of the tracked cells. The numerical computing environment MATLAB® is subsequently used to filter the data and extract features, which were applied to machine learning algorithms to classify the cell cultures. Results obtained indicate that the TZ/T0070907 addition can cause a change in the average behavior of cells, such as the number of cells in the culture, the speed of cells and the average clump size of cells. The work also demonstrates that there is a difference in single cell behavior among different cultures. In summary, it is proposed that the approach described in this project provides a potential way of analyzing the average behavior of cells in different cultures