CAREER: Four-Dimensional Subcellular Structure Tracking and Modeling for Cell Dynamics Study

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

New discoveries in biology have required an extensive knowledge of cell dynamics. Knowledge of subcellular particles/structures such as organelles, vesicles, and mRNAs is critical to understand how cells regulate delivery of specific proteins from the site of synthesis to the site of action for a better understanding of diseases and viral infections. Quantitative spatial-temporal protein mobility study plays an essential role in experimentally comprehending and validating protein signaling mechanisms and protein-protein interactions. The goal of this proposal is to develop a unique and the first web-based open access Cell Dynamics Analysis System (CellDAS) for automating subcellular particle motion estimation, tracking and mobility analysis. First, a divergence filter is developed, analogous to physics concepts, to detect the directed motion that is of biological importance. Then a Markov Chain Monte Carlo (MCMC) sampling framework is presented for multi-object tracking that overcomes the exponential complexity problem. Finally, distinctive features are optimally selected during the tracking process. This project will provide rich resources for training and education in cell biology and computer science. By accessing the proposed open-access web-based CellDAS interface, undergraduates and graduates will have training in basic computer vision algorithms, biological data visualization, and formation understanding to specific interested biology problem. Moreover, the PI will promote and motivate women in science and engineering, and inspire high school students to pursue careers in these fields through the mentoring activities of the Society of Women Engineers (SWE) and the annual high school Robot Programming Contest.