Advisor: Hang Lu, Ph.D. (Georgia Institute of Technology)
Patrick McGrath, Ph.D. (Georgia Institute of Technology)
Robert Butera, Ph.D. (Georgia Institute of Technology)
Mark Stycyznski, Ph.D. (Georgia Institute of Technology)
Yun Zhang, Ph.D. (Harvard University)
A High-Throughput Microfluidic Platform for High-Content
Learning Studies in C. elegans
Learning is a complex process governed by many genetic and environmental factors. C. elegans serves as a useful model organism for learning studies, as it contains a simple nervous system with only 302 neurons, and yet exhibits a large repertoire of behaviors, including learning and memory. My thesis aims to develop a platform for high-throughput learning-phenotype classification through the extraction and analysis of high-content behavioral data. The platform will consist of a microfluidic behavioral arena to provide a standardized and finely tunable closed-loop assay environment to detect preference changes due to learning. Hardware and software will enable simultaneous multiple-animal behavioral tracking as animals navigate through their environment during the learning assays. Methodologies will be developed to extract relevant behavioral features and subsequently quantify learning phenotypes based on the temporal, feature-rich behavioral data. The platform will be utilized to assess the roles, interactions, and functional effects of a putative insulin-like peptide (ILP) network on pathogenic learning in C. elegans. This platform will improve learning-phenotype classification through advancements in microenvironment standardization, presentation of dynamic spatial stimuli, and the amount of information collected. Additionally, this high-content information can be used to better infer putative biological mechanisms from observed morphologies.