High-throughput Tools and Techniques to Investigate Environmental Effects on Aging Behaviors in Caenorhabditis elegans

Aging is modulated by genetic and environmental cues; however, it is difficult to study how these perturbations modulate the aging process in a robust, high-throughput manner. Methods to gather large-scale behavioral data for aging studies are labor-intensive, lack individual-level resolution, or lack precise spatiotemporal environmental control. In addition, tools to analyze large-scale behavioral data sets are difficult to scale, unable to be broadly applied across complex environments, or fail to detect subtle behavioral changes. In this thesis I develop tools to enable robust, microfluidic culture and behavioral analysis of C. elegans to examine how environmental cues, such as dietary restriction, influence longevity and behavior with age. In Aim 1, I engineer a robust pipeline for the long-term longitudinal culture and behavioral monitoring of C. elegans in aging studies with precise spatiotemporal environmental control. In Aim 2, I develop a flexible deep learning based pipeline for detecting and extracting postural information from large-scale behavioral datasets across heterogeneous environments. In Aim 3, I characterize how the full behavioral repertoire of individuals change with age, along with examining how these age-related behavioral changes are modulated by different dietary restriction regimes. The completion of this thesis provides 1) a new toolset to robustly explore how genetic or environmental effects influence longevity and healthspan, 2) a flexible pipeline for analyzing large-scale behavioral data in C. elegans, and 3) insight into how environmental perturbations influence health through age-related changes in behavior.Ph.D