Executive Summary : | The cGMP-gated channel is a key signal transduction system in chemosensation, involving the identification of specific genes involved in the process. However, there is limited exploration into how these genes influence spatial coverage and worm behavior. This project aims to study the behavioral and spatial dynamics of C. elegans under various environmental conditions generated by chemicals, including isotropic chemical environments and chemical gradients. The project will also investigate how spatial coverage and worm behavior change due to age, using chemotaxis assays for worms aged from L2 to Late adult stages and tracking their behavior. Traditional chemotaxis assays will be conducted on an agar plate, while microfluidics will be used for chemosensation studies. Detection and tracking of individual C. elegans worms are essential tasks in behavioral studies. To complement these experimental studies, state-of-the-art deep learning-based software will be developed for image segmentation, object detection, and worm tracking in videos. Traditional machine learning methods may face computational performance issues and errors in identification under noisy backgrounds. The proposed end-to-end deep learning model will combine YOLO object detection model and DeepSORT tracking backbone, trained on a challenging manually curated dataset. This behavioral analysis will help correlate behavior with the chemical and gene in question, providing a better understanding of the correlation between chemosensory signals, networks, and behavior. This study has great relevance in our understanding of chemosensation, which falls under the larger umbrella of olfaction. |