Age is the major risk factor for many diseases, including diabetes, heart attack, cancer, and neurodegeneration. Genetic and environmental factors converge on hormonal pathways in the brain to affect the ageing process. These pathways are highly conserved, enabling studies in the experimentally tractable roundworm C. elegans to provide new insights into the neuroendocrine regulation of ageing. Our project combines experimental and computational approaches to delineate the neuroendocrine network involving TGF-beta, serotonin, and catecholamines that are conserved from roundworms to humans.
Year 1: Single-neuron transcriptomics to reveal gene network activity patterns that encode food stimuli. Training in programming, machine learning, and image analysis.
Year 2: Develop machine learning/deep learning pipeline for image analysis of microscopy data. Use this pipeline to validate transcriptomics with microscopy of reporter genes in live animals.
Year 3 Investigate the effects of food-gene interactions on lifespan by testing mutants in neuroendocrine pathways under different food levels.
The student will discover how connectivity of a hormonal network in the nervous system modulates the effects of food on lifespan. These results will help explain how nutrient information is processed by the brain and communicated to the body.
The student will work closely with both supervisors to design experiments and interpret results. Dr Ch’ng will train the student in molecular genetics, microscopy, and lifespan measurements. Dr Coxy will train the student in data handling, image analysis, and artificial intelligence. This project provides a unique opportunity to learn systems biology to as a new approach in biomedicine.