Improving detection of gene expression in microscopy, using Artificial Intelligence techniques in order to investigate how food affects ageing in C El

This project investigates how food affects ageing via conserved neuroendocrine factors. In C. elegans and humans, neuroendocrine factors such as insulin-like peptides, growth factors, and biogenic amines regulate each other in complex networks to modulate ageing, metabolism, and other physiological outputs. However, the information processing mechanisms in these networks for discriminating food inputs are unclear. We address this question by enhancing high-throughput C. elegans experiments with artificial intelligence and machine learning.

These studies have two key limitations: first, the neurons are identified by hand, which is very labour intensive; and second, the need for highly standardised images means many images are discarded. The first issue offers an opportunity to automate cell identification, either by applying supervised machine learning (e.g. random forest classifier) to tens of thousands of annotated image stacks; or by using deep learning so that the algorithm can select the parameters to optimise. The second issue means that there are many unannotated image stacks that can be analysed by unsupervised deep learning and cross-validated against the annotated subset to check performance.

We will then apply decoding analysis and machine learning to relate food inputs, combinatorial patterns of gene expression, and ageing phenotypes. Using these results, we will predict the lifespans of novel mutants based on their effects on gene expression, and verify these hypotheses experimentally by exploiting high-throughput imaging and the automated image analysis developed above. This work thus decodes food-sensing gene networks in the nervous system that impact ageing and other health-related outputs.