Numbers of older people are growing globally. Age is the main risk factor for many diseases, as starkly illustrated by the Covid-19 pandemic. Identifying mechanisms that can improve older-age health is a key task for contemporary biology. Ageing itself is plastic but our knowledge of the underlying mechanisms is limited due to the complex genetics of ageing phenotypes that hamper the use of animal models for unbiased gene identification. In this project, we propose to overcome this limitation by using machine learning on gene expression data to identify candidate genes and directly test their role in fruit fly ageing.
Martínez Corrales & Alic (2020) Evolutionary Conservation of Transcription Factors Affecting Longevity. Trends in Genetics
Partridge, Deelen and Slagboom (2018) Facing up to the global challenges of ageing. Nature
Filer, Thompson, Takhaveev, Dobson, Kotronaki, Green, Heinemann, Tullet and Alic (2017) RNA polymerase III limits longevity downstream of TORC1. Nature