Uncovering new dimensions in evolution of insect pathogens through large-scale computational data mining
Supervisors:
Liam Brierley, Centre for Virus Research (University of Glasgow)
Marta Shocket, Lancaster Environment Centre, (Lancaster University)
Maxwell Farrell, School of Biodiversity, One Health & Veterinary Medicine, (University of Glasgow)
Summary:
Insects are a vital part of global biodiversity, though we know relatively little about how they interact with pathogens. Fungal pathogens in particular are under-studied, even though they have important implications for conservation where they can threaten rare species, and for agriculture where they are used as bioagents to control crop pests.
This interdisciplinary project aims to tackle this challenging knowledge gap by combining computer science and infectious disease evolution. The project will develop new specialised language models to identify, process, and extract information on insect-fungus relationships from scientific literature. The created data will then be used to develop evolutionary analyses exploring why some fungal pathogens specialise on particular insect hosts yet others can infect many different insect hosts.
The student will have access to training in high-performance computing, artificial intelligence (including deep learning and large language models), and evolutionary and phylogenetic comparative analyses. This project would therefore suit a student with biology experience and strong competency in programming and quantitative skills, or a student with computational experience with genuine motivation for biological model application.