EVOLVE-AI: Evolutionary-Informed Deep Learning for Deciphering and Modifying Cell-Cell Communication During Infection, Inflammation, and Cancer

Cellular communication is a fundamental component of life and is a core driver of health as well as disease. As such, these communication systems are fantastic drug targets for medicines but we often lack a complete understanding of their biology as well as their diversity across the tree of life. Without this knowledge, the potential of such interventions will not be realised. However, with the explosive growth in genomic data available, combined with developments in Artificial Intelligence-based approaches, we can begin to unravel the story of cellular communication systems.

To this end, in collaboration with the supervisory team, whose expertise spans molecular virology, cancer biology and machine learning, the student will combine evolutionary biology (aim 1: structure-informed phylogenomics of >200 systems in >3,000 genomes), machine learning (aim 2: deep learning, BERT-like language models to probe protein-protein co-evolution), and molecular cellular biology (aim 3: testing synthetic systems in cancer and virus infection models) to further our understanding of cytokine signalling, which could identify new avenues for therapeutic discovery.

The student will gain specific training in both computational (evolutionary and structural biology, machine learning) and wet-lab based skills (cell culture, molecular biology, disease models), attend courses at the European Molecular Biology Labs, and carry out a placement with relevant industry partners. The student will regularly meet with their interdisciplinary team and their training augmented by institution-led PhD programmes on core scientific aspects like hypothesis generation, statistics and writing. 

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