The emergence of powerful artificial intelligence (AI) tools, now supports complex prediction and generation tasks at molecular, systems, and genomic levels. In this project, AI will be harnessed to study the bacterial-phage relationship by training it on publicly available phage and bacterial genome datasets, alongside large empirical bacteria-phage infection matrices, to enable an AI-guided synthetic genomics approach for phage design.
Using AI-assisted high-throughput analyses spanning resolutions from genes to genomes and infection matrices on designed phage genomes to predict bacterial infection susceptibility. By using established DNA construction facilities, build a range of predicted phage genomes and test their infectivity profiles to validate and refine our predictions on a collection of over 30,000 genome-sequenced clinical bacterial isolates. This approach will deliver tailored therapeutic phages, reducing reliance on trial-and-error methods in phage therapy development and accelerating the clinical adoption of this promising technology against emerging antimicrobial resistance.
The University of Manchester believes that diversity strengthens the research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. Encourage applicants from diverse career paths and backgrounds and from all sections of the community. The university also supports applications from those returning from a career break or other roles.
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