[FSE Bicentenary PhD] Generative AI approaches for synthetic phage genome engineering

Phage therapy (PT) is a promising treatment for drug resistant bacterial infections. However, PT faces barriers to implementation, such as identification of effective phage treatments, which currently requires time-consuming and lab-intensive susceptibility testing. Predicting phage infectivity profiles from genomics data would streamline treatment design but is hindered by the complexity of multi-step phage infection processes involving receptor binding and evasion of post-infection immunity. Recent advancements in applying large language models to biology that have revolutionised synthetic genomic design could provide a novel pathway for developing effective phage therapies.  

Emergence of powerful artificial intelligence (AI) tools, such as ‘Evo’, now support complex prediction and generation tasks at molecular, systems, and genomic levels¹. In this project, AI will be harnessed to study the bacterial-phage relationship by training it on publicly available phage and bacterial genome datasets², alongside largescale empirical bacteria-phage infection matrices³, to enable an AI-guided synthetic genomics approach for phage design. We will use AI-assisted high-throughput analyses spanning resolutions from genes to genomes and infection matrices on designed phage genomes to predict bacterial infection susceptibility. Using our established DNA construction facilities⁴, we will 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 accelerate the clinical adoption of this promising technology against emerging antimicrobial resistance.  

Before you apply: We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply: To be considered for this project you’ll need to complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD. Please select University of Manchester funding in the funding section of the form.

When applying, you’ll need to specify the full name of this project, the name of your proposed supervisor/s, details of your previous study, and names and contact details of two referees. You are also required to upload your CV and a Personal Statement describing your motivation for applying for the project.

Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.

Equality, diversity and inclusion: Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or equivalent) in a relevant science or engineering related discipline.

_{FSE_Bicentenary}