PhD position generating microbiology data and paired mathematical modelling

Scientific description of this research project
Vision:
Antimicrobial resistance (AMR), the process by which infectious bacteria become resistant to antibiotics used to control them, is an urgent global health threat. The cost of inaction is estimated to be $100 trillion USD and 10 million deaths by 2050. Among AMR bacterial pathogens, the ESKAPE pathogens, a group of six bacteria including Staphylococcus aureus and Klebsiella pneumoniae (the ‘S’ and ‘K’), driving the majority of hospital acquired infections and WHO priority pathogens such as Shigella are of particular concern. Bacteriophages (phages) are naturally occurring viruses infective for bacteria. Phages and bacteria have been locked in a co-evolutionary arms race and predator-prey relationship as long as they have existed. Phages kill their bacterial hosts at the end of an infection cycle, releasing progeny phages that repeat this process, making them an appealing means of controlling infectious bacteria.

Given the global health threat posed by AMR bacterial pathogens, there is renewed interest in alternative treatment options, including the use of phages in clinical practice, termed ‘phage therapy’. Sporadic compassionate use case studies have demonstrated that phage therapy can resolve otherwise untreatable AMR infections, however, the failure of several clinical trials has emphasised that many knowledge-gaps remain which have hampered progress towards routine phage therapy. These include details of (i) the dynamics of phage-bacteria interactions in the presence of human components, required to optimise dosages and determine fundamental drivers of successful phage cocktail therapy, and (ii) rates and mechanisms of phage resistance. PHACTS will bring together researchers from a variety of disciplines to address these knowledge gaps.

Approach and aims:
PHACTS will focus on fundamental biological interactions to improve our ability to understand one of the most ancient evolutionary interactions and generate criteria to optimise phage therapy. PHACTS unites distinct but highly complementary expertise in (i) cellular microbiology, in vivo infection models, high resolution microscopy techniques (Serge Mostowy), (ii) pathogen genomics, evolutionary biology, bioinformatics (Zoe Dyson), and (iii) mathematical modelling of microbiological interactions (Gwen Knight). Using a combination of in silico, in vitro and in vivo experimental analyses, PHACTS will improve (i) our understanding of phage-bacteria interactions and (ii) use this understanding to generalise predictive therapeutic benefit. PHACTS will provide a stepwise change in biology as well as underlying evidence to inform strategies for successful phage therapy for S. aureus, K. pneumoniae, and Shigella spp.

Objectives:

Characterise phage-bacteria dynamics in vitro
Explore phage-bacteria interactions in vivo using zebrafish infection models
Generalize, support and extend in vitro and in vivo experimental work using mathematical modelling
Techniques:

Microbiology, cell biology, and host response using infection of tissue culture cells and zebrafish infection models
Whole genome sequencing and bioinformatic analysis of phages and bacteria to understand the evolutionary relationships between phages, bacteria, and antimicrobial resistance. This work will also focus on the spread of bacteria within hospital settings
Data analysis and model fitting to determine parametric distributions for resistance diversity in different settings
Evolutionary mathematical models fit to existing and newly generated data from resistance transfer experiments
Develop transfer assays to explore stability and resistance diversity levels under different competition assays
Dynamic transmission models to explore bacterial transmission within hospital settings fit to data on within host populations
Further reading
Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

https://doi.org/10.1021/acssynbio.2c00465
https://doi.org/10.1073/pnas.2006110117

Type
PhD position
Institution
LSHTM
City
London
Country
UK
Closing date
January 14th, 2026
Posted on
November 11th, 2025 09:33
Last updated
November 11th, 2025 09:33
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