Mathematical modelling and data integration for Candida species and identify marker for oral infections

The human fungal pathogen Candida albicans, the causative agent of oral thrush, causes millions of infections annually in people worldwide, whilst other Candida species also cause severe mucosal and systemic infections. The rates of disease and mortality associated with fungal infections such as Candida are high due to a dearth of antifungal drugs and a lack of a single vaccine. The search for an explanation for precisely how these fungi are able to cause mucosal disease and immune activation has remained at the forefront of medical mycology and immunology for several decades, but there is now a need to use cutting edge technologies to improve current research.

Recent and rapidly evolving progress with high-throughput analysis techniques has led to the need to develop new mathematical models that successfully integrate the large data sets generated. These models are simplified representations of more complex, real systems. Using these models, we can deduce or infer properties of the modelled system that can be tested in in vitro/in vivo settings. This studentship will model the complex interactions between Candida species and epithelial cells. In doing so, we will identify targets for novel antifungal drugs, discover new antimicrobial drugs, as well as identifying important factors in mucosal disease pathogenesis.

Person specification:
- Candidates must possess, or be expected to achieve a first or high upper second class degree, or master’s degree in a relevant subject (bioinformatics / data science / computational biology / computer science / applied mathematics or related disciplines).
- Candidates must also have experience in one of the following software R / MATLAB / PERL / PYTHON or related.

Please note: Applicants must include the project ID number in the ’Research proposal’ and ’Funding (point 5)’ sections of their application.