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This 3-year self-funded PhD will be based in the School of Mathematics and Physics and will be supervised by Dr Marianna Cerasuolo, and .

Prostate cancer (PCa) is the second most common cause of cancer among men worldwide. Advances in screening and diagnosis have allowed detection of the disease in early stages, but for late-stage disseminated diseases current therapies are merely palliative.

Historically, treatment for metastatic prostate cancer was limited to androgen deprivation therapy, by which the patients invariably developed castration-resistant prostate cancer (CRPC). Recent interdisciplinary studies emphasized the need to evaluate new therapeutic strategies to control PCa dynamics and the onset of drug-resistance; and current experimental evidence suggests that multi-drugs therapy is the way to succeed.

Experimental frameworks based on the combination of biology/chemistry and mathematics have become increasingly common in cancer research, and the use of continuum and discrete-continuum hybrid models has proved to have a great potential in suggesting and designing new personalised therapeutic strategies.

The aim of this project is to develop and validate continuum and hybrid mathematical models to gain insights into the interplay between newly-discovered drugs and PCa cells. These tools will help us to understand how the drug-cells interaction can affect the dynamics of the tumour growth and, at the same time, will allow for the exploration of different therapeutic strategies' effectiveness.

The project will focus on representing mathematically different-scales mechanisms that contribute towards drug resistance in cancer; and cells metabolic changes during treatments and the impact of such changes on cancer behaviour. The analytical and numerical study of the dynamical systems –&²Ô²ú²õ±è;and the statistical comparison between numerical simulations and experimental data â€“ will also be part of the project.

Candidates should ideally have previous experience in mathematics applied to biology. However, the successful candidate will receive training in all relevant areas and have the opportunity to learn new skills in applied mathematics, cancer modelling, data analysis and computer programming.

The student will also have access to a large number of training resources available through the Graduate School at the ºÚÁÏ³Ô¹Ï including those geared toward improving presentation skills, time-management, project organization skills, thesis writing, data analysis and statistics, and other various related training modules.

Fees and funding

Visit the research subject area page for fees and funding information for this project.
Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK (UK and EU students only).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.