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On the dynamics of drug-cancer cells interactions with the aid of continuous and hybrid multiscale models

   School of Mathematics and Physics

About the Project

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Mathematics and Physics and will be supervised by Dr Marianna Cerasuolo and Dr Andrew Burbanks.

The work on this project could involve:

  • Development and qualitative analysis of mathematical models for cancer dynamics and drug-cancer cells interaction
  • Data analysis
  • Computer programming
  • External collaboration with interdisciplinary teams (Experimental oncologists and biochemists)

Project description

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. However, for late stage disseminated diseases current therapies are merely palliative. 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. In the last few years, mathematical oncology has become increasingly important in supporting experimental studies to gain insights into cancer research and find new personalised therapeutic strategies to fight this deadly disease.

The aim of this project is to develop and validate continuum and hybrid mathematical models of vascular tumour growth to gain insights into the interplay between newly discovered drugs and PCa cells. These tools will help understanding how the drug-cells interaction can affect the dynamics of the tumour growth and, at the same time, will allow to explore the effectiveness of different therapeutic strategies. In particular the project will focus on representing mathematically different-scales mechanisms that contribute towards drug resistance in cancer; cells metabolic changes during treatments and the impact of such changes on cancer behaviour (development of metastasis, etc). The analytical and numerical study of the dynamical systems as well as the statistical comparison between numerical simulations and experimental data will also be part of the project.

Candidates 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 University of Portsmouth including those geared toward improving presentation skills, time-management, project organization skills, thesis writing, data analysis and statistics, and other various related training modules.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

You should ideally have a 1st class honours degree (or Master) in Mathematics, Physics, Engineering or Computer Science and have a genuine interest in Biomedical Sciences. Previous research experience in applied mathematics, computational biology or computer programming is welcome.

How to Apply

We encourage you to contact Dr Cerasuolo ()to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Mathematics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:SMAP5821023.

Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).

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