Gaining insight into the dynamics of drug-cancer cell interactions with the aid of continuous and hybrid multiscale models
PhDs and postgraduate research
Self-funded PhD students only
School of Mathematics and Physics
Applications accepted all year round
The work on this project will:
- develop and validate continuum and hybrid mathematical models to gain insights into the interplay between newly discovered drugs and PCa cells
- focus on representing mathematically different-scales mechanisms that contribute towards drug resistance in cancer
- Involve analytical and numerical study of the dynamical systems as well as the statistical comparison between numerical simulations and experimental data
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 – and the statistical comparison between numerical simulations and experimental data – 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 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.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a**
Home/EU/CI part-time students: £2,250 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
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.
- You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Mathematics, Physics, Engineering or Computer Science and have a genuine interest in Biomedical Sciences.
- 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.
Previous research experience in applied mathematics, computational biology or computer programming is welcome.
How to apply
We’d encourage you to contact Dr Marianna Cerasuolo (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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.
Please note: to be considered for this self-funded PhD opportunity you must quote project code MPHY4450219 when applying.