Computational Modelling of Tumour–Microenvironment Dynamics (25008)

Project Title:

Computational Modelling of Tumour–Microenvironment Dynamics and Radiotherapy Resistance in Paediatric High-Grade Gliomas 25008)

Primary Supervisor:

Dr Jamie Dean -  (Medical Physics and Biomedical Engineering, UCL)  jamie.dean@ucl.ac.uk

Introduction:

A fully funded 4-year PhD studentship is available in the Computational Radiation Biology Lab in the UCL Department of Medical Physics and Biomedical Engineering. Funding will be at least the UCL minimum stipend rate. Read more details on funding here. 

The successful candidate will join our Research Degree in Medical Physics (application portal here), and benefit from the activities and events organised by the department.

The Computational Radiation Biology Lab seeks to unlock the “fourth dimension” of radiation biology — time — to design superior radiotherapy strategies. Although radiotherapy is highly effective for many cancer patients, treatment failure remains common. Improving outcomes requires understanding and ultimately manipulating how tumours and normal tissues evolve over time in response to therapy. 

Using an innovative, multidisciplinary, and collaborative approach, the lab combines mathematical and computational modelling of molecular and cellular radiation response dynamics with longitudinal experimental and clinical data. Through this integration of computational and biological approaches, the lab is developing a detailed quantitative understanding of tumour and normal tissue responses to radiotherapy and drug-radiation combination therapies. 

The lab then leverages this understanding of the dynamic biology of treatment response to design and evaluate more effective therapeutic strategies. 

Project Background: 

Paediatric high-grade gliomas are highly aggressive brain tumours with poor patient outcomes despite multimodal therapy. Radiotherapy, using high-energy x-rays to induce DNA damage and cell death, is a central component of current treatment strategies. However, tumours typically exhibit only partial and transient responses before rapid progression. This reflects an incomplete understanding of how the complex tumour ecosystem adapts under therapeutic pressure. 

Recent advances in experimental and computational technologies now enable these tumours to be studied as complex dynamical systems. Single-cell and spatial molecular profiling of patient tumours and preclinical models generate high-dimensional, time-resolved datasets encompassing interacting cellular populations within the tumour microenvironment. These data capture coupled spatiotemporal processes involving malignant, immune, and stromal cell populations that are reshaped by therapy. 

This setting provides a rich opportunity for mathematical and computational modelling. Machine learning can be used to infer latent structure and construct lower-dimensional representations of system behaviour from high-dimensional data. Dynamical systems approaches can then be used to model, simulate, and analyse the evolution of interacting cellular populations under radiotherapy. Together, these approaches provide a framework that links data-driven inference with mechanistic modelling to understand emergent tumour–microenvironment dynamics and the development of therapy resistance in paediatric high-grade gliomas. 

Research aims: 

We aim to understand cellular and molecular mechanisms of radiotherapy response and resistance in paediatric high-grade gliomas. The student will investigate how tumour and microenvironmental cell populations survive treatment, whether cells transition between sensitive and resistant states, and how interactions with immune and stromal compartments shape these dynamics. A further goal is to identify molecular programmes underpinning these processes. The student will apply machine learning and dynamical systems approaches to multimodal preclinical and clinical datasets, integrating single-cell, spatial, and imaging data. The project will also involve collaboration with experimental partners to design and interpret validation studies informed by computational predictions. 

Person specification & requirements:

• This studentship is only open to Home Fee-paying candidates. More information about fee status criteria.
• Candidates with backgrounds in computer science, engineering, physical sciences, mathematics or computational biology, or comparable experience in computer programming and machine learning or computational modelling are welcome to apply. 
• Hands-on experience in advanced machine learning or computational modelling and its application to complex biomedical data, and interdisciplinary communication of complex concepts will be a plus. 
• Candidates should have an interest in neuro-oncology and the application of advanced computational methods to solving high impact problems in biomedicine, and the desire to work collaboratively within a multidisciplinary team. 
• Candidates should hold a UK (or international equivalent) first or upper-second class Bachelor’s degree. 

Funding:

This is a full studentship available to Home fee applicants only.

The successful student will receive a stipend starting from at least the UCL minimum (£22,780 in 2025/26) as well as the cost of tuition fees for Home fee students (£6,215 in 2025/26). 

The stipends awarded to PhD students at UCL are tax free and incur no income tax or national insurance contributions. The amount received increases each year over the duration of the studentship. 

UCL’s fee eligibility criteria

How to apply:

Please complete the following steps to apply: 

• Send an expression of interest and current CV to Dr Jamie Dean and medphys.pgr@ucl.ac.uk, quoting Project Code 25008 in the email subject line. jamie.dean@ucl.ac.uk
• Make a formal application via the UCL application portal: Apply | Prospective Students Graduate - UCL – University College London. Please select the programme code ‘Medical Physics RRDMPHSING01’ and enter Project Code 25008 under ‘Name of Award 1’
• If shortlisted, candidates will be invited for an interview. 

Application Timeline:

• After the deadline, all applicants that expressed their interest and specified Project 25008 in their Portico application will be considered for interview.
• Candidates will normally be invited for interview within three weeks of the deadline. If you have not been contacted within this time-period, you have unfortunately not been successful in being shortlisted.
• The interview panel will normally consist of the supervision team on the project.
• Note that applications without specifying the project they are applying for and/or making a formal Portico application will be automatically rejected.
• If you are offered and accept a studentship position, a formal UCL Offer of Admission will be sent to you as well as an offer of studentship funding.