Mapping and Modelling the Dynamics of Tumours

Dr Xiao Fu

Lab: Integrative Modelling
Duration: 4 years, starting October 2026
Closing Date: 5 June 2025

Application Instructions - please read before applying

Please note, for your application to be considered, you must upload your CV and a completed document CRUK EDI Recruitment Form(107 KB) .

• We ask that you do not add your name or any Institution details to the CRUK EDI Recruitment Form.
• Applications will be shortlisted initially based on the CRUK EDI Recruitment Form only. CVs will be used in further rounds of shortlisting to invite candidates to interview.
• Please upload your CV and Recruitment Form as two seperate documents. 
• References will only be requested after an initial shortlisting stage.  

 APPLY HERE

Background

Complex and dynamic interactions between cancer cells and elements of the tumour microenvironment shape tumour progression and contribute to therapy resistance. In our Integrative Modelling Lab, we focus on developing novel computational approaches to investigate the co-evolutionary dynamics and organisational principles of the tumour and its microenvironment.
Our vision is that these computational approaches, in integration with clinical and pre-clinical laboratory research, will increase our insights into the fundamental mechanisms underpinning tumour progression and therapy resistance and, ultimately, improve our strategies for stratification and treatment of patients.

This PhD project focuses on developing novel mathematical models, in particular agent-based models (ABMs), to study tumour growth dynamics and interactions with the cellular ecosystem. These models will be built iteratively side by side with experimental systems (e.g., genetically engineered mouse models, in vivo perturbation experiments), with an aim to reveal a tumour’s vulnerabilities and identify novel strategies for more effective treatment.

Ultimately, the calibrated models will be employed as Virtual Laboratories to screen diverse perturbations in silico and to help study the role of cancer-intrinsic and microenvironmental factors that underpin the dynamics of disease progression in patients.

The student will have an exciting opportunity to work closely with cancer biologists, wet-lab experimentalists, clinicians, and data scientists in the highly collaborative and multi-disciplinary environment at the CRUK Scotland Institute and the University of Glasgow. The student will further benefit from our established collaborations in several cancer contexts nationally and internationally.

The successful candidate will likely come from a quantitative background (e.g., Mathematics, Physics, Computer science) with strong interest in cancer biology. The project may also be suitable for candidates from biological backgrounds with strong motivations to learn (and ideally with experiences in developing/applying) computational approaches to biology.

Research Question

The student will be driving the cross-disciplinary project in the context of liver, colorectal, or pancreatic cancer, building upon our established collaborations at the Institute and internationally. We will tailor the project details to the successful candidate’s research interests.

Broadly speaking, we are interested in these research questions:

• What architectural and spatial features in temporally sparse “snapshots” of tumour samples (e.g., spatial transcriptomics, multiplex images) are associated with tumour aggressiveness and therapy responsiveness?
• What are organisational principles and molecular/cellular mechanisms sculpting clinically relevant architectural features of tumours?
• Can we infer dynamic histories and trajectories from “snapshots” of tumours and discover novel dynamic features for risk stratification?
• Can we predict outcomes of different drugs treated to “snapshots” of tumours and identify novel combinatory strategies for better treatment outcomes?
• Methodologically, how to best integrate/iterate in silico models with experimental systems to address the questions above and ultimately inform the management of patient disease?

Skills/Techniques that will be gained

Biological foundation and knowledge in the specific context of the project (liver, colorectal, or pancreatic).

Development of computational models and of frameworks to link models with experimental data. In different aspects of the project addressing different questions, models will be written using a programming language such as Python or C++, or constructed using a multi-cellular simulation software such as PhysiCell or CompuCell3D.

Large-scale simulations on High-Performance Computing (HPC) clusters.

Bioimage (e.g., QuPath, Fiji) and spatial data analysis (e.g., ScanPy, SquidPy) relevant to the project.

Code management and version control vis GitHub.

Funding 

• stipend at CRUK rate
• tuition fees at home or international rate
• consumables funding

For questions regarding the application process, PhD programme/studentships at the CRUK Scotland Institute or any other queries, please contact phdstudentships@crukscotlandinstitute.ac.uk.

Closing date: 5 June 2026

Applications are open to all individuals irrespective of nationality or country of residence.

 APPLY HERE

Application Instructions - please read before applying

 Please note, for your application to be considered, you must upload your CV and a completed document CRUK EDI Recruitment Form(107 KB) .

• We ask that you do not add your name or any Institution details to the CRUK EDI Recruitment Form.
• Applications will be shortlisted initially based on the CRUK EDI Recruitment Form only. CVs will be used in further rounds of shortlisting to invite candidates to interview.
• Please upload your CV and Recruitment Form as two seperate documents. 
• References will only be requested after an initial shortlisting stage. 

Relevant Publications

1. X. Fu*, Y. Zhao*, et al.  Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study. Nat Ecol Evol, 2022. 6(1): p. 88-102. 10.1038/s41559-021-01586-x.
2. Y. Zhao*, X. Fu*, J. I. Lopez*, et al. Selection of metastasis competent subclones in the tumour interior. Nat Ecol Evol, 2021. 5(7): p.1033-1045. 10.1038/s41559-021-01456-6.
3. A. Bhargava*, X. Fu*^, S. Bailey*, …, E. Sahai^, Cell migration and the tumour microenvironment shape evolutionary dynamics. BioRxiv (doi: https://doi.org/10.1101/2025.09.15.675999 )
4. X. Fu^, E. Sahai, A. Wilkins^. Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response. J Pathol. 2023 Aug;260(5):578-591. doi: 10.1002/path.6153. Epub 2023 Aug 8. PMID: 37551703; PMCID: PMC10952145.

GitHub repositories: 

(linked to Papers 1 and 2): https://github.com/iamfuxiao/tumour-growth-patterns-impact-evolution

(an example model of tumour growth in the liver): https://github.com/xiaofu-lab-beatson/ABM-Tutorial-Wellcome-EBEC