Mapping and Modelling the Dynamics of Tumours

About the Project

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 Notes

Annual tax-free stipend £22,500 + 1.75% indexation in Year 2,3&4. Tuition fees will also be paid at home or international rate to the University of Glasgow.