Computational modelling of cell morphology in the spatial organisation of cancer

tissue-scale structures that influence disease progression and treatment response. Examples include disordered vasculature that drives tumour hypoxia, structured immune cell aggregates in the tumour periphery that promote tumour immunoediting, or the shape and dynamics of the tumour invasive front. Understanding how these structures arise remains a major challenge in cancer biology.

The distinct cell phenotypes and functions which influence tissue structures are tightly coupled with cellular morphology. For example, changes in cell shape are central to the epithelial-mesenchymal transition (EMT) associated with tumour invasion, and alterations in immune cell morphology are observed during activation, migration, and exhaustion. Modern spatial imaging technologies can quantify both cell state and morphology across entire tissues, enabling the relationship between shape and function to be studied at unprecedented scale.

A powerful approach for interrogating spatial patterning in imaging data uses agent-based models (ABMs), mathematical and computational tools which simulate each cell in a tissue as a distinct "agent" whose movement and phenotype are determined by microenvironmental cues. There are many distinct types of ABM framework, which balance biological realism against computational complexity [1, 2]. However, many existing models represent cells as points or deformed spheres, and cannot capture the changes in cell morphology associated with EMT or cell migration; frameworks with more complex representations of cell shape are often difficult to scale to large heterogeneous tissues, or are governed by unrealistic biophysical processes [3, 4]. As a result, the relationship between cell morphology and tissue-scale spatial organisation remains underexplored computationally.

Our group is developing a new generation of ABMs that extend force-based approaches with realistic representations of cell shape. These models allow morphology to emerge dynamically from subcellular physical processes and intercellular interactions, while remaining computationally scalable to large numbers of cells and simulation in 3D. Our framework creates opportunities to model the mechanistic underpinnings behind immune cell infiltration of cancers, tumour cell invasion, and interactions between cells and the extracellular matrix.

The successful student will help further develop this computational simulation framework, and use it to mathematically model the role of cellular morphology in cancer spatial biology. A particular focus will be on understanding how changes in morphology influence cell movement in immune infiltration and tumour invasion. The project will combine mathematical modelling with scientific computing and the quantitative spatial analysis of cutting-edge imaging data [5], and will provide new insights into how cell-scale morphology shapes tissue-scale organisation in the tumour microenvironment.

Candidate background

This project would suit candidates with a background in mathematics, computer science, physics, or other quantitative subjects, with an interest in applying their skills to problems in cancer biology. A background in cancer biology is not required, but a demonstrated interest in applying their subject area to problems outside of their field is desirable (particularly where this overlaps with problems in medicine or biology). The project is strongly computational, so a strong coding background and experience in using Python, Rust, or C++ would be highly beneficial.

Lab-specific question

Describe a computational, mathematical, or data analysis project that you are particularly proud of. What challenges did you encounter, how did you overcome them, and what did you learn from the process?

Funding Notes

Successful applicants will be awarded a non-taxable annual stipend of £27,715 plus payment of university tuition fees. Students of all nationalities are eligible to apply.

References

1. Osborne, J.M., Fletcher, A.G., Pitt-Francis, J.M., Maini, P.K. and Gavaghan, D.J. (2017) Comparing individual-based approaches to modelling the self-organization of multicellular tissues. PLOS Computational Biology 13: e1005387.
2. Metzcar, J., Wang, Y., Heiland, R. and Macklin, P. (2019) A Review of Cell-Based Computational Modeling in Cancer Biology. JCO Clinical Cancer Informatics 3: 1–13.
3. Lange, Z., Matthaus, F. and Qiu, M. (2025) Vertex models capturing subcellular scales in epithelial tissues. PLOS Computational Biology 21: e1012993.
4. Bull, J.A. and Byrne, H.M. (2023) Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLOS Computational Biology 19: e1010994.
5. Bull, J.A., Moore, J.W., Corry, S.M., Lin, M., Belnoue-Davis, H.L., Mulholland-Illingworth, E.J., . . . Byrne, H.M. (2026) MuSpAn: a toolbox for multiscale spatial analysis. Nature Communications.