Our research combines theoretical modelling with experimental data to explore how cells grow, migrate, interact, and respond to their environment. We study a diverse range of cellular systems, from infection and immunity to gene regulation and cell differentiation, linking molecular and cellular mechanisms to organismal outcomes.

Mathematical approaches

We employ a wide range of methods, including systems of ordinary and partial differential equations, agent-based and multi-scale models, and stochastic and statistical frameworks. These are complemented by data-driven approaches such as phylogenetic inference, genome sequence analysis, and machine learning for parameter estimation and validation.

Applications

Our models are used to study within-host infection dynamics and immune responses (e.g. tuberculosis), bacterial evolution and antibiotic resistance, and the cellular-scale processes that underpin tissue formation and regeneration.

We also investigate how epigenetic and chromatin-level regulation drives gene expression and cancer metastasis, and how microbiome–host interactions influence ageing and immunity. Collaboration between modellers and experimental biologists ensures that predictions are continually refined against biological observations.