Professor of Mathematical Biology
- Joint Theme Lead, Cancer Big Data, Cancer Research UK Oxford Centre
- Co-investigator, EPSRC-funded Centre for Topological Data Analysis
- Member, EPSRC Mathematical Sciences Strategic Advisory Team
I have undertaken my research in the Mathematical Institute’s Wolfson Centre for Mathematical Biology since 2011 and I took up an additional position at the Ludwig Institute for Cancer Research Oxford Branch in 2022.
My research focuses on the development and analysis of mathematical and computational models that describe biomedical systems, with particular application to cancer and its treatment. My aims in studying such models are two-fold: to identify the mechanisms responsible for observed biomedical phenomena and to abstract novel features from the resulting mathematical models that merit theoretical investigation. More recently, my research interests have broadened to include the development of statistical and mathematical approaches for analysing complex, high-dimensional datasets, especially datasets relating to cancer. In the future, I aim to extend these approaches while also developing innovative ways to combine them with complex, multiscale biomedical datasets in order to progress understanding of disease initiation and progression and, in the longer term, to provide an objective and rational basis to support decision-making in the treatment of cancer and other diseases, including atherosclerosis.
In 2019, I received the Leah Edelstein-Keshet Award from the Society for Mathematical Biology in recognition of my scientific achievements coupled with active leadership in mentoring scientific careers. In 2020 I became a Fellow of the Society for Mathematical Biology.
Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs.
Weeratunga P. et al, (2023), Nature communications, 14
Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics.
Johnson RM. et al, (2023), bioRxiv
The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.
Chulián S. et al, (2023), PLoS computational biology, 19