Research groups
Colleges
Helen Byrne
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.
Key publications
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Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors
Vipond O. et al, (2021), Proceedings of the National Academy of Sciences, 118
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Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer
Paczkowski M. et al, (2021), Communications Biology, 4
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Abnormal morphology biases hematocrit distribution in tumor vasculature and contributes to heterogeneity in tissue oxygenation.
Bernabeu MO. et al, (2020), Proceedings of the National Academy of Sciences of the United States of America, 117, 27811 - 27819
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Combining multiple spatial statistics enhances the description of immune cell localisation within tumours.
Bull JA. et al, (2020), Scientific reports, 10
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Identifying and characterising the impact of excitability in a mathematical model of tumour-immune interactions
Osojnik A. et al, (2020), Journal of Theoretical Biology, 501, 110250 - 110250
Recent publications
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Exploring the relationship between vascular remodelling and tumour growth using agent-based modelling
Fan N. et al, (2025)
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Spatio-temporal dynamics of M1 and M2 macrophages in a multiphase model of tumor growth
Lampropoulos I. et al, (2025)
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Correction to: Combining Mechanisms of Growth Arrest in Solid Tumours: A Mathematical Investigation.
Colson C. et al, (2025), Bulletin of mathematical biology, 87