I am a postdoc working at the Cancer Institute at University College London. My main research interests combine biology and computer science, using computational modelling to predict cancer evolution and plan treatment programmes to avert or overcome resistance.
I completed my PhD at the the University of Cambridge, looking at how computational network models could be used to find more effective combination treatments for breast cancer. As a postdoc at the Fisher Lab in the UCL Cancer Institute I am building upon this work in order to predict resistance mechanisms to radiotherapy and to find the most effective patient-specific treatments to overcome them.
I am keen to share my knowledge and expertise with others. As a mentor to postdocs, PhD students, Masters students, and undergraduates, I take pride in helping to guide and inspire the next generation of scientists.
💻 My website | 
| ⚡ Published Project | 📰 Paper | 💾 Code/Data |
|---|---|---|
| Order of mutations in evolution | Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution | MutationOrder |
| Combination Treatments for COVID-19 | Executable network of SARS-CoV-2-host interaction predicts drug combination treatments | COVID19 |
| Combination Treatment for Myc-driven breast cancer | Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling | HeterogeneousBreastCancer |
| Melanoma Immunotherapy | Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling | Melanoma |
| Blood Cancer Evolution | HOXA9 has the hallmarks of a biological switch with implications in blood cancers | Analysis & Data |
| Executable Modelling Review | Executable cancer models: successes and challenges | NA |
| Views on AML | Prognostic hallmarks in AML | NA |
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