Group members and alumni

My research is based on developing a mechanistic understanding of the interaction between mutational and non-genetic contributors to Non-Small Cell Lung Cancer progression. To do this I use an array of computational and network-based tools to model complex biological data. The eventual goal of my research is to identify novel therapies that can be tailored to patients based on their mutational background and so improve overall responses to treatment.

I am interested in computational modelling of gene regulatory networks in cancer, particularly in breast cancer and glioblastoma (GBM). My work focuses on the DNA damage response (DDR) pathway, how mutations cause it to malfunction leading to cancer, and predicting treatment effects to find optimal and novel therapies. Beyond treatment response, I am investigating the potential of network models to predict the evolution of cancer.

I focus on building a biological executable model of glioblastoma and high-grade glioma, aggressive cancers known for poor prognosis and a lack of widely available targeted therapies. My interest is how DNA damages responses and the driver mutations that differentiate glioblastoma subtypes contribute to tumour resistance or sensitivity to treatment. I am to produce a model able to predict individual patient responses to existing chemo-radiotherapy treatments based on their mutational background.

I use computational modelling to tackle triple-negative breast cancer (TNBC). TNBC is remarkably heterogeneous, and I am interested in how this heterogeneity, along with tumour evolution and architecture confers the ability to resist therapy. Thus, I employ clinical data and executable models of biological networks to stratify tissue states and their potential for state transition and metastasis, which may serve to identify patients that will or will not respond to a given form of therapy.

My work focuses on developing an executable model of triple-negative breast cancer (TNBC), with a particular interest in the tumour microenvironment. This in silico model aims to predict and better understand individual TNBC patient responses to immunotherapy.

Co-supervised with Karen Page and Philip Pearce, Department of Mathematics, UCL. My work focuses on mathematical and computational modelling of clonal evolution in lung cancer. I am particularly interested in how the behaviour of tumour cells is affected by known oncogenic mutations, and in predicting the effects of inter-cellular interactions using evolutionary game theory.

Co-supervised with Clare Bennett, UCL Cancer Institute. A multidisciplinary approach to boosting immune surveillance of primary melanoma in the skin.

Co-supervised with Crispin Hiley at the UCL Cancer Institute, my research investigates causes of treatment failure in locally advanced non-small cell lung cancer. I perform genomic analyses of tumour and plasma samples from patients receiving chemoradiotherapy and use these data to build computational models of tumour behaviour, aiming to understand treatment resistance and guide personalised therapies based on mutational profiles.