Matthew Clarke – Postdoctoral Research Associate
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 and specifically whether we can use gene regulatory network models to predict the order of the acquisition of mutations during tumorigenesis, and the emergence of resistance after targeted therapy.
Yuxin Sun – Postdoctoral Research Associate
My research interests lie in developing and applying machine learning algorithms to understand the mechanisms of the treatment responses to cancer, in particular triple-negative breast cancer and glioblastoma. My work involves exploring interpretable machine learning models to integrate multi-omics data, in order to better explain the underlying pathways of the responses after therapy, as well as designing personalised treatment.
Pedro Victori – Postdoctoral Research Associate
My work focuses on the use of computational modelling to tackle triple-negative breast cancer (TNBC), a disease characterised by its aggressiveness and low survival rate. TNBC is also 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. Moreover, in the case of non-responders, I analyse the mechanisms that underpin resistance to treatment, which can inform the development of new therapeutic strategies.
Daniel Holdbrook – Research Associate
In collaboration with Promatix
My role is to integrate Biological Executable Models into Promatix target discovery workflow. By building models, such as one for colon cancer, we aim to identify druggable targets of high importance to the biology of diseased cells. These targets may have characteristics that prevent the cancer from finding an escape to treatment due to their integral nature within key signalling pathways. With an understanding of these key mechanisms, we hope to develop more effective cancer treatments.
Daniel Jacobson – PhD student
Co-supervised with Maria Secrier, Department of Genetics, UCL
My work focuses on applying computational techniques to identify deficiencies in the DNA damage response (DDR) in cancer, and investigating how these deficiencies are involved in driving tumour evolution. This includes studying signatures of genomic aberrations present within the cancer genome, as well as the application of machine learning and network modelling to study dysregulated processes associated with DDR deficiencies. Additionally, I am interested in the role played by the tumour microenvironment and how this is associated with the DDR process.
Francesco Moscato – PhD student
Co-supervised with Clare Bennett, UCL Cancer Institute
A multidisciplinary approach to boosting immune surveillance of primary melanoma in the skin.
Carla Castignani – PhD student
Co-supervised with Charles Swanton & Peter Van Loo, Francis Crick Institute
My work focuses on the reconstruction of epigenomic evolutionary trajectories in lung cancer and to understand how these help drive carcinogenesis. Ultimately, and in collaboration with Jasmin Fisher’s group, I aim to build an integrated model of genomic, epigenomic and transcriptomic histories to obtain more robust evolutionary models.
Sara Bazan – MSc student Email: email@example.com
Applying computational modelling to study the effects of the inflammatory response in breast cancers with DNA repair defects. This will enable further insight into combination therapies which target both of these processes.
Dan Kviat - MSc student
Building an executable model of ALK inhibitor resistance in non-small cell lung cancer, to understand and predict mechanisms of resistance, and find treatments to overcome it.
Jamie Dean – Senior Research Fellow/Junior Group Leader
My research interests are in the application of mathematical and statistical modelling to inform improved treatment strategies in radiation oncology.