Computational Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed …

Computatational modelling allows screening of thousands of combinations of drugs to find the most effective treatments. This allows for better protection against the emergence of resistance, as well as rapid drug repurposing e.g. to combat emerging diseases such as COVID-19.

Cancers often emerge, in part, due to deficencies in DNA damage repair, making them vulnerable to DNA damaging treatments such as radiotherapy. While radiotherapy efficacy has been increasing, this is mainly due to better targeting of tumour anatomy, but targeted therapy offers the opportunity to radiosensitise tumour cells by targeting vulnerabilities in the tumour biology. We use computational modelling to find such vulnerabilities, and explore potential resistance mechanisms.

Lung cancer is the leading cause of cancer mortality world-wide, and over half of lung cancer patients rely on radiotherapy (RT). …

While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico …

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, …

Cells with higher levels of Myc proliferate more rapidly and supercompetitively eliminate neighboring cells. Nonetheless, tumor cells …

As cancer tumours develop, competition between cells will favour those with some mutations over others, creating a dynamic …