Computational Modelling

Computational models have become essential tools for understanding signalling networks and their non-linear dynamics. However, these …

Computational models have become essential tools for understanding signalling networks and their non-linear dynamics. However, these models are typically constructed manually using prior knowledge and can be over-reliant on study bias. Scaling up the construction and analysisof models to take advantage of increasingly abundant ‘omics data can bridge these gaps by providing a comprehensive view of signalling events and how they influence cellular phenotypes.

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.

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

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, …