BioModelAnalyzer

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

Cancers evolve from normal cells, and continue to evolve to evade treatment. Understanding evolution from tumourigenesis will allow for earlier detection of cancer, while predicting evolution of resistance mechanisms is essential for finding treatments that are effective in the long term.

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.

Blood malignancies arise from the dysregulation of haematopoiesis. The type of blood cell and the specific order of oncogenic events …

The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 …

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 …

Cancer develops via the progressive accumulation of somatic mutations, which subvert the normal operation of the gene regulatory …

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