Mathematical reconstruction of cancer networks


Gene regulatory networks (GRNs) are complex and still uncharacterized sets of regulators and interactions that govern cellular processes, such as proliferation and metabolic adaptation. In cancer, deregulation of their delicate balance may result in uncontrolled tumour growth. Hypoxia, i.e. lack of oxygen, is the main difference between normal and cancerous tissue, and associated with tumour aggressiveness [1] and resistance to treatment [2]. We have used co-expression networksto derive a hypoxia signature [3, 4], which is now being translated to the clinic as biomarker.

This project will develop the first executable GRN model of HIF1 signaling. The model will be trained using data from cell line experiments and clinical studies. This will enable the discovery of new HIF1 targets and generation of testable biological hypotheses on their biology, which we will validate in the lab. It will also highlight candidate therapeutic targets, and inform the design of future biomarker studies.

The candidate will benefit from working closely with members of Prof Buffa’s CRUK Functional Genomics and ERC Tumour Microenvironment Modelling labs, and collaboration with Prof Saez-Rodriguez group. Importantly, we expect the results produced in this project will have broader methodological impact on the development of integrative genomic approaches.

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