Project No.2245

Primary Supervisor

Dr Frances Pearl – University of Sussex

Co-Supervisor(s)

Prof Michelle Garrett – University of Kent

Summary

The ultimate goal in cancer treatment is to identify the therapeutic vulnerabilities of a patient’s tumour and use this to design a personalised medicine regime.

Protein-protein interaction (PPI) networks have been shown to successfully predict which genes a cell depends on for its survival. However, such networks are derived from experiments on many thousands of different cells.  As such, these generic PPI networks cannot capture the variation of genetic dependencies across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis.

Predicting the ‘cell-essential’ genes in a cancer, on which a specific cancer has become dependent is of considerable therapeutic benefit enabling the use of targeted drugs to inhibit the corresponding protein products.

In this PhD, students will use multi-platform, genomic cancer data to personalise PPI networks for a set of cancer cell lines where cell-essential genes have been identified.  The student will extract features from these PPI networks and use a range of AI and data science techniques to predict the cell-essential genes.  Predictions will be experimentally validated in the Garrett lab, with the ultimate aim of identifying a set of novel, biologically validated drug targets for hard-to-treat cancers.

The student will receive state of the art training in computational biology including; programming, bioinformatics, ‘big data’ and data science, as well as cancer biology and therapeutics.  They will also be trained in a range of experimental techniques including; cancer cell culture, cell proliferation analysis, RNA interference technology for target validation, flow cytometry for cell cycle analysis, western blotting for protein detection and confocal microscopy for cell phenotype studies.