Bioscience for an integrated understanding of health

Category: Industry Co-funded Studentships

Dynamic modelling of synthetically lethal pathways to enable the development of cancer therapeutics

Project No. 2144

Primary Supervisor

Dr Frances Pearl – University of Sussex


Dr John George – Oppilotech Ltd,

Dr Helfrid Hochegger – University of Sussex


In many cancers the majority of genetic changes that drive the disease involve loss of function of the affected genes.

This provides a challenge for targeted therapies that take advantage of the distinctive genetic attributes of cancer cells.  Synthetic lethality (SL) is the phenomenon where loss of function in two genes causes cell death, whereas loss of function in either of these genes alone can be tolerated by the cell.   Synthetic lethality provides a therapeutic approach that enables tumour suppressor cancer related genes to be targeted pharmacologically.

The PhD Student will join the Pearl Bioinformatics Lab who have recently published computational methods that reveal existing SL gene pairs within the human genome and have proposed and validated novel therapeutic SL gene pairs. The project involves identifying a pathway for a therapeutic synthetic lethal gene pair and building dynamic computational models for that pathway.  The student will then have the opportunity to optimise and test the model with the industrial partner Oppilotech.

In the final year, experimental validation of the target will take place in the Hochegger laboratory in the Genome Damage Stability Centre at Sussex where the student will be trained in experimental methods (e.g. CRISPR/Cas9).

We expect this project to have high impact. It will validate computational methodologies to inform biology. Findings will be published in peer reviewed journals. The project has the potential to lead into a drug discovery project within oncology at Oppliotech and ultimately impact on human health.


A Emiola, J George, SS Andrews. (2015). A complete pathway model for lipid A biosynthesis in Escherichia coli. PloS one 10 (4), e0121216

G Benstead-Hume, X Chen, SR Hopkins, KA Lane, JA Downs, FMG Pearl (2019).  Predicting synthetic lethal interactions using conserved patterns in protein interaction networks PLoS Computational Biology 15 (4), e1006888