Data integration and hypothesis testing for brain tumour multi-omics data

This project aims to develop computational and statistical methods for the integrated learning of biological networks from multiple sources to further our understanding of Glioblastoma Multiforme (GBM), a highly aggressive primary brain tumour for which there is currently no cure. Current research into the relationship between the transcriptome, proteome and metabolome in GBM is generating large amounts of data that are challenging to analyse, due to the volume and complexity of the data produced. There is a clear need for work to fully exploit this data in a synergistic manner.

The project will develop methods to learn biological networks while integrating data from multiple ‘omics platforms, including metabolomic, proteomic and transcriptomic data. This will help us to understand the interplay between gene regulation and expression and metabolites in GBM. To learn these networks, the project will develop statistical models of biological networks consisting of multiple interacting layers, corresponding to the different data sources, and use approaches from computational statistics to infer the network structure. We will also investigate approaches to identify changes in the network structure between GBM patients and controls.

During the project, the methodologies developed will be applied to existing metabolomic and transcriptomic data, and to new data generated by the project partner, Dr Nelofer Syed at Imperial College London. Dr Syed’s research group routinely employ omics technologies on patient-derived tissue and cell lines generated from them and animal models to identify novel therapeutic strategies for adult GBM. The applicant will be based in the Department of Computer Science at the University of Surrey, but will also work closely with the group of Dr Syed at Imperial College London to ensure the methods developed answer the relevant biological questions about the biological networks involved in disease in GBM.

Applicants are expected to hold a first or upper-second class degree in a relevant discipline (or equivalent overseas qualification), or a lower second plus a good masters degree (distinction normally required).

English languages requirements

IELTS minimum 6.5 overall with minimum 6.0 in each component, or equivalent.

Nature Inspired Computing and Engineering (NICE)