Investigating tumour evolution through graph theoretical analysis of gene regulatory networks

Upton, Alex (2014). Investigating tumour evolution through graph theoretical analysis of gene regulatory networks. University of Birmingham. Ph.D.

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Abstract

The main aim of this work was to develop methods to aid biologists and clinicians investigate the progression and evolution of tumours through the analysis of microarray data, concentrating on the inference and analysis of Gene Regulatory Networks (GRNs) representing different evolutionary and clinical stages of cancer microarray data. Three main areas of work were carried out.

The first was the development and implementation of a network inference method designed to infer GRNs at differently defined classes from a single microarray dataset.

The second was the investigation of appropriate graph theory metrics to quantitatively analyse the different defined stages of disease. Genes identified by the various metrics were scored for the particular disease of interest, allowing the graph theory metrics to be ranked against each other for the various GRNs.

The third was the comparison of GRNs inferred for different disease stages across datasets for the same disease, neuroblastoma, from two different studies.

This work has shown that analysis of GRNs inferred using a method designed to infer multiple GRNs from a single microarray dataset has identified genes involved in different stages of disease, thereby having the potential to aid in the investigation of the progression and evolution of tumours.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):