Investigating tumour evolution through graph theoretical analysis of gene regulatory networks
Year: 2014
Type: dissertation
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. \n \nThe first was the development and implementation of a network inference method designed to infer GRNs at differently defined classes from a single microarray dataset. \n \nThe 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. \n \nThe third was the comparison of GRNs inferred for different disease stages across datasets for the same disease, neuroblastoma, from two different studies. \n \nThis 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. \n
Author: Alex Upton
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W73028604