Biological Network Analysis through Global Pairwise and Multi-way Network Alignment

Abstract

Recent developments into high throughput sequencing technologies have created a large influx of biological data, with a reasonable portion consisting of interaction data. This interaction data has been compiled into several network types. Protein-protein interaction networks that outline the interactions between proteins, gene regulatory networks which describe the regulation of expression of genes by other genes, and transcription networks that model how a collection of regulatory proteins associate with genes across a genome. These networks permit the analysis of biological systems from a different perspective to the classical analysis of biological systems. The analysis of this interaction data can bring about a wealth of information that was not previously available. There have been multiple approaches to the analysis of these networks with a plethora of different algorithms for each approach. This thesis will concentrate on the method of network alignment to compare these biological networks. I will discuss existing methods for the analysis of this new biological data, and propose my own approach for pairwise and multiple network alignment problem which is quick, lightweight and highly scalable. I will also discuss measures of analysis of these resulting alignments, including numerical measures of accuracy, graphical measures of accuracy, and some visual comparison methods.