IRUS Total

Analysing directed network data

File Description SizeFormat 
Sarajlic-A-2015-PhD-Thesis.pdfThesis14.67 MBAdobe PDFView/Open
Title: Analysing directed network data
Authors: Sarajlic, Anida
Item Type: Thesis or dissertation
Abstract: The topology of undirected biological networks, such as protein-protein interaction networks, or genetic interaction networks, has been extensively explored in search of new biological knowledge. Graphlets, small connected non-isomorphic induced sub-graphs of an undirected network, have been particularly useful in computational network biology. Having in mind that a significant portion of biological networks, such as metabolic networks or transcriptional regulatory networks, are directed by nature, we define all up to four node directed graphlets and orbits and implement the directed graphlet and graphlet orbits counting algorithm. We generalise all existing graphlet based measures to the directed case, defining: relative directed graphlet frequency distance, directed graphlet degree distribution similarity, directed graphlet degree vector similarity, and directed graphlet correlation distance. We apply new topological measures to metabolic networks and show that the topology of directed biological networks is correlated with biological function. Finally, we look for topology–function relationships in metabolic networks that are conserved across different species.
Content Version: Open Access
Issue Date: Aug-2015
Date Awarded: Jan-2016
URI: http://hdl.handle.net/10044/1/34381
DOI: https://doi.org/10.25560/34381
Supervisor: Przulj, Natasa
Rueckert, Daniel
Sponsor/Funder: European Research Council
Funder's Grant Number: 278212 (2012-2017)
Department: Computing
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Computing PhD theses

Unless otherwise indicated, items in Spiral are protected by copyright and are licensed under a Creative Commons Attribution NonCommercial NoDerivatives License.

Creative Commons