Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Author:
Description:
Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require ...
Publisher:
Helsingin yliopisto ; Helsingfors universitet ; University of Helsinki
Contributors:
University of Helsinki, Faculty of Science, Department of Computer Science ; Helsingin yliopisto, matemaattis-luonnontieteellinen tiedekunta, tietojenkäsittelytieteen laitos ; Helsingfors universitet, matematisk-naturvetenskapliga fakulteten, institutionen för datavetenskap ; van Helden, Jacques ; Rousu, Juho ; Ukkonen, Esko
Year of Publication:
2010-11-25T12:15:30Z
Document Type:
Doctoral dissertation (article-based) ; Artikkeliväitöskirja ; Artikelavhandling ; Text ; doctoralThesis ; [Doctoral and postdoctoral thesis]
Language:
eng
Subjects:
tietojenkäsittelytiede
Rights:
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. ; This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. ; Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden.
Relations:
URN:ISBN:978-952-10-6641-2 ; Helsinki: 2010, Series of publications / Department of Computer Science, University of Helsinki. A. 1238-8645 ; http://hdl.handle.net/10138/21328
URL:
http://hdl.handle.net/10138/21328
Content Provider:
HELDA – Helsingin yliopiston avoin julkaisuarkisto
Further nameHELDA – University of Helsinki Open Repository  Flag of Finland