Network biology : applications in medicine and biotechnology

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorOresic, Matej, Research Prof.
dc.contributor.authorLindfors, Erno
dc.contributor.departmentDepartment of Biomedical Engineering and Computational Scienceen
dc.contributor.departmentLääketieteellisen tekniikan ja laskennallisen tieteen laitosfi
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorKaski, Kimmo, Prof.
dc.date.accessioned2012-08-31T11:01:37Z
dc.date.available2012-08-31T11:01:37Z
dc.date.issued2011
dc.description.abstractThe concept of systems biology emerged over the last decade in order to address advances in experimental techniques. It aims to characterize biological systems comprehensively as a complex network of interactions between the system's components. Network biology has become a core research domain of systems biology. It uses a graph theoretic approach. Many advances in complex network theory have contributed to this approach, and it has led to practical applications spanning from disease elucidation to biotechnology during the last few years. Herein we applied a network approach in order to model heterogeneous biological interactions. We developed a system called megNet for visualizing heterogeneous biological data, and showed its utility by biological network visualization examples, particularly in a biomedical context. In addition, we developed a novel biological network analysis method called Enriched Molecular Path detection method (EMPath) that detects phenotypic specific molecular paths in an integrated molecular interaction network. We showed its utility in the context of insulitis and autoimmune diabetes in the non-obese diabetic (NOD) mouse model. Specifically, ether phosholipid biosynthesis was down-regulated in early insulitis. This result was consistent with a previous study (Orešič et al., 2008) in which serum metabolite samples were taken from children who later progressed to type 1 diabetes and from children who permanently remained healthy. As a result, ether lipids were diminished in the type 1 diabetes progressors. Also, in this thesis we performed topological calculations to investigate whether ubiquitous complex network properties are present in biological networks. Results were consistent with recent critiques of the ubiquitous complex network properties describing the biological networks, which gave motivation to tailor another method called Topological Enrichment Analysis for Functional Subnetworks (TEAFS). This method ranks topological activities of modules of an integrated biological network under a dynamic response to external stress. We showed its utility by exposing an integrated yeast network to oxidative stress. Results showed that oxidative stress leads to accumulation of toxic lipids.en
dc.format.extentVerkkokirja (1899 KB, 81 s.)
dc.format.mimetypeapplication/pdf
dc.identifier.isbn978-951-38-7759-0 (PDF)
dc.identifier.isbn978-951-38-7758-3 (printed)
dc.identifier.issn1235-0621
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/5038
dc.identifier.urnURN:ISBN:978-951-38-7759-0
dc.language.isoenen
dc.publisherVTTen
dc.relation.haspart[Publication 1]: Erno Lindfors, Peddinti V. Gopalacharyulu, Eran Halperin, and Matej Orešič (2009). Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse. PLoS ONE, 4(10), e7323. 9 p. © 2009 by authors.en
dc.relation.haspart[Publication 2]: Peddinti V. Gopalacharyulu, Erno Lindfors, Catherine Bounsaythip, Teemu Kivioja, Laxman Yetukuri, Jaakko Hollmén, and Matej Orešič (2005). Data integration and visualization system for enabling conceptual biology. Bioinformatics, 21(1):i177-i185. © 2005 by authors.en
dc.relation.haspart[Publication 3]: Peddinti V. Gopalacharyulu, Erno Lindfors, Jarkko Miettinen, Catherine Bounsaythip, and Matej Orešič (2008). An integrative approach for biological data mining and visualisation. International Journal of Data Mining and Bioinformatics, 2(1):54-77. © 2008 Inderscience Enterprises. By permission.en
dc.relation.haspart[Publication 4]: Catherine Bounsaythip, Erno Lindfors, Peddinti V. Gopalacharyulu, Jaakko Hollmén, and Matej Orešič (2005). Network-based representation of biological data for enabling context-based mining. In: Catherine Bounsaythip, Jaakko Hollmén, Samuel Kaski, and Matej Orešič, editors, Proceedings of KRBIO'05, International Symposium on Knowledge Representation in Bioinformatics, Espoo, Finland, Jun 2005. Helsinki University of Technology, Laboratory of Computer and Information Science. 6 p. © 2005 by authors.en
dc.relation.haspart[Publication 5]: Erno Lindfors, Jussi Mattila, Peddinti V. Gopalacharyulu, Antti Pesonen, Jyrki Lötjönen, and Matej Orešič. Heterogeneous Biological Network Visualization System: Case Study in Context of Medical Image Data. Advances in Experimental Medicine and Biology. (In press.)en
dc.relation.haspart[Publication 6]: Peddinti V. Gopalacharyulu, Vidya R. Velagapudi, Erno Lindfors, Eran Halperin, and Matej Orešič (2009). Dynamic network topology changes in functional modules predict responses to oxidative stress in yeast. Molecular BioSystems, 5(3):276-287. © 2009 Royal Society of Chemistry (RSC). By permission.en
dc.relation.ispartofseriesVTT publications, 774en
dc.subject.keywordnetwork biologyen
dc.subject.keywordsystems biologyen
dc.subject.keywordbiological data visualizationen
dc.subject.keywordtype 1 diabetesen
dc.subject.keywordoxidative stressen
dc.subject.keywordgraph theoryen
dc.subject.keywordtechen
dc.subject.keywordnetwork topologyen
dc.subject.keywordubiquitous complex network propertiesen
dc.subject.otherComputer science
dc.subject.otherBiotechnology
dc.titleNetwork biology : applications in medicine and biotechnologyen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotVäitöskirja (artikkeli)fi
dc.type.ontasotDoctoral dissertation (article-based)en
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local.aalto.digifolderAalto_65281
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