Computational models and methods for lipoprotein research

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Ala-Korpela, Mika, Prof., Universtity of Oulu
dc.contributor.author Kumpula, Linda
dc.date.accessioned 2012-08-29T10:07:29Z
dc.date.available 2012-08-29T10:07:29Z
dc.date.issued 2011
dc.identifier.isbn 978-952-60-4078-3 (PDF)
dc.identifier.isbn 978-952-60-4077-6 (printed) #8195;
dc.identifier.issn 1799-4942
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/4937
dc.description.abstract Lipoproteins are self-assembled nanoparticles for water-insoluble lipid transportation in the circulation. Lipoprotein particles form a key metabolic system in a variety of normal physiological processes but also play an essential role in many pathological conditions. In particular, certain lipoprotein abnormalities are associated with the development of atherosclerosis, a disease state of arteries, common in cardiovascular disease. Computational modelling is a potential but so far rarely used method to study lipoprotein particles. This thesis contributes to lipoprotein research by various computational approaches where experimentally isolated and biochemically characterised lipoprotein particles serve as a starting point. This thesis deals with estimating the number of lipid molecules within lipoprotein particles, i.e., composition information, and approximating the molecular structure of lipoprotein particles in each subclass. It also proceed the ultracentrifugal particle isolation by a kind of in silico sub-classification resulting from utilisation of the self-organising map (SOM) method. This, when applied to experimental data, with lipoprotein lipid concentration and composition information combined, shows that there is variability in the compositional/metabolic relations between individuals, i.e., distinct lipoprotein phenotypes. Furthermore, this thesis introduces a method to estimate lipoprotein particle concentrations in each subclass, which also provides a reference particle library for NMR-based lipoprotein particle concentration estimation. Applications of the models to experimental data show that triglyceride and cholesterol ester molecules, which are conventionally held as core lipids, may also locate in significant amounts in the surface. The lipoprotein phenotype analysis shows that per particle compositions, which appear as a fundamental issue in metabolic and clinical corollaries, can not be deduced solely from the regularly measured plasma lipid concentrations nor from the particle concentration estimates. en
dc.format.extent Verkkokirja (13670 KB, 151 s.)
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Aalto University en
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS , 25/2011 en
dc.subject.other Biotechnology
dc.subject.other Medical sciences
dc.title Computational models and methods for lipoprotein research en
dc.type G4 Monografiaväitöskirja fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.department Lääketieteellisen tekniikan ja laskennallisen tieteen laitos fi
dc.contributor.department Department of Biomedical Engineering and Computational Science en
dc.subject.keyword lipoprotein en
dc.subject.keyword ultracentrifugation en
dc.subject.keyword self-organizing map en
dc.subject.keyword lipoprotein composition en
dc.subject.keyword lipoprotein concentration en
dc.identifier.urn URN:ISBN:978-952-60-4078-3
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (monografia) fi
dc.type.ontasot Doctoral dissertation (monograph) en
dc.contributor.supervisor Kaski, Kimmo, Prof.


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