The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
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Computational Models and Methods for Lipoprotein Research
Linda Kumpula
Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the School of Science for public examination and debate in Auditorium of the F-building at the Aalto University School of Science (Espoo, Finland) on the 1st of April 2011 at 12 noon.
Dissertation in PDF format (ISBN 978-952-60-4078-3) [13670 KB]
Dissertation is also available in print (ISBN 978-952-60-4077-6)
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.
Keywords: lipoprotein, ultracentrifugation, self-organizing map, lipoprotein composition, lipoprotein concentration
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© 2011 Aalto University
Last update 2011-05-26