Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma

• Published in: PLoS computational biology Vol. 4; no. 5; p. e1000079
• Main Authors: Hübner, Katrin, Schwager, Thomas, Winkler, Karl, Reich, Jens-Georg, Holzhütter, Hermann-Georg
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2008; Public Library of Science (PLoS)
• Subjects: Apolipoproteins; Biochemistry; Biochemistry/Macromolecular Assemblies and Machines; Biochemistry/Theory and Simulation; Blood Chemical Analysis - methods; Cardiovascular Disorders; Cholesterol; Computational Biology; Computational Biology/Systems Biology; Computer Simulation; Humans; Lipids; Lipoproteins - blood; Medical research; Models, Cardiovascular; Models, Statistical; Plasma; Statistical Distributions; Studies; Lipoproteins; Blood Chemical Analysis; Models, Cardiovascular; Computer Simulation; Humans; Statistical Distributions; Models, Statistical
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000079

Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia.