Atherosclerosis, the primary cause of coronary artery disease (CAD), remains the number one cause of death in the United States and other industrialized nations. A major approach to the prevention and treatment of atherosclerosis is to target genes, mechanisms and pathways that are involved in high density lipoprotein (HDL) metabolism, and the process of reverse cholesterol transport (RCT). Therapeutic interventions of this nature exploit the atherogenic properties of HDL and promote the efflux of cholesterol from the body. Interventions that serve to alter the regulatory pathways of HDL metabolism often have off target system-wide consequences that are poorly understood and cannot be measured experimentally. A Bayesian methodology will be applied to analyze a genetically based dynamic computational model of HDL metabolism. The model will be described by a large system of ordinary differential equations that obeys physiological principles, existing data, enzyme kinetics, laws of mass balance and thermodynamic constraints. In the Bayesian modeling approach, the unknown parameters are related to the data through conditional and marginal probability density functions. In this framework, additional a priori information about the system is brought into the simulations to offset the lack of sufficient data, and allow for more physiological predictions. Monte Carlo sampling techniques will be applied to explore these densities and collect a family of suitable models that reflect the inherent variability of the underlying population. The sample of representative models will be simulated for the purpose of system-wide: (1) dynamic predictions of both measurable and immeasurable quantities, and (2) steady state and dynamic sensitivity studies to establish quantitative and qualitative relationships between system components and pathways. Features of the methodology will be applied to predict how the metabolic pathways of different genetic populations function under variable conditions, e.g., environment, medicine, diet and disease. A model of this type can be applied to assess the system-wide effects of drug targets, and to validate and design future experiments.
| Blair, Rachael Hageman; Kliebenstein, Daniel J; Churchill, Gary A (2012) What can causal networks tell us about metabolic pathways? PLoS Comput Biol 8:e1002458 |
| Aljakna, Aleksandra; Choi, Seungbum; Savage, Holly et al. (2012) Pla2g12b and Hpn are genes identified by mouse ENU mutagenesis that affect HDL cholesterol. PLoS One 7:e43139 |
| Leduc, Magalie S; Blair, Rachael Hageman; Verdugo, Ricardo A et al. (2012) Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross. J Lipid Res 53:1163-75 |
| Hageman, Rachael S; Leduc, Magalie S; Korstanje, Ron et al. (2011) A Bayesian framework for inference of the genotype-phenotype map for segregating populations. Genetics 187:1163-70 |
| Hageman, Rachael S; Leduc, Magalie S; Caputo, Christina R et al. (2011) Uncovering genes and regulatory pathways related to urinary albumin excretion. J Am Soc Nephrol 22:73-81 |
| Hageman, Rachael S; Wagener, Asja; Hantschel, Claudia et al. (2010) High-fat diet leads to tissue-specific changes reflecting risk factors for diseases in DBA/2J mice. Physiol Genomics 42:55-66 |
