Toward a biological network for atherosclerosis

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks. —Ghazalpour, A., S. Doss, X. Yang, J. Aten, E. M. Toomey, A. Van Nas, S. Wang, T. A. Drake, and A. J. Lusis. Toward a biological network for atherosclerosis. J. Lipid Res. 2004. 45: 1793– 1805. Supplementary key words systems biology • transgenic mice • quantitative trait locus mapping • principal components • Bayesian networks • correlation coefficients • genetics • genomics • proteomics Atherosclerosis involves a large genetic network, not a simple linear pathway. This network extends to interactions with the many known risk factors for the disease and involves many cell types and organ systems ( Fig. 1 ). The connectedness of the various risk factors results in their clustering in populations, and these clusters have been given designations such as “the metabolic syndrome” and “the atherogenic lipoprotein phenotype.” Experimentally, the network is commonly studied by perturbing a single element, as in knockout mice, in a single genetic background. Although this approach provides valuable information that is simple to interpret, it may not identify the key regulators. Knockout experiments, for example, are dependent on prior biological information about the candidate gene, and they are not an efficient screen for the many epistatic and pleiotropic interactions that are likely to be involved. Approaches involving multiple perturbations, as in crosses between two genetically distinct strains of mice, may provide greater power to elucidate relevant pathways. In this review, we discuss progress toward unraveling the complex network that influences atherosclerosis. First, we discuss various approaches that have provided much of our present knowledge of the pathways in atherosclerosis. These include genetic studies in humans and in animal models, including transgenic studies. Second, we discuss the use of genomic and proteomic technologies, as well as nonclassical statistics, to identify genes and pathways contributing to atherosclerosis. The combination of genetics and gene expression promises to be a particularly powerful approach in the identification of the interactions underlying complex traits. Third, we discuss the general properties of biological networks and some early results related to networks for atherosclerosis.