title: Systems biology approaches and pathway tools for investigating cardiovascular disease.
abstract: Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.
authors.affiliation: Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Stockholm, Sweden. craig.wheelock@ki.se
authors.forename: Craig E
authors.initials: CE
authors.lastname: Wheelock
authors.forename: Asa M
authors.initials: AM
authors.lastname: Wheelock
authors.forename: Shuichi
authors.initials: S
authors.lastname: Kawashima
authors.forename: Diego
authors.initials: D
authors.lastname: Diez
authors.forename: Minoru
authors.initials: M
authors.lastname: Kanehisa
authors.forename: Marjan
authors.initials: M
authors.lastname: van Erk
authors.forename: Robert
authors.initials: R
authors.lastname: Kleemann
authors.forename: Jesper Z
authors.initials: JZ
authors.lastname: Haeggström
authors.forename: Susumu
authors.initials: S
authors.lastname: Goto
mesh_terms: D000818:Animals; D002318:Cardiovascular Diseases / Q000235:genetics / Q000378:metabolism*; D020869:Gene Expression Profiling; D006801:Humans; D050356:Lipid Metabolism; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D012984:Software; D049490:Systems Biology / Q000379:methods* / Q000639:trends