Abstract
Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.
Highlights
CORONARY ARTERY DISEASEcoronary artery disease (CAD) is a degenerative disease that develops over decades from the stress of circulating blood cells and other plasma constituents that gradually alters the artery wall composition (cellular and extracellular), eventually leading to the formation of atherosclerosis plaques (Fig. 2) [14] The rate of atherosclerosis development depends both on environmental pressures and on the genetic makeup of the individual [15]
Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease
Reverse engineering of biological networks requires perturbations of the biological system followed by measurements of the system response using whole-genome measurement tools [5, 6]
Summary
CAD is a degenerative disease that develops over decades from the stress of circulating blood cells and other plasma constituents that gradually alters the artery wall composition (cellular and extracellular), eventually leading to the formation of atherosclerosis plaques (Fig. 2) [14] The rate of atherosclerosis development depends both on environmental pressures and on the genetic makeup of the individual [15]. That tools to measure the activity of the entire genome are available, systems biology approaches can be used to delineate the regulation and interplay between CAD phenotypes and pathways of atherosclerosis development, both known and unknown. It has been suggested that whole-genome measurements of complex traits like atherosclerosis should be performed on specific disease cell types separately (i.e., smooth muscle cells, foam cells, endothelial cells, and possibly T cells) in order to generate meaningful data [24]. It is quite possible—and in our view necessary—to view the plaque or lesion as a single functional system. Wholegenome technology platforms to screen for single nucleotide polymorphisms are increasingly being used to study cardiovascular disease [32]
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