Thematic Review Multi-organ Whole-genome Measurements and Reverse Engineering to Uncover Gene Networks Underlying Complex Traits

Together with computational analysis and mod-eling, 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 diag-nostics and treatments for complex disorders in the coming era of individualized medicine.—Tegnér, J., J. Skogsberg, and J. Bjö rkegren. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits. Supplementary key words global gene expression & coronary athero-sclerosis & multicellular disease & computational modeling & individualized medicine Candidate gene approaches, such as positional cloning (1), inherited from studies of single-gene disorders, have thus far generated fragmented knowledge of complex traits. Attention is now being redirected toward systems biological approaches. The general belief is that such approaches , unlike those based on candidate genes, can better take into account the inherent complexity of these disorders. Although systems theory has been around for quite some time (2), its applications in biology are flourishing because of the availability of whole-genome measurement technologies such as genomics (3) in combination with computational analysis and modeling (4). With an increasing number of research communities embracing systems biology, it is important to be clear about what this term means. It is tempting to define systems biology as physiology or pathology—that is, the biological functions of an entire system rather than those of its molecular components. A stricter, and in our view more correct, definition of systems biology is research that focuses not on the molecular parts themselves (i.e., genes, proteins, metabolites) but on their interactions within networks. For such studies, the four Ms—manipulation, measurement, mining, and modeling—are key ingredients (4). Reverse engineering (the process of identifying gene networks from whole-genome data using an underlying computational model) of biological networks requires perturbations (i.e., manipulations) of the biological system followed by …