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Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology .
Computional Medicine group KI.
Computional Medicine group KI.
2007 (English)In: Journal of Lipid Research, ISSN 0022-2275, E-ISSN 1539-7262, Vol. 48, no 2, 267-277 p.Article in journal (Other academic) Published
Abstract [en]

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. Copyright ©2007 by the American Society for Biochemistry and Molecular Biology, Inc.

Place, publisher, year, edition, pages
2007. Vol. 48, no 2, 267-277 p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-38397DOI: 10.1194/jlr.R600030-JLR200Local ID: 44167OAI: oai:DiVA.org:liu-38397DiVA: diva2:259246
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13

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Tegnér, Jesper

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