Tegnér Jesper, Skogsberg Josefin, Björkegren Johan
The CoCenter for Molecular Medicine, King Gustaf V Research Institute, Department of Medicine, Karolinska Institute, Karolinska University Hospital, Solna, SE-171 76 Stockholm, Sweden.
J Lipid Res. 2007 Feb;48(2):267-77. doi: 10.1194/jlr.R600030-JLR200. Epub 2006 Dec 1.
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.
全基因组测量技术的发展与计算分析和建模相结合，有可能从根本上改变对诸如冠状动脉疾病等复杂疾病的研究。借助这些工具，揭示复杂疾病中生物成分（基因、蛋白质和代谢物）的全部组成及其在模块和网络中的相互作用的阶段已经就绪。在这里，我们回顾了基于逆向工程的网络识别方法如何从应用于较简单生物体的全基因组数据集，发展到如今适用于更复杂的情况，如与生理和病理状态相关的人类细胞系和器官的数据集。我们重点关注使用系统生物学方法来识别冠状动脉粥样硬化中的基因网络。我们还讨论了在即将到来的个性化医学时代，基因网络如何可能在复杂疾病的早期诊断和治疗发展中发挥关键作用。
