Silverman Edwin K, Loscalzo Joseph
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, Massachusetts 02115, USA.
Discov Med. 2012 Aug;14(75):143-52.
Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.
复杂疾病是由生物网络的扰动引起的。专注于个体遗传决定因素的遗传分析方法不太可能全面表征复杂疾病的网络结构。网络医学将系统生物学和网络科学应用于人类疾病背后的复杂分子网络,专注于识别导致疾病发病机制的相互作用基因和蛋白质。遗传风险变异与复杂疾病发展之间漫长的生物学路径涉及一系列生化中间体,包括编码和非编码RNA、蛋白质和代谢物。转录组学、蛋白质组学、代谢组学和其他“组学”技术有潜力为复杂疾病发病机制提供见解,特别是如果它们在网络生物学框架内应用。以前大多数将遗传学与“组学”数据相关联的努力都集中在单一的“组学”平台上;下一代复杂疾病遗传学研究将需要在网络背景下整合多种类型的“组学”数据集。网络医学还可能为复杂疾病的异质性提供见解,作为反映潜在疾病发病机制的新疾病分类的基础,并指导合理的治疗和预防策略。