Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA.
Wiley Interdiscip Rev Syst Biol Med. 2011 May-Jun;3(3):357-67. doi: 10.1002/wsbm.121. Epub 2010 Sep 23.
The main goal of systems medicine is to provide predictive models of the patho-physiology of complex diseases as well as define healthy states. The reason is clear--we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub-populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated.
系统医学的主要目标是提供复杂疾病病理生理学的预测模型，并定义健康状态。原因很明显——我们希望准确的模型最终将导致更具体和敏感的疾病标志物，帮助临床医生更好地对患者群体进行分层，并优化治疗计划。此外，我们期望这些模型将为对抗疾病定义新的靶点。然而，对于许多复杂疾病，特别是在临床水平上，越来越明显的是，单一或少数几个基因组变异（例如，简单的模型）不能充分解释与疾病状态相关的多种表型，也不能充分解释疾病进展所伴随的可变风险。我们认为，能够解释这些表型内在的更大范围变异性的模型，将考虑到许多相互作用的蛋白质的活性。这些模型将涵盖针对特定疾病和特定患者亚群的失调的蛋白质相互作用网络，通过将蛋白质相互作用数据与多种类型的其他相关细胞信息进行整合来构建。因此，蛋白质相互作用数据库在疾病研究的系统生物学方法中发挥着越来越重要的作用。它们为我们呈现了细胞状态的静态但高度功能化的视图，因此不仅使我们更好地理解了正常表型，而且在某些相互作用失调时，还使我们更好地理解了整个生物体的整体疾病表型。
