Department of Medicine, Mount Sinai School of Medicine, New York, New York 10029, USA.
Kidney Int. 2012 Jan;81(1):22-39. doi: 10.1038/ki.2011.314. Epub 2011 Aug 31.
Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.
肾脏疾病表现为肾功能进行性丧失，最终导致完全肾衰竭。肾脏疾病的起源和进展的机制尚未完全阐明。参与肾脏疾病发病机制的多种因素使得传统的候选基因方法对于充分理解这些疾病的分子机制的价值有限。一种将计算建模与大规模分子变化数据收集相结合的系统生物学方法，可能有助于识别驱动肾脏疾病的多个相互作用基因及其产物。生物技术的进步现在使得收集大量数据集以表征基因组、表观基因组、转录组、蛋白质组和代谢组在肾脏疾病中的作用成为可能。当与计算分析相结合时，这些实验方法将提供对潜在生物学过程的全面理解。将不同肾脏细胞的分子相互作用和细胞生物学与肾脏生理学和病理学联系起来的多尺度分析，可以用于识别在疾病过程中受到干扰的具有生物学和临床重要性的模块。这种实验方法和计算建模的整合预计将产生新的知识，有助于识别标记物集，以指导诊断、监测疾病进展和确定新的治疗靶点。
