Systems Biology of Kidney Diseases

Kidney Int. 2012 Jan;81(1):22-39. doi: 10.1038/ki.2011.314. Epub 2011 Aug 31.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Computational Biology
  • Drug Discovery
  • Epigenomics
  • Gene Expression Profiling
  • Genome-Wide Association Study
  • Genomics
  • Humans
  • Kidney Diseases / drug therapy
  • Kidney Diseases / etiology*
  • Kidney Diseases / genetics*
  • Kidney Diseases / physiopathology
  • Metabolomics
  • MicroRNAs / genetics
  • Models, Biological
  • Proteomics
  • Quantitative Trait Loci
  • Systems Biology

Substances

  • MicroRNAs