Integrative System Biology Strategies for Disease Biomarker Discovery

Haiyuan      Zhang; Hao      Hu; Cao      Deng; Yeona      Chun; Shengtao      Zhou; Fuqiang      Huang; Qin      Zhou

Abstract

Biomarkers are currently widely used to diagnose diseases, monitor treatments, and evaluate potential drug candidates. Research of differential Omics accelerate the advancements of biomarkers discovery. By extracting biological knowledge from the ‘omics’ through integration, integrative system biology creates predictive models of cells, organs, biochemical processes and complete organisms, in addition to identifying human disease biomarkers. Recent development in high-throughput methods enables analysis of genome, transcriptome, proteome, and metabolome at an unprecedented scale, thus contributing to the deluge of experimental data in numerous public databases. Several integrative system biology approaches have been developed and applied to the discovery of disease biomarkers from databases. In this review, we highlight several of these approaches and identify future steps in the context of the field of integrative system biology.

Keywords: Disease biomarkers, high-throughput, integrative system biology, diagnose, drug, post-genomics, mRNA, cDNA, SAGE, mass spectrometry, nuclear magnetic resonance, bioinformatics, pathologic cells, biochemical pathways, metabolic analysis, genes, probes