Network biology methods integrating biological data for translational science

March 5, 2012

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Figure Workflow for network detection. Networks are identified by jactivemodule using P-values from GWAS study (see text). Briefly, based on the P-values for each SNPs from GWAS, each gene has been assigned a P-value, then, they are superposed on the human PPI interactome derived from HPRD, finally, Cytoscape and jactivemodule are used to identify the network that is enriched with significant P-values. The color represents the P-values and nodes with gray color indicate that the P-values are missing from GWAS.

Results from: Bebek G, Koyutürk M, Price ND, Chance MR. Brief Bioinform. 2012.