Increasing evidence points to an important role for ncRNAs in complex disorders. On the level of mutations, microRNAs (miRNAs) have been shown to play a mechanistic role in the effects of often ignored synonymous mutations [14]. A recent work has shown that a network of microRNAs may play Tenofovir ic50 a key role in the epithelial to mesenchymal transformation of ovarian cancers [82•]. The

importance of other ncRNA species have also been highlighted, such as the role of anti-sense RNAs on PTEN regulation [83], broad epigenetic effects of HOTAIR a long intergenic ncRNA (lincRNA) in breast cancer [12], and the role of PCAT-1, another lincRNA, on the progression of prostate cancer [13]. Biological network models still fall short of capturing many important aspects of biological systems. Cells exhibit dynamic responses

to environmental stimuli [84] and cells of different tissue types are characterized by distinct gene expression patterns [10 and 64•]. These properties are key determinants of phenotype but are not captured by the standard static network models that are prevalent in the field. Attempts to estimate the completeness and accuracy of existing protein interaction data suggest that 92% or more of binary human PPIs remain to be uncovered [3 and 85]. These estimates do not account for the possibility check details that distinct protein isoforms participate in different interactions. In addition, new molecular species are still being discovered and have not yet been incorporated into network models [7]. Constructing network models that accurately capture the molecular composition and interactions in specific cell types

and under distinct conditions will be essential for effectively modeling genotype–phenotype relationships. New experimental techniques Bay 11-7085 are rapidly emerging that will enable systematic screens of molecular interactions in mammalian cells. Mass spectrometry (MS)-based techniques promise to enable systematic cell type-specific screens of the proteome and protein post-translational modifications [61]. Proteomics may also aid in discovery of as yet undiscovered protein coding genes [86]. Until now, the majority of GI screens have been performed in model organisms, especially yeast, by exhaustively knocking out pairs of genes and measuring the effects on colony size. Novel approaches using RNAi technologies are now enabling systematic mapping of GIs in mammalian cells [87, 88 and 89]. New strategies for network construction and visualization will also aid the search for disease causing genes and mutations. Reformulating interactomes as hierarchies can provide representations of biological information that are easier to interpret than the typical ‘hairball’ that results when thousands of interactions are simultaneously displayed [41 and 90••] (Figure 2).