To date cellular networks are most available for the “super-model” organisms (Davis, 2004) yeast, worm, fly, and plant. High-throughput interactome mapping relies upon genome-scale resources such as ORFeome resources, a segment of which is shown in the background. Several types of interactome networks discussed are depicted around the periphery. In a protein interaction network nodes represent proteins and edges represent physical interactions. In a transcriptional regulatory network nodes represent transcription factors (circular nodes) or putative DNA regulatory elements (diamond nodes) and edges represent physical binding between the two. In a gene-disease network, nodes represent disease genes and edges represent genes mutation of which is associated with disease. In a virus-host network nodes represent viral proteins (square nodes) or host proteins (round nodes) and edges represent physical interactions between the two. In a metabolic network nodes represent enzymes and edges represent metabolites that are products or substrates of the enzymes. The network depictions seem dense, but they represent only small portions of available interactome network maps, which themselves constitute only a few percent of the complete interactomes within cells.

By interacting with each other, genes and their products form complex cellular networks. The link between perturbations in network and systems properties and phenotypes, such as Mendelian disorders, complex traits, and cancer, might be as important as that between genotypes and phenotypes.

Co-expression and phenotypic profiling can be thought of as matrices comprising all genes of an organism against all conditions that this organism has been exposed to within a given expression compendium and all phenotypes tested, respectively. For any correlation measurement, Pearson Correlation Coefficients (PCCs) being one of the most widely used, the threshold between what is considered co-expressed and non-co-expressed needs to be set using appropriate titration procedures. The resulting integrated networks have powerful predictive value.