The GeneWeaver bipartite data model provides an efficient means to evaluate 
shared molecular components from sets derived across diverse species, disease 
states and biological processes. In order to adapt this model for examining 
related molecular components and biological networks, such as pathway or gene 
network data, we have developed a means to leverage the bipartite data structure 
to extract and analyze shared edges. Using the Pathway Commons database we 
demonstrate the ability to rapidly identify shared connected components among a 
diverse set of pathways. In addition, we illustrate how results from maximal 
bipartite discovery can be decomposed into hierarchical relationships, allowing 
shared pathway components to be mapped through various parent-child 
relationships to help visualization and discovery of emergent kernel driven 
relationships. Interrogating common relationships among biological networks and 
conventional GeneWeaver gene lists will increase functional specificity and 
reliability of the shared biological components. This approach enables 
self-organization of biological processes through shared biological networks.