G LOBAL S IMILARITY B ETWEEN M ULTIPLE B IONETWORKS Yunkai Liu Computer Science Department University of South Dakota

B ACKGROUND Just as the rapid disclosing of genomic data enables the study of sequence conservation, the growth of network quality and availability allows us to ask similar questions at network level. One challenging problem is the characterization of similar patterns among multiple biological networks. However, there is no definition of similarity between networks that has been agreed upon and efficient algorithms for comparing dynamic bio-networks are limited.

G RAPH M ODEL AND P REVIOUS W ORKS The growth of quality and availability of new biotechnology allows us to simulate biological systems with graph models. Generally speaking, nodes represent biological units (e.g., proteins or genes); and edges represent physical or chemical relationships. Previous works: PHUNKEE (2007); Græmlin (2006); NetworkBLAST (2004);

P URPOSE AND S IGNIFICANCE The global similarity of multiple bio-networks, such as anatomical networks, gene regulatory network and protein interaction networks, are expected to evaluate the overall topological likeness among graphs. Biological Applications: Topological structural study Evolution of bio networks Experimental data analysis

M ETHOD Basic method: compare the adjacent matrices of networks. Challenges: Sequence sorting: The nodes are generally weighted by different attributes; however, the occurrence of same nodes in graphs greatly increase the complexity for finding the maximal global similarities between two networks. Transitivity: Especially in functional networks, the transitivity should be considered. Another reason is to allow gaps in study. Global and Local similarity: The optimal solution of global similarity may cause the ignorance of local conserved subgraphs. The comparison of similar graphs may have noises.

C URRENT AND F UTURE W ORK Currently, a research team, consisting of researchers from Computer Science Dept and Medical School, are developing software and biological applications based on bionetwork comparison. We are also looking for collaborators. Please contact,