1. Systematic Analysis of Interactome: A New Trend in Bioinformatics KOCSEA Technical Symposium 2010 Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University

2. History of Bioinformatics Stage 1. Sequence Analysis Gene sequencing Sequence alignment Homolog search Motif finding

3. History of Bioinformatics Stage 1. Sequence Analysis Stage 2. Structure Analysis Protein folding Homolog search Binding site prediction Function prediction Computational Biology Gene sequencing Sequence alignment Homolog search Motif finding

4. History of Bioinformatics Stage 1. Sequence Analysis Stage 2. Structure Analysis Stage 3. Expression Analysis Function prediction Gene clustering Sample classification Functional Genomics Computational Biology Protein folding Homolog search Binding site prediction Function prediction Gene sequencing Sequence alignment Homolog search Motif finding

5. History of Bioinformatics Stage 1. Sequence Analysis Stage 2. Structure Analysis Stage 3. Expression Analysis Stage 4. Network Analysis Network modeling Interaction prediction Function prediction Pathway identification Module detection Systems Biology Functional Genomics Computational Biology Function prediction Gene clustering Sample classification Protein folding Homolog search Binding site prediction Function prediction Gene sequencing Sequence alignment Homolog search Motif finding

6.  Definition  Maps of biochemical reactions, interactions, regulations between genes or proteins  Importance  Provide insights into the mechanisms of molecular function within a cell  Significant resource for functional characterization of genes or proteins  Require computational and systematic approaches  Examples  Metabolic networks  Protein-protein interaction networks  Genetic interaction networks  Gene regulatory networks (Signal transduction networks) Biological Networks

7.  Determination  Experimental methods: Y2H, MS, Protein Microarray  Computational methods: Homolog search, Gene fusion analysis, Phylogenetic profiles  Genome-scale protein-protein interactions  Interactome  Representation  Un-weighted, undirected graph  Challenges  Unreliability  Large scale  Complex connectivity Protein Interaction Networks

8.  Strategy  To resolve complex connectivity  Converts the complex graph to a hierarchical tree structure  Uses the concepts of path strength, functional linkage, and centrality  Process  Input: a protein interaction network  Output: a list of functional modules Network Re-structuring unweighted network edge weighting functional linkage measurement network restructuring hub confidence measurement network clustering weighted network score matrix structured network hubs clusters

9.  Path Strength Model  Assumption: each node in a path chooses a succeeding edge based on the weighted probability   Path Strength Factors  Edge weights  Path length  Node weighted degree Path Strength

10.  Measurements  Path strength of the strongest path between two nodes  Computational problem  Needs a heuristic approach  Uses a user-specified threshold of the max path length  Formula  k-length path strength:  Functional linkage: Functional Linkage shortest path length threshold

11.  Centrality  Weighted closeness:  Algorithm  Computes centrality for each node a  Selects a set of ancestor nodes, T(a), of a by  Selects a parent node, p(a), of a by  Example Network Restructuring

12.  Measurement  Selects a set of child nodes, D(a), of a by  Selects a set of descendent nodes, L a, of a by  Computes the hub confidence, H(a), of a by  Example Hub Confidence

13.  Algorithm  Iteratively select a hub a with the highest hub confidence  Output the sub-tree L a including a as a cluster (functional module)  Cluster Depth  The max path length from the root of the sub-tree to a leaf  Example Clustering

14.  Network Vulnerability  Random attack: repeatedly disrupt a randomly selected node  Degree-based hub attack: repeatedly disrupt the highest degree node  Structural hub attack: repeatedly disrupt the node with the highest hub confidence  For each iteration, observes the largest component  Results Topological Assessment of Hubs

15.  Protein Lethality  Determines lethal / viable proteins by knock-out experiment  Lethality represents functional essentiality  Orders proteins by degree and hub confidence  Observes the cumulative proportion of lethal proteins for every 10 proteins  Results Biological Assessment of Hubs

16.  Modularity  A combined measure of density within each cluster and separability among clusters  Estimated by the ratio of the number of edges within a cluster (sub-graph) to the number of all edges starting from the nodes in the cluster (sub-graph)  Observes the average modularity of clusters with respect to the cluster depth  Results  More specific function module has higher modularity  Justify the general-to-specific concepts of hierarchical functional modules Topological Assessment of Clusters

17.  f-Measure  Compares each output cluster X with the real functional annotation Y (from MIPS)  Recall = (# of common proteins of X and Y) / (# of proteins in Y)  Precision = (# of common proteins of X and Y) / (# of proteins in X)  f-measure = 2 × Recall × Precision / (Recall + Precision)  Results  Compared with the results from previous hierarchical clustering methods, e.g., edge-betweenness (top-down approach) and ProDistIn (bottom-up-approach) Biological Assessment of Clusters

18.  Motivation  Significant functional knowledge in protein interaction networks (interactome)  Complex connectivity  Contributions  Convert an unstructured network to a structured network  Conserve functional information through pathways  High network vulnerability, low functional lethality at hubs as a drug target  Applicable to various fields, e.g., social networks, WWW  Foundation of structural dynamics during network evolution Conclusion

19.  Reference  Y.-R. Cho and A. Zhang, “Identification of functional modules by converting interactome networks into hierarchical ordering of proteins”. BMC Bioinformatics, 11(Suppl 3):S3, 2010 Questions ?