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WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS

WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS. Biological Network Analysis and Representational Implications. 27 - 28 MAR 2008. Richard H. Scheuermann, Ph.D. Chief, Division of Biomedical Informatics U.T. Southwestern Medical Center. Motivation.

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WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS

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  1. WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS Biological Network Analysis and Representational Implications 27 - 28 MAR 2008 Richard H. Scheuermann, Ph.D. Chief, Division of Biomedical Informatics U.T. Southwestern Medical Center

  2. Motivation • The cell functions as a system of integrated components • There is increasing evidence that the cell system is composed of modules • A “module” in a biological system is a discrete unit whose functionis separable from those of other modules • Modules defined based on functional criteria reflect the critical level of biological organization (Hartwell, et al.) • A modular system can reuse existing, well-tested modules • The notion of regulation requires the assembly of individual components into modular networks • Functional modules can then be assembled together into cellular networks • Thus, identifying functional modules and their relationship from biological networks is important to the understanding of the organization, evolution and interaction of the cellular systems they represent

  3. MoNet MoNet

  4. 1 2 3 Definition of network modules

  5. Betweenness = 20 Edge betweeness • Girvan-Newman proposed an algorithm to find social communities within human population networks • Utilized the concept of edge betweenness as a unit of measure • defined as the number of shortest paths between all pairs of vertices that run through it • edges between modules tend to have higher values • Provides a quantitative criterion to distinguish edges inside modules from the edges between modules

  6. A new definition of network modules • Definition of module degree: • Given a graph G, let U be a subgraph of G (U G). The number of edges within U is defined as the indegree of U, ind(U). The number of edges that connect U to remaining part of G (G−U) is defined as the outdegree of U, outd(U). • Definition of module: • A subgraph U G is a module if ind(U) outd(U). • A subgraph is a complex module if it can be separated into at least two modules by removing edges inside it. Otherwise, it is a simple module. • Adjacent relationship between modules: • Given two subgraphs U, V G, U and V are adjacent if UV= and there are edges in G connecting vertices in U and V.

  7. Interaction Networks • Large component of the S. cerevisiae protein interaction network • DIP database • 2440 proteins & 6241 interactions • Large component of the Homo sapiens protein interaction network • BIOGRID database • 6656 proteins & 19022 interactions

  8. dMoNet Modules • 99 dMoNet simple modules • 3 to 201 nodes in size • Include 1700 nodes out of the original 2440 nodes and 3459 of the 6241 edges • 156 dMoNet simple modules • 3 to 1048 nodes in size • Include 3169 nodes out of the original 6656 nodes and 6949 of the 19022 edges

  9. Validation of modules • Annotated each protein with the Gene OntologyTM (GO) terms from the Saccharomyces Genome Database (SGD) (Cherry et al. 1998; Balakrishna et al) • Quantified the co-occurrence of GO terms using the hypergeometric distribution analysis • The results show that each module has statistically significant co-occurrence of functional GO categories

  10. S. cerevisiae dMoNet Module Evaluation Top 10 yeast network modules with lowest co-clustering p-values. The p-value threshold corresponding to a 5% chance of committing a Type I error based on the Bonferroni correction given a data set of size 2440 is 2.05E-05. Of the 99 modules, 84 have biological process co-clustering p-values below this threshold.

  11. Proposed Module Naming Convention

  12. Topologies and Measures

  13. Ontology for Biomedical Investigation (OBI) Data Transformation Branch

  14. Summary of terms • Network analysis methods • Network components - network (graph), node (protein), edge (interaction), module (subgraph) • Component properties (qualities) - connectivity, degree, betweeness, density, modularity, edge weight • Topologies - star, ring, mesh, linear, combinations

  15. Acknowledgements • Support (NIAID) • 1N0140041 • 1N0140076 • Network Analysis • Feng Luo (Clemson) • Roger Chang (UCSD) • Maya El Dayeh (SMU) • Yuhang Wang (SMU) • Preetam Ghosh (UTSW) • OBI Data Tranformation • Helen Parkinson (EBI) • Melanie Courtot (BCCRC) • Ryan Brinkman (BCCRC) • Elisabetta Manduchi (UPenn) • James Malone (EBI) • Monnie McGee (SMU

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