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An Integrated Computational Framework for Systems Biology Ram Samudrala University of Washington

An Integrated Computational Framework for Systems Biology Ram Samudrala University of Washington. How does the genome of an organism specify its behaviour and characteristics?. Proteome – all proteins of a particular system.

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An Integrated Computational Framework for Systems Biology Ram Samudrala University of Washington

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  1. An Integrated Computational Framework for Systems Biology Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

  2. Proteome – all proteins of a particular system

  3. Modelling proteomes – understand the structure of individual proteins

  4. Modelling proteomes – understand their individual functions

  5. Modelling proteomes – understand their expression

  6. Modelling proteomes – understand their interactions

  7. Integrated structural and functional annotation of proteomes structure based methods microenvironment analysis structure comparison sequence based methods sequence comparison motif searches phylogenetic profiles domain fusion analyses zinc binding site? homology function? + assign function to entire protein space * Bioverse * * } * EXPRESSION + INTERACTION * * + experimental data single molecule + genomic/proteomic

  8. Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier

  9. Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier

  10. Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier

  11. Bioverse – explore relationships among molecules and systems http://bioverse.compbio.washington.edu Jason McDermott/Michal Guerquin/Zach Frazier

  12. Bioverse – prediction of protein interaction networks Target proteome Interacting protein database 85% protein α protein A experimentally determined interaction predicted interaction protein B protein β 90% Assign confidence based on similarity and strength of interaction Jason McDermott

  13. Bioverse – E. coli predicted protein interaction network Jason McDermott

  14. Bioverse – M. tuberculosis predicted protein interaction network Jason McDermott

  15. Bioverse – C. elegans predicted protein interaction network Jason McDermott

  16. Bioverse – H. sapiens predicted protein interaction network Jason McDermott

  17. Bioverse – network-based annotation for C. elegans Jason McDermott

  18. Bioverse – identifying key proteins on the anthrax predicted network Articulation point proteins Jason McDermott

  19. Bioverse – identification of virulence factors Jason McDermott

  20. Bioverse - Integrator Aaron Chang

  21. Take home message Prediction of protein structure, function, and networks may be used to model whole genomes to understand organismal function and evolution

  22. Acknowledgements Aaron Chang Chuck Mader David Nickle Ekachai Jenwitheesuk Gong Cheng Jason McDermott Kai Wang Ling-Hong Hung Mike Inouye Michal Guerquin Stewart Moughon Shing-Chung Ngan Tianyun Liu Zach Frazier National Institutes of Health National Science Foundation Searle Scholars Program (Kinship Foundation) UW Advanced Technology Initiative in Infectious Diseases http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu

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