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BioGrids: Why Should Biologists Care?

BioGrids: Why Should Biologists Care?. Bruno Sobral (sobral@vt.edu) Director, The Virginia Bioinformatics Institute (http://www.vbi.vt.edu) MCNC, January 24, 2002. Biological Research Is “Big Science”. Expensive infrastructure Lots of people need to use infrastructure

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BioGrids: Why Should Biologists Care?

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  1. BioGrids:Why Should Biologists Care? Bruno Sobral (sobral@vt.edu) Director, The Virginia Bioinformatics Institute (http://www.vbi.vt.edu) MCNC, January 24, 2002 Virginia Bioinformatics Institute

  2. Biological Research Is “Big Science” • Expensive infrastructure • Lots of people need to use infrastructure • Design, implementation and management of infrastructure is a major opportunity and challenge • Integration of disciplines is needed • Experimentalists and theoreticians must integrate to move knowledge forward • There are lessons from physics! Virginia Bioinformatics Institute

  3. Predicting Life Processes: Reverse Engineering Living Systems (storage) DNA Transcription Gene Expression Translation Proteins Proteomics Biochemical Circuitry Environment Metabolomics Phenotypes (Traits)

  4. About Bioinformatics • ~100 years ago, German theoretical biochemists: • Bioinformatics is the quest to mathematically describe living organisms…. • …BioGrids are needed to compute on mathematical descriptions of living organisms to predict behaviors and performance… • The use of Information Technology (IT) to acquire, store, share, analyze and display large amounts of complex biological information (in either definition, computational biology is included)… • Coupling of biology with data management and supercomputers is driving IT to new levels... • …Biology has become be the driver for IT, replacing physical sciences… Virginia Bioinformatics Institute

  5. Virginia Bioinformatics Institute Source: Phil Miller, BITS

  6. New Technologies Have Produced Explosion of Data Metabolic Pathways Pharmacogenomics Proteins Medical Data Growth Human Genome Petabytes of Data SNPs Combinatorial Chemistry External Research Partnerships The Internet HTS Growth in Clinical Trials ESTs Mergers and Acquisitions 1990 2000 2001 Source: Jeff Augen, IBM

  7. -Omics data: Enabling Arrow ofBioinformatics Products/Results Metabolomics Proteomics Functional Genomics Genomics Virginia Bioinformatics Institute

  8. Computational Grids Needed to Enable Biological Computation: Why? • There may be no other cost-effective alternative for biological computation • Basic infrastructure needed for areas other than life sciences • Data, computation, applications and people are distributed Source: Larry Smarr, NCSA

  9. Comprehensive Measurements Source: Pedro Mendes, VBI

  10. Whole-Cell Measurements • -Omics technologies produce rich data sets • Microarrays / DNA chips / SAGE • 2D-PAGE, MALDI-TOF & ESI-Q-TOF-MS-MS • FTIR, GC-MS, LC-MS, CE-MS & FT-ICR-MS • These data sets are like partial snapshots of the whole cellular machinery in action… • …We need BioGrids to enable running the movie of all the snapshots… Virginia Bioinformatics Institute

  11. Model Organism SwissProt Genbank Integration Helped by Grids Pathways Literature Simulation Analysis of protein and metabolic profiles Analysis of gene expression Source: Pedro Mendes, VBI

  12. Portals: Easier to Build and Deploy with Grids Source: NCSA

  13. Molecules to Ecosystems: Grids Will Help Integrate Data for Analysis • Common frameworks and standards allow new analysis algorithms to be added à la carte… • …providing the opportunity for evolution of interpretation of same data Virginia Bioinformatics Institute

  14. VBI-JHU Cross-Institutional Interactions: An Example with Malaria Proteolytic digestion models Gene expression OpenGeneX DNA Sequencing Mass spectrometry Metabolic modeling Gepasi/Copasi Biotic Stress portal VBI ( Core Lab Facility ) Phylogenetic analysis VBI ( Bioinformatics ) Gene expression Mosquito Gene-flow Clinical diagnostics Host-pathogen interaction Plasmodium Metabolism Bloomberg School ( Malaria )

  15. Big Science, Big Business Biology Government Industry & Academia Established Companies Collaboratories & Portals USDA Start-Ups DoD Contractors Other Government Agencies: NSF, DoE etc. Universities Virginia Bioinformatics Institute

  16. Summary • Biology shifting from observational to predictive • To understand biological systems we must consider data from molecules to ecosystems • Computational infrastructure (hardware, software, networking) must be developed and deployed specifically for life sciences applications • Regional and Global (Grid) partnerships are required to go from research to applications Virginia Bioinformatics Institute

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