Bms 2010
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Bms 2010






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  • Biology is undergoing a revolution in the same way that chemistry and physics did ealier this century. It happened in physics and chemistry thorugh breakthroughs in our understanding of the physical universe. It is happening in biology through a greater understanding of ourselves. As such it is likely to be even more profound since it’s impact is something to which we all relate.A large part of that understanding is coming from analysis of vast amounts of data being generated in biology. So important is this proces that it has its own name - bioinformatics. Initially played down by experiemental biologists, it is a discipline now realized as being critical to our undetsnading of biological systems. Bioinformatics was born thanks to the human genome porject. The genome project for the first time revealed the importance of computation, first for achieving the goals of the project - to have sequenced the 3 billion bases before 2005 and later for using this information effectively in medical care. This happened around 1990, a clear decade after medicine had realized the importance of computation and had spawed the discipline of medical informatics. Bioinformatics can the thought of as the distillation of raw experimental data - data which is groiwng exponentially - and initially turning it into data. That is something of recognized significance

Bms 2010 Bms 2010 Presentation Transcript

  • Philip E. Bourne Skaggs School of Pharmacy and Pharmaceutical Sciences [email_address] The BMS Bioinformatics Focus Sept 27, 2010
  • The Bioinformatics/Comp. Biol. Distinction
    • Bioinformatics – New tools and algorithms for the analysis and use of high throughput data
    • See journal Bioinformatics or BMC Bioinformatics
    • Computational Biology – Application of computational techniques to make new discoveries about living systems
    • See journal PLoS Computational Biology
    There are opportunities to study both Sept 27, 2010
  • Bioinformatics In General Biological Experiment Data Information Knowledge Discovery Collect Characterize Compare Model Infer Sequence Structure Assembly Sub-cellular Cellular Organ Higher-life Year 90 05 Computing Power Sequencing Technology Data 1 10 100 1000 100000 95 00 E.Coli Genome C.Elegans Genome ESTs Yeast Genome Gene Chips Virus Structure Ribosome Metaboloic Pathway of E.coli Complexity Technology Brain Mapping Neuronal Modeling Cardiac Modeling Human Genome # People/Web Site (C) Copyright Phil Bourne 1998 10 6 10 2 1 10 1000000 .1 GWAS 4 th Gen Translational Medicine Meta- genomics
  • Consider one Bioinformatics Growth Area Pioneered by a BMS Alumni Sept 27, 2010
  • Metagenomics: First Look at the Challenges
    • New type of genomics
    • New data (and lots of it) and new types of data
      • 17M new (predicted proteins!) 4-5 x growth in just few months and much more coming
      • New challenges and exacerbation of old challenges
    • PLoS Biology 2007 5(3) e74 Sept 27, 2010
  • What is Metagenomics?
    • Technology
      • Sequencing DNA extracted directly from the environment
      • No cultures, no PCR
      • Short reads
        • 500-800 bp
        • 80-100 bp (454)
      • No assembly
    • Concept
      • Direct study of microbial communities
      • Minimal perturbation – no cultures, no assumptions
      • Fragmentary data, sampling rather than assembling
    Sept 27, 2010
  • Metagenomics: first results
    • More then 99.5% of DNA in every environment studied represent unknown organisms
      • Culturable organisms are exceptions, not the rule
    • Most genes represent distant homologs of known genes, but there are thousands of new families
    • Everything we touch turns out to be a gold mine
    • Environments studied:
      • Water (ocean, lakes)
      • Soil
      • Human body (gut, oral cavity, human microbiome)
    Sept 27, 2010
  • Sept 27, 2010
    • Emphasis on cross training and interdisciplinary activities
    • Multiple departments
    • Over 40 faculty
    Sept 27, 2010
  • Example Courses Sept 27, 2010
  • Support Infrastructure San Diego Supercomputer Center California Institute for Telecommunications & Information Technology Sept 27, 2010
  • Sample Mentors & Project Areas
    • Phil Bourne – Drug discovery, evolution, structure and function of signaling molecules
    • Ruben Abagyan – Molecular Biophysics
    • Steve Briggs – Stem Cells
    • Bing Ren – Gene regulatory networks
    • Palmer Taylor – structure and function of molecules involved in neurotransmission
    • Terry Gaasterland – Microbial Genomics
    Sept 27, 2010
    • Trey Ideker – Network construction and analysis
    • Pavel Pevzner – Genome rearrangements
    • J Andrew McCammon – Electrostatic interactions
    • Wei Wang – Inference of gene regulatory networks
    • Bernhard Palsson – Systems biology
    • Shankar Subramaniam – Functional genomics
    Sample Mentors & Project Areas Sept 27, 2010
  • Rotation Projects Sept 27, 2010
  • Questions? [email_address]
  • Example Projects from My Lab
    • Pharmaceutical Sciences - Competitive Binding of Major Pharmaceuticals
    • From Physical Model of Nucleosome Organization Towards Genome Annotation
    • Earth Sciences Meets Life Sciences
    • Scholarly Communication
    • Exploring the Flexibility versus Designability of Protein Folds
    • What Makes Some Introns’ Positions Ultra-conserved?
    • Building a Meta-method for Assignment of Structural Domains in Proteins
    Sept 27, 2010
  • A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules Dock molecules to both primary and off-targets Statistics analysis of docking score correlations … Computational Methodology Sept 27, 2010
  • Repositioning TB
    • TB Infects 6M people and kills 2M people per year
    • Entacapone and tolcapone shown to have potential as InhA inhibitors
    • Direct mechanism of action avoids M.tuberculosis resistance mechanisms
    • Possess excellent safety profiles with few side effects
    • Commercially available and easy to make
    • Further in vitro , in vivo and clinical studies required
    • Can potentially be applied to clinical practice directly
    S. Kinnings, L. Xie N. Buchmeier and P.E. Bourne Sept 27, 2010
  • A Systems Biology Approach to Explaining & Subsequently Minimizing Side Effects Sept 27, 2010 PNAS Submitted Strong Binding Medium Binding Weak Binding Positive Regulation Negative Regulation Positive & Negative Regulation
  • Bioinformatics Final Examples..
    • Donepezil for treating Alzheimer’s shows positive effects against other neurological disorders
    • Orlistat used to treat obesity has proven effective against certain cancer types
    • Ritonavir used to treat AIDS effective against TB
    • Nelfinavir used to treat AIDS effective against different types of cancers
    Sept 27, 2010