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The Era of Biognostic Machinery

Lawrence Hunter, Ph.D., Director Center for Computational Pharmacology http://compbio.uchsc.edu. The Era of Biognostic Machinery. The Ultimate Biological Irony. Human understanding of our own genome will require partnership with biognostic machines. What is a Biognostic Machine?.

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The Era of Biognostic Machinery

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  1. Lawrence Hunter, Ph.D., Director Center for Computational Pharmacology http://compbio.uchsc.edu The Era of Biognostic Machinery

  2. The Ultimate Biological Irony Human understanding of our own genome will require partnership with biognostic machines

  3. What is a Biognostic Machine? • From the Greek(life) and(knowing) • Two kinds of biognostic machines: • Instruments that produce data about a living things in molecular detail and with genomic breadth • Bioinformaticssystems that bring to bear existing knowledge in the computational analysis of data

  4. Gene Chips as Biognostic Instruments • Good example of the kind of instruments to come... • Gene chips read out the expression (production) of each gene in different tissues • Gene expression is important,but just the first step in realizingthe “blueprints” in our DNA • Overwhelming amounts of data!Each chip is 40,000 genes anddozens of chips for each study

  5. Other kinds of biognostic instruments • High throughput SNP genotyping automation • Finds millions of tiny genetic differences among people Combinatoral Chemistry robotics Tests 50,000 potentialnew drugs per day

  6. Genome sequencing projects So much wonderful data... • More than 11,000,000 biomedical journal articles in Medline • 600,000 new articles per year, accelerating at 10% per year Growth of Protein Databank Growth of Biomedical Literature

  7. ...Is Still Not Enough! • Statistics 101: Never test more hypotheses than you have data, since you will find impressive looking results just by chance. • Each chip is effectively testing 40,000 hypotheses! • Run a lot of chips? Not at $1000 each! • So what can we do with all this data?

  8. Invent Biognostic Computers • Take traditional statistics as far as possible, e.g. • New corrections for multiple testing, randomization approaches But also... • Integrate existing knowledge into computational analysis.Our computer programs have to know about biology! • Bayesian inference • Knowledge-based interpretation of high throughput results • Managing diverse sources of knowledge, including the biomedical literature

  9. Bayesian Inference • An old idea gaining new life • A principled way of combining data with prior knowledge • We balance the belief in new results against how closely they fit with our existing ideas • Where do priors come from? Rev. Thomas Bayes, 1701-1765

  10. A Knowledge-base of Molecular Biology • A knowledge-base encodes facts and concepts in a computationally useful representation • General relationships, e.g.Part-of, Has-parts, Kind-of • Specific relationships, e.g.Binds-to, regulates-gene • Supports many kinds ofinference (not just Bayesian)

  11. Organization Expert Literature Protein Family Gene/ locus Related Gene/locus Protein Variant Phenotype Protein 8 Knowledge visualization tools(in partnership with Accenture)

  12. How do we create biognostic computer programs? • Knowledge management and organization tools from other domains (especially executive information systems) • Still takes a lot of expert human time and effort • Good community efforts in some areas (e.g. Gene Ontology Consortium) can be leveraged effectively • Once a bootstrap knowledge-base exists, extend it by automated information extraction from textbooks, review articles and journals.

  13. A special kind of supercomputer • Recent grant from IBM Life Sciences • Latest p690 “Regatta” architecture • Most important aspect is not speed! • Extraordinarily large memory • 64,000MB of RAM, about 1000x the memory of a desktop machine • Allows us to load both all the data and all the knowledge into memory at once

  14. Why CU? • Talent • World class researchers in many relevant areas:Gene chips, proteomic mass spec, macromolecularstructure determination, high throughput genotyping • Technology • Biognostic instrument facilities are top tier for an academic institution. We are within reach of the very best. • Supercomputing facilities for knowledge-driven applications • Teamwork • Unique culture of collaboration that transcends traditional boundaries

  15. What can we achieve? • Cognitive Disability Applications • Pilot application was in animal models of alcoholism, fetal alcohol syndrome and alcohol-related dementias. • Pharmacology • Identification of synergistic drug targets • Relationships between individual genotype and drug response • Development of novel biotherapies • Stem cell differentiation signals • Metabolic engineering

  16. The Road Ahead Three directions must be pursued simultaneously: • Bringing our instrumentation to the very first rank, including engineering new generations of instruments. • Extending the knowledge-base and developing novel computational methods that take full advantage of the it and supercomputer • Close collaborations on specific bio/medical research projects taking advantage of the latest instruments and bioinformatics techniques. Creation of a broad biognostic infrastructure to support that research.

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