1 / 16

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?.

Download Presentation

The Era of Biognostic Machinery

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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.

More Related