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The Era of Biognostic Machinery Lawrence Hunter, Ph.D., Director Center for Computational Pharmacology http://compbio.uchsc.edu The Ultimate Biological Irony Human understanding of our own genome will require partnership with biognostic machines 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 – Bioinformatics systems that bring to bear existing knowledge in the computational analysis of data 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 realizing the “blueprints” in our DNA Overwhelming amounts of data! Each chip is 40,000 genes and dozens of chips for each study Other kinds of biognostic instruments High throughput SNP genotyping automation Finds millions of tiny genetic differences among people Combinatoral Chemistry robotics Tests 50,000 potential new drugs per day So much wonderful data... Growth of Protein Databank Growth of Biomedical Literature More than 11,000,000 biomedical journal articles in Medline 600,000 new articles per year, accelerating at 10% per year Genome sequencing projects ...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? 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 Bayesian Inference p Data#Hypothesis Å p Hypothesis p Hypothesis#Data = p Data 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 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 of inference (not just Bayesian) Knowledge visualization tools (in partnership with Accenture) Literature Related Gene/locus Protein Expert Gene/ locus Protein Variant Organization Protein Family Phenotype 8 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. 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 Why CU? Talent – World class researchers in many relevant areas: Gene chips, proteomic mass spec, macromolecular structure 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 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 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.