The Era of Biognostic Machinery

1
The Era of Biognostic Machinery

• Lawrence Hunter, Ph.D., Director
• Center for Computational Pharmacology
• http//compbio.uchsc.edu

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
• Bioinformatics systems 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
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

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)

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Knowledge visualization tools(in partnership
with Accenture)
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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

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Why CU?

• Talent
• World class researchers in many relevant
  areasGene 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

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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.