BeeSpace: An Interactive Environment for Functional Analysis of Social Behavior

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BeeSpace An Interactive Environment for
Functional Analysis of Social Behavior

• Bruce Schatz
• Institute for Genomic Biology
• University of Illinois at Urbana-Champaign
• www.beespace.uiuc.edu
• First Annual BeeSpace Workshop
• University of Illinois June 6, 2005

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BeeSpace FIBR Project

• BeeSpace project is NSF FIBR flagship
• Frontiers Integrative Biological Research,
• 5M for 5 years at University of Illinois
• Analyzing Nature and Nurture in Societal Roles
  using honey bee as model
• (Functional Analysis of Social Behavior)
• Genomic technologies in wet lab and dry lab
• Bee Biology gene expressions
• Space Informatics concept navigations

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for Social Beehavior
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Complex Systems I

• Understanding Social Behavior
• Honey Bees have only 1 million neurons
• Yet
• A Worker Bee exhibits Social Behavior!
• She forages when she is not hungry
• but the Hive is
• She fights when she is not threatened
• but the Hive is

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for Functional Analysis
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Complex Systems II

• Understanding Functional Analysis
• Molecular Mechanisms of Social Behavior
• Can only be Discovered via the
• Interactive Navigations of Distributed Systems
• The Interspace is the next generation of
• of the Net (beyond the Web)
• Where Concept Navigation across
• Distributed Communities is routine

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System Architecture

• BeeSpace

• Concepts

• Concepts

• SEQ

• Expressions

• Expressions

• Databases

• Bees

• Flies

• Documents

• Documents

• SEQ

• Community

• Community

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Post-Genome Informatics

• Classical Organisms have extensive Genetic
  Descriptions!
• There will be NO more classical organisms beyond
• Mice and Men other than Worms and Flies, Yeasts
  and Weeds.
• So must use comparative genomics to classical
  organisms,
• Via sequence homologies and literature analysis.
• Automatic annotation of genes to standard
  classifications,
• Such as Gene Ontology via sequence homology.
• Automatic analysis of functions to scientific
  literature,
• Such as concept spaces via text mining.
• Descriptions in Literature MUST be used for
  future
• interactive environments for functional analysis!

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Informational Science

• Computational Science is the Third Branch of
  Science (beyond Experimental and Theoretical)
• Genes are Computed, Proteins are Computed,
• Sequence equivalences are Computed.
• Informational Science is coming to be accepted as
  
• The Fourth Branch of Science
• Based on Information Science technologies for
• Functional Mining of Information Sources
• Comparative Analysis within the
• Dry Lab of Biological Knowledge

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Biology The Model Organism

• The Western Honey Bee, Apis mellifera
• has become a primary model for social behavior
• Complex social behavior in controllable urban
  environment
• Normal Behavior honey bees live in the wild
• Controllable Environment hives can be modified
• Small size manageable with current genomic
  technology
• Capture bees on-the-fly during normal behavior
• Record gene expressions for whole-brain or
  brain-region
• (Note logistical limitations with bees and
  expressions)

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Informatics From Bases to Spaces

• data Bases support genome data
• e.g. FlyBase has sequences and maps
• Genes annotated by GeneOntology and
• linked to biological literature
• BeeBase (Christine Elsik, Texas AM)
• Uses computed homologies to annotate genes
• information Spaces support biological literature
• e.g. BeeSpace uses automatically generated
• conceptual relationships to navigate functions

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Project Investigators

• BeeSpace project is NSF FIBR flagship
• Frontiers Integrative Biological Research,
• 5M for 5 years at University of Illinois
• Biology
• Gene Robinson, Entomology (behavioral
  expression)
• Susan Fahrbach, Wake Forest (anatomical
  localization)
• Sandra Rodriguez-Zas, Animal Sciences (data
  analysis)
• Informatics
• Bruce Schatz, Library Information Science
  (systems) ChengXiang Zhai, Computer Science (text
  analysis)
• Chip Bruce, Library Information Science (users)

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Education and Outreach

• Explaining Social Behavior at all Levels
• Graduate Students and Postdocs as System Users
• 5 early adopter labs then 15 international labs
• Undergraduates to plan Bioinformatics Course
  through Susan Fahrbach at Wake Forest
• Run Workshop for Middle School Minorities through
  UIUC SummerMath (George Reese)
• University High School Biology Courses (David
  Stone)
• Home Hi Middle School for Girls Science (Jim
  Buell)

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BeeSpace GOALS

• Analyze the relative contributions of
• Nature and Nurture in
• Societal Roles in Honey Bees
• Experimentally measure differential gene
  expression for important societal roles during
  normal behavior
• varying heredity (nature) and environment
  (nurture)
• Interactively annotate gene functions for
  important gene clusters using concept navigation
  across biological literature representing
  community knowledge

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Concept Navigation in BeeSpace
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BeeSpace Software Environment

• Will build a Concept Space of Biomedical
  Literature for Functional Analysis of Bee Genes
• -Partition Literature into Community Collections
• -Extract and Index Concepts within Collections
• -Navigate Concepts within Documents
• -Follow Links from Documents into Databases
• Locate Candidate Genes in Related Literatures
  then
• follow links into Genome Databases

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BeeSpace Software Implementation

• Natural Language Processing
• Identify noun phrases
• Recognize biological entities
• Statistical Information Retrieval
• Compute statistical contexts
• Support conceptual navigation
• Network Information System
• Concept switch across community collections
• Semantic Links into biological databases

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BeeSpace Information Sources

• Biomedical Literature
• Medline (medicine)
• Biosis (biology)
• Agricola, CAB Abstracts, Agris (agriculture)
• Model Organisms (heredity)
• -Gene Descriptions (FlyBase, WormBase)
• Natural Histories (environment)
• -BeeKeeping Books (Cornell Library, Harvard
  Press)

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Worm Community System (1991)

• WCS Information Sources
• Literature Biosis, Medline, newsletters,
  meetings
• Data Genes, Maps, Sequences, strains, cells
• WCS Interactive Environment
• Browsing search, navigation
• Filtering selection, analysis
• Sharing linking, publishing
• WCS 250 users at 50 labs across Internet (1991)
• NSF National Collaboratories Flagship

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WCS Molecular
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WCS Cellular
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Medical Concept Spaces (1998)

• Medical Literature (Medline, 10M abstracts)
• Partition with Medical Subject Headings (MeSH)
• Community is all abstracts classified by core
  term
• 40M abstracts containing 280M concepts
• computation is 2 days on NCSA Origin 2000
• Simulating World of Medical Communities
• 10K repositories with gt 1K abstracts
• (1K with gt 10K)

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Navigation in MedSpace

• For a patient with Rheumatoid Arthritis
• Find a drug that reduces the pain (analgesic)
• but does not cause stomach (gastrointestinal)
  bleeding

Choose Domain
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Concept Search
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Concept Navigation
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Retrieve Document
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CONCEPT SWITCHING

• Concept versus Term
• set of semantically equivalent terms
• Concept switching
• region to region (set to set) match

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Biomedical Session
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Categories and Concepts
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Concept Switching
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Document Retrieval
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Biological Concept Spaces (2006)

• Compute concept spaces for All of Biology
• BioSpace across entire biomedical literature
• 50M abstracts across 50K repositories
• Use Gene Ontology to partition literature into
• biological communities for functional analysis
• GO same scale as MeSH but adequate coverage?
• GO light on social behavior (biological process)

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Interactive Functional Analysis

• BeeSpace will enable users to navigate a uniform
  space of diverse databases and literature sources
  for hypothesis development and testing, with a
  software system that goes beyond a searchable
  database, using statistical literature analyses
  to discover functional relationships between
  genes and behavior.
• Genes to Behaviors
• Behaviors to Genes
• Concepts to Concepts
• Clusters to Clusters
• Navigation across Sources

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BeeSpace Information Sources

• General for All Spaces
• Scientific Literature
• -Medline, Biosis, Agricola, Agris, CAB Abstracts
• -partitioned by organisms and by functions
• Model Organisms
• -Gene Descriptions (FlyBase, WormBase, MGI, OMIM,
  SCD, TAIR)
• Special Sources for BeeSpace
• -Natural History Books (Cornell Library, Harvard
  Press)

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XSpace Information Sources

• Organize Genome Databases (XBase)
• Compute Gene Descriptions from Model Organisms
• Partition Scientific Literature for Organism X
• Compute XSpace using Semantic Indexing
• Boost the Functional Analysis from Special
  Sources
• Collecting Useful Data about Natural Histories
• e.g. CowSpace Leverage in AIPL Databases

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Towards the Interspace

• The Analysis Environment technology is
  GENERAL! BirdSpace? BeeSpace?
• PigSpace? CowSpace?
• BehaviorSpace? BrainSpace?
• BioSpace
• Interspace