BeeSpace: An Interactive Environment for Functional Analysis of Social Behavior

1. 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

2. 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

3. for Social Beehavior

4. 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

5. for Functional Analysis

6. 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

9. System Architecture BeeSpace Concepts Concepts SEQ Expressions Expressions Databases Bees Flies Documents Documents SEQ Community Community

10. 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!

11. 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

12. 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)

13. 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 A&M) Uses computed homologies to annotate genes information Spaces support biological literature e.g. BeeSpace uses automatically generated conceptual relationships to navigate functions

14. 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)

15. 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)

16. 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

17. Behavioral Molecular Biologist Biologist Molecular Biology Literature Brain Gene Bee Bee Expression Literature Genome Profiles Flybase, Brain Region WormBase Localization Neuroscience Literature Neuro- scientist Concept Navigation in BeeSpace

18. 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

19. 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

20. 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)

21. 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

22. WCS Molecular

23. WCS Cellular

24. 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 > 1K abstracts • (1K with > 10K)

25. 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

26. Concept Search

27. Concept Navigation

28. Retrieve Document

29. Semantic region term Concept Space Concept Space CONCEPT SWITCHING • “Concept” versus “Term” • set of “semantically” equivalent terms • Concept switching • region to region (set to set) match

30. Biomedical Session

31. Categories and Concepts

32. Concept Switching

33. Document Retrieval

34. 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)

35. 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

36. 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)

37. 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

38. Towards the Interspace The Analysis Environment technology is GENERAL! BirdSpace? BeeSpace? PigSpace? CowSpace? BehaviorSpace? BrainSpace? BioSpace … Interspace