1. Dr. Michael Schroeder Department of Computing City University, London, UK msch@soi.city.ac.uk http://www.soi.city.ac.uk/~msch Visiting Scientist Medical Research Council Cambridge, UK BioGrid

2. Drowning in information... Biology has changed dramatically from an information-light to an information-intensive area Much publicised Human Genome Project is only tip of the iceberg >500 tools online >8000 new abstracts per month LLNE YLEEVE EYEEDE

3. Heureka ! ??????? ?????? BioGrid Provide access to multiple, heterogeneous and geographically distributed information sources. perform active searches for relevant information in non-local domain (includes retrieving, analysing, manipulating, and integrating information)

4. BioGrid Objectives Objectives: Information and knowledge grid allowing knowledge discovery and access to multiple types of structured and unstructured data, including gene expression and protein interaction data Business objectives: Grid for next generation classification research infrastructure for large proteomics and genomics databases; Efficient transactional enterprise collaboration; Faster time to market biotech innovation

5. Example A scientist is interested in a gene, e.g. NOX4 –Search PubMed for articles Too many hits Gene also known under different name –Analyse gene expression data Which genes behave similar to NOX4 Function of NOX4? –Analyse protein interactions Which interactions and processes does expression of NOX4 trigger?

6. Challenges Semantic Complexity –Computer does not “understand” data –DBs and systems cannot inter-operate Computational complexity –generating protein interaction map takes ca. 7 days –analysing large sets of gene expression data can take up to an hour –analysis of large text bodies complex

7. BioGrid Vision BioGrid Interaction data Metabolic pathway data Expression data Sequences Character- isation of target sequence Scientific literature

8. Approach Semantic Web –global and local ontologies to capture meta-data and facilitate semantic inter-operability Grid technology –transparent access to distributed resources Agent technology –personal information agent collecting and presenting relevant information on behalf of its user BioGrid Client BioGrid Client BioGrid Client BioGrid Server Literature Classification Server T he Grid Space Explorer PSIMAP

9. Classification server Finding and processing relevant scientific literature BioGrid Interacti on data Metab olic pathw ay data Express ion data Seque nces Charact er- isation of target sequenc e Scient ific literat ure

10. Results of PubMed Lorenz P,Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44. Fredericks WJ. An engineered PAX3-KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Jul;20(14):5019-31.... Author Title Year Journal However, to a machine things look different!

11. Results of PubMed Lorenz P,Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637- 44. Fredericks WJ. An engineered PAX3-KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Jul;20(14):5019-31.... Solution: tag data (XML)

12. Results of PubMed Lorenz P Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem 2001... However, to a machine things look different!

13. Results of PubMed Lorenz P Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem 2001... Solution: use ontologies (Semantic Web)

14. Semantic Web DAML+OIL is XML-based language to specify ontologies Annotations of data refer to global ontology (where appropriate), hence joint understanding of data possible Ongoing efforts in bioinformatics: e.g. gene ontology

15. Classification Server Scientific objectives: Effective concept recognition Pattern matching Intelligent data sourcing agents and tagging technology Automated categorisation in a biotechnology-domain Metadata hierarchy Functional interoperability methodology design Domain knowledge mapping, Implementing a logical domain ontology Integration of agent & classification logic & visualisation technology.

16. Space Explorer … is a general purpose visualisation tool facilitating interactive exploration of large data sets … deals with multi-variate and proximity data … provides principal component analysis multi-dimensional scaling (principal co-ordinate analysis, spring embedding) clustering … provides dendrograms 2D and 3D (using VRML) scatter plots graphs and colour maps BioGrid Interacti on data Metab olic pathw ay data Express ion data Seque nces Charact er- isation of target sequenc e Scient ific literat ure

18. Example: gene expression data

19. Example: Protein topology

20. Protein Interaction: PSIMAP BioGrid Interacti on data Metab olic pathw ay data Express ion data Seque nces Charact er- isation of target sequenc e Scient ific literat ure Based on 3D structure, PSIMAP determines interactions of proteins Structure of map of great importance for understanding of biological processes Generation and analysis of the map are computationally expensive

21. Partners No. Organisation (abbreviation) Count ry RTD role in the project 1 University of Groningen (RUG) NL User, Bioinformatics on drug discovery 2 ZooRobotics (ZRO) NL Co-ordinator, Supplier of GRID Classification Server, Exploitation Mng. 3 City University London (CIT) UK Supplier of intelligent agents and Space Explorer 4 University of Cyprus (UCY) EL Supplier of GRID knowledge engineering 5 Medical Research Centre (MRC) UK Supplier of PSIMAP, User, bio informatics on Food and Nutrition

22. Pert diagram

23. Work packages Workpackage title WP0Management WP1Source domain analysis WP2Hierarchy creation, Metadata model development WP3Classification logic integration WP4Agent implementation WP5Visualisation implementation WP6Measurement and evaluation WP7Dissemination and exploitation

24. Expression Space: Space Explorer Pathway Space: BioGrid Interaction Space: PSIMAP Literature Space: Classification Server BioGrid Mission: Distributed computational biology platform for fast pharmaceutical research