1. ChEBI and SABIO-RK: Association of chemical compound information and reaction kinetics data Ulrike Wittig Scientific Databases and Visualization Group EML Research, Heidelberg

3. Substrates Products Enzyme ActivatorInhibitor Modifier Biochemical reaction

4. Reaction kinetics V max  maximal enzyme velocity K M  Michaelis-Menten constant (k2+k-1)/k1

5. Essential for simulations of biochemical reactions /pathways (not only parameter values but also rate law equations) Dependency on environmental conditions Dependency on biological location (organism, tissue, cellular location) Data must be easily accessible and interchangeable  Collection and standardization of reaction kinetics data  Links to biochemical, environmental and experimental context  Links to corresponding data of external resources  Complex search and export functions Reaction kinetics

6. SABIO-RK SABIO-RK describes Reaction Kinetics and is an extension of SABIO (System for the Analysis of Biochemical Pathways) SABIO Pathways Reaction Enzymes Reactants Organisms SABIO-RK Concentrations Kinetic Law Environment Reactants Parameters

7. Automatic extraction of information from other databases (e.g. KEGG) Kinetic data manually extracted from literature Kinetic data directly obtained from laboratory experiments Manual curation, assisted by semi-automatic tools for standardization (unification, structuring, normalization, annotation) Access through a web-based user interface Access through web-services Export in SBML (Systems Biology Markup Language) format Data flow

8. Automatic input (XML based) SABIO-RK Database of experimental raw data Storage of kinetic data (e.g. Km, Vmax) Link to original data Data access Generation of experimental data Collaboration with Douglas Kell, Neil Swainston (Manchester Center for Integrative Systems Biology)

9. Manual data input Kinetic data from publications Tables, Formula, Graphs, Pictures Some assay conditions or methods description only noted as reference No controlled vocabulary in the literature (different names of compounds, enzymes etc.) Missing or incomplete information -Incomplete reactions (e.g. products not mentioned) -Missing organism specification -Isoenzyme not specified (old papers)  Web-based input interface

10. Input interface

11. Curation Data first inserted in an intermediate database Curation process (search for errors and inconsistencies) –Manually by biological experts –Semi-automatically by consistency checks –Standardization –Unification –Annotation to controlled vocabularies and external databases Transfer data from intermediate to public SABIO-RK database

12. Curation of chemical compounds  Search for multiple entries for identical compounds –KEGG IDs: C00201, C03802 –SABIO-RK ID: 1674 –ChEBI IDs: 37413, 37076

13. –KEGG IDs: C00677, C04283 –SABIO-RK ID: 1690 –ChEBI IDs: 16381, 16516

14. Identification of identical chemical compounds Goal:  Linking different databases by names of chemical compounds (e.g. ChEBI, KEGG, PubChem) Methods developed to support curation:  Normalization of compound names  Analysis of compound names and generation of SMILES string  Generation of chemical structure based on SMILES string  Classification of chemical compounds

15. Normalization of compound names Examples of normalization rules: -Lowercase letters only -Normalize different types of brackets to one type -Remove spaces -Replace ‘-p’ at end of name by ‘-phosphate’ -Replace all suffixes ‘-phosphate’ by prefixes ‘phospho-’ -Replace ‘ate’ followed by a delimiting character by ‘ic acid’ (acid-base pairs) -Three-letter amino acid code get replaced by full amino acid names -Prefixes of Chemical Compound names are normalised by sorting them: 2-propanone-1-amino-3-(phosphono…  1-amino-2-propanone-3-(phosphono… -Identification of synonymous parts in different names …valpro... ...2-propylpentano...  Generation of lists of normalized compound names for different databases (e.g. SABIO-RK, ChEBI, KEGG, PubChem)  Matching the lists from different databases to find synonyms of chemical compounds

16. Analysis of compound names

17. Classification of compounds -Generation of structural formula, totals formula and molecular weight based on SMILES string -Rules for compound classifications -Graph and sub-graph search for functional groups -Classification using different criteria, for example D-Glucose is a: -Aldose (functional group aldehyde) -Hexose (number of C-Atoms = 6)  SABIO-RK search for general compounds and all sub-classes

18. Access to SABIO-RK  Web-based user interface for browsing and searching the data manually  Web services (API access) can be automatically called by external tools, e.g. by other databases or simulation programs for biochemical network models (http://sabio.villa-bosch.de/SABIORK/webservice.jsp)

20. SABIO-RK web interface Web access to search for general reaction information, kinetic laws, kinetic parameters, experimental conditions etc. Colour-coded representation of results –Kinetic data available matching search criteria –Kinetic data available but not matching search criteria –No kinetic data available Export of kinetic data in SBML (Systems Biology Mark-up Language) Annotations to external databases, ontologies and controlled vocabularies

24. Links to external databases Cellular location –Gene Ontology Protein –UniProt Literature source –PubMed Compound –KEGG –PubChem –ChEBI Enzyme –KEGG –ExPASy Enzyme –IntEnz –IUBMB –Reactome

25. Annotations in SBML

26. ChEBI links to SABIO-RK

28. SABIO-RK links to ChEBI

29. SABIO-RK Summary  curated data based on literature information (ca. 2000 papers with >25000 entries)  offers a platform for kinetic data storage and exchange  free available for academic use  uses existing standard data formats, controlled vocabularies and ontologies  links to original data sources (literature, databases etc.)

30. Kinetic information about reaction mechanism –separate reactions for intermediate steps Kinetic data for signaling reactions Information about in vivo metabolite concentrations SABIO-RK as platform for experimental kinetic data –scientists producing the data can directly enter it into SABIO-RK Extention of web interface (less reaction oriented) –search for inhibitors/ activators (drugs) etc. –search for enzymes Future Perspectives

31. Financial support: Renate Kania Olga Krebs Andreas Weidemann Saqib Mir Henriette Engelken Martin Golebiewski Ulrike Wittig Isabel Rojas SABIO-RK is member of: SABIO-RK team

32. http://sabio.villa-bosch.de/SABIORK