Annotation of SBML Models Through Rule-Based Semantic Integration

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    Notes on slide 1

    At CISBAN, part of our research focuses on telomeres and their relationship to ageing. The model chosen for the use cases involves telomere shortening.From [1]: “Telomeres are repetitive sequences of DNA situated at the ends of linear chromosomes. They require protection from being recognized as double-strand breaks to prevent activation of the DNA damage response.” They shorten with each cell division, and are implicated in ageing.

    This model of telomere uncapping is written in SBML. But what is SBML? At a minimum, SBML models will contain all the maths needed to run the simulations. Linking to biology is not required and therefore many biologist tend to leave this part out. Any integration procedure needs to add information of this type.

    It’s difficult to create models, even with available tools.

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    Annotation of SBML Models Through Rule-Based Semantic Integration - Presentation Transcript

    1. Annotation of SBML Models Through Rule-Based Semantic IntegrationBio-Ontologies SIG, ISMB 200928 June 2009Allyson Lister, Phillip Lord, Matthew Pocock, Anil WipatCISBAN and Newcastle Universityhttp://www.cisban.ac.uk/RBMhelpdesk@cisban.ac.uk
      Presentation slides covered under the CC-BY-SA 2.5
    2. Telomeres and Ageing
      Image: Public Domain, NASA, from http://commons.wikimedia.org/wiki/File:Telomere_caps.gif
    3. Checkpoint response to telomere uncapping in budding yeast
      Proctor et al. [1]
      Image and Model: [1]
    4. What is SBML?
      • Standard format
      • Widely used (100+ tools)
      • Represents the mathematical model
      • Enables linking to the underlying biology
    5. Model Annotation Life Cycle
      Get data
      Read literature
      (Re)build model
      Parameterise
      Examine results
      Run and test model
    6. Challenges in Model Annotation
      • Knowledge in lots of places
      • Hard for a single modeller to retrieve all available knowledge
      • Most existing data integration tools are not suitable for modellers’ needs
      • Many integration methods are purely syntactic
    7. Rule-Based Mediation
      • A core ontology, which is a semantically-rich description of the research domain of interest
      • A set of source ontologies, which are syntactic translations of data formats into OWL
      • Resolution of syntactic and semantic heterogeneity occurs separately
      • Can use existing, independent ontologies
    8. Where is the knowledge?
      • Input data sources: UniProtKB, BioGRID, Pathway Commons
      • Data can end up in SBML format via MFO [2]
    9. How are the data sources integrated?
      XML
      OWL
      UniprotKB
      XML
      OWL
      Instances
      MFO
      BioGRID
      Telomere
      Ontology
      OWL
      PathwayCommons
      Resolve
      syntactic
      heterogeneity
      Resolve
      semantic
      heterogeneity
      Retrieve
      data
      Export
      to
      SBML
      Reviews of integration types [3-6]
      Reviews for ontology mapping [7-11]
    10. Achieving the XML to OWL Conversion
      • The XMLTab plugin for Protégé quickly creates classes and instances from XML
      • Provides syntactic transformation of data
      • But, not multiple XML files
      • May not be in active development
    11. Syntactic Ontologies ->Telomere Ontology
      • Rules written in SWRL
      • Queries over the telomere ontology are written in SQWRL
      • SWRLTab and Jess are used to run these rules and queries
      • While writing rules/queries is mainly manual, running them is automatic
      • Semantic value is added to the information by mapping it to a semantically-rich core ontology
    12. Mappings from UniProtKB
      upkb:locationSlot(?s, ?l) ˄
      upkb:Text(?l, ?value) ˄
      swrlb:equal(?value, “Nucleus”) ->
      tuo:Nucleus(?l)
    13. What is RAD9?
      • Tell me about something called ‘rad9’
      • Results could be added back to the existing model
      • Cross-references
      • SBO annotations
      • Compartment localisations
      • Recommended name
      • These results allow the addition of information required by the MIRIAM guidelines
    14. What interacts with RAD9?
      • In Proctor et al., there are four interacting partners with RAD9
      • Two were identified within the model, and were found by querying the telomere ontology
      • Of the unknown partners, one was provisionally identified as MEC1.
    15. Summary
      • SBML models require large amounts of data to be mathematically and biologically complete
      • A two-step integration “rule-based mediation” helps model annotation
      • Off-the-shelf technology was used
      • Eases the task of fulfilling MIRIAM guidelines
      • SWRL rules were used to populate our semantically-rich telomere ontology
      • SQWRL was used for querying
      • We found new knowledge that the modeller was not aware of
    16. http://www.cisban.ac.uk/RBM
      • Phillip Lord, Matthew Pocock, Anil Wipat
      • Carole Proctor (10.1098/rsif.2006.0148)
      • Tom Kirkwood
      • Reviewers and conference organisers
      Funding for CISBAN provided by BBSRC/EPSRC grant ref BB/C008200/1‏
    17. Thank you!
      xkcd.com
      Funding for CISBAN provided by BBSRC/EPSRC grant ref BB/C008200/1‏
    18. References
      CJ Proctor, DA Lydall, RJ Boys, et al. Modelling the checkpoint response to telomere uncapping in budding yeast. Journal of The Royal Society Interface, Vol. 4, No. 12. (22 February 2007), pp. 73-90.
      AL Lister, M Pocock, A Wipat, Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models. Journal of Integrative Bioinformatics, Vol. 4, No. 3. (2007), 80.
      Stephan Philippi and Jacob Kohler. Addressing the problems with life-science databases for traditional uses and systems biology. Nature Reviews Genetics, 7(6):482–488, May 2006
      W. Sujansky. Heterogeneous database integration in biomedicine. J Biomed Inform, 34(4):285–298, August 2001.
      Lincoln D Stein. Integrating biological databases. Nat Rev Genet, 4(5):337–345, May 2003.
      R. Alonso-Calvo et al. An agent- and ontology-based system for integrating public gene, protein, and disease databases. J Biomed Inform, 40(1):17–29, February 2007.
    19. References
      Marie C. Rousset and Chantal Reynaud. Knowledge representation for information integration. Inf. Syst., 29(1):3–22, 2004.
      Li Xu and David W. Embley. Combining the Best of Global-as-View and Local-as-View for Data Integration. In Anatoly E. Doroshenko, Terry A. Halpin, Stephen W. Liddle, Heinrich C. Mayr, Anatoly E. Doroshenko, Terry A. Halpin, Stephen W. Liddle, and Heinrich C. Mayr, editors, ISTA, volume 48 of LNI, pages 123–136. GI, 2004.
      Natalya F. Noy and Mark A. Musen. The PROMPT suite: interactive tools for ontology merging and mapping. Int. J. Hum.-Comput. Stud., 59(6):983–1024, December 2003.
      HolgerWache et al. Ontology-based integration of information — a survey of existing approaches. In H. Stuckenschmidt, editor, Proceedings of the IJCAI’01 Workshop on Ontologies and Information Sharing, Seattle, Washington, USA, Aug 4-5, pages 108–117, 2001.
      JinguangGu, BaowenXu, and Xinmeng Chen. An XML query rewriting mechanism with multiple ontologies integration based on complex semantic mapping. Information Fusion, 9(4):512–522, October 2008.
    20. Small Print
      This presentation is covered under the CC-BY-SA 2.5 (http://creativecommons.org/licenses/by-sa/2.5/ ) license and can be found on slideshareat: http://www.slideshare.net/allysonlister/annotation-of-sbml-models-through-rulebased-semantic-integration
      The ontologies created by the authors are covered under the CC-BY 3.0 license, and external ontologies are governed under their own licensing requirements (specifically, SBO, which is covered under the Artistic License as defined on its SourceForge project site at http://sourceforge.net/projects/sbo/).
    21. END

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