[unrev-II] Knowledge-Based Modeling of the E. coli Metabolic Network

From: Doug Engelbart - Bootstrap Institute (doug@bootstrap.org)
Date: Fri Nov 03 2000 - 09:18:00 PST

  • Next message: John J. Deneen: "Re: [unrev-II] Towards a summary of documents (including Conceptual Graphs based on existential graphs of Charles Peirce)"

    Talk at St anford Medical Informatics group. Special relevance: the speaker is
    an SRI guy, in the AI Lab.




    Title: Knowledge-Based Modeling of the E. coli Metabolic Network

    Speaker: Peter Karp

       Bioinformatics Research Group

       SRI International

          Date: November 9, 2000

          Time: 12:15 PM to 1:15 PM

    Location: Lee B. Lusted Library (MSOB

       Conference Room x275)


    A knowledge-based model of an organism consists of a set of static facts about
    the molecular components of the organism, plus a set of rules of inference for
    deriving new relationships that are implicit in those static facts. If we
    consider a genome database to be a knowledge-based model, some key questions
    about that genome database are: What range of static facts can the database
    encode? And what new relationships can it infer?

    The ontology (schema) of the Pathway Tools software can encode a wide variety
    of information about the genes, gene products, metabolic pathways,
    transporters, and genetic-regulatory circuitry of an organism. For example,
    the EcoCyc DB describes the full genome and metabolic-pathway complement of E.
    coli, as well as many of its transporters and operons.

    The ontology of a genome database is of central importance because a poorly
    designed ontology will distort the information that the database encodes. The
    Pathway Tools software can infer a wide variety of relationships regarding the
    genome and the biochemical network of the organism that are implicit in the
    static facts within a Pathway Tools database. Each rule of inference is in a
    sense a predefined database query that infers a biological relationship of

    We present a connectivity analysis of the E. coli metabolic network that
    consists of statistics on many of these inferred relationships.

    The schedule for all future talks sponsored by Stanford Medical Informatics may
    be found on the World Wide Web at URL <http://www-smi.stanford.edu/events/>.

    -------------------------- eGroups Sponsor -------------------------~-~>
    eGroups eLerts
    It's Easy. It's Fun. Best of All, it's Free!

    Community email addresses:
      Post message: unrev-II@onelist.com
      Subscribe: unrev-II-subscribe@onelist.com
      Unsubscribe: unrev-II-unsubscribe@onelist.com
      List owner: unrev-II-owner@onelist.com

    Shortcut URL to this page:

    This archive was generated by hypermail 2b29 : Fri Nov 03 2000 - 09:41:06 PST