Knowledge Representation Model for Systems-Level Analysis of Signal Transduction Networks

Dong-Yup Lee[1] (dylee@pse.kaist.ac.kr)
Ralf Zimmer[2] (zimmer@bio.informatik.uni-muenchen.de)
Sang-Yup Lee[1] (leesy@kaist.ac.kr)
Daniel Hanisch[3] (Daniel.Hanisch@aventis.com)
Sunwon Park[1] (sunwon@kaist.ac.kr)

[1]Department of Chemical and Biomolecular Engineering and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea
[2]Intitut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany
[3]Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer GesellschaftSchloss Birlinghoven, 53754 Sankt Augustin, Germany

Abstract

A Petri-net based model for knowledge representation has been developed to describe as explicitly and formally as possible the molecular mechanisms of cell signaling and their pathological implications. A conceptual framework has been established for reconstructing and analyzing signal transduction networks on the basis of the formal representation. Such a conceptual framework renders it possible to qualitatively understand the cell signaling behavior at systems-level. The mechanisms of the complex signaling network are explored by applying the established framework to the signal transduction induced by potent proinflammatory cytokines, IL-1β and TNF-α The corresponding expert-knowledge network is constructed to evaluate its mechanisms in detail. This strategy should be useful in drug target discovery and its validation.

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