Knowledge representation model for systems-level analysis of signal transduction networks

Genome Inform. 2004;15(2):234-43.

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-1beta and TNF-alpha 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Graphics*
  • Cytokines / pharmacology*
  • DNA-Binding Proteins / metabolism
  • Databases, Factual*
  • Models, Biological
  • Signal Transduction / drug effects*
  • Signal Transduction / physiology*
  • Software
  • Systems Analysis*

Substances

  • Cytokines
  • DNA-Binding Proteins