Knowledge representation model for systems-level analysis of signal transduction networks.
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.
Lee
Dong-Yup
DY
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. dylee@pse.kaist.ac.kr
Zimmer
Ralf
R
Lee
Sang-Yup
SY
Hanisch
Daniel
D
Park
Sunwon
S
D003196:Computer Graphics; D016207:Cytokines / Q000494:pharmacology*; D004268:DNA-Binding Proteins / Q000378:metabolism; D016208:Databases, Factual; D008954:Models, Biological; D015398:Signal Transduction / Q000187:drug effects* / Q000502:physiology*; D012984:Software; D013597:Systems Analysis
D016207:Cytokines; D004268:DNA-Binding Proteins