A knowledge model for analysis and simulation of regulatory networks.
BACKGROUND
In order to aid in hypothesis-driven experimental gene discovery, we are designing a computer application for the automatic retrieval of signal transduction data from electronic versions of scientific publications using natural language processing (NLP) techniques, as well as for visualizing and editing representations of regulatory systems. These systems describe both signal transduction and biochemical pathways within complex multicellular organisms, yeast, and bacteria. This computer application in turn requires the development of a domain-specific ontology, or knowledge model.


RESULTS
We introduce an ontological model for the representation of biological knowledge related to regulatory networks in vertebrates. We outline a taxonomy of the concepts, define their 'whole-to-part' relationships, describe the properties of major concepts, and outline a set of the most important axioms. The ontology is partially realized in a computer system designed to aid researchers in biology and medicine in visualizing and editing a representation of a signal transduction system.
Rzhetsky
A
A
Columbia Genome Center, Columbia University, USA. ar345@columbia.edu
Koike
T
T
Kalachikov
S
S
Gomez
S M
SM
Krauthammer
M
M
Kaplan
S H
SH
Kra
P
P
Russo
J J
JJ
Friedman
C
C
D000818:Animals; D001185:Artificial Intelligence; D002965:Classification; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009323:Natural Language Processing; D015398:Signal Transduction; D012984:Software