A knowledge model for analysis and simulation of regulatory networks

Motivation: In order to aid in hypothesis-driven experimental gene discover y, we are designing a computer application for the automatic retrieval of s ignal transduction data from electronic versions of scientific publications using natural language processing (NLP) techniques, as well as for visuali zing and editing representations of regulatory systems. These systems descr ibe both signal transduction and biochemical pathways within complex multic ellular organisms, yeast, and bacteria. This computer application in turn r equires the development of a domain-specific ontology or knowledge model. Results: We introduce an ontological model for the representation of biolog ical knowledge related to regulatory networks in vertebrates. We outline a taxonomy of the concepts, define their 'whole-to-part' relationships, descr ibe the properties of major concepts, and outline a set of the most importa nt axioms. The ontology is partially realized in a computer system designed to aid researchers in biology and medicine in visualizing and editing a re presentation of a signal transduction system.