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Bioinformatics Vol. 16 no. 12 2000
Pages 1120-1128
© 2000 Oxford University Press

Ontology

A knowledge model for analysis and simulation of regulatory networks

Andrey Rzhetsky 1,2,*, Tomohiro Koike 1,3, Sergey Kalachikov 1, Shawn M. Gomez 1, Michael Krauthammer 2, Sabina H. Kaplan 1, Pauline Kra 2,4, James J. Russo 1 and Carol Friedman 2,5

1 Columbia Genome Center, Columbia University,
2 Department of Medical Informatics,, Columbia University,
3 Hitachi Software Engineering Co. Ltd,
4 Department of French, Yeshiva University,
5 Department of Computer Science,, Queens College of the City University of New York,

Received on October 7, 1999 ; revised on April 24, 2000 ; accepted on July 20, 2000

Motivation: 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.

Availability: The knowledge model can be reviewed at http://genome6.cpmc.columbia.edu/tkoike/ontology/

Contact: ar345{at}columbia.edu

* To whom correspondence should be addressed.


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