Abstract #302239

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JSM 2003 Abstract #302239
Activity Number: 374
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #302239
Title: Inference of Regulatory Networks From Microarray Data
Author(s): Guy N. Brock*+ and Laura A. Salter and William D. Beavis
Companies: University of New Mexico and UNM and National Center for Genome
Address: 3618 Calle del Monte NE, Albuquerque, NM, 87110-6106,
Keywords: fuzzy logic ; DNA microarray ; regulatory networks
Abstract:

The analysis of gene expression microarrays plays an important role in elucidating the functionality of genes, including the discovery of genetic interactions that regulate gene expression. Several methods for modeling such gene regulatory networks exist, including a variety of continuous and discrete models. An interesting alternative to these methods is fuzzy logic. However, the guidelines for modeling gene expression with fuzzy logic are fairly open, and the need arises to investigate how adjustments in the modeling scheme will affect the results. We modify an existing fuzzy logic algorithm to involve an arbitrary number of states. We also propose a probabilistic alternative to the fuzzy logic model, which provides a stronger justification of the results and a clearer assessment of the assumptions involved. Both models are allowed to involve an arbitrary number of states, and we investigate the limiting behavior of these models as the number of states tends to infinity. Our methods are tested using yeast cell cycle data and a simulated dataset.

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