Analysis of gene networks for drug target discovery and validation.
Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.
Imoto
Seiya
S
Human Genome Centre, Institute of Medical Science, University of Tokyo, Tokyo, Japan.
Tamada
Yoshinori
Y
Savoie
Christopher J
CJ
Miyano
Satoru
S
D000935:Antifungal Agents / Q000494:pharmacology; D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D015966:Gene Expression Regulation, Fungal / Q000187:drug effects; D053263:Gene Regulatory Networks / Q000187:drug effects; D006118:Griseofulvin / Q000494:pharmacology; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000187:drug effects; D013997:Time Factors
D000935:Antifungal Agents; D006118:Griseofulvin