Identifying Drug Active Pathways from Gene Networks Estimated by Gene Expression Data

Yoshinori Tamada[1] (tamada@kuicr.kyoto-u.ac.jp)
Seiya Imoto[2] (imoto@ims.u-tokyo.ac.jp)
Kousuke Tashiro[3] (ktashiro@grt.kyoshu-u.ac.jp)
Satoru Kuhara[3] (kuhara@grt.kyushu-u.ac.jp)
Satoru Miyano[2] (miyano@ims.u-tokyo.ac.jp)

[1]Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
[2]Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan
[3]Graduate School of Genetic Resource Technology, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan


Abstract

We present a computational method for identifying genes and their regulatory pathways influenced by a drug, using microarray gene expression data collected by single gene disruptions and drug responses. The automatic identification of such genes and pathways in organisms' cells is an important problem for pharmacogenomics and the tailor-made medication. Our method estimates regulatory relationships between genes as a gene network from microarray data of gene disruptions with a Bayesian network model, then identifies the drug affected genes and their regulatory pathways on the estimated network with time course drug response microarray data. Compared to the existing method, our proposed method can identify not only the drug affected genes and the druggable genes, but also the drug responses of the pathways. For evaluating the proposed method, we conducted simulated examples based on artificial networks and expression data. Our method succeeded in identifying the pseudo drug affected genes and pathways with the high coverage greater than 80%. We also applied our method to Saccharomyces cerevisiae drug response microarray data. In this real example, we identified the genes and the pathways that are potentially influenced by a drug. These computational experiments indicate that our method successfully identifies the drug-activated genes and pathways, and is capable of predicting undesirable side effects of the drug, identifying novel drug target genes, and understanding the unknown mechanisms of the drug.

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Japanese Society for Bioinformatics