  We describe a novel approach to the analysis of toxicogenomics data and
elucidation of biological networks affected by drug treatments. In this method
approximately 15,000 linear pathway modules were generated from manually
assembled pathway maps from MetaCore (GeneGo, Inc.). Microarray expression data
from livers of rat exposed to phenobarbital, mestranol and tamoxifen were
mapped onto these modules. Using different analytical techniques we have
identified sets of "differential" pathways featuring highly correlated
expression among multiple repeats of the same treatment while showing strong
anti-correlation across different treatments. Network modules distinguishing
chemical treatments were re-assembled based on these pathways. Unlike
traditional statistical and clustering procedures in expression profiling, our
method takes into account both network connectivity and gene expression in the
course of the analysis. We demonstrate that it enables identification of
important cellular mechanisms involved in drug response that would have been
missed by the analysis based on individual gene expression profiles.
