Discovering drug mode of action using reverse-engineered gene networks

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 6 whole-genome Escherichia coli expression profiles at different time points after treatment with the antibiotic Norfloxacin. We show that the algorithm can correctly identify the known drug targets and associated pathways.