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.
Bansal
Mukesh
M
Mukesh Bansal is PhD Student at Telethon Institute of Genetics and Medicine (TIGEM), Via P.Castellino 111, Naples 80131, Italy. bansal@tigem.it.
Della Gatta
Giusy
G
Wierzbowski
Jamey
J
Gardner
Timothy
T
di Bernardo
Diego
D