A gene regulatory network, where two genes are connected if they are directly, or functionally, regulating each other, can be 'reverse-engineered' from large-scale experimental data such as gene expression profiles. Here used a simple but effective reverse-engineering approach using all the available gene expression profiles in mammals, solving along the way the problems of handling, normalizing and analysing such massive dataset. We reverse-engineered a coexpression network for Homo Sapiens (Mus Musculus) from a set of 20,255 (8895) gene expression profiles. The human (mouse) network is characterized by a set of 22283 (45101) nodes (i.e. genes) and a set of 4,817,629 (14,641,095) edges, where the edge is weighted by the Mutual Information (MI) measure between the two genes.

We show how the resulting network can be then used to understand the function of a gene, the modularity of gene regulation, as well as, as a tool to analyse "gene signatures" to identify the mode of action of a drug.

We will also show how it is possible to use gene expression profile to build a "drug network", where drugs can be automatically grouped in subnetworks ('communities') of drugs sharing a similar mode of action.