The important task of determining the connectivity of gene networks, and at amore detailed level even the kind of interaction existing between genes, cannowadays be tackled by microarraylike technologies. Yet, there is still a largeamount of unknowns with respect to the amount of data provided by a singlemicroarray experiment, and therefore reliable gene network retrieval proceduresmust integrate all of the available biological knowledge, even if coming fromdifferent sources and of different nature. In this paper we present a reverseengineering algorithm able to reveal the underlying gene network by usingtime-series dataset on gene expressions considering the system response todifferent perturbations. The approach is able to determine the sparsity of thegene network, and to take into account possible {\it a priori} biologicalknowledge on it. The validity of the reverse engineering approach ishighlighted through the deduction of the topology of several {\it simulated}gene networks, where we also discuss how the performance of the algorithmimproves enlarging the amount of data or if any a priori knowledge isconsidered. We also apply the algorithm to experimental data on a nine genenetwork in {\it Escherichia coli
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