Abstract: Recent developments in large-scale genomic technologies, such as DNA microarrays and mass spectroscopy have made the analysis of gene networks more feasible. However, it is not obvious how the data acquired through such method can be assembled into unambiguous and predictive models of these networks. In a recent study our group developed an algorithm (Network Identification by multiple regression – NIR) that used a series of steady state RNA expression measurements, following transcriptional perturbations, to construct a model of a 9 gene network that is a part of larger SOS network in E.Coli. Though the NIR method proved highly effective in inferring small microbial gene networks, its practical utility is limited because it requires: (i) prior knowledge of which genes are involved in the network of interest; (ii) the perturbation of all the genes in the network via the construction of appropriate episomal plasmids; (iii) the measurement of gene expressions at steady state (i.e., constant physiological conditions after the perturbation). This experimental setup is unpractical for large networks, it is not easily applied to higher organisms, and, most importantly, it is not applicable if there is no prior knowledge of the genes belonging to the network. Here we are proposing a new algorithm that can infer the network of gene-gene interactions to which a gene of interest belongs and identify its direct targets, using the perturbation of only one of the genes in the network. To this end, we need to measure gene expression profiles at multiple time points following perturbation of only the known gene, or genes, and without the need of the steady-state assumption.