  This paper deals with gene networks whose dynamics is assumed to be generated
by a continuous-time, linear, time invariant, finite dimensional system (LTI)
at steady state. In particular, we deal with the problem of network
reconstruction in the typical practical situation in which the number of
available data is largely insufficient to uniquely determine the network. In
order to try to remove this ambiguity, we will exploit the biologically a
priori assumption of network sparseness, and propose a new algorithm for
network reconstruction having a very low computational complexity (linear in
the number of genes) so to be able to deal also with very large networks (say,
thousands of genes). Its performances are also tested both on artificial data
(generated with linear models) and on real data obtained by Gardner et al. from
the SOS pathway in Escherichia coli.
