  We perform a reverse engineering from the ``extended Spellman data'',
consisting of 6178 mRNA levels measured by microarrays at 73 instances in four
time series during the cell cycle of the yeast Saccharomyces cerevisae. By
assuming a linear model of the genetic regulatory network, and imposing an
extra constraint (the Lasso), we obtain a unique inference of coupling
parameters. These parameters are transfered into an adjacent matrix, which is
analyzed with respect to topological properties and biological relevance. We
find a very broad distribution of outdegrees in the network, compatible with
earlier findings for biological systems and totally incompatible with a random
graph, and also indications of modules in the network. Finally, we show there
is an excess of genes coding for transcription factors among the genes of
highest outdegrees, a fact which indicates that our approach has biological
relevance.
