  The promise of discovering a functional blueprint of a cellular system from
large-scale and high-throughput sequence and experimental data is predicated on
the belief that the same top-down investigative approach that proved successful
in other biological problems (e.g. DNA sequencing) will be as effective when it
comes to inferring more complex intracellular processes. The results in this
paper address this fundamental issue in the specific context of transcription
regulatory networks. Although simple recurring regulatory motifs have been
identified in the past, due to the size and complexity of the connectivity
structure, the subdivision of such networks into larger, and possibly
inter-connected, regulatory modules is still under investigation. Specifically,
it is unclear whether functionally well-characterized transcriptional
sub-networks can be identified by solely analyzing the connectivity structure
of the overall network topology. In this paper, we show that transcriptional
regulatory networks can be systematically partitioned into communities whose
members are consistently functionally related. We applied the partitioning
method to the transcriptional regulatory networks of the yeast Saccharomyces
cerevisiae; the resulting communities of gene and transcriptional regulators
can be associated to distinct functional units, such as amino acid metabolism,
cell cycle regulation, protein biosynthesis and localization, DNA replication
and maintenance, lipid catabolism, stress response and so on. Moreover, the
observation of inter-community connectivity patterns provides a valuable tool
for elucidating the inter-dependency between the discovered regulatory modules.
