  Connectivity networks have recently become widely used in biology due to
increasing amounts of information on the physical and functional links between
individual proteins. This connectivity data provides valuable material for
expanding our knowledge far beyond the experimentally validated via
mathematical analysis and theoretical predictions of new functional
interactions. In this paper we demonstrate an application of several algorithms
developed for the ranking of potential gene-expression regulators within the
context of an associated network. We analyze how different types of
connectivity between genes and proteins affect the topology of the integral
C.elegans functional network and thereby validate algorithmic performance. We
demonstrate the possible definition of co-expression gene clusters within a
network context from their specific motif distribution signatures. We also show
that the method based on the shortest path function (SPF) applied to gene
interactions sub-network of the co-expression gene cluster, efficiently
predicts novel regulatory transcription factors (TFs). Simultaneous application
of other methods, including only interactions with neighborhood genes, allows
rapid ranking of potential regulators that could be functionally linked with
the group of co-expressed genes. Predicting functions of regulators for a
cluster of ribosomal/mRNA metabolic genes we highlight a role of mRNA
translation and decay in a longevity of organisms.
