  Background: Networks in different domains are characterized by similar global
characteristics while differing in local structures. To further extend this
concept, we investigated network regularities on a fine scale in order to
examine the functional impact of recurring motifs in signed oriented biological
networks. In this work we generalize to signaling net works some considerations
made on feedback and feed forward loops and extend them by adding a close
scrutiny of Linear Triplets, which have not yet been investigate in detail.
Results: We studied the role of triplets, either open or closed (Loops or
linear events) by enumerating them in different biological signaling networks
and by comparing their significance profiles. We compared different data
sources and investigated the fine topology of protein networks representing
causal relationships based on transcriptional control, phosphorylation,
ubiquitination and binding. Not only were we able to generalize findings that
have already been reported but we also highlighted a connection between
relative motif abundance and node function. Furthermore, by analyzing for the
first time Linear Triplets, we highlighted the relative importance of nodes
sitting in specific positions in closed signaling triplets. Finally, we tried
to apply machine learning to show that a combination of motifs features can be
used to derive node function. Availability: The triplets counter used for this
work is available as a Cytoscape App and as a standalone command line Java
application. http://apps.cytoscape.org/apps/counttriplets Keywords: Graph
theory, graph analysis, graph topology, machine learning, cytoscape
