Elucidation of functional consequences of signalling pathway interactions.
BACKGROUND
A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions.


RESULTS
A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-kappaB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks.


CONCLUSIONS
It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.
Ihekwaba
Adaoha E C
AE
The Microsoft Research-University of Trento, Centre for Computational Systems Biology, Povo (Trento), Italy. ihekwaba@cosbi.eu
Nguyen
Phuong T
PT
Priami
Corrado
C
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015398:Signal Transduction
D011506:Proteins