%0 Journal Article
%T Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks
%A Daniel C Kirouac
%A Julio Saez-Rodriguez
%A Jennifer Swantek
%A John M Burke
%A Douglas A Lauffenburger
%A Peter K Sorger
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-29
%X We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a ¡°bow tie¡± architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NF¦ÊB, and apoptotic signaling. Individual pathways exhibit ¡°fuzzy¡± modularity that is statistically significant but still involving a majority of ¡°cross-talk¡± interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.Wide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningfu
%U http://www.biomedcentral.com/1752-0509/6/29