Signalling networks have the potential to provide useful insight into mechanisms driving disease progression. It has been estimated that as much as a third of the eukaryotic proteome is phosphorylated at one time indicating the significance of phosphorylation in modulating cell signalling. Nevertheless, the simple identification and quantification of proteins from different conditions is not sufficient to reconstruct the mechanisms underpinning the observed differences. Functional analysis methods have been developed to help with the interpretation of proteomic and phosphoproteomic data, however, these methods suffer from a range of limitations and fail to account for the complexity of cellular signalling networks. Thus, there is a need for tools, methods and frameworks that consider underlying network structures to aid accurate interpretation and reconstruction of the biological mechanisms at play. An important first step is the derivation of the network since most knowledgebases today deal in pathways, which do not properly represent the global flow of information across the entire signalling system. Here we have developed a set of algorithms to extract and interrogate a more-global signalling network from the knowledgebase determined to be the most complete for this purpose. We also demonstrate how phosphoproteomics measurements can be mapped to this network to interpret the functional consequences of the observed changes in protein phosphorylation. This approach will enable a more unbiased and complete analysis to be performed over networks encompassing specific proteins and phosphoproteins of interest.