Abstract:
Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. In the present study, through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations, a novel framework is presented for reconstruction and analysis of a highly-correlated protein interaction network. This network is composed of the candidate proteins for signal transduction mechanisms in Saccharomyces cerevisiae. Identification and scoring of the possible linear pathways in the network enables reconstruction of model sub-networks for two glucose sensing and signaling and four MAPK signaling pathways in Saccharomyces cerevisiae. Almost all of the known components of these pathways are identified together with several new “candidate” proteins, indicating the successful reconstructions of six model pathways involved in yeast. A detailed map including high-scoring physical and functional connections that link the relevant signal transduction components to each other and to adjacent networks is developed. A novel and efficient algorithm is created for determination of topological properties of bio-molecular networks. Additionally, game theoretical concepts are adapted into bioinformatics to understand the underlying design principles of MAPK signaling pathways. The hierarchical organization of signal transduction is also investigated with classification of signal transducer proteins according to their topological characteristics. Reporter GO terms indicated the correlation between classification and protein functionality and the organization of functionality is further investigated using a layer decomposition technique. A novel technique is described and used in detection of basic regulatory motifs and high-level modules in the integrated network of transcriptional regulation and protein-protein interaction. The inter-connected clusters of these modules are indicating several regulatory mechanisms active in Saccharomyces cerevisiae.
