Active modules of bipartite metabolic network

The thesis investigates the problem of identifying active modules of bipartite metabolic network. We devise a method of motif projection, and the extraction of clusters from active modules based on the concentration of active-motifs in the network. Our results reveal the existence of hierarchical structure. We model regulation of metabolism as an interaction between a metabolic network and a gene regulatory network in the form of interconnected network. We devise two module detection algorithms for interconnected network to evaluate the molecular changes of activity that are associated with cellular responses. The first module detection algorithm is formulated based on information map of random walks that is capable of inferring modules based on topological and activity of nodes. The proposed algorithm has faster execution time and produces comparably close performance as previous work. The second algorithm takes into account of strong regulatory activities in the gene regulatory layer to support the active regions in the metabolic layer. The integration of gene information allows the formation of large modules with better recall. In conclusion, our findings indicate the importance of no longer modelling complex biological systems as a single network, but to view them as flow of information of multiple molecular spaces