Application of random hierarchical clustering in metabolic networks

Computational methods, especially topological structure based methods are increasingly important for the study of more and more biological networks these days. Generally speaking, identification of the communities (or functional modules) is critical for these networks. By using a random hierarchical clustering algorithm, the present paper studied community structure of B. thuringiensis metabolic network (mainly focused on the giant strong component of the network). With the random hierarchical clustering algorithm, we obtained 11 communities for the network, and we also discussed the biological function of these communities.