Research Of Community Structure In Metabolic Network

Cellular metabolism is essential for the maintenance of life. In metabolic process, throughspecific biochemical reactions, some materials are broken down to yield energy for vitalprocesses while other substances, necessary for life, are synthesized. All metabolic reactionsoccurring within a living cell of an organism constitute the metabolic network of this organism.Although a large variety of metabolic reactions can be found in different organisms, metabolicnetworks are highly conserved across them. As the results of various genome projects, thegenome sequences of many organisms are available and the organism specific metabolicnetworks can be faithfully reconstructed from genome information. Thus the prediction offunction from the metabolic networks has become an essential step in the post-genomic era. Theanalysis of metabolic networks can help to understand and utilize cellular metabolic process inorder to promote the development of ferment technology and medicine industry. On the otherhand, the topology of metabolic networks reflects the dynamics of their formation and evolution,studying of which may help to understand the evolutionary history of life.Community structure is an important characteristic in complex networks.Seeking andanalyzing communities is an invaluable tool of understanding the structure of networks.Findingcommunity structure is a very popular issue in researches of complex networks.A lot ofalgorithms have been proposed to detect the community structure in networks. This articleprimarily introduces the research progress of structure,function and their relation of metabolicnetwork.It has particular introduction of the method on community structure in complexnetworks and analyses both advantage and disadvantage of it.On that basis, it elaborates theanalytical method of community structure in metabolic network based on link clustering.In thisarticle, we engage a links clustering based method for analyzing the giant strong component of S.aurous metabolic network from published high-quality models, we obtain 10 functional moduleswith better biological significance, which suggest that links clustering could identify functionalcommunities in metabolic networks.