Abstract
Recent studies of complex networks offer new methods for characterizing large scale of networks and provide new insights on how such networks are developed. In particular, researchers focused on biological networks such as gene regulatory systems, protein interactions and metabolic pathways in order to understand how these elemental reactions are integrated as an organism. Although various statistical features of network structures, such as scale-free or small-world, have been studied to approach underlying principles of network organization, more detailed analysis on network properties is required to understand their functions.
The community finding algorithm proposed by Girvan and Newman provides another useful technique for investigating topological structures of large networks. Applying this method to metabolic networks, we found that behavior like that of Zipf’s law of the distribution of community size is shared very generally among a wide range of organisms. With the aim of realizing how this property is achieved, we present a new evolutionary model of metabolic reactions based on artificial chemistry.
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Ono, N., Fujiwara, Y., Yuta, K. (2005). Artificial Metabolic System: An Evolutionary Model for Community Organization in Metabolic Networks. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_72
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DOI: https://doi.org/10.1007/11553090_72
Publisher Name: Springer, Berlin, Heidelberg
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