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The Small World Inside Large Metabolic Networks


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  • Andreas Wagner
  • David Fell
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    We analyze the structure of a large metabolic network, that of the energy and biosynthesis metabolism of Escherichia coli. This network is a paradigmatic case for the large genetic and metabolic networks that functional genomics efforts are beginning to elucidate. To analyze the structure of networks involving hundreds or thousands of components by simple visual inspection is impossible, and a quantitative framework is needed to analyze them. We propose a graph theoretical description of the E. coli metabolic network, a description that we hope will prove useful for other genetic networks. We find that this network is a small world graph, a type of graph observed in a variety of seemingly unrelated areas, such as friendship networks in sociology, the structure of electrical power grids, and the nervous system of C. elegans. Moreover, its connectivity follows a power law, another unusual but by no means rare statistical distribution. This architecture may serve to minimize transition times between metabolic states, and also reflect the evolutionary history of metabolism.

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    Bibliographic Info

    Paper provided by Santa Fe Institute in its series Working Papers with number 00-07-041.

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    Date of creation: Jul 2000
    Date of revision:
    Handle: RePEc:wop:safiwp:00-07-041

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    Cited by:
    1. Petra M. Gleiss & Peter F. Stadler & Andreas Wagner & David A. Fell, 2000. "Small Cycles in Small Worlds," Working Papers 00-10-058, Santa Fe Institute.
    2. How to Reconstruct a Large Genetic Network from n Gene Perturbations in Fewer than n2 Easy Steps, 2001. "How to Reconstruct a Large Genetic Network from," Working Papers 01-09-047, Santa Fe Institute.
    3. Andreas Wagner, 2001. "The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes," Working Papers 01-04-022, Santa Fe Institute.
    4. Andreas Wagner, 2001. "Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data," Working Papers 01-09-051, Santa Fe Institute.


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