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The small world inside large metabolic networks

Open access
Wagner, Andreas, Fell, David A.·2001·Proceedings of the Royal Society of London. Series B: Biological Sciences

Abstract

The metabolic network of the catabolic, energy and biosynthetic metabolism of Escherichia coli is a paradigmatic case for the large genetic and metabolic networks that functional genomics efforts are beginning to elucidate. To analyse the structure of previously unknown networks involving hundreds or thousands of components by simple visual inspection is impossible, and quantitative approaches are needed to analyse them. We have undertaken a graph theoretical analysis of the E. coli metabolic network and find that this network is a small-world graph, a type of graph distinct from both regular and random networks and 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 Caenorhabditis elegans. Moreover, the connectivity of the metabolites follows a power law, another unusual but by no means rare statistical distribution. This provides an objective criterion for the centrality of the tricarboxylic acid cycle to metabolism. The small-world architecture may serve to minimize transition times between metabolic states, and contains evidence about the evolutionary history of metabolism.

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Metadata

Authors
Wagner, A., & Fell, D. A.
Venue
Proceedings of the Royal Society of London. Series B: Biological Sciences
Published
2001-09-07
Volume
268
Issue
1478
Pages
1803-1810
ISSN
  • 0080-4649 (ISSN-L)
  • 0962-8452
  • 1471-2954
OpenAlex
W1983202741
Cited by
981
References
3
FWCI
14.66
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The small world inside large metabolic networks. https://app.cochat.ai/papers/10.1098/rspb.2001.1711
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Wagner, A., & Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rspb.2001.1711

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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