2002
DOI: 10.1126/science.1065103
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Specificity and Stability in Topology of Protein Networks

Abstract: Molecular networks guide the biochemistry of a living cell on multiple levels: its metabolic and signalling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks we quantify correlations between connectivities of interacting nodes and compare them to a null model of a network, in which al links were randomly rewired. We find that for both interaction and regulatory networks,… Show more

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Cited by 2,776 publications
(2,576 citation statements)
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References 14 publications
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“…We explored these potential mechanisms to account for the observed biological response to SSA1 and SSB1 gene deletions by examining the theoretical network structure in the mutants after removing the node in question and its attendant edges. We considered several network and node topology metrics 41, including network degree and node neighbourhood connectivity 47, shown in Supporting Information Fig. 4, and average clustering coefficient and BC, shown in Supporting Information Fig.…”
Section: Resultsmentioning
confidence: 99%
“…We explored these potential mechanisms to account for the observed biological response to SSA1 and SSB1 gene deletions by examining the theoretical network structure in the mutants after removing the node in question and its attendant edges. We considered several network and node topology metrics 41, including network degree and node neighbourhood connectivity 47, shown in Supporting Information Fig. 4, and average clustering coefficient and BC, shown in Supporting Information Fig.…”
Section: Resultsmentioning
confidence: 99%
“…The normalized characteristic path length is the ratio between the real and random characteristic path length: λ=Lp−normalrealLp−normalrand. C p_ rand and L p_ rand denotes, respectively, the averaged clustering coefficient and characteristic path length of 100 matched random networks, which possess the same number of nodes, edges, and degree distribution with the real networks (Maslov & Sneppen, 2002; Sporns & Zwi, 2004). Typically, a small‐word network meets the conditions of Îł>1 and λ  ≈ 1 (Watts & Strogatz, 1998), and therefore, the small‐world scalar σ  =  λ / Îł is larger than 1 (Humphries, Gurney, & Prescott, 2006).…”
Section: Methodsmentioning
confidence: 99%
“…To evaluate the statistical significance of the modularity for TMSN and TCSN, a local rewiring algorithm was performed that maintained the same size of nodes and edges of the network but the connections was rewired. The degree-preserving random rewiring algorithm was implemented as follows[46]:…”
Section: Methodsmentioning
confidence: 99%