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Turnover in the interaction network is dominated by proteins of low connectivity: both newly acquired proteins and proteins lost from the network have power-law scaled connectivity distributions, meaning that most proteins that are gained or lost are connected to only a small number of other proteins.
The mean value of those connectivity distributions tallies with the corresponding mean values estimated using the standard methodology of Seaton.
Node mobility introduces non-homogeneous connectivity distributions that cannot be represented using a simple average.
Node mobility introduces non-homogeneous connectivity distributions that cannot be described using a simple average.
We checked the connectivity distributions of a network generated from ssRNA sequences selected at random from within the E.coli lRNA sequence.
This is reminiscent, both in shape and magnitude, of the small world connectivity distributions that have been widely reported for metabolic and genetic networks [12], [18], [19].
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Each of the 10 randomized networks that we built had the same number of nodes, connections and connectivity distribution as the MADS network (see Methods).
We compared subgraph counts of this network to that of 10 of randomized networks that preserved the number of nodes, connections, and connectivity distribution.
This is unlikely, since we compare each network with randomized versions with the same number of nodes, connections, and connectivity distribution.
To characterize the overall network topology of the 5' and 3' halves, we compared the 2 networks by measuring the distribution of the number of connections per node (connectivity distribution).
Firstly, it has a scale-free Power Law connectivity distribution; a few highly connected nodes or 'hubs', with many poorly connected nodes.
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