Ken A. Hawick
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Betweenness centrality; node vulnerability; regulatory net-works; computational biology; node culling; simulation.
Computational graph analysis metrics can be used to make quantitative comparisons between biological and reg- ulatory networks obtained from real specimens with simu- lated synthetic networks that have well parameterized prop- erties such as a scale-free structure. We compute the be- tweenness centrality metric for five public domain protein- protein network data sets and compare them with synthe- sized NK networks. We employ a node-culling procedure to progressively remove the highest connected nodes in these networks and assess the wholistic system changes as revealed by the resulting Floyd all-pairs distance and the number of component clusters in the networks as they fail and break up. We discuss the potential for this method in assigning characteristic signatures or categories to net- works of this sort as well as for identifying network com- ponents that are most vulnerable to biological attack.
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