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Complex biological networks are strikingly robust to deletion or interruption of their components. This robustness may be mediated by network features that we can now target specifically through network analysis. Network components (‘nodes’) differ greatly in importance depending upon the extent to which a node is connected to other nodes (‘degree’), the extent to which a node connects otherwise unconnected clusters (‘betweenness’) and the extent to which there are alternative pathways through a network (‘redundancy’). While networks are highly robust to random deletions of nodes, they are much more susceptible to targeted deletions prioritised by network properties such as degree and betweenness. A small number of combinations of, for example, five proteins can have a very great impact on network integrity in most biological networks. The strategy of network pharmacology-based approaches such as combinatorial network impact analysis is to find these very few high impact protein combinations that should be addressed simultaneously in order to yield high therapeutic efficacy.

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