Selective pressure on metabolic network structures as measured from the random blind-watchmaker network
arXiv:1010.6185 · doi:10.1088/1367-2630/12/10/103047
Abstract
A random null model termed the Blind Watchmaker network (BW) has been shown to reproduce the degree distribution found in metabolic networks. This might suggest that natural selection has had little influence on this particular network property. We here investigate to what extent other structural network properties have evolved under selective pressure from the corresponding ones of the random null model: The clustering coefficient and the assortativity measures are chosen and it is found that these measures for the metabolic network structure are close enough to the BW-network so as to fit inside its reachable random phase space. It is furthermore shown that the use of this null model indicates an evolutionary pressure towards low assortativity and that this pressure is stronger for larger networks. It is also shown that selecting for BW networks with low assortativity causes the BW degree distribution to slightly deviate from its power-law shape in the same way as the metabolic networks. This implies that an equilibrium model with fluctuating degree distribution is more suitable as a null model, when identifying selective pressures, than a randomized counterpart with fixed degree sequence, since the overall degree sequence itself can change under selective pressure on other global network properties.
11 pages, 5 figures
References in corpus (12)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Error and attack tolerance of complex networks
- Assortative mixing in networks
- Specificity and stability in topology of protein networks
- Resilience of the Internet to random breakdowns
- The Yeast Cell-Cycle Network Is Robustly Designed
- Exploring the assortativity-clustering space of a network's degree sequence
- The Blind Watchmaker Network: Scale-freeness and Evolution
- Random model for RNA interference yields scale free network
- Optimization and Scale-freeness for Complex Networks