io io io io

Figure 5. Flux distribution for the metabolism of E. coli. A) Flux distribution for optimized biomass production on succinate (black) and glutamate (red) rich uptake substrates. The solid line corresponds to the power law fit P(v) - (v + Vo)"a with Vo = 0.00003 and a = 1.5. B) The distribution of experimentally determined fluxes (seerefH) from the central metabolism of £ coli also displays power-law behavior with a best fit to P(y) - V"awitha= 1. A color version of this figure is available online at http://www.Eurekah.com.

larger along the links of the protein interaction network and between proteins occurring in the same complex than for pairs of proteins that are not known to interact direcdy.15'28'32' 1

Taken together, these results indicate that the biochemical activity in both the metabolic and genetic networks is dominated by several 'hot links' that represent a few high activity interactions embedded into a web of less active interactions. This attribute does not seem to be a unique feature of biological systems: hot links appear in a wide range of nonbiological networks where the activity of the links follows a wide distribution.13'30 The origin of this seemingly universal property is, again, likely rooted in the network topology. Indeed, it seems that the metabolic fluxes and the weights of the links in some nonbiological system13'30 are uniquely determined by the scale-free nature of the network. A more general principle that could explain the correlation distribution data as well is currendy lacking.


Power laws are abundant in nature, affecting both the construction and the utilization of real networks. The power-law degree distribution has become the trademark of scale-free networks and can be explained by invoking the principles of network growth and preferential attachment. However, many biological networks are inherently modular, a fact which at first seems to be at odds with the properties of scale-free networks. However, these two concepts can coexist in hierarchical scale-free networks. In the utilization of complex networks, most links represent disparate connection strengths or transportation thresholds. For the metabolic network of E. coli we can implement a flux-balance approach and calculate the distribution of link weights (fluxes), which (reflecting the scale-free network topology) displays a robust power-law, independent of exocellular perturbations. Furthermore, this global inhomogeneity in the link strengths is also present at the local level, resulting in a connected "hot-spot" backbone of the metabolism. Similar features are also observed in the strength of various genetic regulatory interactions. Despite the significant advances witnessed the last few years, network biology is still in its infancy, with future advances most notably expected from the development of theoretical tools, development of new interactive databases and increased insights into the interplay between biological function and topology.

0 0

Post a comment