title: Evolutionary significance of metabolic network properties.
abstract: Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.
authors: Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany. basler@mpimp-golm.mpg.de
authors: Georg
authors: G
authors: Basler
authors: Sergio
authors: S
authors: Grimbs
authors: Oliver
authors: O
authors: Ebenhöh
authors: Joachim
authors: J
authors: Selbig
authors: Zoran
authors: Z
authors: Nikoloski
mesh_terms: D000818:Animals; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D055442:Metabolome / Q000235:genetics*; D008957:Models, Genetic; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D020543:Proteome