Correlation between structure and temperature in prokaryotic metabolic networks.
BACKGROUND
In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks.


RESULTS
For this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large.


CONCLUSIONS
This result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks.
Takemoto
Kazuhiro
K
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. takemoto@kuicr.kyoto-u.ac.jp
Nacher
Jose C
JC
Akutsu
Tatsuya
T
D000220:Adaptation, Biological; D000465:Algorithms; D000818:Animals; D001105:Archaea / Q000145:classification / Q000254:growth & development / Q000378:metabolism*; D001419:Bacteria / Q000145:classification / Q000254:growth & development / Q000378:metabolism*; D016000:Cluster Analysis; D016208:Databases, Factual; D004777:Environment; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism; D015398:Signal Transduction; D013696:Temperature
D020543:Proteome
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