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Temperature Response of Soil Organic Matter Decomposition Rates: Construction and Applications of a Temperature Gradient Block

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Correlation between structure and temperature in prokaryotic metabolic networks.

Kazuhiro Takemoto¹, Jose C Nacher, Tatsuya Akutsu

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. takemoto@kuicr.kyoto-u.ac.jp

BMC Bioinformatics

|August 23, 2007

Summary

Metabolic networks change structure with temperature. Higher temperatures lead to less dense, more homogeneous networks, suggesting temperature influences their design principles.

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Area of Science:

Systems Biology
Metabolic Engineering
Bioinformatics

Background:

Metabolic network structures offer insights into organismal function and evolution.
Environmental factors' influence on metabolic networks remains understudied.
Temperature is investigated as a key environmental factor shaping metabolic network structures.

Purpose of the Study:

To investigate the correlation between metabolic network structural properties and optimal growth temperature.
To understand how environmental temperature influences the design principles of metabolic networks.

Main Methods:

Analysis of metabolic networks from 113 prokaryotes.
Calculation of graph metrics: edge density, degree exponent, clustering coefficient, subgraph concentration.
Correlation analysis between these metrics and optimal growth temperature.

Main Results:

Metabolic network structural properties significantly correlate with optimal growth temperature.
Increasing temperature correlates with decreased edge density, clustering coefficient, and subgraph concentration.
Increasing temperature correlates with an increased degree exponent.

Conclusions:

Metabolic networks transition from heterogeneous, high-modular structures to homogeneous, low-modular structures as temperature increases.
Network connectivity becomes more uniform, resembling random networks at higher temperatures.
Temperature appears to be a significant factor in the evolutionary design of metabolic networks.