Origin of structural difference in metabolic networks with respect to temperature
1 Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan
2 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
BMC Systems Biology 2008, 2:82 doi:10.1186/1752-0509-2-82Published: 22 September 2008
Metabolism is believed to adaptively shape-shift with changing environment. In recent years, a structural difference with respect to temperature, which is an environmental factor, has been revealed in metabolic networks, implying that metabolic networks transit with temperature. Subsequently, elucidatation of the origin of these structural differences due to temperature is important for understanding the evolution of life. However, the origin has yet to be clarified due to the complexity of metabolic networks.
Consequently, we propose a simple model with a few parameters to explain the transitions. We first present mathematical solutions of this model using mean-field approximation, and demonstrate that this model can reproduce structural properties, such as heterogeneous connectivity and hierarchical modularity, in real metabolic networks both qualitatively and quantitatively. We next show that the model parameters correlate with optimal growth temperature. In addition, we present a relationship between multiple cyclic properties and optimal growth temperature in metabolic networks.
From the proposed model, we find that such structural properties are determined by the emergence of a short-cut path, which reduces the minimum distance between two nodes on a graph. Furthermore, we investigate correlations between model parameters and growth temperature; as a result, we find that the emergence of the short-cut path tends to be inhibited with increasing temperature. In addition, we also find that the short-cut path bypasses a relatively long path at high temperature when the emergence of the new path is not inhibited. Even further, additional network analysis provides convincing evidence of the reliability of the proposed model and its conclusions on the possible origins of differences in metabolic network structure.