Analysis and Prediction of Nutritional Requirements Using Structural Properties of Metabolic Networks and Support Vector Machines


Takeyuki Tamura [1](tamura@kuicr.kyoto-u.ac.jp)
Nils Christian [2](nils.christian@mpimp-golm.mpg.de)
Kazuhiro Takemoto [3](takemoto@cb.k.u-tokyo.ac.jp)
Oliver Ebenhöh [4](ebenhoeh@abdn.ac.uk)
Tatsuya Akutsu

[1] Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
[2] Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
[3] Graduate School of Frontier Sciences, University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan
[4] Institute for Complex Systems and Mathematical Biology, University of Aberdeen, United Kingdom

Abstract

Properties of graph representation of genome scale metabolic networks have been extensively studied. However, the relationship between these structural properties and functional properties of the networks are still very unclear. In this paper, we focus on nutritional requirements of organisms as a functional property and study the relationship with structural properties of a graph representation of metabolic networks. In order to examine the relationship, we study to what extent the nutritional requirements can be predicted by using support vector machines from structural properties, which include degree exponent, edge density, clustering coefficient, degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. Furthermore, we study which properties are influential to the nutritional requirements.

[ Full-text PDF |Table of Contents ]


Japanese Society for Bioinformatics