Analysis and Prediction of Nutritional Requirements Using Structural Properties of Metabolic Networks and Support Vector Machines
Takeyuki Tamura (firstname.lastname@example.org)
Nils Christian (email@example.com)
Kazuhiro Takemoto (firstname.lastname@example.org)
Oliver Ebenhöh (email@example.com)
 Bioinformatics Center, Institute for Chemical Research, Kyoto University,
Uji, Kyoto 611-0011, Japan
 Max Planck Institute of Molecular Plant Physiology,
 Graduate School of Frontier Sciences, University of Tokyo,
Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan
 Institute for Complex Systems and Mathematical Biology, University of Aberdeen, United Kingdom
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.
Japanese Society for Bioinformatics