Integration of metabolic databases for the reconstruction of genome-scale metabolic networks
1 Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK
2 Helmholtz-Zentrum München, Technische Universität München, 80333 München, Germany
3 National Centre for Text Mining, University of Manchester, Manchester M1 7DN, UK
4 Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, Japan
5 School of Chemistry, University of Manchester, Manchester M1 7DN, UK
6 Cell Systems Modelling Group, School of Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
7 Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK
8 Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester M1 7DN, UK
9 VIB Department of Plant Systems Biology/Department of Biology, Ghent University, 9052 Gent, Belgium
BMC Systems Biology 2010, 4:114 doi:10.1186/1752-0509-4-114Published: 16 August 2010
Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.
In this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation.
This semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications.