Research article

Toward the automated generation of genome-scale metabolic networks in the SEED

Matthew DeJongh¹ , Kevin Formsma^1,2 , Paul Boillot¹ , John Gould¹ , Matthew Rycenga^3,4 and Aaron Best²

¹Department of Computer Science, Hope College, 27 Graves Place, Holland, MI, USA

²Department of Biology, Hope College, 35 E. 12^th St., Holland, MI, USA

³Department of Chemistry, Hope College, 35 E. 12^th St., Holland, MI, USA

⁴Department of Chemistry, University of Washington, Box # 351700, Seattle, WA, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:139doi:10.1186/1471-2105-8-139

Published:	26 April 2007

Abstract

Background

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process.

Results

We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis.

Conclusion

Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.