TY - JOUR
AU - Best, Aaron
AB - 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 
TI - Toward the automated generation of genome-scale metabolic networks in the SEED
JF - BMC Bioinformatics
DO - 10.1186/1471-2105-8-139
DA - 2007-04-26
UR - https://www.deepdyve.com/lp/springer-journals/toward-the-automated-generation-of-genome-scale-metabolic-networks-in-CX0ma029dV
SP - 1
EP - 17
VL - 8
IS - 1
DP - DeepDyve
ER -