Automated Generation of Genome-Scale Metabolic Networks
Author Information
Author(s): Matthew DeJongh, Kevin Formsma, Paul Boillot, John Gould, Matthew Rycenga, Aaron Best
Primary Institution: Hope College
Hypothesis
Current methods for generating genome-scale metabolic networks are insufficient for creating complete and coherent networks suitable for systems-level analysis.
Conclusion
The developed method allows for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED.
Supporting Evidence
- The method produces substantially complete reaction networks suitable for systems-level analysis.
- Tools were developed for automatically assembling components based on metabolic pathways encoded in an organism's genome.
- The approach was demonstrated by regenerating the reaction network for Staphylococcus aureus from a published model.
Takeaway
This study created a way to automatically build detailed maps of how living things use energy and materials, which helps scientists understand how different organisms work.
Methodology
The method involves partitioning metabolic reactions into components, assembling them based on genome annotations, and verifying their coherence using a database of common components.
Limitations
The method still requires manual effort to refine the generated networks and to add transport and biomass information.
Digital Object Identifier (DOI)
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