The long-term objective is to develop a bioinformatics foundation for deciphering the metabolic network of every organism with a fully sequenced genome, in support of drug discovery, metabolic engineering, and systems biology. Our approach is based on a gold-standard metabolic database, MetaCyc, which is curated by Ph.D.-level biologists, strictly from the experimental literature. The knowledge in MetaCyc is extended computationally to other organisms by the automated creation of hundreds of organism-specific pathway/genome databases.
Our specific aims are (1) To expand MetaCyc, a highly curated multi- organism database of metabolic pathways and enzymes that serves as an encyclopedic reference of metabolic information. MetaCyc can be used to predict the metabolic pathway complement of an organism from its sequenced genome. Information about experimentally determined metabolic pathways and enzymes will be curated into MetaCyc from the biomedical literature, with a focus on microbial and plant pathways and enzymes. (2) To computationally generate BioCyc, a collection of organism-specific pathway/genome databases for all completely sequenced microbes that includes predicted metabolic pathways, predicted metabolic pathway hole fillers, and predicted operons. (3) To enhance the Pathway Tools software that supports the querying, visualization, and analysis of MetaCyc and BioCyc to include a new pathway prediction algorithm, an improved Web site, and scalability to manage 1000 genomes. (4) To make MetaCyc and BioCyc available in several formats. The BioCyc databases will be generated through a computational pipeline using advanced and carefully validated algorithms. MetaCyc and BioCyc data will be captured within a rich database schema using pathway editing software, and made publicly available through multiple access mechanisms, including a user-friendly Web site and the BioPAX standard pathway format. Metabolic pathways form the biochemical foundation of living systems. By quickly characterizing the metabolic pathways of hundreds of microbes, this project will facilitate alterations to those pathways by metabolic engineers, such as to allow bacteria to synthesize drugs. It will speed the development of drugs that kill disease-causing bacteria by enabling identification of essential metabolic pathways for disruption.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM080746-04
Application #
7810709
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Jones, Warren
Project Start
2007-06-19
Project End
2011-04-30
Budget Start
2010-05-01
Budget End
2011-04-30
Support Year
4
Fiscal Year
2010
Total Cost
$1,116,499
Indirect Cost
Name
Sri International
Department
Type
DUNS #
009232752
City
Menlo Park
State
CA
Country
United States
Zip Code
94025
Edison, Arthur S; Hall, Robert D; Junot, Christophe et al. (2016) The Time Is Right to Focus on Model Organism Metabolomes. Metabolites 6:
Caspi, Ron; Billington, Richard; Ferrer, Luciana et al. (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 44:D471-80
Karp, Peter D; Billington, Richard; Holland, Timothy A et al. (2015) Computational Metabolomics Operations at BioCyc.org. Metabolites 5:291-310
Caspi, Ron; Altman, Tomer; Billington, Richard et al. (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 42:D459-71
Caspi, Ron; Dreher, Kate; Karp, Peter D (2013) The challenge of constructing, classifying, and representing metabolic pathways. FEMS Microbiol Lett 345:85-93
Altman, Tomer; Travers, Michael; Kothari, Anamika et al. (2013) A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics 14:112
Karp, Peter D; Paley, Suzanne; Altman, Tomer (2013) Data mining in the MetaCyc family of pathway databases. Methods Mol Biol 939:183-200
Caspi, Ron; Altman, Tomer; Dreher, Kate et al. (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40:D742-53
Latendresse, Mario; Krummenacker, Markus; Trupp, Miles et al. (2012) Construction and completion of flux balance models from pathway databases. Bioinformatics 28:388-96
Karp, Peter D; Caspi, Ron (2011) A survey of metabolic databases emphasizing the MetaCyc family. Arch Toxicol 85:1015-33

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