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. ? ? ?
|Caspi, Ron; Billington, Richard; Fulcher, Carol A et al. (2018) The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 46:D633-D639|
|Karp, Peter D; Billington, Richard; Caspi, Ron et al. (2017) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform :|
|Karp, Peter D; Latendresse, Mario; Paley, Suzanne M et al. (2016) Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology. Brief Bioinform 17:877-90|
|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|
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