Actions for Development of a Knowledgebase (MetRxn) of Metabolites, Reactions and Atom Mappings to Accelerate Discovery and Redesign [electronic resource].

Email RIS file
Bookmark
Report an Issue

Development of a Knowledgebase (MetRxn) of Metabolites, Reactions and Atom Mappings to Accelerate Discovery and Redesign [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
38 pages : digital, PDF file
Additional Creators
United States. Department of Energy. Office of Science and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It
I Want It

Finding items...

Restrictions on Access
Free-to-read Unrestricted online access
Summary
With advances in DNA sequencing and genome annotation techniques, the breadth of metabolic knowledge across all kingdoms of life is increasing. The construction of genome-scale models (GSMs) facilitates this distillation of knowledge by systematically accounting for reaction stoichiometry and directionality, gene to protein to reaction relationships, reaction localization among cellular organelles, metabolite transport costs and routes, transcriptional regulation, and biomass composition. Genome-scale reconstructions available now span across all kingdoms of life, from microbes to whole-plant models, and have become indispensable for driving informed metabolic designs and interventions. A key barrier to the pace of this development is our inability to utilize metabolite/reaction information from databases such as BRENDA [1], KEGG [2], MetaCyc [3], etc. due to incompatibilities of representation, duplications, and errors. Duplicate entries constitute a major impediment, where the same metabolite is found with multiple names across databases and models, which significantly slows downs the collating of information from multiple data sources. This can also lead to serious modeling errors such as charge/mass imbalances [4,5] which can thwart model predictive abilities such as identifying synthetic lethal gene pairs and quantifying metabolic flows. Hence, we created the MetRxn database [6] that takes the next step in integrating data from multiple sources and formats to automatically create a standardized knowledgebase. We subsequently deployed this resource to bring about new paradigms in genome-scale metabolic model reconstruction, metabolic flux elucidation through MFA, modeling of microbial communities, and pathway prospecting. This research has enabled the PI’s group to continue building upon research milestones and reach new ones (see list of MetRxn-related publications below).
Report Numbers
E 1.99:sc0008091
sc0008091
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
12/11/2017.
"sc0008091"
"SC10822882"
Costas D. Maranas.
Pennsylvania State Univ., University Park, PA (United States)
Type of Report and Period Covered Note
Final;
Funding Information
SC0008091
View MARC record | catkey: 23498062