期刊
NATURE METHODS
卷 6, 期 8, 页码 589-U53出版社
NATURE PORTFOLIO
DOI: 10.1038/NMETH.1348
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资金
- US Department of Energy [DE-FG02-07ER64496]
- Jane Coffin Childs Memorial Fund for Medical Research
- National Science Foundation [DGE0504645]
With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.
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