Actions for Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii [electronic resource].

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Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2012.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
3MB : digital, PDF file
Additional Creators
United States. Department of Energy, United States. Department of Energy. Office of Biological and Environmental Research, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals.
Report Numbers
E 1.99:3
3
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
01/13/2012.
"3"
Kourosh Salehi-Ashtiani; Jason A. Papin.
Dana-Farber Cancer Institute
Type of Report and Period Covered Note
Final; 09/15/2007 - 09/14/2011
Funding Information
FG02-07ER64496
View MARC record | catkey: 13817611