%0 Journal Article %1 Bro2006In %A Bro, C. %A Regenberg, B. %A Forster, J. %A Nielsen, J. %D 2006 %J Metabolic Engineering %K bioethanol flux-analysis in-silico metabolic-engineering redox %N 2 %P 102--111 %R 10.1016/j.ymben.2005.09.007 %T In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production %U http://dx.doi.org/10.1016/j.ymben.2005.09.007 %V 8 %X In silico genome-scale cell models are promising tools for accelerating the design of cells with improved and desired properties. We demonstrated this by using a genome-scale reconstructed metabolic network of Saccharomyces cerevisiae to score a number of strategies for metabolic engineering of the redox metabolism that will lead to decreased glycerol and increased ethanol yields on glucose under anaerobic conditions. The best-scored strategies were predicted to completely eliminate formation of glycerol and increase ethanol yield with 10\%. We successfully pursued one of the best strategies by expressing a non-phosphorylating, NADP+-dependent glyceraldehyde-3-phosphate dehydrogenase in S. cerevisiae. The resulting strain had a 40\% lower glycerol yield on glucose while the ethanol yield increased with 3\% without affecting the maximum specific growth rate. Similarly, expression of GAPN in a strain harbouring xylose reductase and xylitol dehydrogenase led to an improvement in ethanol yield by up to 25\% on xylose/glucose mixtures.

@article{Bro2006In, abstract = {In silico genome-scale cell models are promising tools for accelerating the design of cells with improved and desired properties. We demonstrated this by using a genome-scale reconstructed metabolic network of Saccharomyces cerevisiae to score a number of strategies for metabolic engineering of the redox metabolism that will lead to decreased glycerol and increased ethanol yields on glucose under anaerobic conditions. The best-scored strategies were predicted to completely eliminate formation of glycerol and increase ethanol yield with 10\%. We successfully pursued one of the best strategies by expressing a non-phosphorylating, {NADP}+-dependent glyceraldehyde-3-phosphate dehydrogenase in S. cerevisiae. The resulting strain had a 40\% lower glycerol yield on glucose while the ethanol yield increased with 3\% without affecting the maximum specific growth rate. Similarly, expression of {GAPN} in a strain harbouring xylose reductase and xylitol dehydrogenase led to an improvement in ethanol yield by up to 25\% on xylose/glucose mixtures.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Bro, C. and Regenberg, B. and Forster, J. and Nielsen, J.}, biburl = {https://www.bibsonomy.org/bibtex/20fa97fe0de8f7eebde2cf1aa17b5d8f4/karthikraman}, citeulike-article-id = {13172297}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ymben.2005.09.007}, doi = {10.1016/j.ymben.2005.09.007}, interhash = {02224a8a06bf2affdd64a3194d24c112}, intrahash = {0fa97fe0de8f7eebde2cf1aa17b5d8f4}, issn = {10967176}, journal = {Metabolic Engineering}, keywords = {bioethanol flux-analysis in-silico metabolic-engineering redox}, month = mar, number = 2, pages = {102--111}, posted-at = {2016-04-03 11:43:38}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production}, url = {http://dx.doi.org/10.1016/j.ymben.2005.09.007}, volume = 8, year = 2006 }