In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production

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Standard

In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. / Bro, Christoffer; Regenberg, Birgitte; Förster, Jochen; Nielsen, Jens.

In: Metabolic Engineering, Vol. 8, No. 2, 2006, p. 102-111.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Bro, C, Regenberg, B, Förster, J & Nielsen, J 2006, 'In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production', Metabolic Engineering, vol. 8, no. 2, pp. 102-111. https://doi.org/10.1016/j.ymben.2005.09.007

APA

Bro, C., Regenberg, B., Förster, J., & Nielsen, J. (2006). In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metabolic Engineering, 8(2), 102-111. https://doi.org/10.1016/j.ymben.2005.09.007

Vancouver

Bro C, Regenberg B, Förster J, Nielsen J. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metabolic Engineering. 2006;8(2):102-111. https://doi.org/10.1016/j.ymben.2005.09.007

Author

Bro, Christoffer ; Regenberg, Birgitte ; Förster, Jochen ; Nielsen, Jens. / In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. In: Metabolic Engineering. 2006 ; Vol. 8, No. 2. pp. 102-111.

Bibtex

@article{ed02b1e913124e618ff4095090e3e3c4,
title = "In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production",
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.",
keywords = "Ethanol, Genome-scale model, Glycerol, Non-phosphorylating NADP-dependent glyceraldehydes-3-phosphate dehydrogenase, Redox metabolism, Saccharomyces cerevisiae, Xylose",
author = "Christoffer Bro and Birgitte Regenberg and Jochen F{\"o}rster and Jens Nielsen",
year = "2006",
doi = "10.1016/j.ymben.2005.09.007",
language = "English",
volume = "8",
pages = "102--111",
journal = "Metabolic Engineering",
issn = "1096-7176",
publisher = "Academic Press",
number = "2",

}

RIS

TY - JOUR

T1 - In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production

AU - Bro, Christoffer

AU - Regenberg, Birgitte

AU - Förster, Jochen

AU - Nielsen, Jens

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - Ethanol

KW - Genome-scale model

KW - Glycerol

KW - Non-phosphorylating NADP-dependent glyceraldehydes-3-phosphate dehydrogenase

KW - Redox metabolism

KW - Saccharomyces cerevisiae

KW - Xylose

U2 - 10.1016/j.ymben.2005.09.007

DO - 10.1016/j.ymben.2005.09.007

M3 - Journal article

C2 - 16289778

AN - SCOPUS:33644832381

VL - 8

SP - 102

EP - 111

JO - Metabolic Engineering

JF - Metabolic Engineering

SN - 1096-7176

IS - 2

ER -

ID: 239904834