4.4 Article

Flux balance analysis for ethylene formation in genetically engineered Saccharomyces cerevisiae

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IET SYSTEMS BIOLOGY
卷 5, 期 4, 页码 245-251

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INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-syb.2010.0027

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  1. Vinnova (Swedish Governmental Agency for Innovation Systems) [P23619-1]
  2. Austrian Science Fund (FWF) [P23619] Funding Source: Austrian Science Fund (FWF)

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Biosynthesis of ethylene (ethene) is mainly performed by plants and some bacteria and fungi, via two distinct metabolic routes. Plants use two steps, starting with S-adenosylmethionine, while the ethylene-forming microbes perform an oxygen dependent reaction using 2-oxoglutarate and arginine. Introduction of these systems into Saccharomyces cerevisiae was studied in silico. The reactions were added to a metabolic network of yeast and flux over the two networks was optimised for maximal ethylene formation. The maximal ethylene yields obtained for the two systems were similar in the range of 7-8 mol ethylene/10 mol glucose. The microbial metabolic network was used for testing different strategies to increase the ethylene formation. It was suggested that supplementation of exogenous proline, using a solely NAD-coupled glutamate dehydrogenase, and using glutamate as the nitrogen source, could increase the ethylene formation. Comparison of these in silico results with published experimental data for yeast expressing the microbial system confirmed an increased ethylene formation when changing nitrogen source from ammonium to glutamate. The theoretical analysis methods indicated a much higher maximal yield per glucose for ethylene than was experimentally observed. However, such high ethylene yields could only be obtained with a concomitant very high respiration (per glucose). Accordingly, when ethylene production was optimised under the additional constraint of restricted respiratory capacity (i.e. limited to experimentally measured values) the theoretical maximal ethylene yield was much lower at 0.2/10 mol glucose, and closer to the experimentally observed values.

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