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A Fermentation State Marker Rule Design Task in Metabolic Engineering.
Stalidzans, Egils; Muiznieks, Reinis; Dubencovs, Konstantins; Sile, Elina; Berzins, Kristaps; Suleiko, Arturs; Vanags, Juris.
Afiliação
  • Stalidzans E; Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia.
  • Muiznieks R; Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia.
  • Dubencovs K; Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia.
  • Sile E; Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, LV-1006 Riga, Latvia.
  • Berzins K; Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia.
  • Suleiko A; Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia.
  • Vanags J; Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia.
Bioengineering (Basel) ; 10(12)2023 Dec 15.
Article em En | MEDLINE | ID: mdl-38136018
ABSTRACT
There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is IF (specific growth rate µ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Bioengineering (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Letônia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Bioengineering (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Letônia