A Fermentation State Marker Rule Design Task in Metabolic Engineering
-
Published:2023-12-15
Issue:12
Volume:10
Page:1427
-
ISSN:2306-5354
-
Container-title:Bioengineering
-
language:en
-
Short-container-title:Bioengineering
Author:
Stalidzans Egils1ORCID, Muiznieks Reinis1ORCID, Dubencovs Konstantins23, Sile Elina2ORCID, Berzins Kristaps1, Suleiko Arturs23, Vanags Juris23
Affiliation:
1. Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia 2. Bioreactors.net AS, Dzerbenes Street 27, LV-1006 Riga, Latvia 3. Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, LV-1006 Riga, Latvia
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.
Funder
European Regional Development Fund
Reference39 articles.
1. González-Figueredo, C., Alejandro Flores-Estrella, R., and Rojas-Rejón, O.A. (2019). Current Topics in Biochemical Engineering, IntechOpen. 2. Gargalo, C.L., Lopez, P.C., Hasanzadeh, A., Udugama, I.A., and Gernaey, K.V. (2022). Current Developments in Biotechnology and Bioengineering, Elsevier. 3. Zhu, X., Rehman, K.U., Wang, B., and Shahzad, M. (2020). Modern Soft-Sensing Modeling Methods for Fermentation Processes. Sensors, 20. 4. Generalization of Monod Kinetics for Analysis of Growth Data with Substrfate Inhibition;Luong;Biotechnol. Bioeng.,1987 5. The Growth of Bacterial Cultures;Monod;Annu. Rev. Microbiol.,1949
|
|