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Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks
1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Cit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919890/ https://www.ncbi.nlm.nih.gov/pubmed/36772306 http://dx.doi.org/10.3390/s23031266 |
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author | Drożdżyńska, Agnieszka Wawrzyniak, Jolanta Kubiak, Piotr Przybylak, Martyna Białas, Wojciech Czaczyk, Katarzyna |
author_facet | Drożdżyńska, Agnieszka Wawrzyniak, Jolanta Kubiak, Piotr Przybylak, Martyna Białas, Wojciech Czaczyk, Katarzyna |
author_sort | Drożdżyńska, Agnieszka |
collection | PubMed |
description | 1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Citrobacter freundii AD119 was performed using the one-factor-at-a-time method (OFAT) and a two-step statistical experimental design. Eleven mineral components were tested for their impact on the process using the Plackett–Burman design. MgSO(4) and CoCl(2) were found to have the most pronounced effect. Consequently, a central composite design was used to optimize the concentration of these mineral components. Besides minerals, carbon and nitrogen sources were also optimized. Partial glycerol substitution with other carbon sources was found not to improve the bioconversion process. Moreover, although yeast extract was found to be the best nitrogen source, it was possible to replace it in part with (NH(4))(2)SO(4) without a negative impact on 1,3-PD production. As a part of the optimization procedure, an artificial neural network model of the growth of C. freundii and 1,3-PD production was developed as a predictive tool supporting the design and control of the bioprocess under study. |
format | Online Article Text |
id | pubmed-9919890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99198902023-02-12 Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks Drożdżyńska, Agnieszka Wawrzyniak, Jolanta Kubiak, Piotr Przybylak, Martyna Białas, Wojciech Czaczyk, Katarzyna Sensors (Basel) Article 1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Citrobacter freundii AD119 was performed using the one-factor-at-a-time method (OFAT) and a two-step statistical experimental design. Eleven mineral components were tested for their impact on the process using the Plackett–Burman design. MgSO(4) and CoCl(2) were found to have the most pronounced effect. Consequently, a central composite design was used to optimize the concentration of these mineral components. Besides minerals, carbon and nitrogen sources were also optimized. Partial glycerol substitution with other carbon sources was found not to improve the bioconversion process. Moreover, although yeast extract was found to be the best nitrogen source, it was possible to replace it in part with (NH(4))(2)SO(4) without a negative impact on 1,3-PD production. As a part of the optimization procedure, an artificial neural network model of the growth of C. freundii and 1,3-PD production was developed as a predictive tool supporting the design and control of the bioprocess under study. MDPI 2023-01-22 /pmc/articles/PMC9919890/ /pubmed/36772306 http://dx.doi.org/10.3390/s23031266 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Drożdżyńska, Agnieszka Wawrzyniak, Jolanta Kubiak, Piotr Przybylak, Martyna Białas, Wojciech Czaczyk, Katarzyna Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title | Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title_full | Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title_fullStr | Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title_full_unstemmed | Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title_short | Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks |
title_sort | optimization and modeling of citrobacter freundii ad119 growth and 1,3-propanediol production using two-step statistical experimental design and artificial neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919890/ https://www.ncbi.nlm.nih.gov/pubmed/36772306 http://dx.doi.org/10.3390/s23031266 |
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