Ethanol production using Zymomonas mobilis: Development of a kinetic model describing glucose and xylose co-fermentation

Díaz, Víctor Hugo Grisales; Willis, M.J.

doi:10.1016/j.biombioe.2019.02.004

Cited by 21 publications

(9 citation statements)

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“…Saccharomyces cerevisiae is also commonly used in ethanol fermentations of sorghum grains 3,26,30,32 . Among the bacteria, mesophilic Zymomonas mobilis is considered as an alternative to yeast for ethanol fermentation 33–36 . The benefits of using Z. mobilis for ethanol production compared to yeast include a higher productivity in continuous fermentation systems, a higher ethanol yield per unit of sugar consumed, ethanol production three to five times faster per cell, and a higher glucose uptake rate.…”

Section: Introductionmentioning

confidence: 99%

“…3,26,30,32 Among the bacteria, mesophilic Zymomonas mobilis is considered as an alternative to yeast for ethanol fermentation. [33][34][35][36] The benefits of using Z. mobilis for ethanol production compared to yeast include a higher productivity in continuous fermentation systems, a higher ethanol yield per unit of sugar consumed, ethanol production three to five times faster per cell, and a higher glucose uptake rate. In addition, these bacteria do not need controlled doses of oxygen to maintain proper vitality and adequate cell concentration, and they have shown the ability to grow in a completely anaerobic environment.…”

Section: Introductionmentioning

confidence: 99%

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Bioethanol production from sorghum grain with Zymomonas mobilis: increasing the yield and quality of raw distillates

et al. 2023

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BACKGROUND The present study aimed to demonstrate the superiority of bioethanol yield and its quality from sorghum using the granular starch degrading enzyme Stargen™ 002 over simultaneous saccharification and fermentation, and separate hydrolysis and fermentation using Zymomonas mobilis CCM 3881 and Ethanol Red® yeast. RESULTS Bacteria were found to produce ethanol at higher yield than the yeast in all fermentations. The highest ethanol yield was obtained with Z. mobilis during 48 h of simultaneous saccharification and fermentation (83.85% theoretical yield) and fermentation with Stargen™ 002 (81.27% theoretical yield). Pre‐liquefaction in fermentation with Stargen™ 002 did not improve ethanol yields for both Z. mobilis and Saccharomyces cerevisiae. Chromatographic analysis showed twice less total volatile compounds in distillates obtained after bacterial (3.29–5.54 g L−1) than after yeast (7.84–9.75 g L−1) fermentations. Distillates obtained after bacterial fermentation were characterized by high level of aldehydes (up to 65% of total volatiles) and distillates obtained after yeast fermentation of higher alcohols (up to 95% of total volatiles). The process of fermentation using granular starch hydrolyzing enzyme cocktail Stargen™ 002 resulted in low amounts of all volatile compounds in distillates obtained after bacterial fermentation, but the highest amounts in distillates obtained after yeast fermentation. CONCLUSION The present study emphasizes the great potential of bioethanol production from sorghum with Z. mobilis using granular starch hydrolyzing enzyme Stargen™ 002, which leads to reduced water and energy consumption, especially when energy sources are strongly related to global climate change. © 2023 Society of Chemical Industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bioethanol production from sorghum grain with Zymomonas mobilis: increasing the yield and quality of raw distillates

et al. 2023

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“…However, the complexity of intracellular metabolism and the lack of metabolic data increase the difficulty of intracellular kinetic modeling, which is not conducive to the dynamic analysis of large-scale metabolic networks. 22,23 For these reasons, dynamic flux balance analysis (DFBA) [24][25][26] can make up for the shortcomings of flux balance analysis and kinetic models. The genome-scale metabolism (GSM) model of C. butyricum iCbu641 16 can be used to analyze the metabolic mechanism of C. butyricum.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional kinetic models can only rely on extracellular measurable metabolite data to identify model parameters. However, the complexity of intracellular metabolism and the lack of metabolic data increase the difficulty of intracellular kinetic modeling, which is not conducive to the dynamic analysis of large‐scale metabolic networks 22,23 . For these reasons, dynamic flux balance analysis (DFBA) 24–26 can make up for the shortcomings of flux balance analysis and kinetic models.…”

Section: Introductionmentioning

confidence: 99%

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

Pan,

Wang,

Wang

et al. 2023

Biotechnology Progress

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To study the relationship between the yield of 1,3‐propanediol (1,3‐PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome‐scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α‐oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3‐PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3‐PDO. As an important intermediate product, the flux change in the metabolic pathway of α‐oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α‐oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3‐PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.

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“…as ethanol production (Díaz & Willis, 2019;Srimachai, Nuithitikul, Sompong, Kongjan, & Panpong, 2015;van Zyl, van Rensburg, van Zyl, Harms, & Lynd, 2011), and environmental biotechnology systems such as nitrification (Leyva- Díaz, González-Martínez, González-López, Muñío, & Poyatos, 2015;Pambrun, Paul, & Spérandio, 2006). The reason may be that adding statistical analysis to the parameter's estimation is not always straightforward, and normally some advanced numerical methods must be applied.…”

mentioning

confidence: 99%

Making sense of parameter estimation and model simulation in bioprocesses

Sadino-Riquelme

Rivas

Jeison

et al. 2020

Biotech & Bioengineering

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Most articles that report fitted parameters for kinetic models do not include meaningful statistical information. This study demonstrates the importance of reporting a complete statistical analysis and shows a methodology to perform it, using functionalities implemented in computational tools. As an example, alginate production is studied in a batch stirred‐tank fermenter and modeled using the kinetic model proposed by Klimek and Ollis (1980). The model parameters and their 95% confidence intervals are estimated by nonlinear regression. The significance of the parameters value is checked using a hypothesis test. The uncertainty of the parameters is propagated to the output model variables through prediction intervals, showing that the kinetic model of Klimek and Ollis (1980) can simulate with high certainty the dynamic of the alginate production process. Finally, the results obtained in other studies are compared to show how the lack of statistical analysis can hold back a deeper understanding about bioprocesses.

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Ethanol production using Zymomonas mobilis: Development of a kinetic model describing glucose and xylose co-fermentation

Cited by 21 publications

References 40 publications

Bioethanol production from sorghum grain with Zymomonas mobilis: increasing the yield and quality of raw distillates

Bioethanol production from sorghum grain with Zymomonas mobilis: increasing the yield and quality of raw distillates

Dynamic flux balance analysis of 1,3‐propanediol production by clostridium butyricum fermentation

Making sense of parameter estimation and model simulation in bioprocesses

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