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ABSTRACT: The objective of this research was to understand how the initial glucose concentration influences adenosine (AR) production and metabolic flux shift on the cultivation of Bacillus subtilis CGMCC 4484. Experiments confirmed that initial glucose concentration affects cell growth, AR production and metabolites, significantly. The flux distribution at the key nodes of glucose-6-phosphate (G6P), pyruvate (PYR) and acetyl coenzyme-A (AcCoA) could be affected by changing the glucose concentration. Based on kinetic analysis of specific rates, the low-glucose concentration was better for both cell growth and AR production during the first 12 h. However, the high-glucose concentration was more favorable for AR formation after 18 h. Furthermore, different simplified feeding strategies were designed to achieve higher AR accumulation. The final AR concentration of 15.60 g L(-1) was achieved when an optimized constant-feeding strategy was used, which was 21.02 % higher than batch fermentation. This was the first time to investigate the regulation of the glucose metabolism of AR-producing B. subtilis.
Bioprocess and Biosystems Engineering 04/2013; · 1.81 Impact Factor
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ABSTRACT: A glucose utilizing strain, Arthrobacter A302 was used for cyclic adenosine monophosphate (cAMP) production in batch modes. The non-structured model in a 5l stirred
tank bioreactor for understanding, controlling, and optimizing the fermentation process was proposed using the logistic equation
for microbial growth, the Luedeking-Piret equation for product formation and Luedeking-Piret-like equation for substrate uptake,
respectively. The production of cAMP was a mixed-growth-associated pattern. Based on model prediction, a comparison of calculated
value using the parameters evaluated above with another experimental data in 30l bioreactor was used to test the model. The
results predicted from the model were in good agreement with the experimental observations in 30l bioreactor, which demonstrated
that the model might be useful for the development and optimization of production of cAMP in industrial scale. Based on estimated
kinetic parameters, three different fed-batch modes, constant rate and intermittent (once and repeated), were adopted in order
to obtain more cAMP accumulation. Furthermore, the final production of cAMP reached 11.24g l−1 after 72h incubation using three stages feeding strategy. In particular, the cAMP productivity (0.156gl−1h−1) was successfully improved by 22.83, 11.43 and 9.86%, respectively, compared with the modes of the batch, constant rate fed-batch
and intermittent fed-batch once.
Keywords
Arthrobacter A302–Cyclic adenosine monophosphate–Fed-batch–Kinetic model
World Journal of Microbiology and Biotechnology 04/2012; 27(10):2379-2385. · 1.53 Impact Factor
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ABSTRACT: Experiments confirmed dissolved oxygen (DO) definitely affects cyclic adenosine monophosphate (cAMP) production by Arthrobacter A302. Production of cAMP by batch fermentation was investigated under various DO conditions. A two-stage DO control strategy was proposed to achieve optimal production of cAMP based on the kinetic analysis: the DO level was controlled at 40% during the first 18 h and then switched to 30%. Relatively high cAMP production (9.9 g L(-1)) was achieved by applying this strategy. The cAMP productivity (0.14 g L(-1) h(-1)) was also successfully improved by 85.1, 59.3, 15.1 and 28.0%, compared to cases in which DO was uncontrolled or DO levels were held at 20, 30 and 40%, respectively. This is the first report of the use of a two-stage DO control strategy in cAMP production, and it was verified to be an effective method for enhancing the cAMP yield via this strain.
Bioprocess and Biosystems Engineering 03/2012; 35(8):1281-6. · 1.81 Impact Factor
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ABSTRACT: The production of cyclic adenosine monophosphate (cAMP) by Arthrobacter sp. A302 was studied in a 5 L stirred tank fermentor under a range of pH values (6.5-8.0) and glucose feeding rates. In batch fermentation under a controlled pH, the optimum pH for cell growth was 7.5 with dry cell density (X) of 11.43 g L, and the optimum pH for cAMP accumulation was 7.0 with cAMP concentration of 7.41 g L. In order to achieve the high X and cAMP yield simultaneously, a pH-shift control strategy was proposed based on kinetic analysis of specific cell growth rate (μ) and specific cAMP formation rate (q ( s )). In this method, pH was controlled to 7.0 for the first 30 h of fermentation, and then subsequently shifted to 7.5 and maintained until the end of the process. Application of this approach significantly enhanced the cAMP concentration. Thereafter, cAMP production was further improved by combining the above-mentioned pH-control system and fed-batch process with glucose at a constant feeding rate of 1.0 g L(-1 )h(-1). Under optimum conditions, the final cAMP production was 10.87 g L, which is 110.0, 46.7, and 27.7% higher than that of the pH-uncontrolled, pH-controlled, and pH-shift controlled methods, respectively.
MIRCEN Journal of Applied Microbiology and Biotechnology 01/2012; 28(1):121-7. · 1.08 Impact Factor
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ABSTRACT: During ribonucleic acid fermentation, the fermentative processes were researched at pH controlled at 4.0 and under natural conditions. Unstructured models in a 50-L airlift fermentor were established for batch RNA production at pH 4.0 using the Verhulst equation for microbial growth, the Luedeking-Piret equation for product formation and a Luedeking-Piret-like equation for substrate uptake. Parameters of the kinetic models were determined using origin 7.5. Based on the models estimated above, another batch fermentation experiment was conducted in a 300-L airlift fermentor, which demonstrated that the models could be useful for RNA production on an industrial scale. Additionally, continuous fermentation based on kinetic models was proposed to make full use of substrates and reduce the cost of waste water treatment. As a result, although the DCW and RNA concentration were 11.5 and 1.68 g L(-1), which were lower than that of batch fermentation, the sugar utilization increased by 14.3%, while the waste water decreased by more than 90%.
Bioprocess and Biosystems Engineering 08/2011; 35(3):415-22. · 1.81 Impact Factor
