Effect of propionate on the production of natamycin with Streptomyces gilvosporeus XM-172

This study described the influence of feeding short-chain fatty acids and alcohols on natamycin production in the glucose basal medium, produced by Streptomyces gilvosporeus XM-172. The highest natamycin production was obtained with feeding propionate as compared to other precursors. The optimal propionate concentration and feeding time were 6 g L(-1) and early log phase, respectively. This optimal propionate feeding strategy led to a natamycin production of 6.72 g L(-1), which was nearly 85% higher than that of the control. It was firstly revealed that propionate could greatly promote natamycin biosynthesis by S. gilvosporeus

High regioselective acetylation of vitamin A precursors using lipase B from Candida antarctica in organic media

The effect of different reaction parameters was explored on the acylation of primary hydroxyl group of 1,6-diol by lipase B from Candida antarctica catalysis in organic solvent. First, the effect of the organic solvents was investigated, and the highest conversion rate was obtained in n-hexane. Then, the effect of the acyl donor was studied. Among several reactants, including acetic acid and two different acetates, vinyl acetate gave the best yield. A maximum monoester yield of 98.5% was obtained using vinyl acetate as acyl donor in n-hexane at 50 degrees C. The substrate concentration was 25 mmol/L, while the diol to vinyl acetate molar ratio was 1:3. Substrate concentration had to be limited due to an inhibitory effect on enzyme by the diol that caused a decrease on initial reaction rate. To promote initial reaction rate, excess vinyl acetate was used. Under the optimum conditions, the conversion rate and monoacylation selectivity were 98.5 and 100%, respectively. The produced monoester was 6.1 mg/ml, and this amount can be further optimized base on the results presented here

Exploring biostimulation of plant hormones and nitrate supplement to effectively enhance biomass growth and lutein production with thermo-tolerant Desmodesmus sp. F51.

In this study, the interactive effect of plant hormone-salicylic acid and succinic acid on biomass growth, lutein content, and productivity of Desmodesmus sp. F51 were investigated. The results demonstrated that the synergistic action of salicylic acid and succinic acid could effectively enhance the assimilation of nitrate and significantly improve lutein production. The maximal lutein content 7.01 mg/g and productivity 5.11 mg/L/d could be obtained with a supplement of 100 µM salicylic acid and 2.5 mM succinic acid in batch culture. Furthermore, operation strategy of nitrate fed-batch coupled with supplementation for succinic acid and salicylic acid resulted in further enhancement of lutein content and productivity by 7.50 mg/g and 5.78 mg/L/d, respectively. The performance is better than most of the previously reported values

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

From Wiley via Jisc Publications RouterHistory: received 2021-03-22, rev-recd 2021-09-29, accepted 2021-09-30, pub-electronic 2021-10-12Article version: VoRPublication status: PublishedAbstract: Astaxanthin is a high‐value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high‐fidelity model is challenging due to the system's variability, also known as batch‐to‐batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar‐based fermentation and provides a novel approach for bioprocess dynamic modeling

Use of biodiesel-derived crude glycerol for vancomycin production by Amycolatopsis orientalis XMU-VS01

Crude glycerol is a primary by-product in the biodiesel industry. Microbial fermentation on crude glycerol for producing value-added products provides opportunities to utilize a large quantity of this by-product. This study investigates the potential of using the crude glycerol to produce vancomycin (glycopeptide antibiotics) through fermentation of Amycolatopsis orientalis XMU-VS01. The results show that crude glycerol was the most effective carbon source for mycelium growth and vancomycin production, with 4060 g/L glycerol concentration as optimal range. Among other culture medium components, potato protein (nitrogen source) and the phosphate concentration had significant effects (p<0.05) for vancomycin production. A Box-Behnken design and response surface methodology were employed to formulate the optimal medium. Their optimal values were determined as 52.73 g/L of glycerol, 17.36 g/L of potato protein, and 0.1 g/L of dipotassium phosphate. A highest vancomycin yield of 7.61 g/L with biomass concentration of 15.8 g/L was obtained after 120 h flask fermentation. The yield of vancomycin was 3.5 times higher than with basic medium. The results suggest that biodiesel-derived crude glycerol is a promising feedstock for production of vancomycin from A. orientalis culture

Production, purification and cytotoxity of soluble human Fas ligand expressed by Escherichia coli and Dictyostelium discoideum

Human Fas ligancl (hFasL) is a type II membrane protein that induces apoptosis in the Fas-bearing cells. Its special biological activity has the potential for the therapeutic use as an anti-cancer agent directed at enhancing apoptosis in tumor cells. In this study Escherichia coli and eukaryotic Dictyostelium discoideum were used to produce a soluble form of hFasL in large amounts. An expression vector for hFasL production in E. coli was constructed based on plasmid pET32a(+). By cultivation of the hFasL-producing E. coli clone on LB medium and induction with IPTG, a hFasL concentration of 1.0 mg L-1 was achieved. D. discoideum strain AX3-hFasL-H was cultured in a conventional stirred bioreactor on an improved synthetic medium using a simple fed-batch strategy, and cell densities of up to 8.3 x 10(7) cells/mL and a maximum hFasL concentration of 420 mu g/L were obtained. Using Ni-NTA affinity chromatography purification, two kinds of recombinant hFasLs from E. coli and D. discoideum were purified with a purity of 94% and 90%, respectively. They showed similar biological activities in inducing apoptosis in Fas-expressing cells. (C) 2012 Elsevier B.V. All rights reserved.Program for New Century Excellent Talents in Fujian Province, PR China; National Natural Science Foundation of China [31071488, 20736008, 20928006

Review of advanced physical and data‐driven models for dynamic bioprocess simulation: Case study of algae–bacteria consortium wastewater treatment

Microorganism production and remediation processes are of critical importance to the next generation of sustainable industries. Undertaking mathematical treatment of dynamic biosystems operating at any spatial or temporal scale is essential to guarantee their performance and safety. However, constructing physical models remains a challenge due to the extreme complexity of process biological mechanisms. Data‐driven models also encounter severe limitations because datasets from large‐scale bioprocesses are often scarce without complete information and on a restricted operational space. To fill this gap, the current research compares the performance of advanced physical and data‐driven models for dynamic bioprocess simulations subject to incomplete and scarce datasets, which to the best of our knowledge has never been addressed before. In specific, kinetic models were constructed by integrating different classic models, and state‐of‐the‐art hyperparameter selection frameworks were developed to design artificial neural networks and Gaussian process regression models. An algae–bacteria consortium wastewater treatment process was selected to test the accuracy of these modeling strategies, as it is one of the most sophisticated biosystems due to the intricate mutualistic and competitive interactions. Based on the current results and available data, a heuristic model selection procedure is provided. This study paves the way to facilitate future bioprocess modeling

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model’s validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model’s validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering