An unstructured kinetic model for the improvement of triterpenes production by Ganoderma lucidum G0119 based on nitrogen source effect

The kinetics of cell growth and triterpenes production for liquid submerged fermentation of the medicinal mushroom Ganoderma lucidum were investigated. A kinetic model was developed based on the Logistic and Luedeking-Piret equations for cell growth, substrate consumption and triterpene formation. The kinetic parameters of the model were optimized by specifically designed Runge-Kutta genetic algorithms. The mathematical model simulated the experimental data well and was capable of explaining the behavior of triterpenes production. The predictions of the kinetics from this model are very good both for normal fermentation kinetics under nitrogen limitation as well as for predictions of transitions to sluggish fermentations. The resulting model is very useful for scaling up liquid submerged fermentation of the mushroom G. lucidum and its application to the industrial production of triterpene.     

Fermentation process diagnosis using a mathematical model

Summary Intriguing physiology of a solvent-producing strain ofClostridium acetobutylicum led to the synthesis of a mathematical model of the acetone-butanol fermentation process. The model presented is capable of describing the process dynamics and the culture behavior during a standard and a substandard acetone-butanol fermentation. In addition to the process kinetic parameters, the model includes the culture physiological parameters, such as the cellular membrane permeability and the number of membrane sites for active tansport of sugar. Computer process simulation studies for different culture conditions used the model, and quantitatively pointed out the importance of selected culture parameters that characterize the cell membrane behaviour and play an important role in the control of solvent synthesis by the cell. The theoretical predictions by the new model were confirmed by experimental determination of the cellular membrane permeability.     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition roles of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.     

Modeling of an industrial alcohol fermentation and simuiation of the plant by a process simulator

The aim of the present study was the development of a general simulation module for fermentation within the framework of existing chemical process simulators. This module has been applied to an industrial plant which produces ethanol from beet molasses and fresh beet juice by Saccharomyces cerevisiae. An unstructured mechanistic model has been developed with kinetic laws that are based on a chemically defined reaction scheme which satisfies stoichiometric constraints. This model can be applied to different culture conditions and takes into account secondary byproducts such as higher alcohols. These byproducts are of prime importance and need to be correctly estimated because a sequence of distillation columns follow the fermentor in the plant. Important measurement campaigns have been performed on the plant to validate the model. Plant operation has been successfully simulated using the same kinetic model for both continuous and fed-batch modes of production. (c) 1995 John Wiley & Sons, Inc.     

基于遗传算法的麦角固醇分批发酵动力学参数估算

把遗传算法应用于求解麦角固醇分批发酵动力学模型参数,能进一步提高麦角固醇分批发酵过程状态变量的计算值与实验值的吻合程序,在计算机上对动力学模型进行了拟合,模拟值与实验值对比显示,该动力学模型能很好地反映麦角固醇分批发酵过程。     

An unstructured kinetic model to study NaCl effect on volatile ester fermentation by <Emphasis Type="Italic">Candida etchellsii</Emphasis> for soy sauce production

Salt-tolerant aromatic yeast is an important microorganism arising from the solid state fermentation of soy sauce. The fermentation kinetics of volatile esters by Candida etchellsii was studied in a batch system. The data obtained from the fermentation were used for determining the kinetic parameters of the model. Batch experimental results at four NaCl levels (180, 200, 220, and 240 g/L) were used to formulate the parameter estimation model. The kinetic parameters of the model were optimized by specifically designed Runge-Kutta Genetic Algorithms (GA). The resulting mathematical model for volatile ester production, cell growth and glucose consumption simulates the experimental data well. The resulting new model was capable of explaining the behavior of volatile ester fermentation. The optimized parameters (μ_o, X _max, K _i, α, β, Y _X/S, m, and Y _P/S) were characterized by a correlation of functions assuming salinity dependence. The kinetic models optimized by GA describe the batch fermentation process adequately, as demonstrated by our experimental results.     

Study of process variables in industrial acetic fermentation by a continuous pilot fermentor and response surfaces

The modeling and optimization of industrial processes requires an intensive study of the factors involved. In this work, a continuous pilot system for studying the industrial process of acetic fermentation is developed. A Doehlert design is applied to the five variables involved in the pilot process. This experimental design allows reduction of the experimental burden and the maximum amount of information to be obtained, studying the factors at different levels depending on their significance. The experimental system provides a robust measure of the specific growth rate and the rates of substrates consumption and acetic acid production, related to the flow of effluent stream evaluated in the steady state. The results demonstrate the growth-associated kinetics of substrates and product, and the yield factors are calculated with low values of variances for the coefficients, i.e., within the range 1-11%. The specific growth rate suits the quadratic model proposed. The response surfaces generated by the model are applied to explain the behavior of the bacterial growth and, therefore, the effects of the process variables studied over the acetic acid production. Very low levels of ethanol or oxygen make the acetification rate decrease, and a saturation effect with high levels of ethanol or oxygen is also deduced. The effects of the aeration rate, agitation, and overpressure suggest a kind of inhibition of the acetic acid production caused by the oxygen that has not been practically studied before. The temperature strengthens the inhibitory effect of the ethanol and the oxygen. The conclusions of this work consolidate the structure of a hybrid model for the acetic fermentation.     

Kinetic model-based feed-forward controlled fed-batch fermentation of Lactobacillus rhamnosus for the production of lactic acid from Arabic date juice

Arabic date is overproduced in Arabic countries such as Saudi Arabia and Iraq and is mostly composed of sugars (70–80 wt%). Here we developed a fed-batch fermentation process by using a kinetic model for the efficient production of lactic acid to a high concentration from Arabic date juice. First, a kinetic model of Lactobacillus rhamnosus grown on date juice in batch fermentation was constructed in EXCEL so that the estimation of parameters and simulation of the model can be easily performed. Then, several fed-batch fermentations were conducted by employing different feeding strategies including pulsed feeding, exponential feeding, and modified exponential feeding. Based on the results of fed-batch fermentations, the kinetic model for fed-batch fermentation was also developed. This new model was used to perform feed-forward controlled fed-batch fermentation, which resulted in the production of 171.79 g l^?1 of lactic acid with the productivity and yield of 1.58 and 0.87 g l^?1 h^?1, respectively.     

Real‐time monitoring of high‐gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real‐time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm^?1 C–C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high‐ and low‐concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two‐set calibration models for starch (R² = 0.998, percent error = 2.5%) and ethanol (R² = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS‐based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman‐based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Biotechnol. Bioeng. 2013; 110: 1654–1662. © 2013 Wiley Periodicals, Inc.     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process (CAPFM) was established in this study, based on a morphologically structured dynamic penicillin production model, that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation. CAPFM uses the three-dimensional cellular automata as a growth space, and a Moore-type neighborhood as the cellular neighborhood. The transition rules of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes. Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms, and has various state. The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly. __________ Translated from ACTA BIOPHYSICA, 2005, 21(2) [译自: 生物物理学报, 2005,21(2)]     

Kinetic models in industrial biotechnology – Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.     

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.     

金龟子绿僵菌IMI330189液体发酵动力学研究

研究了金龟子绿僵菌IMI330189的液体发酵动力学。利用Sigmoid函数构建了该菌株液体发酵过程中的菌体生长和底物消耗的动力学模型,并运用Origin7.5软件拟合求解出各模型参数。结果表明,模型能够较好地拟合绿僵菌IMI330189液体发酵过程,其比生长速率在发酵第22.8h达到最大值,为0.084h-1;总糖比消耗速率在第9.6h达到最大值,为0.246h-1;总氮比消耗速率在第10.3h达到最大值,为0.007h-1;菌体对总糖的得率系数在39.8h达到最高,为0.861g/g。模型拟合和实验数据具有良好的适应性,基本反映了绿僵菌IMI330189液体发酵过程的动力学特征,为其液体发酵工艺的优化和发展奠定了基础。     

Aerobic submerged fermentation by acetic acid bacteria for vinegar production: Process and biotechnological aspects

Strictly aerobic acetic acid bacteria (AAB) have a long history of use in fermentation processes, and the conversion of ethanol to acetic acid for the production of vinegar is the most well-known application.At the industrial scale, vinegar is mainly produced by submerged fermentation, which refers to an aerobic process in which the ethanol in beverages such as spirits, wine or cider is oxidized to acetic acid by AAB. Submerged fermentation requires robust AAB strains that are able to oxidize ethanol under selective conditions to produce high-titer acetic acid. Currently submerged fermentation is conducted by unselected AAB cultures, which are derived from previous acetification stocks and maintained by repeated cultivation cycles.In this work, submerged fermentation for vinegar production is discussed with regard to advances in process optimization and parameters (oxygen availability, acetic acid content and temperature) that influence AAB activity. Furthermore, the potential impact arising from the use of selected AAB is described.Overcoming the acetification constraints is a main goal in order to facilitate innovation in submerged fermentation and to create new industry-challenging perspectives.     

Acetobacter senegalensis isolated from mango fruits: Its polyphasic characterization and adaptation to protect against stressors in the industrial production of vinegar: A review

It has been more than a decade since Acetobacter senegalensis was isolated, identified and described as a thermotolerant strain of acetic acid bacteria. It was isolated from mango fruits in Senegal and used for industrial vinegar production in developing countries, mainly in sub-Saharan Africa. The strain was tested during several spirit vinegar fermentation processes at relatively high temperatures in accordance with African acclimation. The upstream fermentation process had significant stress factors, which are highlighted in this review so that the fermentation process can be better controlled. Due to its high industrial potential, this strain was extensively investigated by diverse industrial microbiologists worldwide; they concentrated on its microbiological, physiological and genomic features. A research group based in Belgium proposed an important project for the investigation of the whole-genome sequence of A. senegalensis. It would use a 454-pyrosequencing technique to determine and corroborate features that could give this strain significant diverse bio-industrial applications. For instance, its application in cocoa bean fermentation has made it a more suitable acetic acid bacterium for the making of chocolate than Acetobacter pasteurianus. Therefore, in this paper, we present a review that summarizes the current research on A. senegalensis at its microbial and genomic levels and also its specific bio-industrial applications, which can provide economic opportunities for African agribusiness. This review summarizes the physiological and genomic characteristics of Acetobacter senegalensis, a thermotolerant strain isolated from mango fruits and intended to be used in industrial vinegar fermentation processes. It also explores other bio-industrial applications such as cocoa fermentation. Vinegar fermentation is usually performed with mesophilic strains in temperate regions of the world. Developing countries, such as Senegal, import vinegar or make ‘fake’ vinegar by diluting acetic acid obtained from petrochemicals. The use of a thermotolerant Acetobacter senegalensis strain as a solid functional starter culture, as well as the design of a new adapted bioreactor, has significantly contributed to food security and the creation of small- to medium-sized enterprises that produce mango vinegar in West Africa.     

Modeling of glycerol production by fermentation in different reactor states

A kinetic model of glycerol production by fermentation with the osmophilic yeast Candida krusei was studied firstly by analogies to published works. Considering that the glycerol produced competes with glucose, as a second carbon source for energy maintenance, mathematical models of glucose utilization and glycerol accumulation were modified further. By adjusting only two variable macrokinetic parameters, K_S and β, the model simulations could fit experimental data well when the reactor was changed from Airlift Loop Reactor in different scale or airlift mode to Stirred Vessel. To avoid a significant reduction in glycerol production in the latter fermentation stage, the final condition of the fermentation, determined by the concentration ratio of glycerol to glucose, was also investigated in four different Reactor States. The kinetic models and simulation results can provide certain reference for scale up of glycerol production by fermentation.     

模拟青霉素分批补料发酵过程的细胞自动机模型

根据青霉素产生菌的生长机理和青霉素分批补料发酵过程的动力学特性,在Paull等建立的形态学结构动力学模型的基础上,建立了模拟青霉素分批补料发酵过程的细胞自动机模型。模型采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批补料发酵过程中菌体生长机理和简化动力学结构模型设计。模型中的每一个细胞既可代表单个产黄青霉菌体细胞,又可代表特定数量的这种菌体细胞,它具有不同的状态。对模型进行的仿真实验结果表明：模型不但能一致地复现形态学结构动力学模型所描述的青霉素分批补料发酵过程的演化特性,而且较形态学结构动力学模型更加直观地刻画了青霉素分批补料发酵过程的演化行为。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

基于遗传算法的谷氨酸发酵动力学参数估计

把遗传算法应用于求解谷氨酸分批发酵动力学模型参数,取交叉概率Pc=0．8、变异概率Pm=0．06、初始种群为20、遗传世代数为200代,能进一步提高谷氨酸分批发酵过程状态变量的计算值与实验值的吻合程度。模拟值与实验值对比显示,该动力学模型能很好地反映谷氨酸分批发酵过程。     

桔青霉摇瓶发酵生产核酸酶P1的动力学研究

研究了桔青霉发酵生产核酸酶P1的发酵动力学特性：以Logistic方程和Luedeking—Piret方程为基础,进行最优参数估计和非线性拟合,得到了描述整个发酵过程中的茵体生长、产物合成和基质消耗的动力学模型。对实验数据与模型预测值进行比较,发现模型预测值与实验数据能较好地拟合,基本上反映了桔青霉发酵过程的动力学特征,为以后进一步研究和预测核酸酶P1发酵过程奠定了理论基础。