十三碳二元酸发酵过程菌体生长期动力学模型及其应用

介绍了由十三碳烷烃生产十三碳二元酸的发酵过程,对其中的菌体生长期的代谢过程进行了分析。提出了以CO₂释放率判断菌体生长状况的方法,据此可确定进入产酸期的最佳时间．建立了菌体生长期底物消耗及菌体生长的动力学模型,对模型参数进行了回归估值。并对菌体生长期进行了拟合。结果表明,模型的计算值和实测值吻合得较好,平均相对偏差为2．4％。利用所建模型对菌体生长期进行多种操作条件下的模拟计算,结果表明,提高蔗糖浓度及初始菌体浓度均能显著地提高菌体生长期结束时的菌体浓度。     

重组类人胶原蛋白工程菌补料发酵过程建模

对产类人胶原蛋白的重组大肠杆菌Escherichia coli(E. Coli) 的批式和分批-补料培养动力学进行了研究。通过检测发酵过程的基质浓度、菌体量和产物浓度,建立了一组反映发酵的动力学模型,并考虑了非工程菌存在的影响,分析了细胞生长、底物消耗、基因工程产物生成的过程,结果显示该动力学模型可以很好的拟合发酵过程。     

醋酸杆菌AS1.41的醋酸发酵动力学特性

用分批培养的方法研究了醋酸发酵过程的动力学特性。醋酸杆菌AS 1.41的醋酸发酵过程属“非生长偶联型”,发酵初期菌体迅速增殖,发酵中期为醋酸生成的高峰期。高浓度的底物和产物对菌体生长及其产酸活性有抑制作用。在乙醇浓度2—4g/100ml和醋酸浓度小于3g/100ml范围内,发酵反应效率最佳,最大菌体比生长速率可达0.127h^-1,最大醋酸比生成速率为0.12g醋酸/h·OD。在工业常用的初始底物浓度范围内,底物抑制效应主要表现在对菌体生长的影响上。高浓度醋酸的存在显著抑制该菌的产酸能     

十三碳二元羧酸发酵技术的研究

以一株热带假丝酵母菌(Candida tropicalis) SP1为出发菌株,经紫外线反复诱变获取一株难以同化烷烃的突变株SPUV56,摇瓶培养5d平均产酸量达72g/L,较出发菌株提高了1.25倍,并利用突变株SPUV56在137L自控罐上扩试,补加醋酸盐发酵,144h产酸量达153g/L,比不加醋酸盐发酵提高了29.7%。采用提 高搅拌混合效果和低溶解氧发酵过程控制方法,可有效地提高菌体的产酸能力,在20m3发酵罐中发酵生产十三碳二元羧酸,总培养时间144h,产酸量可达172g/L,放罐体积15.0m3,产量为2.25t。     

蓝色犁头霉深层发酵产壳低聚糖生长动力学研究

目的:研究蓝色犁头霉产壳低聚糖分批发酵动力学的模型.方法:在10L发酵罐中,将蓝色犁头霉分批发酵培养,对蓝色犁头霉菌丝体生长、产物形成和基质消耗的实验数据进行分析,根据Logistic和Luedeking-Piret方程分别建立蓝色犁头霉发酵过程菌体生长、壳低聚糖生成和基质消耗的动力学数学模型,并利用1stOpt软件对模型参数进行非线性曲线拟合.结果:研究得到了菌体生长、产物合成及基质消耗的动力学模型及参数,模型的拟合度分别为0.994、0.986、0.992.结论:研究表明蓝色犁头霉多糖合成和菌体生长呈生长偶联型,模型计算值与实验值有良好的拟合性,模型准确度较高.     

柠檬酸发酵的数学模型研究

对柠檬酸发酵过程中菌体生长、基质消耗及产物生成的动力学进行了研究,得到了描述柠檬酸发酵过程的数学模型,并蹦实验统计数据为基础,通过对模型进行分析,推断了模型参数,同时用实验结果对模型进行了验证,结果表明模型计算与实测结果拟合良好,从而显示所建立的模型基本正确地描述了柠檬酸发酵过程,这对应用电子计算机控制发酵过程,实现发酵过程的最佳化有着重要意义。     

甲醇产纤维素菌株的静态发酵动力学研究

目的:为实现甲醇资源化产细菌纤维素发酵过程的优化,研究纤维素生产菌株一木醋杆菌(Gluconacetobacter χγlinus)的静态发酵动力学特性.方法:将木醋杆菌接入甲醇浓度分别为2.7%和4.5%的培养基中驯化,根据Logistic方程和LuedekingPiret方程,研究周期为13d的静态发酵动力学过程.结果:确定静态发酵过程的菌体生长、细菌纤维素合成、底物消耗的动力学参数,得到动力学方程,拟合试验值与模型值,得到甲醇模拟废水培养基平均拟合误差为16%,略高于基础培养基的14%.结论:利用甲醇产纤维索的模型方程可预测菌浓、产物浓度及底物消耗规律,实现静态发酵过程的优化.     

重组毕赤酵母产青霉素G酰化酶的分批发酵动力学研究

构建了重组毕赤酵母产青霉素G酰化酶的分批发酵动力学模型。实验考察了分批发酵过程中甘油消耗、甲醇浓度、菌体浓度、溶氧、补料时间对青霉素G酰化酶活力的影响。应用Matlab软件,对菌体生长、基质消耗和产物生成方程进行最优参数估算和非线性拟合,得到相应的动力学模型。模型的计算值与实验值能较好地拟合,表明所建模型能较好反映重组毕赤酵母产青霉素G酰化酶的分批发酵过程。     

烷烃发酵产生1，13-十三碳二元酸及其诱导作用的研究

从能利用正十二烷产生1,12-十二碳二元酸的热带假丝酵母突变株D28出发,经两次紫外线照射诱变,选育到一株从正十三烷产生1,13-十三碳二元酸较高的突变株2—23号菌。该突变株较出发菌株提高产酸率20％,达40．4g／L。突变株2—23也能将一定链长的长链烷烃以较高的产率转变成相应的单一二元酸。此外,在产酸摇瓶条件试验中观察到烷烃的诱导作用,使突变株产酸能力得以提高。用烷烃预培养的种子发酵正十三烷,其产生1,13一十三碳二元酸的量较糖质碳源培养的种子发酵时要提高30％。     

L-赖氨酸分批发酵连续补糖的研究

用L-赖氨酸产生株纯齿棒状杆菌PI-3-2(Hsc^-,AEC⁺)在8L自控发酵小罐上,用恒稀释率指数递增方式连续补加葡萄糖液进行L-赖氨酸分批发酵的研究。结果表明,一次投糖分批发酵时,较高糖浓度使比产酸速率Qp值下降,不能有效地提高产酸水平。采用连续补糖方式可以改变菌体竞争底物的能力或改善代谢途径,增大耗底物分数a2或真正产酸率yp,;从而增加表观产酸率Yp值,提高葡萄糖转化率。此方式的发酵属Caden动力学分类第I型,在发酵的中后期控制H等条件,可增加比产酸速率Qp值,提高发酵水平。PI-3-2菌株的产酸水平可由47．Mg／ml提高到64．2mg／ml(总糖浓度18．18％时),避离可达73．3mg／m1(总糖浓度22．73mg／ml时)。     

灵芝胞外多糖分批发酵非结构动力学模型

在２Ｌ搅拌发酵罐上提出了描述了灵芝胞外多糖分批发酵过程中菌球生长、底物消耗和胞外多糖形成的非结构动力学模型。首先研究了灵芝分批发酵特性,结果表明该发酵过程属菌体生长和产物形成相偶联型。然后在总结文献的基础上,运用动力学模型,经过非线性回归,得到了模型中的参数值。通过计算机模拟,证明模型预测值与实际实验值具有良好的拟合性。     

法夫酵母分批发酵虾青素动力学研究

通过三联30L全自动发酵罐对虾青素产生菌法夫酵母的分批发酵动力学进行了研究,结果表明,法夫酵母的生长与限制性基质葡萄糖浓度之间符合Logistic方程,建立了细胞生长、产物合成和基质消耗随时间变化的数学模型。应用MATLAB软件对发酵动力学模型进行最优参数估计和非线性拟和,获得最大比生长速率（umax）和产物得率（Yp/x）分别为0.1829/h、0.1524g/g,虾青素分批发酵中细胞生长与产物合成属于偶联型,模型模拟计算结果和实验值能较好地吻合,动力学研究结果表明该模型能较好地反映细胞的生长、底物消耗和产物合成过程机制。     

Modeling and simulation of Streptomyces peucetius var. caesius N47 cultivation and epsilon-rhodomycinone production with kinetic equations and neural networks

This study focuses on comparing different kinetic growth models and the use of neural networks in the batch cultivation of Streptomyces peucetius var. caesius producing epsilon-rhodomycinone. Contois, Monod and Teissier microbial growth models were used as well as the logistic growth modeling approach, which was found best in the simulations of growth and glucose consumption in the batch growth phase. The lag phase was included in the kinetic model with a CO2 trigger and a delay factor. Substrate consumption and product formation were included as Luedeking-Piret and logistic type equations, respectively. Biomass formation was modeled successfully with a 6-8-2 network, and the network was capable of biomass prediction with an R2-value of 0.983. Epsilon-rhodomycinone production was successfully modeled with a recursive 8-3-1 network capable of epsilon-rhodomycinone prediction with an R2-value of 0.903. The predictive power of the neural networks was superior to the kinetic models, which could not be used in predictive modeling of arbitrary batch cultivations.     

A kinetic model for product formation of microbial and mammalian cells

Growth of microbial and mammalian cells can be classified into substrate-limited and substrate-sufficient growth according to the relative availability of the substrate (carbon and energy source) and other nutrients. It has been observed for a number of microbial and mammalian cells that the consumption rate of substrate and energy (ATP) is generally higher under substratesufficient conditions than under substrate limitation. Accordingly, the product formation under substrate excess often exhibits different patterns from those under substrate limitation. The extent of increase or decrease in product formation may depend not only on the nature of limitation and cell growth rate but also on the residual substrate concentration in a relatively wide range. The product formation kinetic models existing in literature cannot describe these effects. In this study, the Luedeking-Piret kinetic is extended to include a term describing the effect of residual substrate concentration. The extended model has a similar structure to the kinetic model for substrate and energy consumption rate recently proposed by Zeng and Deckwer. The applicability of the extended model is demonstrated with three microbial cultures for the production of primary metabolites and three hybridoma cell cultures for the production of ammonia and lactic acid over a wide range of substrate concentration. The model describes the product formation in all these cultures satisfactorily. Using this model, the range of residual substrate concentration, in which the product formation is affected, can be quantitatively assessed. (c) 1995 John Wiley & Sons, Inc.     

产耐热蛋白酶苏云金芽胞杆菌的发酵动力学

对苏云金芽胞杆菌FZ62耐热蛋白酶的代谢过程进行初步研究,酶生成与菌体生长为耦联过程。以Logistic方程和Luedeking—Piret方程为基础,建立该菌发酵过程中菌体生长、产物生成和基质消耗的动力学模型,求得模型参数,该动力学模型与试验结果拟和程度较好。     

Mathematical modeling of ethanol production in solid-state fermentation based on solid medium’ dry weight variation

In this work, mathematical modeling of ethanol production in solid-state fermentation (SSF) has been done based on the variation in the dry weight of solid medium. This method was previously used for mathematical modeling of enzyme production; however, the model should be modified to predict the production of a volatile compound like ethanol. The experimental results of bioethanol production from the mixture of carob pods and wheat bran by Zymomonas mobilis in SSF were used for the model validation. Exponential and logistic kinetic models were used for modeling the growth of microorganism. In both cases, the model predictions matched well with the experimental results during the exponential growth phase, indicating the good ability of solid medium weight variation method for modeling a volatile product formation in solid-state fermentation. In addition, using logistic model, better predictions were obtained.     

Identification of Growth Phases and Influencing Factors in Cultivations with AGE1.HN Cells Using Set-Based Methods

Production of bio-pharmaceuticals in cell culture, such as mammalian cells, is challenging. Mathematical models can provide support to the analysis, optimization, and the operation of production processes. In particular, unstructured models are suited for these purposes, since they can be tailored to particular process conditions. To this end, growth phases and the most relevant factors influencing cell growth and product formation have to be identified. Due to noisy and erroneous experimental data, unknown kinetic parameters, and the large number of combinations of influencing factors, currently there are only limited structured approaches to tackle these issues. We outline a structured set-based approach to identify different growth phases and the factors influencing cell growth and metabolism. To this end, measurement uncertainties are taken explicitly into account to bound the time-dependent specific growth rate based on the observed increase of the cell concentration. Based on the bounds on the specific growth rate, we can identify qualitatively different growth phases and (in-)validate hypotheses on the factors influencing cell growth and metabolism. We apply the approach to a mammalian suspension cell line (AGE1.HN). We show that growth in batch culture can be divided into two main growth phases. The initial phase is characterized by exponential growth dynamics, which can be described consistently by a relatively simple unstructured and segregated model. The subsequent phase is characterized by a decrease in the specific growth rate, which, as shown, results from substrate limitation and the pH of the medium. An extended model is provided which describes the observed dynamics of cell growth and main metabolites, and the corresponding kinetic parameters as well as their confidence intervals are estimated. The study is complemented by an uncertainty and outlier analysis. Overall, we demonstrate utility of set-based methods for analyzing cell growth and metabolism under conditions of uncertainty.     

Lactic acid fermentation of crude sorghum extract

Crude extract from sweet sorghum supplemented with vetch juice was utilized as the carbohydrate source for fermentative production of lactic acid. Fermentation of media containing 7%(w/v) total sugar was complex completed in 60–80 hr by Lactobacillus plantarum, product yield averaging 85%. Maximum acid production rates were dependent on pH, initial substrate distribution, and concentration, the rates varying from 2 to 5 g(liter·hr.) The lactic acid yield was lowered to 67% under limited medium supplementation. The fermented ammoniated product contained over eight times as much equivalent crude protein (N × 6.25) as the original medium. Unstructured kinetic models were developed for cell growth, lactic acid formation, and substrate consumption in batch fermentation. With the provision of experimentally determined kinetic parameters, the proposed models accurately the fermentation process.