樟芝产生三萜类化合物的液体培养条件的响应面法优化

樟芝(Antrodia cinnamomea)是一种非褶菌目、多孔菌科的多年生蕈菌类,樟芝子实体中含有多种生理活性物质包括三萜类化合物,但野生樟芝只生长在台湾特有的牛樟树树干腐朽的心材内壁和枯死倒伏的牛樟树表面,生长极其缓慢,很难获得,价格昂贵。本研究的目的是通过液体培养樟芝的菌丝体以产生三萜类化合物,用响应面法优化了樟芝产生三萜类化合物的液体发酵条件。首先对樟芝的液体发酵的培养基成分及培养条件进行了单因素优化,研究发现樟芝液体发酵产生三萜类化合物的最佳碳源和氮源分别为葡萄糖和酵母提取物,最佳浓度分别为100 g/L和9 g/L,优化的培养基配方为(g/L):葡萄糖100.0,酵母提取物9.0,Mg SO4·7H2O 0.5,KH2PO41.0。优化的培养条件为:初始pH值6.0,温度28℃,接种量10%(v/v),摇床转速180 r/min。在此基础上,用响应面法对樟芝液体发酵产生三萜类化合物影响最大的三个因素即碳源、氮源和培养温度进行了优化,结果优化的三种因素条件为:葡萄糖104.71 g/L,酵母提取物9.93 g/L,温度28.42℃。用优化的培养条件液体培养樟芝,其三萜类化合物的产量达到391.45 mg/L,比优化前的产量223.39 mg/L提高了75.23%,为进一步研究液体培养樟芝产生三萜类化合物奠定了基础。     

L-缬氨酸发酵培养基的神经网络建模与遗传算法优化

运用神经网络对L-缬氨酸发酵培养基组成进行建模,在神经网络模型的基础上采用遗传算法对培养基组成进行优化,得到最佳发酵培养基组成.结果表明,运用神经网络并结合遗传算法是一种行之有效的优化方法.按最佳发酵培养基组成进行发酵实验64h,可在发酵液中积累L-缬氨酸28.5g/L.     

响应面法优化普鲁兰多糖发酵培养基

以出芽短梗霉IFO 4464为实验菌种,采用响应面法(RSM)优化了出芽短梗霉IFO 4464产普鲁兰多糖的发酵培养基。通过实验得到出芽短梗霉最佳发酵培养基为蔗糖59.8g/L,硫酸铵0.7 g/L,硫酸镁0.3 g/L,磷酸二氢钾5.0g/L,氯化钾0.5g/L,氯化钠1.5g/L,酵母浸膏2.5 g/L,多糖产量可达21.92 g/L。     

珍稀药用真菌——樟芝深层发酵培养条件的优化

对樟芝深层发酵培养基进行了筛选,并在此基础上对发酵条件进行了优化。以樟芝深层发酵菌丝体三萜产量为主要目标产物,确定发酵培养条件为:40g/L葡萄糖,6g/L豆饼粉,1g/L K_2HPO_4,0．5g/L MgSO4,VB_1 100mg/L,自然pH,接种量为20%,装液量为100mL/250mL三角瓶,转速100r/min,26℃恒温培养6d,胞内三萜产量达15．25mg/100mL发酵液。     

表皮短杆菌ZJB-07021产R-酰胺酶培养条件的优化

采用响应面分析法(RSM)对R-酰胺酶产生菌Brevibacterium epidermidis ZJB-07021的发酵培养基进行了优化.首先运用了单因子试验筛选出了发酵培养的最佳pH与温度,在此基础上采用Plackett-Burman(PB)设计法,对 8 种影响产酶的因素进行评价,实验结果表明,葡萄糖、酵母粉与乙酰胺含量对菌株产酰胺酶的活力具有显著的影响.通过旋转中心组合实验考察了葡萄糖、酵母粉和乙酰胺这三个主要因素对菌株所产酰胺酶活力的影响.发酵培养基优化结果为葡萄糖 17.00 g/L,酵母粉 15.74 g/L,乙酰胺 7.05 g/L,采用优化后的发酵培养条件进行摇瓶发酵培养,酰胺酶的酶活达到 72.14 U/L,比优化前的初始发酵培养条件下的酶活提高了73.3%.     

丝状真菌Glarea lozoyensis SIIA-F1108产纽莫康定B0发酵条件的研究

【目的】采用响应面法优化丝状真菌Glarea lozoyensis SIIA-F1108发酵生产纽莫康定B_0培养基,提高发酵产量;通过氮源优化,降低发酵液菌体浓度,改善发酵过程的溶氧水平。【方法】采用Plackett-Burman设计和响应面法进行培养基优化,筛选出对纽莫康定B_0产量具有显著影响的因素;通过最陡爬坡实验及Box-Behnken设计,并利用Design-Expert软件对实验数据进行回归分析,得到优化的发酵培养基配方;通过对优化培养基中氮源组分进行全因子实验,最终得到高产量和低菌体浓度发酵培养基。【结果】实验数据表明:甘露醇、脯氨酸和葡萄糖对纽莫康定B_0产量影响最大;最佳浓度分别为甘露醇167.3 g/L、脯氨酸26.1 g/L、葡萄糖28.5 g/L。采用优化后的培养基进行摇瓶发酵,纽莫康定B_0产量达到了1 840 mg/L,较优化前提高了42%,与预测结果一致。用硫酸铵部分替换棉籽饼粉后,发酵液菌体浓度降低,在100 L发酵罐上对优化后的结果做了进一步的验证,纽莫康定B_0产量达到1 980 mg/L。【结论】模型预测值与实验值有较高吻合度,具备较高可信度和显著性,发酵产量提高了42%,响应面实验设计和分析方法能够有效地用于丝状真菌Glarea lozoyensis SIIA-F1108产纽莫康定B_0发酵培养基进行优化。通过调整培养基中的氮源组成,降低了发酵液菌体浓度,改善了发酵过程的溶氧水平。     

大球盖菇产胞外多糖液体优化培养条件初探

以菌丝生物量及胞外多糖(exopolysaccharides,EPS)含量为指标对大球盖菇产胞外多糖液体培养基组成和发酵条件进行了优化。结果表明,最适碳源是麦芽糖,最适氮源是酵母膏,正交试验确定最佳培养基组成为马铃薯150 g/L,麦芽糖20 g/L,酵母膏1 g/L,KH2PO41 g/L,MgSO4.7H2O 2.5 g/L。最佳发酵条件为28℃,摇床转速160 r/min,起始pH值6.5,装液量100 mL/250 mL、接种量10%,发酵时间6 d。在此条件下,大球盖菇菌丝生物量及EPS含量分别比对照增加了31.8%和51.6%。     

利用响应面法优化固氮类芽孢杆菌Paenibacillus sp.1–49的发酵培养基（英文）北大核心CSCD

【目的】固氮类芽孢杆菌Paenibacillus sp.1–49是本实验室分离的具有潜在生物肥料价值的微生物菌剂。该菌在常见的培养基中不能高效生长(OD_(600)≤1)。本文拟优化发酵培养基成分以获得最大菌体生物量。【方法】运用响应面分析中的因素筛选实验设计和最陡爬坡实验以及中心复合设计法,对菌株的发酵培养基进行了响应面优化。【结果】利用响应面优化法最终确定了菌株最佳培养基:蔗糖36.22g/L,Tryptone 5.31 g/L,Yeast Extract 10.92 g/L,Mg SO_4 0.51 g/L,Na Cl 3.5 g/L,Na_2Mo O_4 0.02 g/L,FeS O_4 0.02 g/L。通过摇瓶发酵后菌体OD600=10.280,达到预测值的94.6%。【结论】利用响应面法成功地对Paenibacillus sp.1-49的发酵培养基进行了优化,为该菌株和其他类芽孢杆菌的大规模发酵提供了参考。     

利用响应面法优化固氮类芽孢杆菌Paenibacillus sp.1–49的发酵培养基（英文）

【目的】固氮类芽孢杆菌Paenibacillus sp.1–49是本实验室分离的具有潜在生物肥料价值的微生物菌剂。该菌在常见的培养基中不能高效生长(OD_(600)≤1)。本文拟优化发酵培养基成分以获得最大菌体生物量。【方法】运用响应面分析中的因素筛选实验设计和最陡爬坡实验以及中心复合设计法,对菌株的发酵培养基进行了响应面优化。【结果】利用响应面优化法最终确定了菌株最佳培养基:蔗糖36.22g/L,Tryptone 5.31 g/L,Yeast Extract 10.92 g/L,Mg SO_4 0.51 g/L,Na Cl 3.5 g/L,Na_2Mo O_4 0.02 g/L,FeS O_4 0.02 g/L。通过摇瓶发酵后菌体OD600=10.280,达到预测值的94.6%。【结论】利用响应面法成功地对Paenibacillus sp.1-49的发酵培养基进行了优化,为该菌株和其他类芽孢杆菌的大规模发酵提供了参考。     

竹黄产胞外多糖液态培养条件优化

目的:优化竹黄Shiraia sp. SUPER-H168产胞外多糖的液态培 养基组成和发酵条件.方法:通过单因素和正交试验优化培养基组成和发酵条件,在65L气升式发酵罐中进行验证试验.结果:经分析确定最佳培 养基组成为(g/L)葡萄糖40,玉米浆粉3,KH2PO4 1,MgSO4*7H2O 0.05;最佳发酵条件为起始pH 6.0,温度30℃,装液量75mL/250mL,接种量10%(v/v).在此条件下,摇瓶中 生物量及多糖分别达到11.7g/L和1.12g/L,验证试验中分别可达到12.1g/L和1.35g/L.结论:放大试验结果优于摇瓶水平,实验数据对生产有一定指导作用.     

Optimization of fermentation medium for triterpenoid production from <Emphasis Type="Italic">Antrodia camphorata</Emphasis> ATCC 200183 using artificial intelligence-based techniques

In this study, alteration in morphology of submergedly cultured Antrodia camphorata ATCC 200183 including arthroconidia, mycelia, external and internal structures of pellets was investigated. Two optimization models namely response surface methodology (RSM) and artificial neural network (ANN) were built to optimize the inoculum size and medium components for intracellular triterpenoid production from A. camphorata. Root mean squares error, R ², and standard error of prediction given by ANN model were 0.31%, 0.99%, and 0.63%, respectively, while RSM model gave 1.02%, 0.98%, and 2.08%, which indicated that fitness and prediction accuracy of ANN model was higher when compared to RSM model. Furthermore, using genetic algorithm (GA), the input space of ANN model was optimized, and maximum triterpenoid production of 62.84 mg l⁻¹ was obtained at the GA-optimized concentrations of arthroconidia (1.78 × 10⁵ ml⁻¹) and medium components (glucose, 25.25 g l⁻¹; peptone, 4.48 g l⁻¹; and soybean flour, 2.74 g l⁻¹). The triterpenoid production experimentally obtained using the ANN–GA designed medium was 64.79 ± 2.32 mg l⁻¹ which was in agreement with the predicted value. The same optimization process may be used to optimize many environmental and genetic factors such as temperature and agitation that can also affect the triterpenoid production from A. camphorata and to improve the production of bioactive metabolites from potent medicinal fungi by changing the fermentation parameters.     

Medium optimization for pyrroloquinoline quinone (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network–genetic algorithm

Methylobacillus sp. zju323 was adopted to improve the biosynthesis of pyrroloquinoline quinone (PQQ) by systematic optimization of the fermentation medium. The Plackett–Burman design was implemented to screen for the key medium components for the PQQ production. CoCl₂?·?6H₂O, ρ-amino benzoic acid, and MgSO₄?·?7H₂O were found capable of enhancing the PQQ production most significantly. A five-level three-factor central composite design was used to investigate the direct and interactive effects of these variables. Both response surface methodology (RSM) and artificial neural network–genetic algorithm (ANN–GA) were used to predict the PQQ production and to optimize the medium composition. The results showed that the medium optimized by ANN–GA was better than that by RSM in maximizing PQQ production and the experimental PQQ concentration in the ANN–GA-optimized medium was improved by 44.3% compared with that in the unoptimized medium. Further study showed that this ANN–GA-optimized medium was also effective in improving PQQ production by fed-batch mode, reaching the highest PQQ accumulation of 232.0?mg/L, which was about 47.6% increase relative to that in the original medium. The present work provided an optimized medium and developed a fed-batch strategy which might be potentially applicable in industrial PQQ production.     

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO₄, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.     

试验设计和优化及其在发酵培养基优化中的应用

在发酵工业中,发酵培养基的优化对发酵水平的提高起着举足轻重的作用。而在寻求最佳发酵培养基的过程中,试验设计及统计优化发挥着重要作用。对近年来常用的试验设计及优化方法进行了综述,内容包括单因素轮换法、析因设计、均匀设计、响应面设计、人工神经网络和遗传算法等多种试验设计和优化方法,并对其进行分析和比较。     

Genetic algorithm-based medium optimization for enhanced production of fluorescent pseudomonad R81 and siderophore

Fluorescent pseudomonad R81, a root-colonizing bacterium, is a potential bio-inoculant due to its plant growth promoting characteristics. It produces hydroxamate-type siderophore which is involved in disease suppression in plants. Genetic algorithm (GA) methodology was applied for the optimization of siderophore and cell mass production simultaneously in shake flask experiments. A total of 10 medium components were optimized within 80 experiments. A high siderophore concentration of 1.9 g/L and cell mass concentration of 2.8 g/L was achieved in the optimized medium. The application of GA was well suited for determination of optimum concentration levels of the medium constituents for a bi-objective function. GA was able to increase the siderophore concentration by 2.8-fold when compared to RSM-based optimization. Further, the batch fermentation of the GA-optimized medium in 14 L bioreactor without pH control produced 2.2 g/L siderophore in 36 h, the highest reported so far. GA was also successfully used to estimate the kinetic parameters of the mathematical models of the batch fermentation.     

Genetic algorithm-based medium optimization for enhanced production of fluorescent pseudomonad R81 and siderophore

Fluorescent pseudomonad R81, a root-colonizing bacterium, is a potential bio-inoculant due to its plant growth promoting characteristics. It produces hydroxamate-type siderophore which is involved in disease suppression in plants. Genetic algorithm (GA) methodology was applied for the optimization of siderophore and cell mass production simultaneously in shake flask experiments. A total of 10 medium components were optimized within 80 experiments. A high siderophore concentration of 1.9 g/L and cell mass concentration of 2.8 g/L was achieved in the optimized medium. The application of GA was well suited for determination of optimum concentration levels of the medium constituents for a bi-objective function. GA was able to increase the siderophore concentration by 2.8-fold when compared to RSM-based optimization. Further, the batch fermentation of the GA-optimized medium in 14 L bioreactor without pH control produced 2.2 g/L siderophore in 36 h, the highest reported so far. GA was also successfully used to estimate the kinetic parameters of the mathematical models of the batch fermentation.     

Screening of Ganoderma strains with high polysaccharides and ganoderic acid contents and optimization of the fermentation medium by statistical methods

Polysaccharides and ganoderic acids (GAs) are the major bioactive constituents of Ganoderma species. However, the commercialization of their production was limited by low yield in the submerged culture of Ganoderma despite improvement made in recent years. In this work, twelve Ganoderma strains were screened to efficiently produce polysaccharides and GAs, and Ganoderma lucidum 5.26 (GL 5.26) that had been never reported in fermentation process was found to be most efficient among the tested stains. Then, the fermentation medium was optimized for GL 5.26 by statistical method. Firstly, glucose and yeast extract were found to be the optimum carbon source and nitrogen source according to the single-factor tests. Ferric sulfate was found to have significant effect on GL 5.26 biomass production according to the results of Plackett–Burman design. The concentrations of glucose, yeast extract and ferric sulfate were further optimized by response surface methodology. The optimum medium composition was 55 g/L of glucose, 14 g/L of yeast extract, 0.3 g/L of ferric acid, with other medium components unchanged. The optimized medium was testified in the 10-L bioreactor, and the production of biomass, IPS, total GAs and GA-T enhanced by 85, 27, 49 and 93 %, respectively, compared to the initial medium. The fermentation process was scaled up to 300-L bioreactor; it showed good IPS (3.6 g/L) and GAs (670 mg/L) production. The biomass was 23.9 g/L in 300-L bioreactor, which was the highest biomass production in pilot scale. According to this study, the strain GL 5.26 showed good fermentation property by optimizing the medium. It might be a candidate industrial strain by further process optimization and scale-up study.     

Enhanced antibiotic production by Streptomyces sindenensis using artificial neural networks coupled with genetic algorithm and Nelder-Mead downhill simplex

Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be 95 microgram/ml, which nearly doubled (176 microgram/ml) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production (197 microgram/ml) was obtained by cultivating the cells with (g/l) fructose 2.7602, MgSO4 1.2369, (NH4)2PO4 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.     

Statistical optimization of medium components for the production of Antrodia cinnamomea AC0623 in submerged cultures

The nutritional medium requirement for biomass and triterpenoid production by Antrodia cinnamomea AC0623 strain was optimized. Box–Behnken was applied to optimize biomass and triterpenoid production. According to response surface methodology (RSM), the optimum concentrations of N-source were determined. The results indicate that when a submerged culture in shake flasks was operated at 28°C, initial pH 5.5, and rotation speed 105 rpm, the biomass and triterpenoid content in dry basis could be increased to 3.20% (w/w) and 31.8 mg/g, respectively. The experiments were further scaled up to 100- and 700-l fermentors. Higher content of triterpenoids (63.0 mg/g) was obtained in 700-l fermentations by means of the control of cultural conditions and the modification of medium composition based on the RSM.