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作 者:王嘉[1] 关怡新[1] 王海清[1] 姚善泾[1]
机构地区:[1]浙江大学化学工程与生物工程学系,杭州310027
出 处:《生物工程学报》2006年第4期662-666,共5页Chinese Journal of Biotechnology
基 金:浙江省科技计划(No.2005C31028);杭州市科技计划(No.200433124)资助项目。~~
摘 要:环氧黄体酮羟化产物是多种甾体激素药物的中间体,其11-羟化过程利用犁头霉(Absidiacoerulea)所得转化率远高于其他菌株。采用拟结晶投料方式,将环氧黄体酮颗粒细化后加一定量水,β-环糊精,吐温-80,超声波乳化后投入发酵液中。这种投料方式可避免传统投料中使用有机溶剂毒害细胞的缺点,更利于底物转化。采用多层前传神经网络建立培养基和投料成分配比与转化率关系模型,并将具有全局寻优性能的粒子群优化算法(PSO)应用于培养基和投料成分配比的优化,收敛速度快,效果好。在优化的操作条件下,摇瓶中投料浓度为10g/L时底物转化率达到87.5%,在3.7L发酵罐中投料浓度提高到20g/L时底物转化率仍高达86.6%。The ll-hydroxylation of 16α, 17α-epoxy-4-pregenene-3,20-dione as a useful intermediate for the preparation of hormones can be achieved by the mycelium of Absidia coerulea at higher conversion rate than using other strains. In this paper 16α, 17α-epoxy-4-pregenene-3,20-dione mixed with a little water, β-cyclodextrin, Tween-80 was introduced into the fermentation broth after uhrasonication to increase pseudo-water-solubility of the hydrophobic substrate. This pseudo-crystallo feed could avoid the toxicity of organic solvents and was more available for the microbial transformation. The multi layer feed-forward neural network was used to setup a model which indicated the relationship between medium and feed components and the conversion rate. Particle swarm optimization (PSO), which was a stochastic global optimization algorithm and of which the convergence speed was high, was applied to obtain the optimal concentration of the medium and feed components. At optimum conditions with the pseudo-crystallo feed, the conversion rate of 16α, 17α-epoxy-4-pregenene-3,20-dione at an initial concentration of 10g/L was 87.5% in shaking flasks. The conversion rate of the substrate was up to 86.6% at higher concentration of 20g/L feed in a 3.7L fermentor.
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