【作者】 田宇；

【导师】 陈守文；

【作者基本信息】 华中农业大学 ， 发酵工程， 2006， 硕士

【摘要】 泰乐菌素是由弗氏链霉菌(Streptomyces fradiae)产生的一类十六元大环内酯禽畜专用抗生素，被国内外广泛用作兽药和饲料添加剂，是国家规划重点发展的兽药产品。 本文通过对泰乐菌素产生菌弗氏链霉菌BMB-012进行复壮，获得了一株发酵效价相对较高的菌株A-49。以菌株A-49为出发菌株，依次用紫外线氯化锂复合诱变、硫酸二乙酯诱变、链霉素抗性诱变等多种诱变手段对菌株A-49进行育种工作。筛选得到一株遗传性状稳定的高产菌株S-22，其摇瓶发酵效价较出发菌株提高了68％。 对高产菌株S-22进行了发酵条件进行了初步优化。通过研究不同种子培养条件对菌体生长和发酵的影响，确定了种子的最佳培养条件。对发酵培养基配方进行了单因子实验，研究了发酵培养基中的碳源、氮源、无机盐等对泰乐菌素发酵的影响。对发酵培养基主要组成成分进行了正交实验，得到了培养基配方SH1，从而将泰乐菌素的发酵效价提高到了10886μg／ml。 为了进一步提高泰乐菌素的发酵效价，采用BP神经网络结合遗传算法的方法对发酵培养基中的菜油、鱼粉D、甜菜碱盐酸盐、组份X这四种组份进行优化。首先运用BP神经网络建立有效的发酵效价预测模型，然后在此基础上采用遗传算法对上述四种培养基组份进行全局寻优，得到其最佳配比：菜油36g／L、鱼粉D18.5g／L、甜菜碱盐酸盐1.8g/L、组份X 8.8g／L，泰乐菌素发酵效价达到12878μg／ml，与模型预测值的误差为2.08％。采用上述方法优化后的培养基使泰乐菌素的发酵效价比采用正交优化的培养基SH1提高了18.3％。结果表明，运用BP神经网络结合遗传算法优化泰乐菌素发酵培养基的方法是行之有效的。 采用单因素实验的方法对接种量、初始培养基pH、装液量、发酵温度等发酵条件进行了优化，确定了较适的发酵条件为：灭菌前培养基pH调至7.2，种子液的接种量为12％，发酵摇瓶的装液量为40ml／500ml三角瓶，28℃培养7d。在此优化发酵培养基和发酵条件下进行摇瓶发酵，泰乐菌素发酵效价达到13216μg／ml。更多还原

【Abstract】 Tylosin is a commercially-important, 16-membered macrolide antibiotic produced by Streptomyces fradiae. It exclusively applied in animals to control the mycoplasma induced diseases, improve feed conversion efficiency, and increase average daily weight gain.A stable mutated strain A-49 was obtained from the spores of tylosin producer Streptomyces fradiae BMB-012 by purification. The strain A-49 was chosen as the start strain for mutagenic treatments with ultraviolet irradiation + LiCl, DES and Streptomycin resistant rational screening, after first and second selection, the mutant S-22 was selected. It showed better growth and yield characters than the strain A-49.The tylosin yield of the strain S-22 was 9168μg/ml which improved 68%.The fermentation medium optimized experiment with the strain S-22 was on shaking flask level. The effects of carbon sources, nitrogen sources, inorganic salts, were studied by means of single factor. The medium SH1 was established by orthonormal experiment. The production of tylosin in medium SH1 was 10886μg/ml.Back-Propagation (BP) neural network and genetic algorithms (GA) were employed to optimize the medium for production of tylosin. BP neural network was applied for modeling fermentation medium of tylosin. Based on the model, the fermentation medium compositions which included rape oil、 fish meal D、 betaine hydrochloride and ingredient X ,were optimized by GA. The optimized result was: rape oil 36g/L、 fish meal D 18.5g/L、 betaine hydrochloride 1.8g/L、 ingredient X8.8g/L. The production of tylosin in optimized medium was 12878μg/ml, which was increased by 18.3% compared to that in medium SH1.The relative error between the experimental value and the predictive value of tylosin production from the optimized medium was2.08%.Those results showed it was effective by the method of BP neural network and genetic algorithms in optimizing the medium for production of tylosin.The optimal fermentation conditions of the strain S-22 were studied by means of single factor. From the shake-flask, the optimal fermentation conditions were: initial pH 7.2,12% inoculums, 28℃, 40ml/500ml shake flask, culture 7 days. With the optimal fermentation medium and condition, the productivity of tylosin could be up to 13216μg/ml.更多还原