摘要
为了获得怀槐悬浮细胞合成异黄酮的最适培养条件 ,采用ANNs(人工神经网络 )结合RAGA(实数编码加速遗传算法 )对培养基组成进行全局寻优。培养基中影响异黄酮染料木素产率的主要组成是KNO3、(NH4 ) 2 SO4 、2 ,4 D和 6 BA。在它们的有效作用浓度范围内 ,用随机 1 0组培养基组合及细胞染料木素产率为输入和输出由ANNs对数据建模 ,由RAGA优化模型参数。建立的优化模型准确性高 ,依赖模型由RAGA全局寻优获得的最佳培养基组合是 1 4 9 6 8mg L (NH4 ) 2 SO4 、2 936 1 0mg LKNO3、0 0 1mg L 2 ,4 D和 0 1 9mg L 6 BA ,染料木素产率达 1 4 1 3mg L ,与模型预测值的误差为 7 38%。结果表明 ,运用神经网络结合遗传算法优化怀槐细胞合成异黄酮的培养条件是可行的 。
The medium for isoflavone production in Maackia amurensis suspension cells has been optiwised through the artificial neural networks (ANNs) and the real coding based accelerating genetic algorithm (RAGA). Among the ingredients of the medium, nitrogen sources and plant growth regulators were found to be the main factors affecting the production of isoflavone genistein. (NH 4) 2SO 4, KNO 3, 2,4 D and 6 BA, 100～800 mg/L, 1500～3000 mg/L, 0～3 mg/L and 0～1 mg/L respectively, significantly increased genistein yield,in the ranges of effective concentrations. The random ten combinations of these four components generated by RAGA as input data and the genistein yields of ten combinations as output data were used for ANNs RAGA (the artificial neural networks associated with the accelerating genetic algorithm) modeling. The resultant model showed a high fit between the experimental data and calculating values by ANNs RAGA. Based on the prediction of the model, the optimum combination of four factors for genistein production was determined on 149 68 mg/L for (NH 4) 2SO 4, 2936.10 mg/L KNO 3, 0 01 mg/L 2,4 D and 0 19 mg/L 6 BA. When cells were cultured in the optimized medium, their capability of genistein production was remarkably enhanced to 14 13 mg/L, which was about 19 times higher than that in the original medium. The relative discrepancy between the experimental value and the predictive value of genistein yield from the optimized medium was 7 38%.
出处
《生物工程学报》
CAS
CSCD
北大核心
2004年第5期759-763,共5页
Chinese Journal of Biotechnology
基金
国家自然科学基金资助 (No .3 0 170 0 5 9)~~
