A freshwater alkaliphilic strain of Pseudomonas aeruginosa, grown on waste frying oil-basal medium, produced a surface-active metabolite identified as glycolipopeptide. Bioprocess conditions namely temperature, pH, agitation and duration were comparatively modeled using statistical and artificial neural network (ANN) methods to predict and optimize product yield using the matrix of a central composite rotatable design (CCRD). Response surface methodology (RSM) was the statistical approach while a feed-forward neural network, trained with Levenberg–Marquardt back-propagation algorithm, was the neural network method. Glycolipopeptide model was predicted by a significant (P < 0.001, R² of 0.9923) quadratic function of the RSM with a mean squared error (MSE) of 3.6661. The neural network model, on the other hand, returned an R² value of 0.9964 with an MSE of 1.7844. From all error metrics considered, ANN glycolipopeptide model significantly (P < 0.01) outperformed RSM counterpart in predictive modeling capability. Optimization of factor levels for maximum glycolipopeptide concentration produced bioprocess conditions of 32 °C for temperature, 7.6 for pH, agitation speed of 130 rpm and a fermentation time of 66 h, at a combined desirability function of 0.872. The glycosylated lipid-tailed peptide demonstrated significant anti-bacterial activity (MIC = 8.125 µg/mL) against Proteus vulgaris, dose-dependent anti-biofilm activities against Escherichia coli (83%) and Candida dubliniensis (90%) in 24 h and an equally dose-dependent cytotoxic activity against human breast (MCF-7: IC50 = 65.12 µg/mL) and cervical (HeLa: IC50 = 16.44 µg/mL) cancer cell lines. The glycolipopeptide compound is recommended for further studies and trials for application in human cancer therapy.

一种淡水嗜碱铜绿假单胞菌菌株，在废弃的煎炸油基础培养基上生长，产生了一种表面活性代谢物，被鉴定为糖脂肽。使用统计和人工神经网络 (ANN) 方法对生物工艺条件（即温度、pH、搅拌和持续时间）进行比较建模，以使用中央复合可旋转设计 (CCRD) 的矩阵预测和优化产品产量。响应面方法 (RSM) 是统计方法，而使用 Levenberg-Marquardt 反向传播算法训练的前馈神经网络是神经网络方法。糖脂肽模型预测显着 ( P  < 0.001, R ²0.9923) RSM 的二次函数，均方误差 (MSE) 为 3.6661。另一方面，神经网络模型返回的R ²值为 0.9964，MSE 为 1.7844。从所有考虑的误差指标来看，ANN 糖脂肽模型在预测建模能力方面显着 ( P  < 0.01) 优于 RSM 对应模型。优化最大糖脂肽浓度的因子水平产生了温度为 32°C、pH 为 7.6、搅拌速度为 130 rpm 和发酵时间为 66 小时的生物工艺条件，组合合意函数为 0.872。糖基化的脂尾肽对普通变形杆菌表现出显着的抗菌活性 (MIC = 8.125 µg/mL), 24 小时内对大肠杆菌(83%) 和都柏林念珠菌(90%) 的剂量依赖性抗生物膜活性和对人乳房 (MCF-7: IC50 = 65.12 µg/mL) 和宫颈的同等剂量依赖性细胞毒活性(HeLa: IC50 = 16.44 µg/mL) 癌细胞系。推荐将糖脂肽化合物用于进一步研究和试验以应用于人类癌症治疗。