Ennouri Karim, Ben Ayed Rayda, Triki Mohamed Ali, Ottaviani Ennio, Mazzarello Maura, Hertelli Fathi, Zouari Nabil
Centre of Biotechnology of Sfax, Sfax University, 3038, Sfax, Tunisia.
Laboratoire Ressources et Amélioration Génétiques de l'Olivier, du Pistachier et de l'Amandier, Institut de l'Olivier, Sfax, 3000, Tunisia.
3 Biotech. 2017 Jul;7(3):187. doi: 10.1007/s13205-017-0799-1. Epub 2017 Jun 29.
The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.
本研究的目的是开发一种模型，该模型能够准确预测苏云金芽孢杆菌库尔斯塔克变种HD-1产生的δ-内毒素和蛋白酶的产量。以可用的培养基成分作为变量，采用基于Plackett-Burman设计（PB）的数学方法，对通过Bootstrap方法生成并经多元线性回归（MLR）和人工神经网络（ANN，包括多层感知器（MLP）和径向基函数（RBF）模型）处理的数据进行分析和比较。通过确定系数（R）和均方误差（MSE）值来比较输出数据，从而评估这些模型的预测能力。结果表明，与MLR方法（δ-内毒素和蛋白酶的确定系数分别为73.1%和77.2%）相比，ANN技术（δ-内毒素和蛋白酶的确定系数分别为87%和89%）对δ-内毒素和蛋白酶产量的预测更准确，这表明所提出的人工神经网络，尤其是MLP，是一种适用于确定细菌产物产量的新方法，它使我们能够在更短的时间内、以更少的工程工作量做出更合适的预测。
