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Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis

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Abstract

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 2) 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.

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Acknowledgements

This work was supported by grants from the ‘‘Ministère de l’Enseignement Supérieur et de la Recherche Scientifique” of Tunisia. The authors are grateful to Kamel MAALOUL, translator and English professor, for having proofread the manuscript.

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  1. Senior author: Nabil Zouari.

Authors and Affiliations

  1. Centre of Biotechnology of Sfax, Sfax University, 3038, Sfax, Tunisia

    Karim Ennouri, Rayda Ben Ayed & Fathi Hertelli

  2. Laboratoire Ressources et Amélioration Génétiques de l’Olivier, du Pistachier et de l’Amandier, Institut de l’Olivier, Sfax, 3000, Tunisia

    Mohamed Ali Triki

  3. On Air s.r.l., via Carlo Barabino 26/4B, Genoa, Italy

    Ennio Ottaviani & Maura Mazzarello

  4. Biological and Environmental Sciences Department, College of Arts and Sciences, Qatar University, Doha, Qatar

    Nabil Zouari

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  1. Karim Ennouri
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Ennouri, K., Ben Ayed, R., Triki, M.A. et al. Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis . 3 Biotech 7, 187 (2017). https://doi.org/10.1007/s13205-017-0799-1

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