Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Abstract

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH2PO4, K2HPO4, FeSO4, MnSO4, and MgSO4 and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett—Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO4, K2HPO4, starch and soybean meal. Indeed, it was found that soybean meal, K2HPO4, KH2PO4 and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett—Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation.