Vlajkov Vanja, Anđelić Stefan, Pajčin Ivana, Grahovac Mila, Budakov Dragana, Jokić Aleksandar, Grahovac Jovana
Faculty of Technology Novi Sad, University of Novi Sad, 21000 Novi Sad, Serbia.
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
Microorganisms. 2022 Jun 6;10(6):1165. doi: 10.3390/microorganisms10061165.
One of the leading limiting factors for wider industrial production and commercialization of microbial biopesticides refers to the high costs of cultivation media. The selection of alternative sources of macronutrients crucial for the growth and metabolic activity of the producing microorganism is a necessary phase of the bioprocess development. Gaining a better understanding of the influence of the medium composition on the biotechnological production of biocontrol agents is enabled through bioprocess modelling and optimization. In the present study, after the selection of optimal carbon and nitrogen sources, two modelling approaches were applied to mathematically describe the behavior of the examined bioprocess-the production of biocontrol agents effective against aflatoxigenic strains. The modelling was performed using four independent variables: cellulose, urea, ammonium sulfate and dipotassium phosphate, and the selected response was the inhibition-zone diameter. After the comparison of the results generated by the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN) approach, the first model was chosen for the further optimization step due to the better fit of the experimental results. As the final investigation step, the optimal cultivation medium composition was defined (g/L): cellulose 5.0, ammonium sulfate 3.77, dipotassium phosphate 0.3, magnesium sulfate heptahydrate 0.3.
微生物生物农药更广泛的工业化生产和商业化的主要限制因素之一是培养基成本高昂。选择对生产微生物的生长和代谢活性至关重要的替代大量营养素来源是生物工艺开发的必要阶段。通过生物工艺建模和优化，可以更好地了解培养基组成对生物防治剂生物技术生产的影响。在本研究中，在选择了最佳碳源和氮源后，应用了两种建模方法来数学描述所研究的生物过程——对产黄曲霉毒素菌株有效的生物防治剂的生产。使用四个自变量进行建模：纤维素、尿素、硫酸铵和磷酸二氢钾，选定的响应是抑菌圈直径。在比较了响应面法（RSM）和人工神经网络（ANN）方法产生的结果后，由于实验结果拟合度更好，选择了第一个模型进行进一步的优化步骤。作为最终的研究步骤，确定了最佳培养基组成（g/L）：纤维素5.0、硫酸铵3.77、磷酸二氢钾0.3、七水硫酸镁0.3。
