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Multi-objective optimization in Aspergillus niger fermentation for selective product enhancement

机译：黑曲霉发酵中的多目标优化，以增强选择性产物

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摘要

A multi-objective optimization formulation that reflects the multi-substrate optimization in a multi-product fermentation is proposed in this work. This formulation includes the application of ε-constraint to generate the trade-off solution for the enhancement of one selective product in a multi-product fermentation, with simultaneous minimization of the other product within a threshold limit. The formulation has been applied to the fed-batch fermentation of Aspergillus niger that produces a number of enzymes during the course of fermentation, and of these, catalase and protease enzyme expression have been chosen as the enzymes of interest. Also, this proposed formulation has been applied in the environment of three control variables, i.e. the feed rates of sucrose, nitrogen source and oxygen and a set of trade-off solutions have been generated to develop the pareto-optimal curve. We have developed and experimentally evaluated the optimal control profiles for multiple substrate feed additions in the fed-batch fermentation of A. niger to maximize catalase expression along with protease expression within a threshold limit and vice versa. An increase of about 70% final catalase and 31% final protease compared to conventional fed-batch cultivation were obtained. Novel methods of oxygen supply through liquid-phase H_2O_2 addition have been used with a view to overcome limitations of aeration due to high gas-liquid transport resistance. The multi-objective optimization problem involved linearly appearing control variables and the decision space is constrained by state and end point constraints. The proposed multi-objective optimization is solved by differential evolution algorithm, a relatively superior population-based stochastic optimization strategy.
机译：在这项工作中，提出了反映多产品发酵中多底物优化的多目标优化公式。该配方包括应用ε约束来生成权衡解决方案，以增强多产品发酵中一种选择性产品的性能，同时将另一种产品的阈值限制降至最低。该制剂已应用于黑曲霉的补料分批发酵中，其在发酵过程中产生多种酶，并且其中过氧化氢酶和蛋白酶表达被选为目的酶。同样，该提议的配方已经在三个控制变量的环境中应用，即蔗糖，氮源和氧气的进料速率，并且已经产生了一组折衷方案以形成最佳的曲线。我们已经开发并实验评估了黑曲霉补料分批发酵中添加多种底物饲料的最佳控制特性，以在阈值限制内最大化过氧化氢酶表达以及蛋白酶表达，反之亦然。与常规分批补料培养相比，最终的过氧化氢酶和最终的蛋白酶增加了约70％。为了克服由于高的气液传输阻力而引起的曝气限制，已经使用了通过液相H_2O_2添加来提供氧气的新方法。多目标优化问题涉及线性出现的控制变量，并且决策空间受状态和端点约束的约束。提出的多目标优化是通过差分进化算法解决的，差分进化算法是一种相对优越的基于种群的随机优化策略。

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