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首页> 外文期刊>Biotechnology Progress >Using Highly Efficient Nonlinear Experimental Design Methods for Optimization of Lactococcus lactis Fermentation in Chemically Defined Media
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Using Highly Efficient Nonlinear Experimental Design Methods for Optimization of Lactococcus lactis Fermentation in Chemically Defined Media

机译：使用高效非线性实验设计方法优化化学限定培养基中乳酸乳球菌的发酵

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摘要

Optimization of fermentation media and processes is a difficult task due to the potential for high dimensionality and nonlinearity. Here we develop and evaluate variations on two novel and highly efficient methods for experimental fermentation optimization. The first approach is based on using a truncated genetic algorithm with a developing neural network model to choose the best experiments to run. The second approach uses information theory, along with Bayesian regularized neural network models, for experiment selection. To evaluate these, methods experimentally, we used them to develop a new chemically defined medium for Lactococcus lactis IL1403, along with an optimal temperature and initial pH, to achieve maximum cell growth. The media consisted of 19 defined components or groups of components. The optimization results show that the maximum cell growth from the optimal process of each novel method is generally comparable to or higher than that achieved using a traditional statistical experimental design method, but these optima are reached in about half of the experiments (73-94 vs. 161, depending on the variants of methods). The optimal chemically defined media developed in this work are rich media that can support high cell density growth 3.5-4 times higher than the best reported synthetic medium and 72% higher than a commonly used complex medium (M17) at optimization scale. The best chemically defined medium found using the method was evaluated and compared with other defined or complex media at flask- and fermentor-scales.
机译：发酵培养基和工艺的优化是一项艰巨的任务，因为它具有高维和非线性的潜力。在这里，我们开发和评估两种新型高效发酵发酵优化方法的变异。第一种方法基于将截短的遗传算法与正在开发的神经网络模型结合使用，以选择最佳的实验进行。第二种方法使用信息论以及贝叶斯正则化神经网络模型进行实验选择。为了通过实验评估这些方法，我们使用它们为乳酸乳球菌IL1403开发了一种新的化学成分确定的培养基，以及最佳温度和初始pH，以实现最大的细胞生长。介质由19个定义的组件或组件组组成。优化结果表明，每种新方法的最佳工艺产生的最大细胞生长通常与传统统计实验设计方法可比或更高，但这些优化在大约一半的实验中得以实现（73-94 vs 161，取决于方法的变体）。在这项工作中开发的最佳化学定义培养基是富培养基，在优化规模下，它可以支持高细胞密度生长，比最佳报道的合成培养基高3.5-4倍，比常用的复杂培养基（M17）高72％。对使用该方法找到的最佳化学成分确定的培养基进行了评估，并与烧瓶和发酵罐规模的其他成分明确或复杂的培养基进行了比较。

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