Institute of Biochemical Engineering, Faculty of Mechanical Engineering, Technische Universität München, Boltzmannstrasse 15, Garching, Germany.
Biotechnol Prog. 2012 Nov-Dec;28(6):1499-506. doi: 10.1002/btpr.1635. Epub 2012 Oct 18.
Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem-specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D-optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (~1.400 U g(-1) refolded activity), which were 3.4-fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two-step statistical DOE.
实验问题的优化在工程和科学领域都是一项具有挑战性的任务。原则上，存在两种不同的实验设计（DOE）策略：统计和随机方法。这两种方法都旨在有效地、精确地在特定于问题的搜索空间内识别最佳解决方案。在这里，我们在同一个实验问题上评估和比较了这两种策略，该问题是优化脂肪酶Thermomyces lanuginosus 的折叠条件，该酶有 26 个研究变量。蛋白质折叠是重组蛋白过程开发的主要瓶颈之一。尽管付出了巨大的努力，但仅从序列信息预测折叠仍然不适用。相反，合适的折叠条件通常是通过大量筛选实验经验性地得出的。因此，蛋白质折叠应该是 DOE 策略的一个很好的性能测试。我们比较了一种迭代随机优化方法，该方法应用遗传算法，以及一种由 D-最优筛选步骤和响应面方法优化组成的标准统计设计。我们的结果表明，只有随机优化能够识别最佳折叠条件（~1.400 U g(-1)折叠活性），比标准方法高 3.4 倍。此外，随机优化证明非常稳健，因为三个独立的优化结果相似。相比之下，统计 DOE 导致了次优的解决方案，并且未能识别出可比的活性。过程变量之间的相互作用被证明是这种优化的关键。因此，线性筛选模型无法正确识别最重要的过程变量。由此，本研究强调了经典两步统计 DOE 的局限性。
