LaCroix Ryan A, Palsson Bernhard O, Feist Adam M
Department of Bioengineering, University of California, San Diego, California, USA.
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
Appl Environ Microbiol. 2017 Mar 31;83(8). doi: 10.1128/AEM.03115-16. Print 2017 Apr 15.
The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., laboratory supplies, personnel, and time). To design and to better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to the optimization of ALE experiments. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim, to explain differences in experimental outcomes. Furthermore, a beneficial mutation rate of 10 to 10 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results described here will aid in the design of ALE experiments to fit the exact needs of a project while taking into account the resources required and will lower the barriers to entry for this experimental technique. ALE is a widely used scientific technique to increase scientific understanding, as well as to create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform, with little optimization for efficiency. Such inefficiencies result in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve.
突变的发生是适应性进化理论的基石，它利用了突变赋予适应性优势的罕见机会。在工业和基础科学应用的实验室环境中，自然选择正越来越多地得到利用。尽管其应用日益广泛，但目前尚无用于设计和开展适应性实验室进化（ALE）实验的标准化程序。因此，有必要优化实验设计，特别是要确定何时可认为实验完成，以及如何在实验结果与可用资源（即实验室耗材、人员和时间）之间取得平衡。为了设计并更好地理解ALE实验，开发并验证了一个模拟器ALEsim，并将其应用于ALE实验的优化。通过实验确定了不同传代规模的影响，随后用ALEsim进行评估，以解释实验结果的差异。此外，得出了每个细胞分裂产生10至10个有益突变的速率。对ALE实验的回顾性分析表明，连续传代分批培养ALE实验中通常采用的传代规模导致有益突变的产生和固定效率低下。ALEsim及本文所述结果将有助于设计符合项目确切需求的ALE实验，同时考虑所需资源，并将降低这项实验技术的入门门槛。ALE是一种广泛应用的科学技术，可增进科学认识，并创造与工业相关的生物体。ALE实验的开展方式高度依赖人工且缺乏变化，几乎没有针对效率进行优化。这种低效率导致实验效果欠佳，可能需要数月才能完成。随着自动化和计算机模拟技术的出现，我们现在可以以优化的方式进行这些实验，并设计实验以在更短的时间内产生更高的适应性，从而突破适应性实验室进化所能达到的极限。
