Artificial selection methods from evolutionary computing show promise for directed evolution of microbes.
Artificial selection methods from evolutionary computing show promise for directed evolution of microbes.
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来自进化计算的人工选择方法显示了微生物定向进化的希望。
DOI：
10.7554/elife.79665
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发表时间：
2022-08-02
期刊：
影响因子：
7.7
通讯作者：
Zaman L
中科院分区：
文献类型：
--
作者：
Lalejini A;Dolson E;Vostinar AE;Zaman L
定向微生物进化利用实验室中的进化过程来构建具有增强或新功能特性的微生物。试图指导应用目标的进化过程是进化计算的基础，它利用达尔文进化论的原理作为解决具有挑战性的计算问题的通用搜索引擎。尽管它们的方法重叠，但来自进化计算的人工选择方法通常不适用于实验室中的生命系统。在这项工作中，我们问父母的选择算法程序选择有前途的祖先进化计算可能是有用的指导微生物种群的进化时，选择多个功能性状。要做到这一点，我们引入了一个基于代理的定向微生物进化模型，我们用它来评估如何以及三个选择算法从进化计算（锦标赛选择，lexicase选择，和非支配精英选择）执行相对于实验室中常用的方法（精英和前10%的选择）。我们发现，多目标选择技术从进化计算（词典和非支配精英）一般优于常用的定向进化方法时，选择多个感兴趣的性状。我们的研究结果激发了正在进行的工作，将这些多目标选择程序转移到实验室中，并继续评估更复杂的人工选择方法。人类早就知道如何为了自己的利益而选择进化过程。他们仔细选择要繁殖的个体，以便使有益的性状得以保持，他们驯养了狗、小麦、奶牛和许多其他物种来满足他们的需要。生物学家最近改进了这些“人工选择”方法，将重点放在微生物上。希望获得具有理想特征的微生物，例如降解塑料或产生有价值分子的能力。然而，现有的对微生物进行人工选择的方法是有限的，有时是无效的。计算机科学家也利用进化原理来实现自己的目的，开发出高效的人工选择协议，用于寻找具有挑战性的计算问题的解决方案。然而，由于这两个领域之间的交流有限，在进化计算中磨练了几十年的复杂选择协议尚未被评估用于生物种群。在他们的工作中，Lalejini等人比较了为进化计算或微生物工作开发的流行人工选择协议。两种计算选择方法显示出在实验室中改进定向进化的希望。至关重要的是，这些选择协议不同于传统使用的方法，选择多样性和性能，而不是性能单独。这些有前途的方法现在正在实验室中进行测试，对医疗，生物技术和农业应用具有潜在的深远意义。虽然进化计算的起源归功于我们对生物过程的理解，但它可以提供很多回报来帮助我们利用这些相同的机制。Lalejini等人的结果有助于弥合计算和生物社区之间的差距，这两个社区都可以从增加的合作中受益。
Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods. Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs. Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective. Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations. In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications. While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.