Modelling The Fitness Landscapes of a SCRaMbLEd Yeast Genome

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI:10.1109/CIBCB49929.2021.9562918

Bill Yang, Goksel Misirli, A. Wipat, J. Hallinan

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引用次数: 2

Abstract

The use of microorganisms for the production of industrially important compounds and enzymes is becoming increasingly important. Eukaryotes have been less widely used than prokaryotes in biotechnology, because of the complexity of their genomic structure and biology. The Yeast2.0 project is an international effort to engineer the yeast Saccharomyces cerevisiae to make it easy to manipulate, and to generate random variants using a system called SCRaMbLE. SCRaMbLE relies on artificial evolution in vitro to identify useful variants, an approach which is time consuming and expensive. We developed an in silico simulator for the SCRaMbLE system, using an evolutionary computing approach, which can be used to investigate and optimize the fitness landscape of the system. We applied the system to the investigation of the fitness landscape of one of the S. cerevisiae chromosomes, and found that our results fitted well with those previously published. Our simulator can be applied to the analysis of the fitness landscapes of any organism for which SCRaMbLE has been implemented.

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酵母基因组的适应性景观建模

利用微生物生产工业上重要的化合物和酶正变得越来越重要。真核生物由于其基因组结构和生物学特性的复杂性，在生物技术中的应用不如原核生物广泛。Yeast2.0项目是一项国际合作，旨在改造酿酒酵母，使其易于操作，并使用一种名为SCRaMbLE的系统生成随机变体。SCRaMbLE依靠体外人工进化来识别有用的变异，这种方法既耗时又昂贵。我们为SCRaMbLE系统开发了一个硅模拟器，使用进化计算方法，可用于调查和优化系统的适应度景观。我们将该系统应用于研究酿酒酵母一条染色体的适合度景观，发现我们的结果与先前发表的结果非常吻合。我们的模拟器可以应用于分析任何有机体的适应性景观，其中SCRaMbLE已经实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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