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组合优化策略在合成生物学中的应用。

Application of combinatorial optimization strategies in synthetic biology.

机构信息

Institut für Chemie, Humboldt Universität zu Berlin, 12489, Berlin, Germany.

Center for Biotechnology, Rensselaer Polytechnic Institute, Troy, NY, USA.

出版信息

Nat Commun. 2020 May 15;11(1):2446. doi: 10.1038/s41467-020-16175-y.

Abstract

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.

摘要

在合成生物学的第一波浪潮中,遗传元件被组合成简单的电路,用于控制单个细胞功能。在合成生物学的第二波浪潮中,简单的电路被组合成复杂的电路,形成系统级的功能。然而,构建复杂电路的努力往往受到我们对单个电路最佳组合的有限知识的阻碍。例如,在大多数代谢工程项目中,一个基本问题是最佳酶水平以最大化输出。为了解决这一问题,已经建立了组合优化方法,允许在没有单个基因表达水平最佳组合的先验知识的情况下进行自动优化。这篇综述重点介绍了当前的组合优化方法和新兴技术,这些技术促进了它们的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7a0/7229011/9e7efed21537/41467_2020_16175_Fig1_HTML.jpg

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