利用实验设计指导基因优化工程代谢途径。

IF 3.2 4区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal of Industrial Microbiology & Biotechnology Pub Date : 2024-01-09 DOI:10.1093/jimb/kuae010
Seonyun Moon, Anna Saboe, Michael J Smanski
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引用次数: 0

摘要

实验设计(DoE)是一个术语,用于描述应用统计方法来分析多个变量对多元系统性能的影响。它通常用于化学工程和材料科学等领域的工艺优化。最近,定量控制生物系统中基因表达的能力取得了进展,这为将 DoE 应用于基因优化提供了可能。在这篇以基因和代谢工程师为对象的综述中,我们从高层次介绍了 DoE 的几种方法,并描述了将这些方法应用于分析或优化工程基因系统的实例。我们讨论了应用 DoE 所面临的挑战,并提出了缓解这些挑战的策略。
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Using design of experiments to guide genetic optimization of engineered metabolic pathways.

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

One-sentence summary: This is a review of literature related to applying Design of Experiments for genetic optimization.

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来源期刊
Journal of Industrial Microbiology & Biotechnology
Journal of Industrial Microbiology & Biotechnology 工程技术-生物工程与应用微生物
CiteScore
7.70
自引率
0.00%
发文量
25
审稿时长
3 months
期刊介绍: The Journal of Industrial Microbiology and Biotechnology is an international journal which publishes papers describing original research, short communications, and critical reviews in the fields of biotechnology, fermentation and cell culture, biocatalysis, environmental microbiology, natural products discovery and biosynthesis, marine natural products, metabolic engineering, genomics, bioinformatics, food microbiology, and other areas of applied microbiology
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