吞吐量
序列空间
适应度函数
集合(抽象数据类型)
计算机科学
序列(生物学)
DNA测序
选择(遗传算法)
计算生物学
人工智能
生物
机器学习
DNA
遗传算法
遗传学
数学
纯数学
巴拿赫空间
无线
程序设计语言
电信
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2022-01-01
卷期号:: 123-146
标识
DOI:10.1016/b978-0-12-824469-2.00011-7
摘要
Directed evolution and deep mutational scanning are two high-throughput techniques used to navigate the sequence space using biological fitness. In directed evolution, variants with improved fitness are selected from mutant libraries for engineering better biological catalysts. In deep mutational scans, the fitness/activity of several variants is measured in parallel to better understand the function and evolution of biological systems. With the advent of cheap DNA synthesis, sequencing, and CRISPR-based platforms, high-throughput technologies are becoming increasingly accessible and indispensable across fields. In this chapter, I present how biological fitness is measured and exploited in high-throughput technologies. I discuss the complexity of fitness and the challenges associated with measuring fitness accurately. Then I lay down a set of postulates that are important to remember while planning high-throughput experiments. In the second half of the chapter, I summarize methods for constructing libraries at the protein and genome-wide scales.
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