合成生物学
工作流程
计算机科学
领域(数学)
数据科学
人工智能
机器学习
管理科学
生化工程
工程类
计算生物学
生物
数学
数据库
纯数学
作者
Brendan Fu‐Long Sieow,Ryan De Sotto,Zhi Ren Darren Seet,In Young Hwang,Matthew Wook Chang
出处
期刊:Methods in molecular biology
日期:2022-10-13
卷期号:: 21-39
被引量:6
标识
DOI:10.1007/978-1-0716-2617-7_2
摘要
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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