利用机器学习增强和加速合成生物学

IF 4.7 3区 工程技术 Q2 ENGINEERING, BIOMEDICAL Current Opinion in Biomedical Engineering Pub Date : 2024-08-02 DOI:10.1016/j.cobme.2024.100553
{"title":"利用机器学习增强和加速合成生物学","authors":"","doi":"10.1016/j.cobme.2024.100553","DOIUrl":null,"url":null,"abstract":"<div><p>Engineering synthetic regulatory circuits with precise input–output behavior—a central goal in synthetic biology—remains encumbered by the inherent molecular complexity of cells. Non-linear, high-dimensional interactions between genetic parts and host cell machinery make it difficult to design circuits using first-principles biophysical models. We argue that adopting data-driven approaches that integrate modern machine learning (ML) tools and high-throughput experimental approaches into the synthetic biology design/build/test/learn process could dramatically accelerate the pace and scope of circuit design, yielding workflows that rapidly and systematically discern design principles and achieve quantitatively precise behavior. Current applications of ML to circuit design are occurring at three distinct scales: 1) learning relationships between part sequence and function; 2) determining how part composition determines circuit behavior; 3) understanding how function varies with genomic/host-cell context. This work points toward a future where ML-driven genetic design is used to program robust solutions to complex problems across diverse biotechnology domains.</p></div>","PeriodicalId":36748,"journal":{"name":"Current Opinion in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using machine learning to enhance and accelerate synthetic biology\",\"authors\":\"\",\"doi\":\"10.1016/j.cobme.2024.100553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Engineering synthetic regulatory circuits with precise input–output behavior—a central goal in synthetic biology—remains encumbered by the inherent molecular complexity of cells. Non-linear, high-dimensional interactions between genetic parts and host cell machinery make it difficult to design circuits using first-principles biophysical models. We argue that adopting data-driven approaches that integrate modern machine learning (ML) tools and high-throughput experimental approaches into the synthetic biology design/build/test/learn process could dramatically accelerate the pace and scope of circuit design, yielding workflows that rapidly and systematically discern design principles and achieve quantitatively precise behavior. Current applications of ML to circuit design are occurring at three distinct scales: 1) learning relationships between part sequence and function; 2) determining how part composition determines circuit behavior; 3) understanding how function varies with genomic/host-cell context. This work points toward a future where ML-driven genetic design is used to program robust solutions to complex problems across diverse biotechnology domains.</p></div>\",\"PeriodicalId\":36748,\"journal\":{\"name\":\"Current Opinion in Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468451124000333\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468451124000333","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

设计具有精确输入输出行为的合成调控电路--这是合成生物学的核心目标--仍然受到细胞固有分子复杂性的制约。基因部件与宿主细胞机器之间非线性、高维的相互作用,使得使用第一原理生物物理模型设计电路变得困难。我们认为,采用数据驱动的方法,将现代机器学习(ML)工具和高通量实验方法整合到合成生物学的设计/构建/测试/学习过程中,可以大大加快电路设计的速度和范围,产生快速、系统地辨别设计原理并实现定量精确行为的工作流程。目前,ML 在电路设计中的应用有三种不同的规模:1)学习部件序列与功能之间的关系;2)确定部件组成如何决定电路行为;3)了解功能如何随基因组/宿主细胞环境而变化。这项工作为未来指明了方向,即使用 ML 驱动的基因设计来为不同生物技术领域的复杂问题提供稳健的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Using machine learning to enhance and accelerate synthetic biology

Engineering synthetic regulatory circuits with precise input–output behavior—a central goal in synthetic biology—remains encumbered by the inherent molecular complexity of cells. Non-linear, high-dimensional interactions between genetic parts and host cell machinery make it difficult to design circuits using first-principles biophysical models. We argue that adopting data-driven approaches that integrate modern machine learning (ML) tools and high-throughput experimental approaches into the synthetic biology design/build/test/learn process could dramatically accelerate the pace and scope of circuit design, yielding workflows that rapidly and systematically discern design principles and achieve quantitatively precise behavior. Current applications of ML to circuit design are occurring at three distinct scales: 1) learning relationships between part sequence and function; 2) determining how part composition determines circuit behavior; 3) understanding how function varies with genomic/host-cell context. This work points toward a future where ML-driven genetic design is used to program robust solutions to complex problems across diverse biotechnology domains.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current Opinion in Biomedical Engineering
Current Opinion in Biomedical Engineering Medicine-Medicine (miscellaneous)
CiteScore
8.60
自引率
2.60%
发文量
59
期刊最新文献
Editorial Board Contents Computational modeling of autonomic nerve stimulation: Vagus et al. Synthetically programming natural cell–cell communication pathways for tissue engineering What can protein circuit design learn from DNA nanotechnology?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1