{"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。
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.