摘要
合成生物学采用工程化设计理念,对生物体进行有目标的设计、改造,甚至从头合成具有特定功能的“人造生命”,用于探索生命活动规律和进行生物技术创新.由于生命系统高度复杂,人工设计的合成生命体很难完全按照预期工作,往往需要长时间的反复调谐.目前,“试错”过程主要依靠研究者手动完成,存在通量低、重复性差、迭代慢等局限.针对这一难题,自动化合成生物技术通过低成本、多循环地完成海量工程试错性实验,提高研究通量和效率,大幅增加实验设计的复杂度和系统性,从而快速实现特定功能,揭示人工生命体的设计原理.近年来,在合成生物学“设计-构建-测试-学习”的各个研究环节,自动化技术正在以前所未有的速度加速发展.与此对应,在全球范围内已建成或在建多个大型工程化平台,用于支撑相关研究和应用.本文旨在对自动合成生物技术的关键要素进行总结,并对合成生物研究基础设施的发展情况和未来方向进行讨论.
Synthetic biology applies engineering principles for the deliberate design,engineering,and de novo creation of artificial biological systems with certain functions.Due to the complexity of living systems and lack of rational design principles,iterative trial-and-error experiments are often necessary,but the dependence on human researchers limits the throughput,efficiency,and consistency of such endeavor.To overcome these limitations,biofoundries are developed as an integrated infrastructure for accelerating the“design-build-test-learn”cycles in synthetic biology research and biotechnology applications.Computer-aided design and robotic automation are applied in the physical manufacturing and prototyping of engineered DNA and genetically reprogrammed organisms.Currently,many biofoundries are being created around the world,and a Global Biofoundry Alliance has been established to promote coordination and collaboration.This paper aims to summarize the recent advances in synthetic biology automation and introduce design and construction of current and future biofoundries.We start with key technologies that promote automation in synthetic biology,including computer-aided design,highthroughput instrumentation,robotic integration,automation-compatible workflows for DNA assembly and chassis engineering,and analytical approaches.For bio-design automation,we introduce existing software tools such as j5 from Agile Biofoundry,iBioCAD from Illinois Biological Foundry for Advanced Biomanufacturing(iBioFAB),and CUBA from Edinburgh Genome Foundry(EGF).Moreover,software and hardware for creating automated build and test workflows are summarized,followed by recent advances in DNA synthesis,DNA assembly,nucleic acid extraction and analysis,chassis manipulation,and high-throughput testing.Notable examples are discussed,including automated DNA assembly using the Golden-Gate method,multiplex automated yeast genome engineering,and artificial intelligence(AI)-guided optimization of biosynthetic pathways.Then,representative biofoundries and their software systems,robotic platforms,and available workflows are discussed.Biofoundries are categorized by various formats of integration,including full integration(e.g.,iBioFAB and EGF),modular integration(e.g.,London Biofoundry,Singapore Biofoundry,and Concordia Genome Foundry),and manual integration.Commercial biofoundries are also compared and contrasted with their academic counterparts,using the ones at Gingko Bioworks Inc.as an example.Furthermore,the design of Shenzhen Biofoundry is discussed in detail,whereby a centralized cloud lab is envisioned to serve the domestic and international synthetic biology communities.Shenzhen Biofoundry will consist of a build platform for engineered DNA,phage,bacteria and yeast,a test platform for optics,chromatography and mass spectrometry,and fermentation scale-up,and a design/learn/cloud platform to coordinate and integrate the whole facility.We conclude with the future challenges and promises of biofoundries and synthetic biology automation.We propose key research directions,including new automation technologies,data-driven and intelligent design,and automation-compatible workflows in synthetic biology.Systems approach and interdisciplinary collaboration are necessary for biofoundries development,which requires synergistic integration of synthetic biology,analytical chemistry,robotics,instrumentation,informatics,and smart manufacturing.Just like foundries initiated the magnificent prosperity of the semiconductor industry,biofoundries will unleash the potential of synthetic biology to revolutionize our society.
作者
唐婷
付立豪
郭二鹏
张振坤
王子宁
马辰飞
张智彧
张建志
黄建东
司同
Ting Tang;Lihao Fu;Erpeng Guo;Zhenkun Zhang;Zining Wang;Chenfei Ma;Zhiyu Zhang;Jianzhi Zhang;Jiandong Huang;Tong Si(Key Laboratory of Quantitative Engineering Biology,Shenzhen Institute of Synthetic Biology,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)
出处
《科学通报》
EI
CAS
CSCD
北大核心
2021年第3期300-309,共10页
Chinese Science Bulletin
基金
深圳市科技创新委员会项目(KQTD2015033117210153)资助。
