Progress in bioreactors and instruments for phenotype testing with synthetic biology research

GUO Xiaojie; JIAN Xingjin; WANG Liyan; ZHANG Chong; XING Xinhui

doi:10.12211/2096-8280.2023-067

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Progress in bioreactors and instruments for phenotype testing with synthetic biology research

Invited Review | 更新时间：2025-03-20

- Progress in bioreactors and instruments for phenotype testing with synthetic biology research
- Synthetic Biology Journal Vol. 5, Issue 1, Pages: 16-37(2024)
- 作者机构：
  
  1.清华大学化学工程系，生物化工研究所，北京 100084
  2.工业生物催化教育部重点实验室，北京 100084
  3.清华大学无锡应用技术研究院生物育种中心，无锡 214000
  4.清华大学合成与系统生物学中心，北京 100084
  5.清华大学深圳国际研究生院生物医药与健康工程研究院，深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2023-067
  CLC： Q816
- Received：19 September 2023，
  
  Revised：2023-11-02，
  
  Published：29 February 2024
- 稿件说明：
移动端阅览
郭肖杰，剪兴金，王立言，张翀，邢新会. 合成生物学表型测试生物反应器及其装备化研究进展［J］. 合成生物学， 2024， 5（1）： 16-37

GUO Xiaojie， JIAN Xingjin， WANG Liyan， ZHANG Chong， XING Xinhui. Progress in bioreactors and instruments for phenotype testing with synthetic biology research［J］. Synthetic Biology Journal， 2024， 5（1）： 16-37
郭肖杰，剪兴金，王立言，张翀，邢新会. 合成生物学表型测试生物反应器及其装备化研究进展［J］. 合成生物学， 2024， 5（1）： 16-37 DOI： 10.12211/2096-8280.2023-067.

GUO Xiaojie， JIAN Xingjin， WANG Liyan， ZHANG Chong， XING Xinhui. Progress in bioreactors and instruments for phenotype testing with synthetic biology research［J］. Synthetic Biology Journal， 2024， 5（1）： 16-37 DOI： 10.12211/2096-8280.2023-067.

摘要

合成生物学经过多年的发展，形成了典型的细胞工厂创制“设计-构建-测试-学习”（design-build-test-learn， DBTL）循环，成为支撑面向加速生物制造发展的智慧生物育种的重要方法。其中，测试环节是对前期所设计与构建的生物体系进行表型测试，以提供大量数据用于后续的学习和迭代升级。测试阶段的通量主要依赖于细胞自身或其培养测试所使用的生物反应器及其装备，是整个DBTL流程的限速步骤。本文系统综述了合成生物学表型测试所开发的面向不同通量的生物反应器及装备，从单细胞检测和筛选及不同尺度生物反应器包括皮纳升级生物反应器、微升级生物反应器、毫升级生物反应器和升级生物反应器等，系统地介绍了各自的特点和应用场景。同时，指出了现有生物反应器及其装备的应用潜力、面临的挑战与发展趋势，为合成生物学表型测试技术研究提供重要参考。

Abstract

Over the past years

synthetic biology has seen significant development

establishing a typical "Design-Build-Test-Learn (DBTL)" cycle for engineering cell factories. This cycle has been becoming an enabling methodology for smart breeding to accelerate the development of biomanufacturing. In the DBTL cycle

the testing step primarily aims to evaluate the phenotypes of constructed cell factories

which can provide a large amount of data for further learning and iterative optimization. Due to the complexity of cellular metabolic networks and regulatory mechanisms

as well as the complicated associations between genotypes and phenotypes

the design and construction of cell factories have traditionally involved long-term and labor-intensive iterative experiments. In synthetic biology

the construction of cell factories with designed synthetic pathways is often combined with random mutation and evolution to build up a large screening library

which always requires a high throughput and efficient technology and equipment in the testing step. The testing step is the rate-limiting process in the entire DBTL cycle

and its efficiency is largely dependent on chassis cells themselves

as well as the throughput of bioreactors and instruments needed for their phenotype testing. Here

this review article focuses on an overview of bioreactors and instruments with different throughput scales used for the phenotype testing in synthetic biology. We introduce their characteristics and application scenarios

including single-cell detecting and screening technology as well as pico-

nano-

micro-

milli-

and liter-scale bioreactors. Moreover

this article also points out the application potential of existing phenotype testing bioreactors and instruments

and illustrates how they can be selected for specific research purposes. Finally

the challenges and perspectives for phenotype testing bioreactors and instruments are summarized

which hopefully provides a reference for a wide range of synthetic biology researchers to properly select and use phenotype testing instruments.

关键词

Keywords

references

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