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Zhang J, Chen Y, Fu L, Guo E, Wang B, Dai L, Si T. Accelerating strain engineering in biofuel research via build and test automation of synthetic biology. Curr Opin Biotechnol 2021; 67:88-98. [PMID: 33508635 DOI: 10.1016/j.copbio.2021.01.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
Biofuels are a type of sustainable and renewable energy. However, for the economical production of bulk-volume biofuels, biosystems design is particularly challenging to achieve sufficient yield, titer, and productivity. Because of the lack of predictive modeling, high-throughput screening remains essential. Recently established biofoundries provide an emerging infrastructure to accelerate biological design-build-test-learn (DBTL) cycles through the integration of robotics, synthetic biology, and informatics. In this review, we first introduce the technical advances of build and test automation in synthetic biology, focusing on the use of industry-standard microplates for DNA assembly, chassis engineering, and enzyme and strain screening. Proof-of-concept studies on prototypes of automated foundries are then discussed, for improving biomass deconstruction, metabolic conversion, and host robustness. We conclude with future challenges and opportunities in creating a flexible, versatile, and data-driven framework to support biofuel research and development in biofoundries.
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Affiliation(s)
- Jianzhi Zhang
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yongcan Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Erpeng Guo
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Bo Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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