期刊
ACS SYNTHETIC BIOLOGY
卷 8, 期 6, 页码 1337-1351出版社
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.9b00020
关键词
DBTL; machine learning; dodecanol; proteomics; synthetic biology
资金
- U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research
- U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office [DE-AC02-05CH11231]
- Ajinomoto Co., Inc., Tokyo, Japan
- Basque Government through the BERC 2018-2021 program
- Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation [SEV-2017-0718]
The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain development than historical efforts in biofuels and biobased products. Here, we report on implementation of two DBTL cycles to optimize 1-dodecanol production from glucose using 60 engineered Escherichia coli MG1655 strains. The first DBTL cycle employed a simple strategy to learn efficiently from a relatively small number of strains (36), wherein only the choice of ribosome-binding sites and an acyl-ACP/acyl-CoA reductase were modulated in a single pathway operon including genes encoding a thioesterase (UcFatB1), an acyl-ACP/acyl-CoA reductase (Maqu_2507, Maqu_2220, or Acr1), and an acyl-CoA synthetase (FadD). Measured variables included concentrations of dodecanol and all proteins in the engineered pathway. We used the data produced in the first DBTL cycle to train several machine-learning algorithms and to suggest protein profiles for the second DBTL cycle that would increase production. These strategies resulted in a 21% increase in dodecanol titer in Cycle 2 (up to 0.83 g/L, which is more than 6-fold greater than previously reported batch values for minimal medium). Beyond specific lessons learned about optimizing dodecanol titer in E. coli, this study had findings of broader relevance across synthetic biology applications, such as the importance of sequencing checks on plasmids in production strains as well as in cloning strains, and the critical need for more accurate protein expression predictive tools.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据