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ACS Synth Biol. 2019 Jun 21;8(6):1337-1351. doi: 10.1021/acssynbio.9b00020. Epub 2019 May 24.

Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

Opgenorth P1,2, Costello Z1,2,3, Okada T4, Goyal G1,2,3, Chen Y1,2,3, Gin J1,2,3, Benites V1,2,3, de Raad M5,6, Northen TR1,5,6, Deng K7, Deutsch S6, Baidoo EEK1,2,3, Petzold CJ1,2,3, Hillson NJ1,2,3,6, Garcia Martin H1,2,3,8, Beller HR1,2.

Author information

1
Joint BioEnergy Institute (JBEI) , Emeryville , California 94608 , United States.
2
Biological Systems & Engineering Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States.
3
DOE Agile BioFoundry , Emeryville , California 94608 , United States.
4
Research Institute for Bioscience Product & Fine Chemicals , Ajinomoto Co., Inc. , Kawasaki 210-8680 , Japan.
5
Environmental Genomics and Systems Biology Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States.
6
DOE Joint Genome Institute , Walnut Creek , California 94598 , United States.
7
Sandia National Laboratories , Livermore , California 94550 , United States.
8
BCAM, Basque Center for Applied Mathematics , 48009 Bilbao , Spain.

Abstract

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.

KEYWORDS:

DBTL; dodecanol; machine learning; proteomics; synthetic biology

PMID:
31072100
DOI:
10.1021/acssynbio.9b00020

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