Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning
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 ribosomebinding 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, thismore »
- Authors:
-
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, DOE Agile BioFoundry, Emeryville, California 94608, United States
- Research Institute for Bioscience Product & Fine Chemicals, Ajinomoto Co., Inc., Kawasaki 210-8680, Japan
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, DOE Joint Genome Institute, Walnut Creek, California 94598, United States
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States, Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, DOE Joint Genome Institute, Walnut Creek, California 94598, United States
- Sandia National Laboratories, Livermore, California 94550, United States
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, DOE Agile BioFoundry, Emeryville, California 94608, United States, DOE Joint Genome Institute, Walnut Creek, California 94598, United States
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, DOE Agile BioFoundry, Emeryville, California 94608, United States, BCAM, Basque Center for Applied Mathematics, 48009 Bilbao, Spain
- Publication Date:
- Research Org.:
- Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office
- OSTI Identifier:
- 1515590
- Alternate Identifier(s):
- OSTI ID: 1529116; OSTI ID: 1877605
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- ACS Synthetic Biology
- Additional Journal Information:
- Journal Name: ACS Synthetic Biology Journal Volume: 8 Journal Issue: 6; Journal ID: ISSN 2161-5063
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 59 BASIC BIOLOGICAL SCIENCES; DBTL; machine learning; dodecanol; proteomics; synthetic biology
Citation Formats
Opgenorth, Paul, Costello, Zak, Okada, Takuya, Goyal, Garima, Chen, Yan, Gin, Jennifer, Benites, Veronica, de Raad, Markus, Northen, Trent R., Deng, Kai, Deutsch, Samuel, Baidoo, Edward E. K., Petzold, Christopher J., Hillson, Nathan J., Garcia Martin, Hector, and Beller, Harry R. Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning. United States: N. p., 2019.
Web. doi:10.1021/acssynbio.9b00020.
Opgenorth, Paul, Costello, Zak, Okada, Takuya, Goyal, Garima, Chen, Yan, Gin, Jennifer, Benites, Veronica, de Raad, Markus, Northen, Trent R., Deng, Kai, Deutsch, Samuel, Baidoo, Edward E. K., Petzold, Christopher J., Hillson, Nathan J., Garcia Martin, Hector, & Beller, Harry R. Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning. United States. https://doi.org/10.1021/acssynbio.9b00020
Opgenorth, Paul, Costello, Zak, Okada, Takuya, Goyal, Garima, Chen, Yan, Gin, Jennifer, Benites, Veronica, de Raad, Markus, Northen, Trent R., Deng, Kai, Deutsch, Samuel, Baidoo, Edward E. K., Petzold, Christopher J., Hillson, Nathan J., Garcia Martin, Hector, and Beller, Harry R. Thu .
"Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning". United States. https://doi.org/10.1021/acssynbio.9b00020.
@article{osti_1515590,
title = {Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning},
author = {Opgenorth, Paul and Costello, Zak and Okada, Takuya and Goyal, Garima and Chen, Yan and Gin, Jennifer and Benites, Veronica and de Raad, Markus and Northen, Trent R. and Deng, Kai and Deutsch, Samuel and Baidoo, Edward E. K. and Petzold, Christopher J. and Hillson, Nathan J. and Garcia Martin, Hector and Beller, Harry R.},
abstractNote = {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 ribosomebinding 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.},
doi = {10.1021/acssynbio.9b00020},
journal = {ACS Synthetic Biology},
number = 6,
volume = 8,
place = {United States},
year = {Thu May 09 00:00:00 EDT 2019},
month = {Thu May 09 00:00:00 EDT 2019}
}
https://doi.org/10.1021/acssynbio.9b00020
Web of Science
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