Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli Article Swipe
Adrian J. Jervis
,
Pablo Carbonell
,
María Vinaixa
,
Mark S. Dunstan
,
Katherine A. Hollywood
,
Christopher Robinson
,
Nicholas J. W. Rattray
,
Cunyu Yan
,
Neil Swainston
,
Andrew Currin
,
Rehana Sung
,
Helen S. Toogood
,
Sandra Taylor
,
Jean‐Loup Faulon
,
Rainer Breitling
,
Eriko Takano
,
Nigel S. Scrutton
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1021/acssynbio.8b00398
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1021/acssynbio.8b00398
The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence-phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acssynbio.8b00398
- https://pubs.acs.org/doi/pdf/10.1021/acssynbio.8b00398
- OA Status
- hybrid
- Cited By
- 109
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2903740678
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2903740678Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acssynbio.8b00398Digital Object Identifier
- Title
-
Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coliWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-18Full publication date if available
- Authors
-
Adrian J. Jervis, Pablo Carbonell, María Vinaixa, Mark S. Dunstan, Katherine A. Hollywood, Christopher Robinson, Nicholas J. W. Rattray, Cunyu Yan, Neil Swainston, Andrew Currin, Rehana Sung, Helen S. Toogood, Sandra Taylor, Jean‐Loup Faulon, Rainer Breitling, Eriko Takano, Nigel S. ScruttonList of authors in order
- Landing page
-
https://doi.org/10.1021/acssynbio.8b00398Publisher landing page
- PDF URL
-
https://pubs.acs.org/doi/pdf/10.1021/acssynbio.8b00398Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://pubs.acs.org/doi/pdf/10.1021/acssynbio.8b00398Direct OA link when available
- Concepts
-
Escherichia coli, Model predictive control, Synthetic biology, Computational biology, Computer science, Control (management), Biology, Machine learning, Chemistry, Artificial intelligence, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
109Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 12, 2023: 12, 2022: 23, 2021: 20Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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