Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli

Adrian J. Jervis, Pablo Carbonell, Maria Vinaixa, Mark S. Dunstan, Katherine A. Hollywood, Christopher J. Robinson, Nicholas J.W. Rattray, Cunyu Yan, Neil Swainston, Andrew Currin, Rehana Sung, Helen Toogood, Sandra Taylor, Jean Loup Faulon, Rainer Breitling, Eriko Takano, Nigel S. Scrutton

Research output: Contribution to journalArticle

19 Citations (Scopus)
34 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)127-136
Number of pages10
JournalACS Synthetic Biology
Volume8
Issue number1
Early online date18 Dec 2018
DOIs
Publication statusPublished - 18 Jan 2019

Keywords

  • machine learning
  • pathway engineering
  • ribosome binding site
  • synthetic biology
  • terpenoids
  • translational tuning

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  • Cite this

    Jervis, A. J., Carbonell, P., Vinaixa, M., Dunstan, M. S., Hollywood, K. A., Robinson, C. J., Rattray, N. J. W., Yan, C., Swainston, N., Currin, A., Sung, R., Toogood, H., Taylor, S., Faulon, J. L., Breitling, R., Takano, E., & Scrutton, N. S. (2019). Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli. ACS Synthetic Biology, 8(1), 127-136. https://doi.org/10.1021/acssynbio.8b00398