Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli

Jervis, Adrian J.; Carbonell, Pablo; Vinaixa, Maria; Dunstan, Mark S.; Hollywood, Katherine A.; Robinson, Christopher J.; J. W. Rattray, Nicholas; Yan, Cunyu; Swainston, Neil; Currin, Andrew; Sung, Rehana; Toogood, Helen; Taylor, Sandra; Faulon, Jean-Loup; Breitling, Rainer; Takano, Eriko; Scrutton, Nigel S.;
  • Publisher: Figshare
  • Related identifiers: doi: 10.1021/acssynbio.8b00398.s004, doi: 10.1021/acssynbio.8b00398.s003, doi: 10.1021/acssynbio.8b00398.s002
  • Subject: Molecular Biology | Biotechnology | screening | Escherichia coli | Microbiology | Plant Biology | ribosome binding sites | combinatorial RBS libraries | tool | Genetics | Biochemistry | Pharmacology | producer | monoterpenoid production pathway | Immunology | Translational Control Allows Predictive Pathway Optimization
    • FOR: 80699 Information Systems not elsewhere classified | 69999 Biological Sciences not elsewhere classified

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 s... View more
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