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Predictive design of sigma factor-specific promoters

Maarten Van Brempt (UGent) , Jim Clauwaert (UGent) , Friederike Mey (UGent) , Michiel Stock (UGent) , Jo Maertens (UGent) , Willem Waegeman (UGent) and Marjan De Mey (UGent)
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Abstract
To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (sigma (70))- and B. subtilis sigma (B)-, sigma (F)- and sigma (W)-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the sigma -specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems. Automated design tools and tailored subunits are beneficial in fine-tuning all components of a complex genetic circuit. Here the authors create E. coli and B. subtilis promoter libraries using FACS and HTS, from which an online promoter design tool has been developed using CNN.
Keywords
RIBOSOME BINDING-SITES, ESCHERICHIA-COLI, GENE-EXPRESSION, PROTEIN, EXPRESSION, BACILLUS-SUBTILIS, RNA-POLYMERASE, TRANSCRIPTION, SEQUENCE, ELEMENTS, TRANSLATION

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MLA
Van Brempt, Maarten, et al. “Predictive Design of Sigma Factor-Specific Promoters.” NATURE COMMUNICATIONS, vol. 11, no. 1, 2020, doi:10.1038/s41467-020-19446-w.
APA
Van Brempt, M., Clauwaert, J., Mey, F., Stock, M., Maertens, J., Waegeman, W., & De Mey, M. (2020). Predictive design of sigma factor-specific promoters. NATURE COMMUNICATIONS, 11(1). https://doi.org/10.1038/s41467-020-19446-w
Chicago author-date
Van Brempt, Maarten, Jim Clauwaert, Friederike Mey, Michiel Stock, Jo Maertens, Willem Waegeman, and Marjan De Mey. 2020. “Predictive Design of Sigma Factor-Specific Promoters.” NATURE COMMUNICATIONS 11 (1). https://doi.org/10.1038/s41467-020-19446-w.
Chicago author-date (all authors)
Van Brempt, Maarten, Jim Clauwaert, Friederike Mey, Michiel Stock, Jo Maertens, Willem Waegeman, and Marjan De Mey. 2020. “Predictive Design of Sigma Factor-Specific Promoters.” NATURE COMMUNICATIONS 11 (1). doi:10.1038/s41467-020-19446-w.
Vancouver
1.
Van Brempt M, Clauwaert J, Mey F, Stock M, Maertens J, Waegeman W, et al. Predictive design of sigma factor-specific promoters. NATURE COMMUNICATIONS. 2020;11(1).
IEEE
[1]
M. Van Brempt et al., “Predictive design of sigma factor-specific promoters,” NATURE COMMUNICATIONS, vol. 11, no. 1, 2020.
@article{8687584,
  abstract     = {To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (sigma (70))- and B. subtilis sigma (B)-, sigma (F)- and sigma (W)-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the sigma -specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems. Automated design tools and tailored subunits are beneficial in fine-tuning all components of a complex genetic circuit. Here the authors create E. coli and B. subtilis promoter libraries using FACS and HTS, from which an online promoter design tool has been developed using CNN.},
  articleno    = {5822},
  author       = {Van Brempt, Maarten and Clauwaert, Jim and Mey, Friederike and Stock, Michiel and Maertens, Jo and Waegeman, Willem and De Mey, Marjan},
  issn         = {2041-1723},
  journal      = {NATURE COMMUNICATIONS},
  keywords     = {RIBOSOME BINDING-SITES,ESCHERICHIA-COLI,GENE-EXPRESSION,PROTEIN,EXPRESSION,BACILLUS-SUBTILIS,RNA-POLYMERASE,TRANSCRIPTION,SEQUENCE,ELEMENTS,TRANSLATION},
  language     = {eng},
  number       = {1},
  pages        = {13},
  title        = {Predictive design of sigma factor-specific promoters},
  url          = {http://dx.doi.org/10.1038/s41467-020-19446-w},
  volume       = {11},
  year         = {2020},
}

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