| Literature DB >> 35922621 |
Friederike Mey1, Jim Clauwaert2, Maarten Van Brempt1, Michiel Stock2, Jo Maertens1, Willem Waegeman2, Marjan De Mey3.
Abstract
A major goal in synthetic biology is the engineering of synthetic gene circuits with a predictable, controlled and designed outcome. This creates a need for building blocks that can modulate gene expression without interference with the native cell system. A tool allowing forward engineering of promoters with predictable transcription initiation frequency is still lacking. Promoter libraries specific for σ70 to ensure the orthogonality of gene expression were built in Escherichia coli and labeled using fluorescence-activated cell sorting to obtain high-throughput DNA sequencing data to train a convolutional neural network. We were able to confirm in vivo that the model is able to predict the promoter transcription initiation frequency (TIF) of new promoter sequences. Here, we provide an online tool for promoter design (ProD) in E. coli, which can be used to tailor output sequences of desired promoter TIF or predict the TIF of a custom sequence.Entities:
Keywords: Biotechnology; Machine learning; Metabolic engineering; Promoter design; Synthetic biology; Toolbox
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Year: 2022 PMID: 35922621 DOI: 10.1007/978-1-0716-2413-5_4
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745