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 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-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.
Highlights
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
The synthesis rate of RNA and subsequent protein can be directly regulated by altering the DNA sequence of the promoter and ribosome binding site (RBS), which determine their affinity for, respectively, the RNA polymerase and ribosome[11,12]
The synthetic biology community’s demand for readily applicable forward engineering tools is, for example, expressed by the success of the RBS sequence design tools created by the Salis Lab
Summary
These were cloned in the pLibrary vector by insertion in the promoter chassis used for library creation. As a preliminary indication of the in vivo performance of model predictions for σB, σF, and σW-specific promoters, we predicted the TIF class and orthogonality of a limited set of library promoters were constructed and characterized in our previous study[56]. The tested promoters do show an ordinal relation between measured expression level and predicted class, especially for the sigmaB-specific promoters, though the number of observations is small and a fraction of the sequences was present in the model training sets (Fig. 6). An overview of the promoter sequences and predicted data is presented in Supplementary Table 3
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