Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000, Ghent, Belgium.
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium.
Nat Commun. 2020 Nov 16;11(1):5822. doi: 10.1038/s41467-020-19446-w.
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 (σ)- and B. subtilis σ-, σ- and σ-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.
为了设计合成基因电路，需要开发分子构建块，这些构建块可以在不相互干扰或与宿主细胞机制干扰的情况下调节基因表达。随着基因电路的复杂性增加，需要自动化设计工具和定制的构建块来确保网络中所有组件的完美调整。尽管已经努力开发可以正向工程启动子转录起始频率（TIF）的预测工具，但仍然缺乏这样的工具。在这里，我们使用大肠杆菌σ因子 70（σ）和枯草芽孢杆菌σ-、σ-和σ-依赖启动子的启动子文库来构建预测模型，这些模型能够同时预测启动子 TIF 和 σ-特异性启动子的正交性。这是通过使用来自荧光激活细胞分选启动子文库的高通量 DNA 测序数据训练卷积神经网络来实现的。该模型是在线启动子设计工具（ProD）的基础，为定制遗传系统提供定制的启动子。
