Predictive design of sigma factor-specific promoters

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

The quest for deus ex machina : harnessing the power of machine learning for synthetic biology

Machine learning is nowadays an ever-present part of many aspects of modern life and has increasingly been used in the field of synthetic biology as well. Examples of studies that successfully harnessed the superb pattern recognition abilities of machine learning algorithms range from a molecular to a big-scale production level and include the prediction of transcription factor activity, enzyme expression balancing, gene annotation and the prediction of production parameters. However, a closer look reveals the lack of a standard for such published models, with no known guidelines on what metrics and analyses should be included in a publication. Studies are often highlighting the fact that the community needs more data in machine-readable format, but it has rarely been discussed how such a study should be conducted and analyzed to obtain a meaningful, robust, high quality and predictive model. Here, we present a guideline specifically aimed at synthetic biologists who wish to use machine learning in their research, in particular on smaller, in-house collected datasets. We discuss key aspects on how to evaluate and interpret a model’s performance with focus on regression, and common problems and pitfalls that arise during the workflow. Together with the increasing availability of vast datasets, the implementation of such guidelines can contribute to the strive for standardization and strong application of engineering principles in the synthetic biology community

The quest for deus ex machina : harnessing the power of machine learning for synthetic biology

Machine learning is nowadays an ever-present part of many aspects of modern life and has increasingly been used in the field of synthetic biology as well. Examples of studies that successfully harnessed the superb pattern recognition abilities of machine learning algorithms range from a molecular to a big-scale production level and include the prediction of transcription factor activity, enzyme expression balancing, gene annotation and the prediction of production parameters. However, a closer look reveals the lack of a standard for such published models, with no known guidelines on what metrics and analyses should be included in a publication. Studies are often highlighting the fact that the community needs more data in machine-readable format, but it has rarely been discussed how such a study should be conducted and analyzed to obtain a meaningful, robust, high quality and predictive model. Here, we present a guideline specifically aimed at synthetic biologists who wish to use machine learning in their research, in particular on smaller, in-house collected datasets. We discuss key aspects on how to evaluate and interpret a model’s performance with focus on regression, and common problems and pitfalls that arise during the workflow. Together with the increasing availability of vast datasets, the implementation of such guidelines can contribute to the strive for standardization and strong application of engineering principles in the synthetic biology community

Improving the performance of machine learning models for biotechnology : the quest for deus ex machina

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues

ProD : a tool for predictive design of tailored promoters in Escherichia coli

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