Author Correction: A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity.

In the originally published version of this Article, financial support was not fully acknowledged. The PDF and HTML versions of the Article have now been corrected to include support from the National Science Foundation (NSF), award number 1745645.

A mixed antagonistic/synergistic miRNA repression model enables accurate predictions of multi-input miRNA sensor activity.

MicroRNAs (miRNAs) regulate a majority of protein-coding genes, affecting nearly all biological pathways. However, the quantitative dimensions of miRNA-based regulation are not fully understood. In particular, the implications of miRNA target site location, composition rules for multiple target sites, and cooperativity limits for genes regulated by many miRNAs have not been quantitatively characterized. We explore these aspects of miRNA biology at a quantitative single-cell level using a library of 620 miRNA sensors and reporters that are regulated by many miRNA target sites at different positions. Interestingly, we find that miRNA target site sets within the same untranslated region exhibit combined miRNA activity described by an antagonistic relationship while those in separate untranslated regions show synergy. The resulting antagonistic/synergistic computational model enables the high-fidelity prediction of miRNA sensor activity for sensors containing many miRNA targets. These findings may help to accelerate the development of sophisticated sensors for clinical and research applications.

Open-source, community-driven microfluidics with Metafluidics.

Microfluidic devices have the potential to automate and miniaturize biological experiments, but open-source sharing of device designs has lagged behind sharing of other resources such as software. Synthetic biologists have used microfluidics for DNA assembly, cell-free expression, and cell culture, but a combination of expense, device complexity, and reliance on custom set-ups hampers their widespread adoption. We present Metafluidics, an open-source, community-driven repository that hosts digital design files, assembly specifications, and open-source software to enable users to build, configure, and operate a microfluidic device. We use Metafluidics to share designs and fabrication instructions for both a microfluidic ring-mixer device and a 32-channel tabletop microfluidic controller. This device and controller are applied to build genetic circuits using standard DNA assembly methods including ligation, Gateway, Gibson, and Golden Gate. Metafluidics is intended to enable a broad community of engineers, DIY enthusiasts, and other nontraditional participants with limited fabrication skills to contribute to microfluidic research.

An end-to-end workflow for engineering of biological networks from high-level specifications.

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.