Genetic circuit engineering

Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Here, using quantitative high-throughput experiments and machine learning, the authors achieve rational control of a DNA attachment site sequence to predictably modulate site-specific recombination rates both in vitro and in cells.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.

Mycoplasmas are minimal cell model organisms but lack genetic tools. Here the authors provide a robust genetic toolkit for Mycoplasma demonstrating gene circuit engineering applications.

Phosphorylation networks are frequently at the heart of complex cellular decision making. Here the authors engineer synthetic phosphorylation devices with feedback regulation in mammalian cells and demonstrate how to use these to achieve tunable and robust control of cell behaviours.

Zhang et al. create an oscillator circuit that can be orthogonally controlled by two inducers in Escherichia coli and by systematically tuning the concentration of the inducers, the amplitude and period can be modulated independently. This design allows the oscillator to be modularized and used in more complex circuit designs, which can be a valuable contribution to the Synthetic Biology field.