Synthetic biology: a new approach to study biological pattern formation

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

Engineering robust and tunable spatial structures with synthetic gene circuits

Controllable spatial patterning is a major goal for the engineering of biological systems. Recently, synthetic gene circuits have become promising tools to achieve the goal; however, they need to possess both functional robustness and tunability in order to facilitate future applications. Here we show that, by harnessing the dual signaling and antibiotic features of nisin, simple synthetic circuits can direct Lactococcus lactis populations to form programmed spatial band-pass structures that do not require fine-tuning and are robust against environmental and cellular context perturbations. Although robust, the patterns are highly tunable, with their band widths specified by the external nisin gradient and cellular nisin immunity. Additionally, the circuits can direct cells to consistently generate designed patterns, even when the gradient is driven by structured nisin-producing bacteria and the patterning cells are composed of multiple species. A mathematical model successfully reproduces all of the observed patterns. Furthermore, the circuits allow us to establish predictable structures of synthetic communities and controllable arrays of cellular stripes and spots in space. This study offers new synthetic biology tools to program spatial structures. It also demonstrates that a deep mining of natural functionalities of living systems is a valuable route to build circuit robustness and tunability.