While there has been significant progress in the development of engineering principles for synthetic biology, a substantial challenge is the construction of robust circuits in a noisy cellular environment. Such an environment leads to considerable intercellular variability in circuit behavior, which can hinder functionality at the colony level. Here, we engineer the synchronization of thousands of oscillating colony “biopixels” over centimetre length scales through the use of synergistic intercellular coupling involving quorum sensing within a colony and gas-phase redox signaling between colonies. We use this platform to construct an LCD-like macroscopic clock that can be used to sense arsenic via modulation of the oscillatory period. Given the repertoire of sensing capabilities of bacteria such as E. coli, the ability to coordinate their behavior over large length scales sets the stage for the construction of low cost genetic biosensors that are capable of detecting heavy metals and pathogens in the field.

One promise of synthetic biology is the creation of genetic circuitry that enables the execution of logical programming in living cells. Such “wet programming” is positioned to transform a wide and diverse swath of biotechnology ranging from therapeutics and diagnostics to water treatment strategies. While progress in the development of a library of genetic modules continues apace1–4, a major challenge for their integration into larger circuits is the generation of sufficiently fast and precise communication between modules5,6. An attractive approach is to integrate engineered circuits with host processes that facilitate robust cellular signaling7. In this context, recent studies have demonstrated that bacterial protein degradation can trigger a precise response to stress by overloading a limited supply of intracellular proteases8–10. Here, we use protease competition to engineer rapid and tunable coupling of genetic circuits across multiple spatial and temporal scales. We characterize coupling delay times that are more than an order of magnitude faster than standard transcription-factor based coupling methods (less than one minute compared with ~20–40 minutes) and demonstrate tunability through manipulation of the linker between the protein and its degradation tag. We use this mechanism as a platform to couple genetic clocks at the intracellular and colony level, then synchronize the multi-colony dynamics to reduce variability in both clocks. We show how the coupled clock network can be used to encode independent environmental inputs into a single time series output, thus enabling the possibility of frequency multiplexing in a genetic circuit context. Our results establish a general framework for the rapid and tunable coupling of genetic circuits through the use of native queueing processes such as protein degradation.

With the expanding interest in cellular responses to dynamic environments, microfluidic devices have become important experimental platforms for biological research. Microfluidic “microchemostat” devices enable precise environmental control while capturing high quality, single-cell gene expression data. For studies of population heterogeneity and gene expression noise, these abilities are crucial. Here, we describe the necessary steps for experimental microfluidics using devices created in our lab as examples. First, we discuss the rational design of microchemostats and the tools available to predict their performance. We carefully analyze the critical parts of an example device, focusing on the most important part of any microchemostat: the cell trap. Next, we present a method for generating on-chip dynamic environments using an integrated fluidic junction coupled to linear actuators. Our system relies on the simple modulation of hydrostatic pressure to alter the mixing ratio between two source reservoirs and we detail the software and hardware behind it. To expand the throughput of microchemostat experiments, we describe how to build larger, parallel versions of simpler devices. To analyze the large amounts of data, we discuss methods for automated cell tracking, focusing on the special problems presented by Saccharomyces cerevisiae cells. The manufacturing of microchemostats is described in complete detail: from the photolithographic processing of the wafer to the final bonding of the PDMS chip to glass coverslip. Finally, the procedures for conducting Escherichia coli and S. cerevisiae microchemostat experiments are addressed.

Almost every aspect of cryo electron microscopy (cryoEM) has been automated over the last few decades. One of the challenges that remains to be addressed is the robust and reliable preparation of vitrified specimens of suitable ice thickness. We present results from a new device for preparing vitrified samples. The successful use of the device is coupled to a new “self-blotting” grid that we have developed to provide a method for spreading a sample to a thin film without the use of externally applied filter paper. This new approach has the advantage of using small amounts of protein material, resulting in large areas of ice of a well defined thickness containing evenly distributed single particles. We believe that these methods will in the future result in a system for vitrifying grids that is completely automated.

We have developed a method combining microfluidics, time-lapsed single-molecule microscopy and automated image analysis allowing for the observation of an excess of 3000 complete cell cycles of exponentially growing Escherichia coli cells per experiment. The method makes it possible to analyse the rate of gene expression at the level of single proteins over the bacterial cell cycle. We also demonstrate that it is possible to count the number of non-specifically DNA binding LacI–Venus molecules using short excitation light pulses. The transcription factors are localized on the nucleoids in the cell and appear to be uniformly distributed on chromosomal DNA. An increase in the expression of LacI is observed at the beginning of the cell cycle, possibly because some gene copies are de-repressed as a result of partitioning inequalities at cell division. Finally, a size–growth rate uncertainty relation is observed where cells living in rich media vary more in the length at birth than in generation time, and the opposite is true for cells living in poorer media.