Natural genetic circuits enable cells to make sophisticated digital decisions. Building equally complex synthetic circuits in eukaryotes remains difficult, however, because commonly used components leak transcriptionally, do not arbitrarily interconnect or do not have digital responses. Here, we designed dCas9-Mxi1-based NOR gates in Saccharomyces cerevisiae that allow arbitrary connectivity and large genetic circuits. Because we used the chromatin remodeller Mxi1, our gates showed minimal leak and digital responses. We built a combinatorial library of NOR gates that directly convert guide RNA (gRNA) inputs into gRNA outputs, enabling the gates to be ‘wired' together. We constructed logic circuits with up to seven gRNAs, including repression cascades with up to seven layers. Modelling predicted the NOR gates have effectively zero transcriptional leak explaining the limited signal degradation in the circuits. Our approach enabled the largest, eukaryotic gene circuits to date and will form the basis for large, synthetic, cellular decision-making systems.

Chewing gum provides an excellent everyday example of viscoelastic behavior, and understanding its rheological properties is important for application purposes. Here, we compare the rheological behavior of selected commercial chewing gums and bubble gums. Small amplitude oscillatory shear, shear creep, and steady shear demonstrated that both chewing and bubble gums behave like power-law critical gels in the linear regime. Nonlinear viscoelastic behavior was investigated using large amplitude oscillatory shear, shear creep, and start-up flows (in shear and uniaxial extension). Bubble gums showed more pronounced strain hardening and greater stresses to break in start-up of steady uniaxial extension than chewing gums. We argue that this combination of rheological signatures is sufficient to provide a new robust definition of chewing gum that is independent of specific molecular composition. There are potentially many different formulations and design routes that can achieve this distinctive rheological fingerprint.

Dynamic control of gene expression is emerging as an important strategy for controlling flux in metabolic pathways and improving bioproduction of valuable compounds. Integrating dynamic genetic control tools with CRISPR-Cas transcriptional regulation could significantly improve our ability to fine-tune the expression of multiple endogenous and heterologous genes according to the state of the cell. In this mini-review, we combine an analysis of recent literature with examples from our own work to discuss the prospects and challenges of developing dynamically regulated CRISPR-Cas transcriptional control systems for applications in synthetic biology and metabolic engineering.

With progress toward inexpensive, large-scale DNA assembly, the demand for simulation tools that allow the rapid construction of synthetic biological devices with predictable behaviors continues to increase. By combining engineered transcript components, such as ribosome binding sites, transcriptional terminators, ligand-binding aptamers, catalytic ribozymes, and aptamer-controlled ribozymes (aptazymes), gene expression in bacteria can be fine-tuned, with many corollaries and applications in yeast and mammalian cells. The successful design of genetic constructs that implement these kinds of RNA-based control mechanisms requires modeling and analyzing kinetically determined co-transcriptional folding pathways. Transcript design methods using stochastic kinetic folding simulations to search spacer sequence libraries for motifs enabling the assembly of RNA component parts into static ribozyme- and dynamic aptazyme-regulated expression devices with quantitatively predictable functions (rREDs and aREDs, respectively) have been described (Carothers et al., Science 334:1716-1719, 2011). Here, we provide a detailed practical procedure for computational transcript design by illustrating a high throughput, multiprocessor approach for evaluating spacer sequences and generating functional rREDs. This chapter is written as a tutorial, complete with pseudo-code and step-by-step instructions for setting up a computational cluster with an Amazon, Inc. web server and performing the large numbers of kinefold-based stochastic kinetic co-transcriptional folding simulations needed to design functional rREDs and aREDs. The method described here should be broadly applicable for designing and analyzing a variety of synthetic RNA parts, devices and transcripts.