Robust digital logic circuits in eukaryotic cells with CRISPR/dCas9 NOR gates

SummaryNatural genetic circuits enable cells to make sophisticated digital decisions. Building equally complex synthetic circuits in eukaryotes remains difficult, however, because commonly used genetic components leak transcriptionally, do not allow arbitrary interconnections, or do not have digital responses. Here, we designed a new dCas9-Mxi1 based NOR gate architecture in S. cerevisiae that allows arbitrary connectivity and large genetic circuits. Because we used the strong chromatin remodeler Mxi1, our system showed very little leak and exhibits a highly digital response. In particular, we built a combinatorial library of NOR gates that each directly convert guide RNA (gRNA) input signals into gRNA output signals, enabling NOR gates to be “wired” together. We constructed and characterized logic circuits with up to seven independent gRNAs, including repression cascades with up to seven layers. Modeling predicted that the NOR gates have Hill Coefficients of approximately 1.71 ± 0.09, explaining the minimal signal degradation we observed in these deeply layered circuits. Our approach enables the construction of the largest, eukaryotic gene circuits to date and will form the basis for large, synthetic, decision making systems in living cells.

HIERARCHICAL AND MIXING PROPERTIES OF STATIC COMPLEX NETWORKS EMERGING FROM FLUCTUATING CLASSICAL RANDOM GRAPHS

The Erdös–Rényi classical random graph is characterized by a fixed linking probability for all pairs of vertices. Here, this concept is generalized by drawing the linking probability from a certain distribution. Such a procedure is found to lead to a static complex network with an arbitrary connectivity distribution. In particular, a scale-free network with the hierarchical organization is constructed without assuming any knowledge about the global linking structure, in contrast to the preferential attachment rule for a growing network. The hierarchical and mixing properties of the static scale-free network thus constructed are studied. The present approach establishes a bridge between a scalar characterization of individual vertices and topology of an emerging complex network. The result may offer a clue for understanding the origin of a few abundance of connectivity distributions in a wide variety of static real-world networks.