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CRISPR-based gene editing is a powerful tool with great potential for applications in the treatment of many inherited and acquired diseases. The longer that CRISPR gene therapy is maintained within a patient, however, the higher the likelihood that it will result in problematic side effects such as off-target editing or immune response. One approach to mitigating these issues is to link the operation of the therapeutic system to a safety switch that autonomously disables its operation and removes the delivered therapeutics after some amount of time. We present here a simulation-based analysis of the potential for regulating the time delay of such a safety switch using one or two transcriptional regulators and/or recombinases. Combinatorial circuit generation identifies 30 potential architectures for such circuits, which we evaluate in simulation with respect to tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation. This modeling predicts one of these circuit architectures to have the desired dynamics and robustness, which can be further tested and applied in the context of CRISPR therapeutics.

Efficient clustered regularly interspaced short palindromic repeat (CRISPR) guide RNA (gRNA) expression from RNA Polymerase II (Pol II) promoters will aid in construction of complex CRISPR-based synthetic gene networks. Yet, we require tools to properly visualize gRNA directly to quantitatively study the corresponding network behavior. To address this need, we employed a fluorescent gRNA (fgRNA) to visualize synthetic CRISPR network dynamics without affecting gRNA functionality. We show that studying gRNA dynamics directly enables circuit modification and improvement of network function in Pol II-driven CRISPR circuits. This approach generates information necessary for optimizing the overall function of these networks and provides insight into the hurdles remaining in Pol II-regulated gRNA expression.

An ideal in vivo gene therapy platform provides safe, reprogrammable, and precise strategies which modulate cell and tissue gene regulatory networks with a high temporal and spatial resolution. Clustered regularly interspaced short palindromic repeats (CRISPR), a bacterial adoptive immune system, and its CRISPR-associated protein 9 (Cas9), have gained attention for the ability to target and modify DNA sequences on demand with unprecedented flexibility and precision. The precision and programmability of Cas9 is derived from its complexation with a guide-RNA (gRNA) that is complementary to a desired genomic sequence. CRISPR systems open-up widespread applications including genetic disease modeling, functional screens, and synthetic gene regulation. The plausibility of in vivo genetic engineering using CRISPR has garnered significant traction as a next generation in vivo therapeutic. However, there are hurdles that need to be addressed before CRISPR-based strategies are fully implemented. Some key issues center on the controllability of the CRISPR platform, including minimizing genomic-off target effects and maximizing in vivo gene editing efficiency, in vivo cellular delivery, and spatial-temporal regulation. The modifiable components of CRISPR systems: Cas9 protein, gRNA, delivery platform, and the form of CRISPR system delivered (DNA, RNA, or ribonucleoprotein) have recently been engineered independently to design a better genome engineering toolbox. This review focuses on evaluating CRISPR potential as a next generation in vivo gene therapy platform and discusses bioengineering advancements that can address challenges associated with clinical translation of this emerging technology.

A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIP's precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.