Mapping the operational landscape of microRNAs in synthetic gene circuits. (December 2018) - Quarton, Tyler; Ehrhardt, Kristina; Lee, James; Kannan, Srijaa; Li, Yi; Ma, Lan; Bleris, Leonidas
Abstract MicroRNAs are a class of short, noncoding RNAs that are ubiquitous modulators of gene expression, with roles in development, homeostasis, and disease. Engineered microRNAs are now frequently used as regulatory modules in synthetic biology. Moreover, synthetic gene circuits equipped with engineered microRNA targets with perfect complementarity to endogenous microRNAs establish an interface with the endogenous milieu at the single-cell level. The function of engineered microRNAs and sensor systems is typically optimized through extensive trial-and-error. Here, using a combination of synthetic biology experimentation in human embryonic kidney cells and quantitative analysis, we investigate the relationship between input genetic template abundance, microRNA concentration, and output under microRNA control. We provide a framework that employs the complete operational landscape of a synthetic gene circuit and enables the stepwise development of mathematical models. We derive a phenomenological model that recapitulates experimentally observed nonlinearities and contains features that provide insight into the microRNA function at various abundances. Our work facilitates the characterization and engineering of multi-component genetic circuits and specifically points to new insights on the operation of microRNAs as mediators of endogenous information and regulators of gene expression in synthetic biology. Synthetic biology: microRNA-based regulation MicroRNAs are increasingly important regulatory modules in genetic circuits. To probe their versatile nature, a systems and synthetic biology research team led by Dr. Leonidas Bleris at the University of Texas at Dallas, combined experiments and mathematical modeling to study the microRNA operational landscape. The team engineered custom genetic circuits that contain microRNA-based regulation and introduced an analytical strategy, that includes clustering and superposition of discrete experiments, to produce a "bird's-eye view" perspective. The team then developed mathematical models with informative features and predictive value. One feature highlighted in this work correlates the function of a microRNA to its concentration and the genetic circuit abundance. The results of this work may help towards rationally utilizing microRNAs in engineered gene circuits.