Transcriptional programming leverages systems of engineered transcription factors to impart decision-making (e.g., Boolean logic) in chassis cells. The number of components used to construct said decision-making systems is rapidly increasing, making an exhaustive experimental evaluation of iterations of biological circuits impractical. Accordingly, we posited that a predictive tool is needed to guide and accelerate the design of transcriptional programs. The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations. Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates). In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates). These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit. Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.
Keywords: antirepressors; biological circuit prediction; synthetic gene circuits; synthetic transcription factors; transcriptional programming.