Performance Prediction of Fundamental Transcriptional Programs

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.