Synthetic negative feedback circuits using engineered small RNAs

Abstract

Negative feedback is known to endow biological and man-made systems with robust performance in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules which can inhibit translation of target messenger RNAs (mRNAs). In this paper, we designed, modelled and built two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal input-output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA which negatively regulates the translation of the mRNA encoding this output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.