Synthetic negative feedback circuits using engineered small RNAs

Abstract

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated 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, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the 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.

Design of a Synthetic sRNA-based Feedback Filter Module

“…These simulations suggest that the in cis design attenuates noise better in comparison with the no sRNA (classical autorepressor) circuit for a wider range of parameters than the in trans design. Note that with sufficient increase of the feedback strength the noise levels can be amplified by the in trans design, which is consistent with previous studies [Kelly et al, 2018]. In our in cis design the noise amplification does not occur for the simulated range of parameters (noise amplification is still possible for larger aTc concentrations).…”

“…In both designs we assumed that mRNA is translated into a protein at the rate k t and that the degradation/dilution rate for every species is different, as mRNA generally degrades faster than proteins and the reported values for the degradation rate of sRNA vary [Hussein and Lim, 2012]. We also assumed that the rate of mRNA-sRNA unbinding is negligibly small, as previously reported [Hussein and Lim, 2012, Kelly et al, 2018], and therefore we did not include it in our model. Modelling both designs using mass-action kinetics yielded the model presented in Figure 1.III, with the difference that for the in cis design, we have β m = β s = β ms .…”

“…Over the past few years, focus has shifted towards designing self-contained in vivo controllers. While the vast majority of these experimental implementations were protein-based [Hsiao et al, 2014, Folliard et al, 2017, Rosenfeld et al, 2002] small non-coding RNAs (sRNAs) have also been recently used in this context [Ghodasara and Voigt, 2017, Takahashi et al, 2014, Hu et al, 2018, Kelly et al, 2018]. sRNAs are found in all domains of life and have been shown to play critical regulatory roles in many processes [Cech and Steitz, 2014], [Michaux et al, 2014], [Robledo et al, 2018], [Gottesman and Storz, 2011], [Livny and Waldor, 2007], [Nitzan et al, 2017].…”

Ultrasensitive molecular controllers for quasi-integral feedback

“…3 and Table 1). As several other reaction networks for closed-loop control [15,16,14,18], the Brink motif relies on sequestration, which is expected to yield a high inputoutput gain. There is evidence that this mechanism can also produce integral action [11,12], however only in the ideal condition of fast sequestration and negligible degradation/dilution rates [19]; the latter requirement may be difficult to achieve in living cells.…”

“…Recent theoretical work has explored the use of molecular sequestration to build molecular controllers that perform integral action [11]. Ongoing experimental research aims at building sequestration-based integral controllers using sigma and anti-sigma factors in E. coli [12]; this research builds on ample evidence that sequestration is suited to achieve concentration reference tracking, with implementations that go beyond sigma and anti-sigma factors [13,14] and range from nucleic acid networks in vitro [15], to protein [16] and RNA-based sequestration [17,18]. A significant challenge in implementing integral action with sequestration alone is posed by the presence of dilu-tion, which introduces steady-state error (leaky integration) [19].…”