Design of a Synthetic sRNA-based Feedback Filter Module

Abstract

Filters are widely used in engineering to reduce noise and/or the magnitude of a signal of interest. Feedback filters, or adaptive filters, are preferred if the signal noise distribution is unknown. One of the main challenges in Synthetic Biology remains the design of reliable constructs but these often fail to work as intended due, e.g. to their inherent stochasticity and burden on the host. Here we design, implement and test experimentally a biological feedback filter module based on small non-coding RNAs (sRNAs) and self-cleaving ribozymes. Mathematical modelling demonstrates that it attenuates noise for a large range of parameters due to negative feedback introduced by the use of ribozymes and sRNA. Our module modifies the steady-state response of the filtered signal, and hence can be used for tuning the feedback strength while also reducing noise. We demonstrated these properties theoretically on the TetR autorepressor, enhanced with our sRNA module.