Fine-tuning gene networks using simple sequence repeats

Abstract

The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch's behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis-suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks.gene network optimization | evolvability | synthetic biology E ngineering reliable and predictable synthetic gene networks presents unique challenges because genetic parts such as promoters, ribosome binding sites, and protein coding regions often behave unexpectedly when used in novel designs. Noise (1, 2), metabolic load (3), poorly characterized interactions with the host (4), and general uncertainty about the detailed functionality of parts conspire to limit the complexity of synthetic gene networks to a small number of interacting genes (5, 6). As a result, a complex gene network that has been predicted analytically to perform well may in fact perform poorly, if it works at all, when ultimately implemented in cells. Furthermore, even if a gene network can be made to perform well in a particular strain and environment, there is no guarantee that the same network will perform well if ported to a new strain or environment (7). More generally, synthetic networks often operate in substantially different parameter regimes than expected during the design process and must, therefore, be tuned (2, 8-11) before they function properly, and even retuned when used in new environments or with different hosts.One way to improve the performance of a poorly tuned gene network is to introduce focused variability into the design, generating a library of circuits (12-15) with the same genetic components and connectivity, but with each member of the library operating in a different parameter regime. In bacteria, for example, promoters (16, 17), ribosome binding sites (RBS) (18,19), RNA stability (20, 21), protein stability (22), and other biochemical details such as transcription factor regulation (23) or enzyme catalysis (24) can be varied to sample different regions of parameter space. Furthermore, sensitivity analysis (25) can guide the designer to parameters that, when tuned, are most likely to result in improved performance. Subject to screening or selection, the effectiveness of a tuning lib...

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Design of a bistable switch to control cellular uptake

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