SUMMARYEngineering artificial gene networks from modular components is one of the major goals of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized non-essential sequence) with in silico modeling to guide predictable gene network construction without the need for post-hoc tweaking. We demonstrate our approach in S. cerevisiae by synthesizing regulatory promoter libraries and using them to construct feedforward loop networks with different predicted inputoutput characteristics. We then expand our method to produce a synthetic gene network acting as a predictable timer, modifiable by component choice. We utilize this network to control the timing of yeast sedimentation, illustrating how the plug-and-play nature of our design can be readily applied to biotechnology.

Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here we report two complementary synthetic genetic counters in E. coli that can count up to three induction events, the first comprised of a riboregulated transcriptional cascade and the second of a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.A counter is a key component in digital circuits and computing that retains memory of events or objects, representing each number of such as a distinct state. Counters would also be useful in cells, which often must have accurate accounting of tightly controlled processes or biomolecules to effectively maintain metabolism and growth. Counting mechanisms have been reportedly found in telomere length regulation (1,2) and cell aggregation (3). These system behaviors appear to be the result of a thresholding effect in which some critical molecule number or density must be reached for the observed phenotypic change.In this study, we first develop a counter, termed the Riboregulated Transcriptional Cascade (RTC) Counter, which is based on a transcriptional cascade with additional translational regulation. Figs. 1A and 1C illustrate two such cascades that can count up to 2 and 3, respectively (hence, the designations RTC 2-Counter and RTC 3-Counter). For the RTC 2-Counter, the constitutive promoter P Ltet0-1 drives transcription of T7 RNA polymerase (RNAP), whose protein binds the T7 promoter and transcribes the downstream gene, in this case Green Fluorescent Protein (GFP). Both genes are additionally regulated by riboregulators (4), whose cis and trans elements silence and activate post-transcriptional gene expression, respectively. The cis-repressor sequence (cr) is placed between the transcription start site and the ribosome binding site (RBS), and its complementarity with the RBS causes a stem-loop structure to form upon transcription. This secondary structure prevents binding of the 30S ribosomal subunit to the RBS, inhibiting translation. A short, trans-activating, noncoding RNA (taRNA) driven by the arabinose promoter P BAD binds to the cis-repressor in trans, relieving * This manuscript has been accepted for publication in Science. This version has not undergone final editing. Please refer to the complete version of record at http://www.sciencemag.org/. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS. * These authors contributed equally to this work. 1A). With cis-repressed T7 RNAP mRNAs in the cell, the first pulse of arabinose drives a short burst of taRNA production and consequently expression of...

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.