De novo engineering of gene circuits inside cells is extremely difficult, and efforts to realize predictable and robust performance must deal with noise in gene expression and variation in phenotypes between cells. Here we demonstrate that by coupling gene expression to cell survival and death using cell-cell communication, we can programme the dynamics of a population despite variability in the behaviour of individual cells. Specifically, we have built and characterized a 'population control' circuit that autonomously regulates the density of an Escherichia coli population. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate. As predicted by a simple mathematical model, the circuit can set a stable steady state in terms of cell density and gene expression that is easily tunable by varying the stability of the cell-cell communication signal. This circuit incorporates a mechanism for programmed death in response to changes in the environment, and allows us to probe the design principles of its more complex natural counterparts.

Promoters control the expression of genes in response to one or more transcription factors (TFs). The architecture of a promoter is the arrangement and type of binding sites within it. To understand natural genetic circuits and to design promoters for synthetic biology, it is essential to understand the relationship between promoter function and architecture. We constructed a combinatorial library of random promoter architectures. We characterized 288 promoters in Escherichia coli, each containing up to three inputs from four different TFs. The library design allowed for multiple À10 and À35 boxes, and we observed varied promoter strength over five decades. To further analyze the functional repertoire, we defined a representation of promoter function in terms of regulatory range, logic type, and symmetry. Using these results, we identified heuristic rules for programming gene expression with combinatorial promoters. Molecular Systems Biology 13 November 2007; doi:10.1038/msb4100187 Subject Categories: metabolic and regulatory networks; synthetic biology Keywords: combinatorial promoter; logic gate; signal integration; synthetic biology; transcription regulationThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission. IntroductionIn many promoters gene expression is regulated in response to two or more transcription factors (TFs). A classic example is the lac operon, where promoter activity depends on both the repressor LacI (Jacob and Monod, 1961) and the activator CRP (Zubay et al, 1970). Such combinatorial regulation of gene expression underlies diverse cellular programs (Ptashne, 2005), including responses to environmental conditions (Ligr et al, 2006) and multicellular development. Combinatorial promoters with multiple TF binding sites, or operators, can facilitate the integration of multiple signals. For example, a synthetic combinatorial promoter responding to LuxR and l cI was recently used to construct a genetic pulse generator (Basu et al, 2004), a band-pass filter, and a bulls-eye pattern formation system (Basu et al, 2005). Furthermore, circuits containing combinatorial promoters are predicted to generate robust oscillations (Hasty et al, 2002;Atkinson et al, 2003) or to create sign-sensitive filters, signal averaging, and response acceleration or delay (Mangan and Alon, 2003).Bacterial promoters typically occupy a region of 100 bp or less, surrounding the start site ( þ 1) of transcription, approximately from positions À75 to þ 25. This sequence includes the primary binding sites for the polymerase, the À10 and À35 boxes (Hawley and McClure, 1983), additional upstream (Chan andBusby, 1989;Ross et al, 1993) and downstream (Kammerer et al, 1986;Haugen et al, 2006) regulatory sequences, along with operators for activator and/or r...

Gene regulatory interactions are context–dependent, active in some cellular states but not others. Stochastic fluctuations, or ‘noise,’ in gene expression propagate through active, but not inactive, regulatory links [1, 2]. Thus, correlations in gene expression noise could provide a non-invasive means to probe the activity states of regulatory links. However, global, ‘extrinsic’, noise sources generate correlations even without direct regulatory links. Here we show that single-cell time-lapse microscopy, by revealing time lags due to regulation, can discriminate between active regulatory connections and extrinsic noise. We demonstrate this principle mathematically, using stochastic modeling, and experimentally, using simple synthetic gene circuits. We then use this approach to analyze dynamic noise correlations in the galactose metabolism genes of E. coli. We find that the CRP-GalS-GalE feed-forward loop is inactive in standard conditions, but can become active in a GalR mutant. These results show how noise can help analyze the context-dependence of regulatory interactions in endogenous gene circuits.

Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.

BackgroundCurrent methods for analyzing the dynamics of natural regulatory networks, and quantifying synthetic circuit function, are limited by the lack of well-characterized genetic measurement tools. Fluorescent reporters have been used to measure dynamic gene expression, but recent attempts to monitor multiple genes simultaneously in single cells have not focused on independent, isolated measurements. Multiple reporters can be used to observe interactions between natural genes, or to facilitate the 'debugging' of biologically engineered genetic networks. Using three distinguishable reporter genes in a single cell can reveal information not obtainable from only one or two reporters. One application of multiple reporters is the use of genetic noise to reveal regulatory connections between genes. Experiments in both natural and synthetic systems would benefit from a well-characterized platform for expressing multiple reporter genes and synthetic network components.ResultsWe describe such a plasmid-based platform for the design and optimization of synthetic gene networks, and for analysis of endogenous gene networks. This network scaffold consists of three distinguishable fluorescent reporter genes controlled by inducible promoters, with conveniently placed restriction sites to make modifications straightforward. We quantitatively characterize the scaffold in Escherichia coli with single-cell fluorescence imaging and time-lapse microscopy. The three spectrally distinct reporters allow independent monitoring of genetic regulation and analysis of genetic noise. As a novel application of this tool we show that the presence of genetic noise can reveal transcriptional co-regulation due to a hidden factor, and can distinguish constitutive from regulated gene expression.ConclusionWe have constructed a general chassis where three promoters from natural genes or components of synthetic networks can be easily inserted and independently monitored on a single construct using optimized fluorescent protein reporters. We have quantitatively characterized the baseline behavior of the chassis so that it can be used to measure dynamic gene regulation and noise. Overall, the system will be useful both for analyzing natural genetic networks and assembling synthetic ones.

Transcription factors (TFs) with regulatory action at multiple promoter targets is the rule rather than the exception, with examples ranging from the cAMP receptor protein (CRP) in E. coli that regulates hundreds of different genes simultaneously to situations involving multiple copies of the same gene, such as plasmids, retrotransposons, or highly replicated viral DNA. When the number of TFs heavily exceeds the number of binding sites, TF binding to each promoter can be regarded as independent. However, when the number of TF molecules is comparable to the number of binding sites, TF titration will result in correlation (“promoter entanglement”) between transcription of different genes. We develop a statistical mechanical model which takes the TF titration effect into account and use it to predict both the level of gene expression for a general set of promoters and the resulting correlation in transcription rates of different genes. Our results show that the TF titration effect could be important for understanding gene expression in many regulatory settings.

The ability to regulate gene expression is of central importance for the adaptability of living organisms to changes in their external and internal environment. At the transcriptional level, binding of transcription factors (TFs) in the promoter region can modulate the transcription rate, hence making TFs central players in gene regulation. For some model organisms, information about the locations and identities of discovered TF binding sites have been collected in continually updated databases, such as RegulonDB for the well-studied case of E. coli. In order to reveal the general principles behind the binding-site arrangement and function of these regulatory architectures we propose a random promoter architecture model that preserves the overall abundance of binding sites to identify overrepresented binding site configurations. This model is analogous to the random network model used in the study of genetic network motifs, where regulatory motifs are identified through their overrepresentation with respect to a “randomly connected” genetic network. Using our model we identify TF pairs which coregulate operons in an overrepresented fashion, or individual TFs which act at multiple binding sites per promoter by, for example, cooperative binding, DNA looping, or through multiple binding domains. We furthermore explore the relationship between promoter architecture and gene expression, using three different genome-wide protein copy number censuses. Perhaps surprisingly, we find no systematic correlation between the number of activator and repressor binding sites regulating a gene and the level of gene expression. A position-weight-matrix model used to estimate the binding affinity of RNA polymerase (RNAP) to the promoters of activated and repressed genes suggests that this lack of correlation might in part be due to differences in basal transcription levels, with repressed genes having a higher basal activity level. This quantitative catalogue relating promoter architecture and function provides a first step towards genome-wide predictive models of regulatory function.

Expansion of the genetic alphabet by an unnatural base pair system provides a platform for the site-specific, enzymatic incorporation of extra, functional components into nucleic acids. Recently, several unnatural base pairs that exhibit high fidelity and efficiency in PCR have been developed. Functional groups of interest, such as fluorescent dyes, can be linked to the unnatural bases, and the modified base substrates are site-specifically incorporated into nucleic acids by polymerases. Furthermore, unique unnatural base pairs between fluorophore and quencher base analogs have been developed for imaging PCR amplification and as molecular beacons. Here, we describe the recent progress in the development of unnatural base pairs that function in PCR amplification and their applications as sensing and diagnostic tools.