ABSTRACT Gene expression control based on CRISPRi (clustered regularly interspaced short palindromic repeats interference) has emerged as a powerful tool for creating synthetic gene

circuits, both in prokaryotes and in eukaryotes; yet, its lack of cooperativity has been pointed out as a potential obstacle for dynamic or multistable synthetic circuit construction. Here

we use CRISPRi to build a synthetic oscillator (“CRISPRlator”), bistable network (toggle switch) and stripe pattern-forming incoherent feed-forward loop (IFFL). Our circuit designs,

conceived to feature high predictability and orthogonality, as well as low metabolic burden and context-dependency, allow us to achieve robust circuit behaviors in _Escherichia coli_

DCAS9 REGULATOR TO NEUTRALIZE COMPETITION IN CRISPRI CIRCUITS Article Open access 16 March 2021 CHARACTERIZATION AND MITIGATION OF GENE EXPRESSION BURDEN IN MAMMALIAN CELLS Article Open

access 15 September 2020 ENGINEERING A SYNTHETIC GENE CIRCUIT FOR HIGH-PERFORMANCE INDUCIBLE EXPRESSION IN MAMMALIAN SYSTEMS Article Open access 17 April 2024 INTRODUCTION Synthetic biology

aims to build artificial decision-making circuits that are programmable, predictable and perform a specific function1. Since the rise of synthetic biology in the 2000s, most synthetic

circuits have been governed by protein-based regulators. Recently, however, there has been growing interest in circuits based on RNA regulators as a means to overcome some of the intrinsic

limitations of protein regulators2. The prokaryotic adaptive immunity system CRISPR constitutes a powerful platform for the construction of RNA-driven synthetic circuits3. The

catalytically-dead mutant dCas9 can be easily directed to virtually any sequence by a single-guide RNA molecule (sgRNA). When a prokaryotic promoter (or downstream) region is targeted,

steric hindrance by the dCas9-sgRNA complex results in transcriptional repression—an approach known as CRISPR interference (CRISPRi). CRISPRi offers several advantages over protein

regulators for synthetic circuit design. Due to its RNA-guided nature, CRISPRi is highly programmable4, allows for easy design of sgRNAs that can be highly orthogonal5 and whose behavior in

different environments can be easily predicted in silico6,7. It also imposes low burden on host cells2 and is encoded in shorter sequences than protein-based repressors, thereby facilitating

circuit handling and delivery and reducing cost. A potential drawback of CRISPRi is the lack of cooperativity8. Cooperative protein transcription factors typically function non-linearly, a

difference that might prevent the successful implementation of CRISPRi-based dynamic and multistable circuits8,9,10. Besides, unlike other RNA circuits that exhibit fast dynamics, low rates

of dCas9:DNA dissociation in the absence of DNA replication11,12 effectively slow down CRISPRi dynamics. Potential strategies to overcome such limitations and yield multistable and dynamic

circuits have been proposed, and include the precise tuning of active dCas9 degradation, an increased CRISPRi fold-repression, faster dCas9:DNA binding9, fusing dCas9 to additional repressor

domains13 or incorporating activators in the circuit design10,14. Alternatively, competition for dCas915 and cellular resources may render non-cooperative systems non-linear, as

demonstrated for a T7 RNA polymerase-controlled positive feedback circuit16. The last few years have seen a growing interest in developing CRISPRi-based synthetic

circuits8,13,17,18,19,20,21,22,23,24,25. However, despite the enormous potential of CRISPRi for synthetic circuit design, the use of CRISPRi circuits in prokaryotes has been largely focused

on logic gates and to the best of our knowledge none of the flagship circuits in synthetic biology (namely, the bistable toggle switch26 and the repressilator27) have been re-constructed

using CRISPRi. Here, we fill this unaddressed gap by demonstrating that CRISPRi can be used for building some of the most notorious (synthetic) circuit topologies: the repressilator, a

bistable toggle switch, and an incoherent feed-forward loop (IFFL, a.k.a. band-pass filter) that drives stripe pattern formation. Our circuit designs, conceived to feature high

predictability and orthogonality, as well as low metabolic burden and context-dependency, allow us to achieve robust circuit behaviors in _Escherichia coli_ populations. Mathematical

modeling suggests that unspecific binding in CRISPRi is essential to establish multistability. RESULTS AND DISCUSSION DESIGN FEATURES AND CRISPRI TESTING To maximize circuit predictability

and orthogonality and minimize metabolic burden on the host, we employed a series of design features in all our synthetic networks. The main circuit components were all expressed from a

single vector (so-called variable vector) to avoid fluctuations in their stoichiometry. Circuit transcriptional units (TUs) were isolated from each other by strong transcriptional

terminators and 200 bp spacer sequences, to avoid transcriptional readthrough and minimize compositional context28. To prevent mRNA context-dependency and provide transcriptional insulation

within TUs, a 20 bp csy4 cleavage sequence29 was used flanking sgRNAs and upstream of the ribosome binding sites (RBS) of the reporter genes. Csy4 is an RNase of the CRISPR system of

_Pseudomonas aeruginosa_ that processes the transcript of the CRISPR array to release the CRISPR-derived RNAs (crRNAs)30. Here, we employed Csy4 processing to release parts that are

transcribed together in the same mRNA molecule but need to act independently once transcribed—e.g., sgRNAs that need to bind dCas9 for function, and fluorescent reporter transcripts that

have to be translated. To avoid cross-talk between fluorescent reporters, orthogonal degradation tags31 were employed. Sequence repetition at the DNA level was minimized to prevent unwanted

recombination events. The levels of dCas9 and Csy4 were kept constant by expressing them from constitutive promoters in a separate constant vector. All gene circuits were tested in _E. coli_

(MK0132) incubated in a rich defined medium (EZ, Teknova) for maximizing cell fitness while reducing variability. In order to speed up the design-build-test cycle, we adopted a previously

described cloning strategy that allows for fast and modular assembly of synthetic networks33. We first devised a simple circuit to assess the dose-response of CRISPRi repression. This 2-node

network design displays Boolean NOT logic, in which node 1 (N1) is induced by L-arabinose (Ara) and represses expression of node 2 (N2) (Fig. 1). Levels of N1 and N2 in the NOT circuit were

monitored via the fluorescent reporters mKO2 and sfGFP (Fig. 1b) carrying degradation tags (MarA and MarAn20) to accelerate reporter turnover31. We chose four single-guide RNAs (sgRNA-1 to

-4) and their corresponding binding sites (bs-1 to -4), previously shown to be orthogonal to each other17. All binding sites were placed at the same distance from the promoter to prevent

differences in the repression derived from binding site position5. Maximal induction (0.2% Ara) of N1 produced sgRNA levels that repressed the expression of N2 in the range of 10 to 30-fold

(Fig. 1c), with sgRNA-2 being the strongest repressor. Truncation of the four 5′ nucleotides (“t4”) of sgRNA-1 and -4 reduced maximal repression by 20 and 45%, respectively, providing a

means to tune down repression (Fig. 1c). We also measured N2 fluorescence in the presence of different Ara concentrations (Fig. 1d), which showed a modulable, dose-dependent CRISPRi

repression, a prerequisite for building dose-dependent circuits. A CRISPRI TOGGLE SWITCH We next sought to build a classical genetic circuit that has never been constructed before using

CRISPRi: the toggle switch (TS)26, where two nodes mutually repress each other to produce bistability, i.e., two mutually-exclusive stable states. We designed a TS circuit in which two nodes

produce sgRNAs that repress each other (Fig. 2), resulting in two non-concurrent stable states: HIGH, in which the fluorescent reporter sfGFP-MarAn20 is produced at high levels, and LOW, in

which sfGFP-MarAn20 is expressed at negligible levels. The two states can be toggled through a controller plasmid, in which the addition of Ara or AHL (N-(β-Ketocaproyl)-L-homoserine

lactone) triggers the expression of sgRNA-1 or -4, thus favoring the LOW and the HIGH states, respectively (Fig. 2b). The TS was assessed by incubating cells in four successive media

containing the first inducer (Ara or AHL), followed by no inducer, then the second inducer (AHL or Ara), and finally no inducer again (Fig. 2c, d). Most cells adopted the LOW state following

an initial incubation with Ara and kept this state even when Ara was removed. When AHL was added, cells switched to the HIGH state and kept this state after AHL removal (Fig. 2c left, and

d). The reverse induction scheme (AHL-nothing-Ara-nothing) also displayed bistability and hysteresis (Fig. 2c). Conversely, the control circuits lacking one of the repressors (Fig. 2a)

failed to display any bistability: both cL (biased towards the HIGH state) and cR (biased towards the LOW state) showed a monostable behavior and were unable to keep the LOW and the HIGH

states in the absence of inducer, respectively (Fig. 2c). Together, these results show that CRISPRi can be applied to build multistable gene circuits. ORIGIN OF BISTABILITY Because CRISPRi’s

non-cooperative binding8 contrasts with the requirement for cooperativity in the protein-based TS26, we aimed to explain how CRISPRi can generate multistability. Theory shows that

cooperativity is sufficient, but not necessary for a two-gene circuit with mutual inhibition to be bistable; alternative mechanisms include a significant depletion of free repressor34. We

considered this a plausible mechanism for CRISPRi because of dCas9’s scanning of PAM sites—the effective affinity of binding to sites not matching the sgRNA sequence is comparatively low35

(considered here as unspecific binding), but PAM sites are abundant in the genome. We therefore developed mechanistic mathematical models in the form of reaction networks with mass-action

kinetics that comprise dCas9/sgRNA binding to DNA, as well as production and degradation of all components (see Supplementary Figs. 7–12 for details). We then used methods from chemical

reaction network theory36,37 to evaluate the potential of a network structure for bistability, without prior knowledge on parameter values. Consistent with theory, we did not find

bistability for networks with specific binding only. However, network extensions by unspecific binding to target genes, with independent (that is, non-cooperative) specific and unspecific

binding events (Fig. 3a) could show bistability. This held even when unspecific binding did not inhibit gene expression and when we constrained parameters to experimentally determined ranges

(Supplementary Table 4), as illustrated in Fig. 3b. Thus, our model-based analysis suggests that the interplay between specific and unspecific binding of dCas9/sgRNA complexes to DNA

explains bistability in the CRISPRi TS. CRISPRI INCOHERENT FEED-FORWARD LOOPS We next decided to build another flagship synthetic circuit, a stripe-forming IFFL (Fig. 4)38,39,40. We designed

a CRISPRi-based 3-node IFFL type 2 (I2), which relies exclusively on repression interactions41. To monitor the behavior of all three nodes of the circuit, we employed compatible fluorescent

reporters fused to orthogonal degradation tags31: mKO2-MarA, sfGFP-MarAn20 and RepA70-mKate2 (Fig. 4b). The rationally designed circuit performed as expected: with increasing Ara levels N1

levels raised, resulting in a concomitant decrease of N2; a peak (stripe) of N3 was observed in the open window between N1 and N2 (Fig. 4c). Our three-reporter IFFL allowed us to readily

monitor network behavior and to optimize it—and to use the circuit as a concentration-detector (Supplementary Fig. 1). To our knowledge, no IFFL with three reporters had been constructed

before. The inclusion of three reporter protein-coding genes was enabled by the small size and transcriptional nature of the circuit, causing low burden on host cells. Even more, due to the

relatively small size of the circuit in terms of DNA sequence, all 3 nodes of the network could be carried within a single plasmid, thus keeping their relative abundance constant and

avoiding fluctuations in their stoichiometry due to plasmid copy number variations. To corroborate that the CRISPRi IFFL was capable of true spatial patterning, we homogeneously spread

bacteria on an agar plate and applied a central source of arabinose, which diffused forming a radial gradient. Consistent with the French flag model42, the initially identical, isogenic

bacterial population carrying the synthetic network interpreted the Ara gradient into three discrete gene expression programs: orange, green and blue (Fig. 4d). We next wanted to explore the

robustness of the CRISPRi stripe circuit with respect to variations in its molecular components. We assessed the performance of (i) a circuit carrying a single reporter (sfGFP-MarAn20 in

N3), (ii) a circuit with a different set of sgRNA regulators (sgRNA-4, sgRNA-4t4 and sgRNA-3 instead of sgRNA-1, sgRNA-1t4 and sgRNA-2), and (iii) a network with swapped N2 and N3 reporters

(Supplementary Fig. 2). All these circuits showed a stripe behavior (Supplementary Fig. 2), demonstrating the robustness of our CRISPRi-based IFFL, which is enabled by our design aiming at

high orthogonality and low context-dependency. An important limitation that commonly affects synthetic gene circuits is the difficulty to expand their functionality by including existing

circuits into more complex networks or by making them operate in a different environment, such as in co-existence with another circuit. To test whether a CRISPRi-based IFFL could operate in

a similar manner in a different context, we decided to build two orthogonal CRISPRi IFFLs that would operate in parallel within the same bacterial cell, each producing a single (and

independent) stripe in response to a common arabinose gradient (Fig. 4e). When bacteria containing both parallel circuits were submitted to different Ara concentrations, a double peak of

gene expression could be observed at intermediate inducer levels corresponding to two overlapping peaks, each generated by one of the circuits, as designed (Fig. 4f). Importantly, the

doubling of the CRISPRi circuit (i.e., two stripe networks instead of one) did not result in a growth defect compared to bacteria carrying only one of the IFFLs or a control without IFFL

circuits (Supplementary Fig. 3). To our knowledge, no other study has reported to date a double synthetic stripe driven by a single (isogenic) cell population. The simultaneous yet

independent operation of the double-stripe system was enabled by the intrinsic properties of CRISPRi (orthogonality, low burden, short encoding sequences) and the low context-dependency of

the design. We also assessed the combination of two CRISPRi circuits exhibiting different functions within the same cellular environment. We combined a stripe-forming IFFL with an orthogonal

double-inverter circuit33, a concatenation of two NOT gates that inverts twice the input signal to give an output that mimics the input. This design can be used when the output needs to

resemble the input with some additional modification, e.g., a shifted position33 or a time delay13. Bacteria carrying the two circuits perform both functions: an IFFL-generated stripe

pattern and a double-inverter-driven behavior that mimics the Ara gradient (Fig. 4g, h). Thus, the two networks behaved as expected, demonstrating the possibility to combine multiple CRISPRi

circuits with distinct functions in the same bacteria. We also designed an IFFL in which both the control and the reporting are RNA-based. To this aim, we modified our CRISPRi IFFL (in

which the control is sgRNA-based) to remove the orange and blue reporters and substituted the original green reporter (sfGFP) with 8 copies of the dBroccoli (dimeric Broccoli) RNA aptamer43

(Supplementary Fig. 4). When we incubated the engineered bacteria with the DFHBI-1T fluorophore we observed a stripe of fluorescence (Supplementary Fig. 4b), showing that the band-pass

behavior of an RNA-controlled IFFL can indeed be reported by an RNA molecule. A CRISPRI OSCILLATOR (THE CRISPRLATOR) Finally, we wanted to explore whether CRISPRi-based circuits could be

employed not only for spatial patterning, as demonstrated above, but also to create temporal patterns. Oscillations are temporal patterns with key roles in many different biological

phenomena. We sought to engineer an oscillatory pattern controlled by a dynamic CRISPRi circuit. To this aim, we designed a 3-node ring oscillator: the CRISPRlator (Fig. 5), in honor of the

first synthetic oscillator, called the repressilator27. Few (2–3) bacteria carrying the CRISPRlator were loaded per microfluidic chamber (Supplementary Fig. 5), grown there continuously for

3 days (over 100 generations) and imaged every 10 min. The CRISPRlator generated robust oscillations between three states (red, green and blue) with a period of 10–12 h (14–17 generations,

Fig. 5c, d), while a control with an open-ring topology did not show any oscillations (Supplementary Fig. 6). After the chambers were filled with cells (~110 cells/chamber), we observed

synchronous long-term oscillations, even in the absence of any active cell communication or synchronization mechanism (Supplementary Movie 1). We hypothesize that the observed synchrony

stems from the robust inheritance of the oscillatory state across cell divisions from the few cells initially seeded in the microfluidic chamber. This is reminiscent of improved versions of

the protein-based repressilator44,45, where synchronous oscillations were observed due to lower levels of molecular fluctuations and consequently reduced phase drift and increased

inheritance of the period compared to the original repressilator27. Together, our results demonstrate that the lack of cooperativity of CRISPRi is not an insurmountable obstacle for

(complex) synthetic circuit construction and CRISPRi can effectively be used for generating dynamic and multistable behaviors. Specifically, our mathematical analysis suggests that

multistability requires unspecific binding (i.e., dCas9’s scanning of PAM sites) in CRISPRi. The benefits inherent to CRISPRi circuits will likely prompt the construction of new and extended

synthetic systems, including designs hard to achieve with protein repressors, e.g., two independent clocks operating within the same cell. The universality of CRISPR should facilitate the

transfer of our circuits into other species. We anticipate that our results will encourage the synthetic biology community to employ CRISPRi for gene circuit design and inspire future

construction of more complex synthetic networks. METHODS CIRCUIT CONSTRUCTION Genes encoding sfGFP, mKO2, mKate2, Csy4, and dCas9 were obtained as previously described33. mCherry and

mCitrine were amplified from plasmids (pLacI(r)-mCherry(ASV) and pCI-Citrine-(ASV)) kindly provided by Sebastian Maerkl44. dBroccoli was amplified from plasmid pET28c-F30-2xdBroccoli, which

was a gift from Samie Jaffrey (Addgene plasmid #66843). Cerulean46 was purchased as an _E. coli_ codon-optimized gBlock from IDT, and LuxR (BBa_C0062) was also codon-optimized and

synthesized by GenScript. Primers were purchased desalted from Microsynth or Sigma-Aldrich (Supplementary Table 1). The circuits were constructed employing a previously described

Gibson-based cloning framework that allows for the fast and modular cloning of synthetic gene networks33. Briefly, the method consists of two steps: step 1 involves Gibson assembly of

transcriptional units into individual intermediate plasmids; in step 2, these plasmids are digested with restriction enzymes so that the resulting flanking regions contain overlaps that

drive a second Gibson assembly into a single plasmid to yield the final circuit. For step 1, all DNA parts carried the same Prefix (CAGCCTGCGGTCCGG) and Suffix (TCGCTGGGACGCCCG) sequences

for modular Gibson assembly using MODAL47. Basically, forward and reverse primers annealing to Prefix and Suffix sequences, respectively, were used for PCRs that added unique linkers to the

DNA parts. PCR amplifications were column-purified using the Monarch PCR & DNA Cleanup Kit (NEB), and assembled using NEBuilder HiFi DNA Assembly Master Mix (NEB, 1 h 50 °C) into

backbones previously digested with corresponding restriction enzymes (NEB, 1 h 37 °C) to yield intermediate plasmids containing individual transcriptional units. In step 2, these

intermediate plasmids were digested with enzyme sets yielding overlapping sequences, purified and assembled as described above. One microliter of non-purified Gibson reaction was transformed

into 50 µl of electrocompetent NEB5α cells, and 2/5 of them were plated onto selective agar plates. Plasmids used in this study (Supplementary Table 2) are available through Addgene

[https://www.addgene.org/Yolanda_Schaerli/] and all sequences are provided in a supplementary file (Supplementary Data 1). MICROPLATE READER EXPERIMENTS Gene expression of fluorescent

reporters was used to assess synthetic circuit performance; fluorescence was measured in microplate readers (except for the flow cytometry in Fig. 2c, d, the agar plate experiment in Fig. 4d

and the microfluidic experiment in Fig. 5). MK0132 electrocompetent cells were transformed with a constant plasmid encoding proteins required for circuit function (namely dCas9 and Csy4,

plus LuxR when applicable), as well as with a variable vector bearing AraC (when needed) and the CRISPRi circuit (see Supplementary Table 2 for a detailed plasmid description). Two mililiter

of selective LB were inoculated with single colonies and incubated at 37 °C for ~6 h with 200 rpm shaking; cells were pelleted at 4000 rcf and resuspended in selective EZ medium (Teknova)

containing 0.4% glycerol. One hundred and twenty microliter of 0.05 OD600 bacterial suspensions were added per well on a 96-well CytoOne plate (Starlab), and 2.4 µl of L-arabinose (Sigma)

were added to yield the indicated concentrations. Plates were incubated at 37 °C with double-orbital shaking in a Synergy H1 microplate reader (Biotek) running Gen5 3.04 software.

Fluorescence was determined after 16 h (for protein reporters) or 6 h (for Broccoli RNA reporter) with the following settings: mKO2: Ex. 542 nm, Em. 571 nm; sfGPF: Ex. 479 nm, Em. 520 nm;

mKate2: Ex. 588 nm, Em. 633 nm. For the Broccoli RNA aptamer, 40 µM DFHB1-1T (Tocris Bioscience) were added to the medium. Fluorescence levels were treated as follows: (i) the fluorescence

signal in a blank sample was subtracted, (ii) the resulting value was divided by the absorbance at 600 nm to correct for differences in bacterial concentration, and finally (iii) the

bacterial auto-fluorescence of a strain with no reporter genes was subtracted. Subsequently, corrected fluorescence was normalized to a percentage scale by dividing all values of a given

color by the highest value of that color. Normalized data were plotted in R48 using RStudio 1.0.143 (running R 3.4.0). FLOW CYTOMETRY MK0132 electrocompetent cells were transformed with a

constant vector encoding dCas9, Csy4 and LuxR plus the controller vector (which carries PBAD-controlled and PLUX-controlled sgRNA-1 and -4, respectively, plus AraC) together with the TS

circuit or the control networks cL or cR. Single colonies were used to inoculate 2 ml of selective EZ medium (Teknova) containing 0.2% Ara or 10 µM AHL and incubated at 37 °C for 10 h in a

tilted 200 rpm shaker. Next, cells were pelleted at 5000 rcf for 10 min and resuspended in selective EZ medium free of inducers; 1 µl of the resuspension was used to inoculate 120 µl of

inducer-free selective EZ in a 96-well plate, which was incubated at 37 °C for 12 h with double-orbital shaking in a Synergy H1 microplate reader (Biotek). Cells were then diluted 1:120 in

120 µl of selective EZ containing the opposed inducer (10 µM AHL or 0.2% Ara) and grown for 10 h more in the microplate reader under identical conditions. Cells were then diluted into an

inducer-free medium as before (resuspension in inducer-free selective EZ followed by 1:120 dilution in the same medium) and incubated for another 12 h in the microplate reader under

identical conditions. At the end of each induction period (inducer 1, no inducer, inducer 2, no inducer) a sample was taken, diluted 1:100 in phosphate-buffered saline (PBS) and analyzed

with a BD LSRFortessa flow cytometer using a 488 nm laser in combination with FITC filter for sfGFP fluorescence determination. 20,000 events were acquired with BD FACSDiva 8.0 software and

data were exported using FlowJo 10.5.2 (FlowJo, LLC). Green cells were gated (green > 330 FITC-H a.u.) in the FITC-H histogram to differentiate the two states (green and non-green) using

non-fluorescent cells as control. Both the gating and the plotting were performed in R48 using RStudio 1.0.143 (R 3.4.0). AGAR PLATE ASSAY MK0132 electrocompetent cells were transformed with

the constant plasmid pJ1996_v233, which carries dCas9 and Csy4, and a variable plasmid encoding the three-color IFFL (pJ2042.2). Two mililiter of selective LB were inoculated with single

colonies and grown at 37 °C for ~6 h; cells were pelleted at 4000 rcf and resuspended in selective EZ medium (Teknova) with 0.4% glycerol. Three hundred microliter of 0.15 OD600 bacterial

suspensions were added to pre-warmed (1 h 37 °C) Petri dishes containing selective EZ medium (Teknova) with 0.4% glycerol and 0.9% agar, and suspensions were spread with sterile glass beads.

After incubating 1 h at 37 °C, a filter paper disk was placed in the center of the agar, and 15 µl of 5% Ara were delivered onto the disk. Plates were incubated at 37 °C and imaged after 20

 h with an Amersham Typhoon 5 Biomolecular Imager (GE Healthcare) running Amersham Typhoon 1.1.0.7 using the following settings: mKO2: Ex. 532 nm, Em. 560–580 nm; sfGPF: Ex. 488 nm, Em.

515–535 nm; mKate2: Ex. 635 nm, Em. 655–685 nm. Grayscale images were converted to color images using Fiji49 2.0.0 and overlaid. MICROFLUIDIC EXPERIMENTS MK0132 electrocompetent cells were

transformed with the constant plasmid pJ1996_v233 and a variable plasmid encoding the CRISPRlator. Single colonies were used to inoculate 5 ml of selective LB, which were grown overnight at

37 °C. Next morning, 3 ml of selective EZ containing 0.85 g l−1 Pluronic F-127 (Sigma) were inoculated with 30 µl of the overnight preculture and grown for 3–4 h at 37 °C. Cells were

centrifuged for 10 min at 4000 rcf and resuspended in ~10 µl of the supernatant to obtain a dense suspension, which was loaded into the PDMS microfluidics device. Cells were grown in a

continuous culture inside microfluidic chambers (dimensions: 1.2 µm × 12 µm × 60 µm, h × w × l, purchased from Wunderlichips, see design in Supplementary Fig. 5) for 3 days with a constant

0.5 ml h−1 supply of fresh medium (selective EZ plus 0.85 g l−1 Pluronic F-127) and removal of waste and excess of bacteria, powered by an AL-300 pump (World Precision Instruments). Imaging

was performed using a Leica DMi8 microscope and a Leica DFC9000 GT camera controlled by the Leica Application Suite X 3.4.2.18368, with the following settings: Cerulean: Ex. 440 nm 10% 50 

ms, Em. 457–483 nm; mCitrine: Ex. 510 nm 10% 50 ms, Em. 520–550 nm; mCherry: Ex. 550 nm 20% 200 ms, Em. 600–670 nm. Imaging started after 20 h to allow cells to fill the chamber and

oscillations to stabilize, and images were collected every 10 min with LAS X software, and analyzed using Fiji49 for both quantification and montage. Fluorescence quantifications were

normalized as follows: background fluorescence was removed by subtracting to each channel the minimum value of that channel, and data were normalized to a percentage scale by dividing all

values of a given channel by the highest value of that channel. Normalized data were plotted in R48 using RStudio 1.0.143 (R 3.4.0). MATHEMATICAL MODELING OF THE CRISPRI TOGGLE SWITCH We

performed all analyses in Matlab 2018b (MathWorks, Natick, MA) and used the BioSwitch toolbox36 version 1.0.0 (available at https://github.com/ireneotero/BioSwitch) to evaluate network

structures that could potentially generate bistability. Model structures are defined in Supplementary Tables 3, 4 and Supplementary Figs. 7–13. We performed parameter exploration for a limit

point in the range [10−2 102] for all parameters, except for the simplest model (Supplementary Fig. 7; ranges [10−3 103]) and for the most complex model (Supplementary Fig. 12) with

experimentally constrained parameter ranges (Supplementary Table 4). Code for the analysis is available as Supplementary folder. REPORTING SUMMARY Further information on research design is

available in the Nature Research Reporting Summary linked to this article. DATA AVAILABILITY The source data underlying Figs. 1, 2, 4 and 5 and Supplementary Figs. 1–4 and 6 are provided as

a Source Data file. The plasmids used in this study (Supplementary Table 2) and their annotated sequences (Supplementary Data 1) are available through Addgene

[https://www.addgene.org/Yolanda_Schaerli/] 124421, 124422, and 140664–140689. All other data are available from the authors upon reasonable request. CODE AVAILABILITY The BioSwitch

toolbox36 is available at https://github.com/ireneotero/BioSwitch. The code for the analysis is available as a Supplementary folder within the Source Data file. CHANGE HISTORY * _ 19 MAY

2021 A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-23622-x _ REFERENCES * Xie, M. Q. & Fussenegger, M. Designing cell function: assembly of synthetic

gene circuits for cell biology applications. _Nat. Rev. Mol. Cell. Biol._ 19, 507–525 (2018). Article  CAS  PubMed  Google Scholar  * Chappell, J., Watters, K. E., Takahashi, M. K. &

Lucks, J. B. A renaissance in RNA synthetic biology: new mechanisms, applications and tools for the future. _Curr. Opin. Chem. Biol._ 28, 47–56 (2015). Article  CAS  PubMed  Google Scholar 

* Jusiak, B., Cleto, S., Perez-Pinera, P. & Lu, T. K. Engineering synthetic gene circuits in living cells with CRISPR technology. _Trends Biotechnol._ 34, 535–547 (2016). Article  CAS 

PubMed  Google Scholar  * Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. _Cell_ 152, 1173–1183 (2013). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Bikard, D. et al. Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. _Nucleic Acids Res._ 41,

7429–7437 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. _Nucleic Acids Res._ 31, 3406–3415

(2003). Article  CAS  PubMed  PubMed Central  Google Scholar  * Zadeh, J. N. et al. NUPACK: Analysis and design of nucleic acid systems. _J. Comput. Chem._ 32, 170–173 (2011). Article  CAS

  PubMed  Google Scholar  * Nielsen, A. A. & Voigt, C. A. Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks. _Mol. Syst. Biol._ 10, 763 (2014). Article 

PubMed  PubMed Central  CAS  Google Scholar  * Clamons, S.E. & Murray, R.M. Modeling dynamic transcriptional circuits with CRISPRi. Preprint at bioRxiv: 225318 (2017). * Lebar, T. et al.

A bistable genetic switch based on designable DNA-binding domains. _Nat. Commun._ 5, 5007 (2014). Article  ADS  CAS  PubMed  Google Scholar  * Ma, H. et al. CRISPR-Cas9 nuclear dynamics and

target recognition in living cells. _J. Cell Biol._ 214, 529 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Jones, D. L. et al. Kinetics of dCas9 target search in

_Escherichia coli_. _Science_ 357, 1420–1423 (2017). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Gander, M. W., Vrana, J. D., Voje, W. E., Carothers, J. M. & Klavins, E.

Digital logic circuits in yeast with CRISPR-dCas9 NOR gates. _Nat. Commun._ 8, 15459 (2017). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Samaniego, C. C., Subramanian, H.

K. K. & Franco, E. Design of a bistable network using the CRISPR/Cas system. In _IEEE Conference on Control Technology and Applications (CCTA)_ 973–978 (IEEE, 2017). * Chen, P. Y., Qian,

Y. L. & Del Vecchio, D. A model for resource competition in CRISPR-mediated Gene repression. In _IEEE Conference on Decision and Control (CDC)_ 4333–4338 (IEEE, 2018). * Tan, C.,

Marguet, P. & You, L. C. Emergent bistability by a growth-modulating positive feedback circuit. _Nat. Chem. Biol._ 5, 842–848 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar

  * Didovyk, A., Borek, B., Hasty, J. & Tsimring, L. Orthogonal modular gene repression in Escherichia coli using engineered CRISPR/Cas9. _ACS Synth. Biol._ 5, 81–88 (2016). Article  CAS

  PubMed  Google Scholar  * Kiani, S. et al. CRISPR transcriptional repression devices and layered circuits in mammalian cells. _Nat. Methods_ 11, 723–U155 (2014). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Kiani, S. et al. Cas9 gRNA engineering for genome editing, activation and repression. _Nat. Methods_ 12, 1051–1054 (2015). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Menn, D. J., Pradhan, S., Kiani, S. & Wang, X. Fluorescent guide RNAs facilitate development of layered pol II-driven CRISPR circuits. _ACS Synth. Biol._ 7,

1929–1936 (2018). Article  CAS  PubMed  Google Scholar  * Nissim, L., Perli, S. D., Fridkin, A., Perez-Pinera, P. & Lu, T. K. Multiplexed and programmable regulation of gene networks

with an integrated RNA and CRISPR/Cas toolkit in human cells. _Mol. Cell._ 54, 698–710 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Liu, Y. C. et al. Synthesizing AND gate

genetic circuits based on CRISPR-Cas9 for identification of bladder cancer cells. _Nat. Commun._ 5, 5393 (2014). Article  ADS  CAS  PubMed  Google Scholar  * Gao, Y. C. et al. Complex

transcriptional modulation with orthogonal and inducible dCas9 regulators. _Nat. Methods_ 13, 1043–1049 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Cress, B. F. et al.

Rapid generation of CRISPR/dCas9-regulated, orthogonally repressible hybrid T7-lac promoters for modular, tuneable control of metabolic pathway fluxes in _Escherichia coli_. _Nucleic Acids

Res._ 44, 4472–4485 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kim, H., Bojar, D. & Fussenegger, M. A CRISPR/Cas9-based central processing unit to program complex

logic computation in human cells. _Proc. Natl Acad. Sci. USA_ 116, 7214–7219 (2019). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Gardner, T. S., Cantor, C. R. & Collins,

J. J. Construction of a genetic toggle switch in _Escherichia coli_. _Nature_ 403, 339 (2000). Article  ADS  CAS  PubMed  Google Scholar  * Elowitz, M. B. & Leibler, S. A synthetic

oscillatory network of transcriptional regulators. _Nature_ 403, 335–338 (2000). Article  ADS  CAS  PubMed  Google Scholar  * Yeung, E. et al. Biophysical constraints arising from

compositional context in synthetic gene networks. _Cell Syst._ 5, e12 (2017). Google Scholar  * Tsai, S. Q. et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome

editing. _Nat. Biotechnol._ 32, 569–576 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Haurwitz, R. E., Jinek, M., Wiedenheft, B., Zhou, K. & Doudna, J. A. Sequence- and

structure-specific RNA processing by a CRISPR endonuclease. _Science_ 329, 1355–1358 (2010). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Butzin, N. C. & Mather, W. H.

Crosstalk between diverse synthetic protein degradation tags in _Escherichia coli_. _ACS Synth. Biol._ 7, 54–62 (2018). Article  CAS  PubMed  Google Scholar  * Kogenaru, M. & Tans, S. J.

An improved _Escherichia coli_ strain to host gene regulatory networks involving both the AraC and Lacl inducible transcription factors. _J. Biol. Eng._ 8, 2 (2014). Article  PubMed  PubMed

Central  CAS  Google Scholar  * Santos-Moreno, J. & Schaerli, Y. A Framework for the modular and combinatorial assembly of synthetic gene circuits. _ACS Synth. Biol._ 8, 1691–1697

(2019). Article  CAS  PubMed  Google Scholar  * Cherry, J. L. & Adler, F. R. How to make a biological switch. _J. Theor. Biol._ 203, 117–133 (2000). Article  ADS  CAS  PubMed  Google

Scholar  * Martens, K. J. A. et al. Visualisation of dCas9 target search in vivo using an open-microscopy framework. _Nat. Commun_. 10 1–11 (2019). * Yordanov, P., Stelling, J. &

Otero-Muras, I. BioSwitch: a tool for the detection of bistability and multi-steady state behaviour in signalling and gene regulatory networks. _Bioinformatics_ 36, 1640–1641 (2020). *

Otero-Muras, I., Yordanov, P. & Stelling, J. Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling. _PLoS Comput. Biol._ 13, e1005454 (2017). *

Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. _Nature_ 434, 1130–1134 (2005). Article  ADS  CAS 

PubMed  Google Scholar  * Schaerli, Y. et al. A unified design space of synthetic stripe-forming networks. _Nat. Commun._ 5, 4905 (2014). Article  ADS  CAS  PubMed  Google Scholar  *

Santos-Moreno, J. & Schaerli, Y. Using synthetic biology to engineer spatial patterns. _Adv. Biosyst._ 3, 1800280 (2019). Article  Google Scholar  * Mangan, S. & Alon, U. Structure

and function of the feed-forward loop network motif. _Proc. Natl Acad. Sci. USA_ 100, 11980–11985 (2003). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Wolpert, L. Positional

information and pattern formation. _Curr. Top. Dev. Biol._ 6, 183–224 (1971). Article  CAS  PubMed  Google Scholar  * Filonov, G. S., Moon, J. D., Svensen, N. & Jaffrey, S. R. Broccoli:

rapid selection of an RNA mimic of green fluorescent protein by fluorescence-based selection and directed evolution. _J. Am. Chem. Soc._ 136, 16299–16308 (2014). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Niederholtmeyer, H. et al. Rapid cell-free forward engineering of novel genetic ring oscillators. _ELife_ 4, e09771 (2015). Article  PubMed  PubMed Central 

Google Scholar  * Potvin-Trottier, L., Lord, N. D., Vinnicombe, G. & Paulsson, J. Synchronous long-term oscillations in a synthetic gene circuit. _Nature_ 538, 514–517 (2016). Article 

ADS  PubMed  PubMed Central  CAS  Google Scholar  * Rizzo, M. A., Springer, G. H., Granada, B. & Piston, D. W. An improved cyan fluorescent protein variant useful for FRET. _Nat.

Biotechnol._ 22, 445–449 (2004). Article  CAS  PubMed  Google Scholar  * Casini, A. et al. One-pot DNA construction for synthetic biology: the modular overlap-directed assembly with linkers

(MODAL) strategy. _Nucleic Acids Res._ 42, e7 (2014). Article  CAS  PubMed  Google Scholar  * R Core Team. R: a language and environment for statistical computing. (R Foundation for

Statistical Computing, Vienna, Austria, 2017). * Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. _Nat. Methods_ 9, 676 (2012). Article  CAS  PubMed  Google

Scholar  Download references ACKNOWLEDGEMENTS We thank Imre Banlaki for cloning _luxR_ gene into pJ1996_v2 to yield pJ2018, Içvara Barbier for help with microfluidics and feedback about

bistable circuits, and Marc García-Garcerà for help with R language. We also thank Mariapia Chindamo, Aysun El Wardani, Florence Gauye, Virginie Kahabdian, Borany Kim and Léo Moser for

excellent technical assistance; Marc Güell, Mark Isalan, and Jan-Willem Veening for critical reading of the manuscript, and all Schaerli lab members for useful discussions. This work was

funded by Swiss National Science Foundation Grant 31003A_175608. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Fundamental Microbiology, University of Lausanne, Biophore

Building, 1015, Lausanne, Switzerland Javier Santos-Moreno & Yolanda Schaerli * Department of Biosystems Science and Engineering, ETH Zurich and SIB Swiss Institute of Bioinformatics,

Basel, Switzerland Eve Tasiudi & Joerg Stelling Authors * Javier Santos-Moreno View author publications You can also search for this author inPubMed Google Scholar * Eve Tasiudi View

author publications You can also search for this author inPubMed Google Scholar * Joerg Stelling View author publications You can also search for this author inPubMed Google Scholar *

Yolanda Schaerli View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.S.M. and Y.S. designed the experimental research. J.S.M. performed

experiments, analyzed data, and prepared the corresponding figures. E.T. and J.S. performed the mathematical modeling, and ET prepared the modeling figures. J.S.M., Y.S., E.T., and J.S.

wrote the manuscript. All authors have given approval to the final version of the manuscript. CORRESPONDING AUTHOR Correspondence to Yolanda Schaerli. ETHICS DECLARATIONS COMPETING INTERESTS

The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Communications_ thanks the anonymous reviewer(s) for their contribution to the peer review

of this work. Peer reviewer reports are available. PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION PEER REVIEW FILE SUPPLEMENTARY MOVIE SUPPLEMENTARY DATA 1 REPORTING SUMMARY DESCRIPTION OF ADDITIONAL SUPPLEMENTARY FILES SOURCE DATA

SOURCE DATA RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution

and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if

changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the

material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to

obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS

ARTICLE Santos-Moreno, J., Tasiudi, E., Stelling, J. _et al._ Multistable and dynamic CRISPRi-based synthetic circuits. _Nat Commun_ 11, 2746 (2020).

https://doi.org/10.1038/s41467-020-16574-1 Download citation * Received: 07 October 2019 * Accepted: 07 May 2020 * Published: 02 June 2020 * DOI: https://doi.org/10.1038/s41467-020-16574-1

SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to

clipboard Provided by the Springer Nature SharedIt content-sharing initiative