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Dynamic control of endogenous metabolism with combinatorial logic circuits

2018; Springer Nature; Volume: 14; Issue: 11 Linguagem: Inglês

10.15252/msb.20188605

1744-4292

Felix Moser, Amin Espah Borujeni, Amar Ghodasara, E. S. Cameron, Yongjin Park, Christopher A. Voigt,

Advanced Control Systems Optimization

Article29 November 2018Open Access Transparent process Dynamic control of endogenous metabolism with combinatorial logic circuits Felix Moser Felix Moser Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Amin Espah Borujeni Amin Espah Borujeni Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Amar N. Ghodasara Amar N. Ghodasara Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Ewen Cameron Ewen Cameron Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Yongjin Park Yongjin Park Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Christopher A. Voigt Corresponding Author Christopher A. Voigt [email protected] orcid.org/0000-0003-0844-4776 Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Felix Moser Felix Moser Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Amin Espah Borujeni Amin Espah Borujeni Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Amar N. Ghodasara Amar N. Ghodasara Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Ewen Cameron Ewen Cameron Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Yongjin Park Yongjin Park Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Christopher A. Voigt Corresponding Author Christopher A. Voigt [email protected] orcid.org/0000-0003-0844-4776 Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Author Information Felix Moser1, Amin Espah Borujeni1, Amar N. Ghodasara1, Ewen Cameron1, Yongjin Park1 and Christopher A. Voigt *,1 1Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA *Corresponding author. Tel: +1 617 617 4851; E-mail: [email protected] Molecular Systems Biology (2018)14:e8605https://doi.org/10.15252/msb.20188605 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation. Synopsis Genetically encoded sensors responding to feedstock (glucose), growth conditions (oxygen), and byproduct accumulation (acetate) are designed and their response is measured during growth. Sensor integration by combinatorial logic circuits generates different temporal responses and can be used to control metabolism. Genetically encoded sensors for glucose and oxygen are built based on synthetic Escherichia coli promoters that respond to native regulators and optimized using oligo synthesis and high-throughput screening. As shown computationally, combinatorial logic circuits that implement different truth tables can integrate the three sensors to produce different dynamic responses during a growth experiment. The circuit output can be used to quickly and strongly repress a target gene by combining CRISPRi (transcriptional control) and a targeted protease (post-translational control). Genetic circuits with CRISPRi/protease outputs were able to dynamically repress native target genes and regulate acetate metabolism. Introduction Genetic modifications made to an organism to optimize the production of a chemical or biologic are typically static (Holtz & Keasling, 2010; Brockman & Prather, 2015b). For example, knocking out a gene to redirect metabolic flux implements its impact permanently and continuously (Schellenberger et al, 2011). Similarly, introduced pathways are often under unchanging constitutive control (Zhang et al, 2002; Burgard et al, 2003; Price et al, 2004; Schellenberger et al, 2011; Morse & Alper, 2016; Deparis et al, 2017). While these changes are required to make the product and optimize yield, they can have a detrimental effect when activated at the wrong time, such as early in growth when resources need to be dedicated to building biomass (San & Stephanopoulos, 1984; Park et al, 2007; Michener et al, 2012; Brockman & Prather, 2015a; Ceroni et al, 2016). Static functions contrast with natural cellular systems that continuously monitor environmental conditions and respond by adjusting gene expression as needed (Shen-Orr et al, 2002; Zaslaver et al, 2004; Cho et al, 2014). Implementing flexible synthetic versions of this regulation would be valuable in engineering projects. For instance, product yields could be optimized by re-balancing enzyme expression to respond to growth phase, the buildup of precursor metabolites, or feedstock concentration (Farmer & Liao, 2000; Liu et al, 2015; Zhang et al, 2015; Morse & Alper, 2016). Additionally, less external intervention would be required if cells could be pre-programmed to undergo a series of steps during a bioprocess or respond as autonomous agents to bioreactor-borne stresses. Dynamic gene expression has begun to be implemented in academic metabolic engineering projects (Liu et al, 2016; Qian & Cirino, 2016; Min et al, 2017; Liu & Zhang, 2018; Zhou et al, 2018). These projects depend on genetically encoded sensors that respond to external environmental signals (O2, temperature, pH), the internal cell state (metabolites, growth phase, stress response, redox), the depletion of carbon feedstock (glucose), cell density, or the accumulation of products and by-products (acetate) (Farmer & Liao, 2000; Bayly et al, 2002; March & Bentley, 2004; Boccazzi et al, 2006; Nevoigt et al, 2007; Kang et al, 2008; Tsao et al, 2010; Liang et al, 2011; Michener et al, 2012; Zhang et al, 2012; Anesiadis et al, 2013; Siedler et al, 2014; Afroz et al, 2015; Liu & Lu, 2015; Soma & Hanai, 2015; Xie et al, 2015; Guan et al, 2016; Immethun et al, 2016; Lo et al, 2016; Qian & Cirino, 2016; Rajkumar et al, 2016; preprint: Borkowski et al, 2017; Bothfeld et al, 2017; Gupta et al, 2017; He et al, 2017; Juarez et al, 2017; Klamt et al, 2017; Pham et al, 2017; Kasey et al, 2018). The information transmitted by these sensors can be used to implement feedback control or switch the carbon flux through alternative pathways at the opportune time (Xu et al, 2014; Brockman & Prather, 2015b; Liu et al, 2015; Ceroni et al, 2016). For many products, this approach has been shown to increase yields by maintaining a toxic intermediate below a critical level or separating growth and production phases (Farmer & Liao, 2000; Michener et al, 2012; Zhang et al, 2012, 2015; Xu et al, 2014; Brockman & Prather, 2015b; Liu et al, 2015; Soma & Hanai, 2015; Xie et al, 2015; Ceroni et al, 2016; Morse & Alper, 2016). Several strategies can be taken to build such sensors. The ideal sensors consist of a regulator that directly binds to a known signal, such as the metabolite, and then strongly regulates the activity of a promoter (Tang & Cirino, 2011; Zhang et al, 2012; Rogers et al, 2015; Albanesi & de Mendoza, 2016; Libis et al, 2016; Morgan et al, 2016; Rogers & Church, 2016). When a sensor for a specific metabolite is unavailable, native promoters that respond to a given stimulus have also been co-opted as sensors (Dahl et al, 2013; Yuan & Ching, 2015). However, many native promoters integrate multiple signals, making them respond to alternative or unknown stimuli (Kang et al, 2008; Dahl et al, 2013; Boyarskiy et al, 2016; Rajkumar et al, 2016; preprint: Borkowski et al, 2017; Hoynes-O'Connor et al, 2017; Kasey et al, 2018; Siu et al, 2018). One approach to address this is to put the operators for a transcription factor into the "clean" background of a constitutive promoter (Cox et al, 2007). A sensor can be genetically modified to change the threshold of signal required to activate it. For example, increasing the expression level of the regulator can make the sensor turn on earlier and mutations can tune the binding constant to the ligand (Nevoigt et al, 2007; Moser et al, 2013; Afroz et al, 2015; Feher et al, 2015; Wang et al, 2015; Gupta et al, 2017; Mannan et al, 2017; Landry et al, 2018). However, an individual sensor can only implement a switch at a one defined cell state and cannot be used to drive a series of events (Wang et al, 2015; Gupta et al, 2017). An alternative approach to modifying the sensors is to select a set of sensors that turn on at different times during a bioprocess and then use a genetic circuit that responds to a pattern of sensor activities to turn on at a defined point. During a bioprocess, many conditions change dynamically inside the reactor and inside of individual cells. Therefore, the same set of sensors can be integrated in different ways to generate different dynamic responses. There is precedent for using genetic circuits to alter a sensor's response (Karig & Weiss, 2005; Slusarczyk et al, 2012; Brophy & Voigt, 2014; Hoynes-O'Connor & Moon, 2015). Connecting a sensor to a circuit is simplified when both are transcriptional; that is, when the output of the sensor is a promoter and the inputs/outputs of a circuit are promoters. Circuits have been used to integrate multiple sensors, change their threshold, amplify the response, convert a transient input to a permanent response, and toggle between outputs (Chen & Bailey, 1994; Kobayashi et al, 2004; Bennett et al, 2008; Moon et al, 2011; Wang et al, 2011; Moser et al, 2012; Solomon et al, 2012; Soma et al, 2014; Soma & Hanai, 2015; Rantasalo et al, 2016; preprint: Borkowski et al, 2017; Bothfeld et al, 2017; He et al, 2017; Ryo et al, 2017; Kasey et al, 2018). One way to respond to a pattern of sensor activities is to use genetic circuits that implement logic operations. Combinatorial logic is defined as a relationship in steady state in which the circuit outputs are a function of only the inputs. While circuits themselves do not implement dynamics, when the inputs (sensors) are changing over time, the output of the circuit will also change. Integrating more sensors makes the response more specific to a set of conditions or period of time during growth (Immethun et al, 2016; He et al, 2017). Larger logic gates can simultaneously integrate many sensors and control multiple output promoters, each turning on in response to a different pattern of sensor activities (Callura et al, 2012; Moon et al, 2012; Guan et al, 2016; Nielsen et al, 2016; Green et al, 2017). There are a number of genetic tools to connect the output promoters of a circuit to the control of endogenous or recombinant genes. The output promoter could be used to directly express enzymes (Temme et al, 2012; Immethun et al, 2016) or orthogonal RNA polymerases that transcribe multi-gene pathways (Temme et al, 2012; Segall-Shapiro et al, 2014; Bonde et al, 2015; Song et al, 2017; Harder et al, 2018). The output promoter can also be used to turn genes off using CRISPRi or sRNA/RNAi (Drinnenberg et al, 2009; Qi et al, 2013). These methods have been used to optimize titers by knocking down enzymes of central metabolism at an opportune time or to redirect flux through a heterologous pathway (Callura et al, 2012; Solomon et al, 2012; Anesiadis et al, 2013; Na et al, 2013; Oyarzun & Stan, 2013; Soma et al, 2014; Brockman & Prather, 2015a; Lv et al, 2015; Wu et al, 2015; Zalatan et al, 2015; Deaner & Alper, 2017; Harder et al, 2018; Kasey et al, 2018). Proteases have also been developed that target a tag that can be added to an enzyme, though this requires modification of the target enzyme (Cameron & Collins, 2014). The ability to degrade the enzyme pool is critical for rapidly eliminating its activity, particularly when the growth rate is low and proteins are only slowly diluted (Soma et al, 2014; Brockman & Prather, 2015a). In this manuscript, we develop three sensors that respond to generic signals that change over the course of bioproduction and are agnostic to a particular product pathway. Oxygen and glucose sensors are constructed by placing FNR/CRP operators into a constitutive promoter and optimizing for dynamic range using oligonucleotide arrays (Kosuri et al, 2010) and fluorescence-assisted cell sorting (FACS). A third sensor that responds to acetate was selected from the literature (Bulter et al, 2004) and modified to improve its response. Each of these signals responds at a different time during growth: The low oxygen sensor turns on first, followed by the turning off of the glucose sensor, and finally the acetate sensor turns on. Simulations of many genetic circuits implementing these sensors' signals into different logic operations show that diverse responses are possible. From these, we select several based on layered AND and ANDN gates, construct them, and verify their temporal response. As a proof-of-principle, we design two genetic circuits to respond during periods of endogenous poxB and pta expression, respectively, as determined using RNA-seq. The circuit controlling poxB is turned on during the transition to stationary phase, and the circuit controlling pta is turned on early in growth. When the circuits are on, they repress the native genes using a combination of CRISPRi and proteases. The resulting circuits are able to control the appropriate genes at early and late stages of growth, thus reducing acetate accumulation. This demonstrates how different configurations of sensors and gates can be used to generate responses at different times and thereby control carbon flux through endogenous metabolism. Results Design of glucose, oxygen, and acetate sensors The simultaneous use of multiple sensors requires that they respond to independent stimuli and do not interfere with each other's response. Further, they require a large dynamic range to facilitate their connection to circuits. For oxygen and glucose, we and others have built sensors based on native promoters and heterologous transcription factors (Anderson et al, 2007; Garcia et al, 2009; Immethun et al, 2016). However, we were concerned that these would either respond to additional unwanted cellular signals or that their reported dynamic ranges were insufficient. Initially, a number of natural Escherichia coli promoters were gleaned from the literature and tested, but their dynamic range proved to be too low (Appendix Fig S1). Therefore, synthetic promoters were designed to respond only to select regulatory proteins and screened variations to identify those that produced a large dynamic range. The approach to build the glucose and oxygen sensors utilizes a previously published method to generate large libraries of constitutive promoters (Kosuri et al, 2013). A library of 11,964 synthetic promoters was computationally designed by varying the promoter backbone and the placement of operators for E. coli transcription factors that respond to each signal (Fig 1A). First, twelve constitutive promoter variations were generated, each made up of one of four σ70-associated promoter sequences (−35 to +1) and one of three randomly generated spacer sequences for the −60 to −35 and +1 to +50 (Fig 1B). Within these sequences, the operators for the glucose- and oxygen-sensing transcription factors were placed at all possible locations (Cox et al, 2007; Stanton et al, 2014b). For glucose, the operators bind to either the global regulators cAMP receptor protein (CRP; Lawson et al, 2004) or FruR (Kochanowski et al, 2013), although no promoters with the latter operator ultimately emerged from the screen. For oxygen, the operator is for the fumarate and nitrate reductase (FNR) transcriptional activator, which is directly modified by oxygen via a Fe-S cluster (Constantinidou et al, 2006). The full set of promoters was synthesized using a CustomArray oligo array and cloned into a reporter plasmid (p15A origin) upstream of green fluorescent protein (gfp). Constitutive expression of a red fluorescent protein (rfp) enabled us to correct for variation in copy number of the plasmid (Materials and Methods). RiboJ was included upstream of gfp in order to insulate against genetic context effects that occur when it is transcribed from different promoters (Lou et al, 2012). Figure 1. Design and optimization of glucose, acetate, and oxygen sensors A. Scheme for sensor design including (left to right): computational design and DNA chip oligo library synthesis, insertion into a plasmid reporter, FACS enrichment, and screening of select mutants in the presence (black) and absence (white) of stimulus. All three sensors were synthesized on a single chip (indicated by the colors). B. Design of the promoter library. The location, number, and name of promoter elements are shown. The permutations included different constitutive core promoters (blue) flanked by random spacers (orange). Single operators (colored bars) were varied across the ranges shown with single nucleotide resolution. When two operators were included, they were inserted at multiple sites and the distance between them was varied by up to 6 bp from each site. C. Shown are the responses of the glucose sensor promoter (PgluA7), a negative control lacking the CRP operator (PgluA7*), and a constitutive promoter (BBa_J23101) to the presence (+) and absence (−) of glucose. D. Shown are the responses of the oxygen sensor promoter (PfnrF8), a negative control lacking the FNR operator (PfnrF8*), and a constitutive promoter (BBa_J23101) to the presence (+) and absence (−) of oxygen. E. The orthogonality of the sensors is shown. The averages and standard deviations for these data are provided in Appendix Fig S5. F–H. Shown are the schematics and responses for the glucose, oxygen, and acetate sensors, respectively. The response functions (center) are shown for each sensor (black circles) compared to promoter variants where the operators are removed (PgluA7*, PfnrF8*; open circles). Horizontal error bars in the PfnrF8 response reflect one standard deviation of three dissolved oxygen (DO) measurements. For the acetate sensor, the response of the sensor is shown in unmodified Escherichia coli MG1655 with glnL intact (open diamonds). The dynamics of induction are shown (right graph) where cells are induced at the time indicated by the dashed line (see text). Representative cytometry florescence distributions for Fig 1F–H are shown in Appendix Fig S2. Data information: Error bars represent one standard deviation of three independent experiments done on different days. Download figure Download PowerPoint The promoter library was then transformed into E. coli MG1655, and FACS sorting was used to screen for activity. For the glucose sensor, cells were grown in the presence of 0.4% glucose and then sorted using a threshold for high GFP:RFP fluorescence (Fig 1A). The recovered variants were then grown in the absence of glucose and re-sorted, this time recovering cells below a threshold GFP:RFP fluorescence. This was repeated for three cycles, after which 95 promoter variants were recovered and tested for their on/off response. The same approach was applied to identify oxygen sensors, where the three FACS cycles were performed by iterating between aerobic and anaerobic growth (Materials and Methods). The top glucose- and oxygen-responsive promoters to emerge from these screens were PgluA7 and PfnrF8, respectively. Their responses were compared to native promoters and the strong constitutive promoter BBa_J23101 (Fig 1C and D; 2016; Kelly et al, 2009). The replacement of each sensor's operator with a random sequence eliminated its response (Fig 1C and D). The promoters only respond to their corresponding signal (Fig 1E). To characterize the promoters as sensors, their response was measured as a function of inducer concentration under conditions that approximate steady state (Materials and Methods). The best glucose sensor (PgluA7) shows a maximum 18-fold dynamic range and achieves half-maximum induction at 0.1% glucose (Fig 1F). The best oxygen sensor (PfnrF8) produces a 25-fold induction and achieves its half-maximum output at a dissolved oxygen (DO) concentration of 36 μmol/l (Fig 1G). For both promoters, the transition between the off and on states occurs uniformly throughout the population of cells (Appendix Fig S2). The responses of both the glucose and oxygen promoters are rapid, achieving 8-fold and 7-fold activation, respectively, after 1 h (Fig 1F and G, and Appendix Fig S3). For the acetate sensor, we tested one previously designed by Liao and co-workers based on the PglnAP2 promoter, which responds to phosphorylated NtrC (glnG) in a glnL knockout stain (Fig 1H; Bulter et al, 2004). In our hands, this promoter produces a 16-fold induction in E. coli MG1655, but requires knocking out the receptor NtrB (ΔglnL; Materials and Methods), which limits its use to strains in which this gene is deleted or repressed. We found that truncating the promoter at the +1 start site (PglnAP2s) improved the dynamic range to 250-fold by reducing the leakiness of the off state (Fig 1H). The half-maximum response occurs at 13.8 mM acetate, and the response to intermediate concentrations is bimodal (Appendix Fig S2). In addition, the response is slower than the other two sensors. It should be noted that the response is sensitive to the pH of the media and changes when other genes are knocked out (Appendix Fig S4; Bulter et al, 2004). Because ΔglnL knockout mutation interferes with the nitrogen starvation response, we used a nitrogen-rich media and did not observe any growth defects due to this mutation (Appendix Table S1). The three sensors (PfnrF8, PgluA7, PglnAPs) were tested for orthogonality to each other's signals (low oxygen, glucose, acetate; Fig 1E). The three sensors are activated by their cognate stimuli, with minimal measurable cross-reactivity between the acetate and glucose sensors (Appendix Fig S5). Thus, they can all be used together within one circuit, although some care needs to be taken to avoid crosstalk. The three sensors were then evaluated in shake flask experiments where cells were seeded into a defined glucose-based media common in industry (Moser et al, 2012) and grown for over 24 h (Materials and Methods). For these experiments, GFP was fused to a degradation tag to better measure off-times (McGinness et al, 2006). Glucose, dissolved oxygen (DO), and acetate were monitored throughout growth by offline liquid chromatography and an oxygen sensor probe (Materials and Methods). Glucose and DO decrease over time due to cell growth and metabolism (Fig 2A and B, Appendix Fig S6). The inoculum culture is first grown without glucose, but when cells are added to glucose-containing media (t = 0 h), the glucose sensor rapidly turns on and remains on until glucose is consumed after 15 h (Fig 2A). The DO sensor turns on to the absence of oxygen, which is consumed during growth, causing the sensor to turn on after 8 h (Fig 2B). Acetate accumulates late in growth and the sensor turns on when the acetate concentration passes the 15 mM threshold after 14 h (Fig 2C and Appendix Fig S7). Figure 2. Response of sensors and circuits during growth in batch experimentsAll of the temporal responses shown on the left-hand side of this figure were measured under identical experimental conditions (Materials and Methods). A–C. The responses of the glucose, oxygen, and acetate sensors during growth in shake flasks are shown. The colored lines (right axes) correspond to the measured changes in the stimuli (Materials and Methods). The colored bars under (C) show the times when the output promoter of each sensor should be on, based on the response functions shown in Fig 1. D. Simulations of circuit dynamics. Examples of different characteristic responses are shown, selected from the full set of simulated circuits (Appendix Fig S8). The lines shown in bold blue colors correspond to those circuits experimentally tested. The simulated output promoter activities are in relative promoter units (RPU; Nielsen et al, 2016). E. The responses of a 3-input, 2-output circuit are shown. F. Shown are the circuit (left) and genetic diagram (right) of the circuit corresponding to (E). In the genetic diagram, the dashed line and * indicates a second copy of the PgluA7 promoter that drives rfp expression and is repressed by PhlF via an immediately downstream PhlF operator. G. The response of the circuit in (E, F) to different combinations of stimuli under the same conditions as Fig 1 (Materials and Methods). The (+) and (−) indicate whether the output promoter of each sensor is active under those conditions. Bars where the circuit is predicted to be on are shown in gray and white when predicted to be off. H. The response of a 3-input 1-output circuit is shown. I. Shown are the circuit (left) and genetic diagram (right) of the circuit corresponding to (H). J. The response of the circuit in (H, I) to different combinations of stimuli under the same conditions as Fig 1 (Materials and Methods). Data information: Representative cytometry florescence distributions for (A–C and E–J) are shown in Appendix Figs S7 and S10, respectively. Error bars represent one standard deviation of three independent experiments done on different days. Download figure Download PowerPoint Sensor integration with combinatorial logic circuits Over the course of a growth experiment, the output of the three sensor promoters is continuously changing. These promoters can be connected as inputs to a logic circuit that responds only when each sensor is at the correct level. Thus, by connecting the sensors to circuits that implement different logic operations (truth tables), the circuits will produce different responses over time. Because the circuits are based on the layered expression of regulators (a cascade), different circuits that encode the same truth table can result in different dynamics due to delays in signal propagation. To determine the range of possible dynamics, simulations were run for all possible 3-input logic circuits designed based on layered AND, ANDN, and NOR gates (Moon et al, 2012; Nielsen et al, 2016; Appendix Fig S8). The inputs to the circuit are the empirically measured output promoter activities of the three sensors over time (Fig 2A–C). The circuit response is simulated using a simple set of ordinary differential equations (ODEs) to model the on- and off-times of each gate (Materials and Methods). The simulation results for the full set of circuits are shown in Appendix Fig S8, of which a subset of characteristic responses are shown in Fig 2D. Circuits are predicted to yield a diverse range of dynamic behaviors with varying on- and off-times. Two modeled circuits were built and tested experimentally (Fig 2E and H). The circuits are built using AND gates that utilize an activator (InvF) that requires the expression of a second protein (SicA) to turn on an output promoter (PsicA; Appendix Fig S9). In addition, the repressor PhlF is used to build ANDN gates (Stanton et al, 2014a). The first circuit (Fig 2E–G) has three inputs and two outputs, where each output is designed to respond to a different combination of signals, and thus, each will turn on and off at different times. In this circuit, the glucose and acetate sensors drive the SicA/InvF system to compose an AND gate. The oxygen sensor drives the repressor PhlF to turn off a second copy of the glucose-inducible promoter to compose AND NOT (ANDN) logic that activates in the presence of glucose AND NOT low oxygen. The second circuit (Fig 2H–J) is based on a three-input, one-output logic gate that implements (A and B) AND NOT (C) logic, where the C signal (low O2) turns off the gate by expressing PhlF, which re