Principles of genetic circuit design

Abstract

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

Figure 1. Potential uses of synthetic genetic circuits

Here, we present several hypothetical uses of circuits in different application areas. (a) In industrial applications, most synthetic metabolic pathways are overexpressed at all times or are under simple inducible control. This could be improved by incorporating timing, feedback of metabolic intermediates, or dynamic control. Here, we show a circuit that is controlling the production of a diesel fuel alternative (bisobolane) by regulating the accumulation of a toxic intermediate (HMG-CoA) by sensing sugar, which induces oscillations in the production of HMGR. This type of oscillatory control occurs in natural metabolic networks. (b) Gene therapy circuits could be built based on CRISPRi technology by detecting SNPs and integrating this information with tissue-specific sensors. As a hypothetical example, we show a circuit that could detect two SNPs associated with colon cancer susceptibility (rs4444235 and rs9929218) and this is integrated with a promoter that is specific to colon cells (pAMUC2) to control the expression of misregulated genes (DLGAP5, NO3, and DDX28). (c) Bacteria could be programmed to colonize human microbiota and implement a therapeutic response. An example is envisioned where a commensal bacterium is used to stabilize pH to treat gastoesophageal acid reflux (GERD). A bacterium that is naturally commensal with the stomach could be programmed to maintain the pH using a circuit that enables set point control via a PI controller^, whose output is proton pump inhibitors (PPIs). The circuit also restricts acid regulation to the stomach by terminating the bacterium via an irreversible switch if it leaves this organ. (d) Genetic circuits could also be used to build “smart plants” that are able to sense environmental stimuli and implement a response. Currently, traits are produced all the time whether or not they are needed by the plant. Here, we envision a circuit that would operate in the chloroplast integrate sensors for drought (pSpark), temperature (pCBF), and plant maturity (pSAG12) to control multiple traits. This could reduce the amount of recombinant protein that is produced and enters the food supply without reducing the effectiveness of the trait.

Figure 2. Logic gates built based on different regulator types

All of the gates are transcriptional, where there are two input promoters (P_IN1 and P_IN2) and one output promoter (P_OUT). Two-input transcriptional logic gates have not yet been built for CRISPRi and RNA-IN/OUT so we hypothesize how these biochemistries could be used. The graphs at the right show how the gates will respond to inputs introduced at the same time (graphs at left) or sequentially (right). In all panels, the on state is assumed to generate ten fold higher than the off state. (a) A NOR gate is shown based on a repressor that binds DNA. The lines are based on measured induction (τ_1/2 ~36 min) and relaxation (τ_1/2 ~35 min) half-lives. (b) An AND gate based on an activator that binds DNA that requires a second protein to be active. The lines are based on a measured induction (τ_1/2 ~36 min) and approximate relaxation (τ_1/2 ~35 min) half-life. (c) A NOR gate based on integrases that flip two terminators to turn off the output^,. We assume a small readthrough probability, which leads to a change in the rate when only one terminator is flipped. Conceptually, a NOR gate could also be constructed by having two input promoters in series drive the expression of a single integrase. The lines are based on an on-rate of 1.8 hours^,,. (d) An AND gate based on integrases. The same on- and off- rates are used as in part c. (e) A NOR gate could be built based on CRISPRi by setting a constitutive level of Cas9 expression and then having the two input promoters drive the expression of two guide RNAs. The lines are based on measured induction (τ_1/2 = 35 min) and relaxation (τ_1/2 = 47 min) half-lives. (f) A NOR gate could be built based on the RNA-IN/RNA-OUT system developed by Arkin and co-workers. RNA-OUT represses translation of tnaC, which allows Rho to bind the mRNA and repress transcription of the output. The CRISPR machinery needed to process RNA-IN mRNA for this circuit is not shown. The lines are based on theoretical induction (τ_{1/2 ~}30 min) and relaxation (τ_1/2 ~35 min) half-lives^,.

Figure 3. Methods of Modifying Circuit Behavior

ODE models were built to simulate a NOT gate and an oscillator. Model equations and parameters are included in the SI in SMBL format^,. Parameters were adjusted to demonstrate the effect of each tuning knob on circuit behavior. Every panel displays circuit outputs with original parameter values (black) or tuning knob variations (grey). Inputs in (a-f) are IPTG. (a/h) Architecture and ideal response functions for the NOT gate (a) and oscillator (h). (b/i) Promoter strength. In both circuits, promoter strength is increased (dashed grey line) or decreased (solid grey line) by a factor of two. (c/j) Enzymatic degradation of the reporter protein was modeled as a five fold increase in the protein degradation rate. (d/k) Gene dosage. The circuits are moved between a high copy plasmids (dashed grey line) and the genome (solid grey line) to tune expression. The high copy plasmid is assumed to be ten times more abundant than the original circuit. (e/l) Ribosome binding site strength. Repressor RBSs (RBS1) are increased (dashed grey line) or decreased (solid grey line) by a factor of five. Altering the reporter RBS would shift the output of both circuits vertically (not pictured). (f/m) Small RNA designed to bind repressor mRNA are modeled with the introduction of a new species that binds repressor mRNA with the same affinity as a ribosome (this value was chosen arbitrarily and can be modulated to change circuit dynamics). In this model, small RNAs are produced constitutively and sRNA/mRNA duplexes are degraded faster than either RNA alone. (g/n) Decoy operators that bind repressor proteins. Decoy operators were modeled by introducing a new species that binds repressor protein with the same K_d as the repressible promoter. The oscillator model has 25 decoy operator sites, however circuits can be tuned with more or less as needed.

Figure 4. Common failure modes and their impact on circuit dynamics. (a)

An AND gate and oscillator are used as model systems to demonstrate the assembly of parts to build more complex circuits. Repression is indicated with a blunt ended connector and activation is indicated with an arrow. For the AND gate, the input promoters are P_IN1 and P_IN2 and the output promoter is P_R3. Promoters are named by the repressor to which it responds (e.g., P_R1 is repressed by R1). The steady-state response to different combinations of inputs is shown as a bar graph, where the OFF states are grey and the ON state is black. For the oscillator, the promoters P_A-R are repressed by R and activated by A. The impact of various failures (red lines) are shown for the AND gate (left) and oscillator (right) with expected dynamics shown in black. Models were used to simulate the R2 NOT gate, the AND gate, and oscillator. Parameters and model equations are included in the SI in SBML format^,. Unless indicated otherwise, Input 2 is the input to the NOT gate transfer function. (b) Mismatched response functions. In the AND gate, R3 was modeled as a different repressor: Bet1 (k_d = 0.2, n = 2.4, max = 13, min = 0.4) instead of Orf2 (k_d = 0.4, n = 6.1, max = 16, min = 0.2). R2 is the input for the R3 transfer function. In the oscillator, the R translation rate is increased ten-fold. (c) Promoter context. Strength of the indicated promoters is reduced by 50% in both circuits. (d) RBS context. The translation rates of R2 (AND gate) and R (oscillator) are set to zero. Input 1 is the input for the R2 transfer function (e) Transcriptional read-through. 30% read-through from upstream operons through the red terminator is simulated in both circuits. (f) Part-junction interference. A new constitutive promoter (AND gate) is simulated as approximately 20% of the strength of P_IN2. New terminator (oscillator) decreases transcription 40%. (g) Orthogonality. R3_max is set as R2_min to simulate repression of P_R3 by R2. Additional equations are added to the oscillator model to simulate repressor-activator complex formation. (h) Recombination. R2 and R were removed from the AND gate and oscillator models, respectively.

Figure 5. Circuit performance within the context of a living cell. (a)

Recombinant protein expression can cause a growth defect by reducing the availability of host resources (e.g., RNAP and ribosomes). Here, synthetic sRNAs compete with mRNA for ribosomes to illustrate the impact of exogenous protein expression on host resource allocation. When sRNAs are produced (left graph, grey bars), ribosomes are titrated away from fluorescent protein mRNA and observed fluorescence is reduced relative to no sRNA (left graph, white bars). Center graph, colored circles represent the overexpression of different proteins in E. coli (blue: Pu promoter β-Galactosidase, red: T7 promoter β-Galactosidase, black: tac promoter ΔEF-Tu, green: bla promoter β-Lactamase). Right graph, colored circles represent growth of different bacterial strains as a function of rRNA supply (blue: E. coli 30°C, green: A. aerogenes 37°C, red: C. utilis 25°C, orange: C. utilis 30°C, black: N. crassa 30°C). (b) Queuing as a result of overloading the ClpXP protease machinery with proteins from a synthetic oscillator. The graph shows the difference between expected (black) and measured (red) dynamics for an oscillator affected by queuing. (c) An additional output (P_R2) on a high copy plasmid is added to the NOT gate. This causes retroactivity, which alters the activation dynamics of the original output (P_R1) (black line: original dynamic response, orange line: retroactive effect). (d) One plasmid with two reporter proteins is transformed into different E. coli strains. The ratio of expression varies in some strains (left graph: wild type E. coli strains, right graph: KEIO collection knockouts). (e) Different media impact the performance of an AND gate based on T7 RNAP^,. Data are shown for the circuit in the absence (white) and presence (black) of both inputs in different medias (LB: luria broth, Min: minimal media, #T and/or #L: minimal media supplemented with tryptone (#T = #g/L) or yeast extract (#Y = #g/L).

Figure 6. Conceptual circuit for a therapeutic bacterium that colonizes a niche in the human microbiome and delivers a drug

This circuit demonstrates how the different classes of regulators and circuits described in this review could be combined into a single system. The leftmost panel shows genetically modified bacteria that have colonized the interior of a human gastrointestinal tract. The upper right panel focuses on the conceptual circuit that the bacteria use to regulate their growth and deliver drugs to the human patient. An analog circuit (left) and irreversible recombinases (right) are highlighted in the insets to emphasize the diverse biochemistries used to build this circuit.