Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies

Abstract

Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.

Figure 1.

Design scheme for assembly-mediated regulatory control of synthetic gene circuits. (A) Nodes in cellular networks use one-to-one regulatory interactions to execute simple computational tasks (top); cooperative interactions within multivalent assemblies enable complex, nonlinear signal processing (bottom). (B) Design of synthetic TF regulatory assemblies. Complexes are built from interaction domains (ZF and PDZ) and their respective binding partners (DBM and PDZ ligand). Clamp-mediated synTF complex-formation (TA, transcriptional activator domain drives coding sequence transcription). Interaction affinities (K_t and K_p) and the number of repeated complex units (n_c) determine thermodynamics of assembly. (C) In vitro assembly of purified cooperative complex components. Fluorescence anisotropy for synTF titration of DBM oligo probe (FITC labeled) was measured in the presence or absence of clamp (5 μM) and converted to fraction probe bound (see Supplementary Materials and Methods) (Nb, synTFs with binding-deficient PDZ ligand mutants; n_c, number of PDZ domains per clamp and DBMs probe). Points represent mean values for three measurements ± SE. (D) Testing in vivo complex assembly in yeast using a synthetic gene circuit (GFP output). All genes are chromosomally-integrated (left, see Table S3). Small molecule-inducible expression systems (ncTET and ncZEV) control intracellular expression levels (see fig. S4) of synTF (induced by ATc, 2 – 500 ng/mL) and clamp (EST, 0.05 – 12.5 nM), respectively. Adjusting molecular features of assembly (K_t, K_p, n_c) tunes overall circuit transfer function (right). (E) Parameterization of thermodynamic complex assembly model. Dose response data for circuit configurations with various K_t, K_p, n_c values were fit to a thermodynamic model (fig. S7–8, Supplementary Materials and Methods); regression plot showing residual from fit are shown at the right. MAE, mean absolute error.

Figure 2.

Constructing gene circuits using cooperative TF assemblies enables expanded steady-state signal processing behavior. (A) Computational model-driven approach for exploring engineerable circuit behavior. Our parts collection (left) defines available configuration space (middle: PDZ-ligand variants, K_p; synTF-DBM variants, K_t; clamp/synTF/DBM units, n_c). Our model (fig. S7–9) computes input-output functions for this space, mapping the potential circuit behavior range (behavior space, right). (B) Programmed complex assembly enables tuning of single-input circuit dose response. For a single-input (2-node) circuit, synTF is induced by ATc addition (input node) and assembles with constitutively-expressed clamp to regulate GFP transcription (reporter node, left). In model-computed circuit behavior space (right) colors indicate different complex sizes (n_c). Five circuits with different assemblies (parameters: K_t affinity in blue, K_p affinity in red, n_c), were constructed and tested by inducing with ATc and measuring GFP by FACS after 16 h (below). Points represent mean values for three experiments ± SE. (C) Programmed complex assembly enables tuning of two-input dose response between linear and non-linear computations. In a two-input circuit, synTFI and synTF2 are induced by ATc and EST respectively (input nodes), assembling with constitutively-expressed clamp to regulate a downstream reporter node (left). Behavior space for the full set of available circuit configurations (center) are plotted as Kullback-Leibler divergence (D_KL): “similarity” between model-computed output surfaces and archetypal Boolean AND and OR surfaces (see fig. S13). Grey areas in the plot indicate regions of AND- and OR-like behavior. Selected circuits, with corresponding reporter complex parameters (K_t affinity, blue; K_p affinity, red; n_c), were constructed and their 2D output surfaces experimentally measured (right) by inducing with ATc and EST and measuring GFP by FACS after 16 h (see Supplementary Materials and Methods).

Figure 3.

Controlling gene circuit dynamics using programmed complex assembly. (A) Tuning assembly cooperativity to control phases of circuit activation. synTF complex formation (left) determines circuit activation and deactivation kinetics (green) in response to transient inducer input (orange). Model-generated dose response profiles for two-node cascades regulated by non-cooperative two-TF (grey) or cooperative four-TF (green) assemblies are shown. Data from time course experiments using time-lapse microscopy in a microfluidic device (lines = mean fluor. Cell⁻¹, shaded boundaries = ± 1 SD of population mean; τ_a, activation half-time; τ_d, deactivation half-time) were compared to model-fitted behavior (dots) (see Supplementary Materials and Methods). (B) Cooperative assemblies enable activation and decay phases to be broadly and independently tuned for a three-node cascade motif. Model-predicted dynamic behavior space compares τ_a and τ_d for two-node (orange) and three-node motifs with (light blue) and without (light green) feedback (‘No decay’, configurations did not return to basal activity upon input removal). Highlighted circuits were tested by time-lapse microscopy/microfluidics (16 h Dox induction, light orange; lines = mean fluor. Cell⁻¹ normalized to maximum output, shaded boundaries = ± 1 SD) and compared to model simulations (dots). See movies S1 and S2 for time-lapse videos.

Figure 4.

Circuits engineered with cooperative assemblies perform temporal signal processing behavior. (A) Persistence filtering in cooperative assembly circuits. Computationally identified two- and three-node motifs reveal persistence filtering behavior – activation in response to an input of a sufficient duration (left, fig. S21). Circuits predicted to show linear (shallow, grey) and nonlinear (sharp, green) filtering were tested in microfluidic time course experiments (lines = mean fluor. Cell⁻¹, shaded region = ± 1 SD of cell population) by inducing with increasing Dox pulse lengths (light orange). “temporal dose response” curves (line = measured data, dots = model) were generated from normalized time course output maxima (see Supplementary Materials and Methods). (B) Population-level temporal decoding of frequency input. Computationally identified (fig. S22) two- and three-node network motifs (left) were tested for frequency filtering behavior in microfluidic experiments. Mixed populations of yeast harboring low-pass filtering (LPF) and band-stop filtering (BSF) feed-forward circuits (driving mKate and GFP, respectively) were tested for their ability to decode a frequency-modulated square wave Dox pulses (fig. S23). Model-predicted frequency responses are shown next to each circuit. Experimentally measured frequency response is shown to the right. Maximum reporter output for each input frequency was normalized to maximum output for constitutive Dox. (fig. S22). Representative mixed population fluorescence images are shown for frequencies highlighted in orange: constitutive Dox (‘always ON’), medium frequency treatment (~10⁻⁵ Hz, 33%), and high frequency (~10⁻⁴ Hz, 33%). Cells without Dox treatment (‘OFF’) are shown to the right. In images, cells harboring LPF and BSF circuits are false-colored red and green, respectively; cell boundaries were determined by segmentation software (see Supplementary Materials and Methods). Scale bar, 10 μM.