Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia

Abstract

Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit's complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined.

Fig 1. Schematic diagram of modular distributed computation and the scalability of this architecture in logic circuits.

(A) Schematic diagram of spatially distributed computation. A spatially organized set of chambers defining a set of modules provides a source of modularity by separating different subsets of cells (consortia) into different groups. Each module contains one or more cells from the Input Layer (IL) library (that respond according to an ID or NOT logic), all of which sense only one signal from a given repertoire of N inputs (X) and respond by secreting a communication molecule ω. All modules also include Output Layer (OL) cells that produce the output β implementing a NOT logic, in response to molecule ω. The concentration of β can be quantified directly from the module if the OL is a reporter cell and then the output of the whole circuit (Final output) is the OR combination of each consortium output. Alternatively, the final output can be quantified by using an optional Buffer Layer (BL) cell (BUF) that integrates the final outcome from the different modules if β is a secretable molecule. (B) Schematic diagram explaining the differences between a circuit with a secreted output (upper panel) and transducer circuits (bottom panel). The buffer cell (BUF) collects the output of the different chambers and produces the final computation (top). (C) Illustration of the combinatorial potential of spatially restricted distributed computation architecture. The plot shows the required number of cells, the number of modules and the number of potential N-input logic functions against the number of inputs. Spatial modules (M) and cells number (Z) scale slowly with the number of inputs (N), whereas the repertoire of logic functions shows super exponential increases even for small N.

Fig 2. A library of engineered cells for implementation of complex biological circuits.

(A) Schematic representation of the Input (IL), Output (OL) and Buffer (BUF) Layer cells library. Each color indicates cells that respond to a different input with Identity (ID) or NOT logic (see Materials and Methods and S1 Text for a complete cell library description and characterization). (B) Quantification of single cell computational output with the optional BUF cells. IL cells (#11) stimulated with 17-β-estradiol (EST) was mixed with OL₃ cells in the absence (-EST) or presence (+EST) of the input (left). After 4 h of computation the supernatant of the mix was added to the BL cells, incubated for 4 h and the percentage of GFP-positive cells was analyzed using FACS (right). (C) Quantification of single cell computational output without the optional Buffer Layer cells. IL cells (#11) were stimulated with 17-β-estradiol (EST) and were mixed with OL₁ cells in the absence (-EST) or presence (+EST) of the input (left). After 4 h of computation the percentage of GFP-positive cells was analyzed using FACS (right). (D) Crosstalk analysis of IL cells (ID, left; NOT, right). IL cells were mixed with OL₁ cells (GFP) in the presence of all 6 inputs individually, all 6 inputs together (ALL), or all inputs except for the specific one (ALL-I). Results are expressed as a percentage of GFP-positive cells.

Fig 3. Design and in vivo implementation of a 3-input majority rule.

(A) Schematic representation and spatial organization of the cells in the majority rule using OL₃ and the optional Buffer Layer cells (left), or OL₁ (right). (B) Image of the open-flow computation device for one combination of inputs. The device contains three chambers that are connected to a final chamber that contains BL cells. The flow in the circuit is managed by an air pump. (C) Truth table (left), microscope images (middle) and percentage of GFP-positive cells (right). Cells were mixed proportionally in the corresponding chamber as described in Fig 3A. Different combinations of inputs (dexamethasone (DEX), 17-β-estradiol (EST) and progesterone (PRO)) were added at the same time to the three chambers. Grey refers to the implementation of all the combination of the majority rule circuits using the open-flow computation device. After computing, for each combination of inputs, valves were open and output liquid from the three chambers was collected into a BL cell chamber containing Buffer cells and let them compute for 4 h at 30°C. The percentage of GFP positive BL cells was analyzed using FACS and cells were also collected for microscopic analysis. Data represent the mean and standard error of three independent experiments. Green refers to the circuit implemented as a biosensor using OL₁. Here, after computing, the percentage of GFP positive cells in each chamber was analyzed using FACS and cells were also collected for microscopic analysis. Data were analyzed and processed as described in Material and Methods and in S11 Fig and represent the mean and standard error of the chamber with highest % of positive cells from three independent experiments.

Fig 4. Design and in vivo implementation of a 4-input comparator.

(A) Schematic representation and spatial distribution of the cells used in the 2 bit magnitude comparator. (B) Truth table (left), microscope images (middle) and percentage of FACS fluorescent-positive cells (right). Cells were mixed proportionally and combinations of four inputs (17-β-estradiol (EST), a₁; doxycycline (DOX), a₀; progesterone (PRO), b₁; and dexamethasone (DEX), b₀) were added simultaneously to the six chambers. After computing, for each combination of inputs, the percentage of GFP or mCherry positive cells from the corresponding three chambers were analyzed using FACS. Green (GFP) and red (mCherry, (mCh) bars represent output quantification (A>B red; A<B green; A = B no signal). Data represent the mean and standard error of the chamber with highest % of positive cells from three independent experiments.

Fig 5. Design and in vivo implementation of a 6-input multiplexer (MUX 4-to-1).

(A) Schematic representation of the input stimuli (I₀-I₃) and selectors (S₀-S₁) in the MUX 4-to-1 circuit. (B) Schematic representation and spatial distribution of the cells used in the MUX 4-to-1 circuit. (C) Truth table (bottom) and percentage of GFP-positive cells analyzed by FACS (top). Cells were mixed proportionally and treated with a combination of progesterone (PRO; selector 0, S₀), doxycycline (DOX; selector 1, S₁), aldosterone (ALD; input 0, I₀, violet), C. albicans α-factor (αCA; input 1, I₁, pink), 17-β-estradiol (EST; input 2, I₂, orange), and dexamethasone (DEX; input 3, I₃, yellow). After computing, for each combination of inputs, the percentage of GFP positive cells from the four chambers was analyzed using FACS. Data represent the mean and standard error of the chamber with highest % of positive cells from three independent experiments.