specification_based_synthetic_biology.ipynb

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    "## Scaling up designs in synthetic biology using _Pacti_"
   ]
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    "Research in synthetic biology has accelerated in the past decade due to its enormous promise in engineering biological systems for desired behavior ([here's a review article on synthetic biology](https://www.cell.com/trends/plant-science/fulltext/S1360-1385(15)00049-7), and [here's another](https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201609229)). Applications range from biomedicine to engineered living materials and environmental remediation. From a design standpoint, mathematical models have been used to study the behavior of biological systems and analyze various properties (see for example [this modeling paper](https://pubs.acs.org/doi/10.1021/acssynbio.2c00534)). However, current experimental design approaches in synthetic biology are largely heuristics and trial-and-error based, hence, difficult to scale. To alleviate this, various modeling and analysis and logic gate design tools (like [Cello](https://www.nature.com/articles/s41596-021-00675-2)) have been proposed but none have yet been used to successfully design large biological circuits. We believe that the _Pacti_ framework holds the potential to address this issue of scale due to its generality in writing system specifications and in decoupling the modeling details of each component from their specification. "
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    "![main_figure](https://github.com/pacti-org/media/blob/main/case_studies/biocircuit_specifications/main_figure.png?raw=true.png)"
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    "Some notes on the biological jargon in this notebook:\n",
    "0. Genetic circuit: A biological system (DNA/group of DNA/DNA and other enzymes, or any other engineered biological system)\n",
    "1. Protein Expression: This simply means the process of production of a protein, often used when talking about the quantitiy of the protein that is produced.\n",
    "2. Inducer: A chemical that is used as an input to \"induce\" a system, that is, a chemical that switched something ON.\n",
    "3. Plasmid: A plasmid is a small DNA molecule within a cell. Often used interchangeably in this notebook with a \"gene\".\n",
    "4. Regulator: A system or a molecule that controls (regulates) something else (expression of a protein or a chemical or enzyme).\n",
    "5. RPU: This stands for relative promoter units, this is a commonly used unit to measure the quantity of a protein output. You can think of RPU as an analogoue of concentration of a protein.\n",
    "6. Fold-change: The ratio between the ON and the OFF level of something. You can think of fold-change as \"SNR\" - the ratio between the output of the system when it is ON (there is a signal that we measure, in this case, RFP) and when it is OFF (so there is only some noisy output, which we refer to as the \"leak value of RFP\")."
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    "This case study shows the use of Pacti to aid the design of engineered biological circuits for altering and observing the dynamics of bacterial gene transfer in soil and its effects on protein expression in plants (schematic shown in Figure A above). As the full system design is large, we focus on the design of a biological NAND gate system. This system has three subsystems, as shown in Figure B. We will use contract _composition_ as part of an optimization strategy to maximize the system fold-change, defined as the ratio between the on and off levels of the system's output. The _quotient_ will be used to infer a specification of a missing subsystem that needs to be designed in our system. We say that an input or an output is \"ON\" when its level is higher than a minimum  threshold, and is \"OFF\" when its level is lower than a maximum threshold. We formalize these notions with polyhedral constraints in the contract descriptions. \n",
    "\n",
    "The biological NAND gate consists of the three subsystems:\n",
    "\n",
    "1) a sensor with tetracycline (aTc) input that outputs a dCas9 protein, \n",
    "\n",
    "2) another sensor (to be chosen) that outputs the xRFP-gRNA protein, and \n",
    "\n",
    "3) a dCas9 repressor subsystem, which takes as inputs the xRFP-gRNA and the dCas9 proteins. \n",
    "\n",
    "When both inputs to this repressor subsystem are ON, it suppresses the output, RFP, a red fluorescent protein. RFP is also the output of the top-level system. RFP is ON only when either of the sensors inputs are OFF. In this way, the system behavior is that of a NAND logic gate. We will denote the contract for the tetracycline sensor as $C_{\\text{aTc}}$, and the contract for dCas9 repression mechanism as $C_{\\text{dCas9}}$. Our first task will be to find the second sensor (choose spec for subsystem 2) that maximizes the top-level system fold-change. Then we will consider the task of finding a specification for the repression component (subsystem 3) assuming that the contracts for both sensors and the top-level system are available.\n",
    "\n",
    "Biological motivation: \n",
    "\n",
    "The input in this system design is a target gene of interest introduced in the soil through bacteria. The goal is to measure the protein expression of the plant in response to the target gene input. The way we detect whether this gene is present is with many different complex biological processes and the output of whether we detect the gene, is \"read out\" from the plant leaf. So, the input is the gene of interest that we want to detect and the output is the protein production out of the plants (measured as protein fluorescence). Designing such a biological system is difficult due to the lack of information about key biological mechanisms, absence of characterization data for system parts, unknown effects of the soil context, etc. There are many biological processes that would need to be engineered to build a system like this but we focus on a small part of it => the engineered bacteria (see Figure). We assume that the top-level designer has asked us to design this functionally complete bacteria as a NAND gate to serve the overall purpose. To optimize the design, we would like the bacterial system to maximize its fold-change, that is, maximize the ratio of its output in the on and the off levels (fold-change = output on / output off). \n",
    "\n",
    "To summarize, we address the following three tasks in this notebook:\n",
    "\n",
    "1. Characterized library of parts as assume-guarantee contracts: Using existing experimental data from literature, we present a library of sensors as assume-guarantee contracts that can be used to reason about specifications of diverse biological circuits. For the engineered bacteria case study, we use previously published data (for the Marionette cell lines) to construct a library of 14 sensors.\n",
    "2. Speeding up the experimental design: Current experimental design approaches in synthetic biology are heavily reliant on screening experiments where all possible choices are explored experimentally to choose the one that works. This is not only time consuming and expensive but also does not scale beyond three or four independent choices. With the use of _Pacti_ , we demonstrate how scientists may describe the desired top-level behavior as contracts and then computationally choose from a library of available parts to ensure that the components meet the top-level system specification. For the engineered bacteria case study, we find the specification of the sensors that meet the top-level criteria on fold-change of the circuit response.\n",
    "3. Finding specifications of missing parts: In synthetic biology, it is common to have parts in the system for which no characterization data is available. Using quotient operation on contracts, we can find the constraints that this missing part must satisfy to meet the desired top-level criteria. For the bacterial case study, we find the missing contract of the dCas9-mediated repression mechanism (as shown in the B panel of the Figure above) for which characterization data is not available in the literature."
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    "## Modeling the specifications to construct a library of parts\n",
    "\n",
    "Using the experimental characterization data for the sensors in the _Marionette_ bacterial cell strain [1], we model the sensor behavior as assume-guarantee contracts. We observe that each sensor has three characteristic behaviors: (1) the off state, where the output of the sensor stays close to zero (the non-zero expression in this state is termed as \"leaky response\"), (2) the linear rate of output , where the sensor output responds linearly to the input (in log scale), (3) the saturation state, where the sensor output saturates to a maximum constant v\n",
    "\n",
    "[1] Meyer, Adam J., et al. \"Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors.\" Nature chemical biology 15.2 (2019): 196-204."
   ]
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    "# Load the Pandas library to read experimental data from the Marionette paper [1]\n",
    "import pandas as pd\n",
    "# Import pacti PolyhedralIoContract class to read contracts\n",
    "from pacti.contracts import PolyhedralIoContract\n",
    "# Import utility functions for this case study\n",
    "from utils.synbio_utils import display_sensor_contracts, remove_quantization_errors\n",
    "# Import pacti PolyhedralIoContract parent for operations on guarantees\n",
    "import pacti.terms.polyhedra as gtp\n",
    "# Import pacti function to write contracts to a file\n",
    "from pacti import write_contracts_to_file\n",
    "# Import matplotlib for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "# Import Python symbolic computation library: sympy\n",
    "import sympy\n",
    "# Import Python numpy\n",
    "import numpy as np"
   ]
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    "# Read the data from the paper using the CSV file \"marionette_data.csv\"\n",
    "df = pd.read_csv(\"data/marionette_data.csv\", delimiter=\",\", engine=\"python\")"
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    "The following table corresponds to this data:\n",
    "\n",
    "|    | Inducer   | Plasmid   | Regulator   | Promoter   |   ymax (RPU) |   ymin (RPUx10-3) |   K (µM) |   n |   Dynamic range |    start |   ymax Linear |\n",
    "|---:|:----------|:----------|:------------|:-----------|-------------:|------------------:|---------:|----:|----------------:|---------:|--------------:|\n",
    "|  0 | DAPG      | pAJM.847  | PhlFAM      | PPhlF      |         2.5  |               2.5 |    1.7   | 2.1 |            1000 |   0.03   |          2    |\n",
    "|  1 | Cuma      | pAJM.657  | CymRAM      | PCymRC     |         3.7  |               4.3 |    8.9   | 2.4 |             870 |   0.3    |          2    |\n",
    "|  2 | OC6       | pAJM.474  | LuxR        | PLuxB      |         1.3  |               2.4 |    0.12  | 1.8 |             540 |   0.002  |          0.5  |\n",
    "|  3 | Van       | pAJM.773  | VanRAM      | PVanCC     |         3    |               2.4 |   26     | 2.3 |            1200 |   1      |          1.8  |\n",
    "|  4 | IPTG      | pAJM.336  | LacIAM      | PTac       |         3.3  |               4.8 |  140     | 1.8 |             690 |   2      |          1.5  |\n",
    "|  5 | aTc       | pAJM.011  | TetR        | PTet*      |         2.4  |               4.9 |    0.013 | 3.8 |             500 |   0.0018 |          1    |\n",
    "|  6 | Ara       | pAJM.677  | AraCAM      | PBAD       |         1.2  |               2.4 |   37     | 1.5 |             500 |   0.2    |          0.7  |\n",
    "|  7 | Cho       | pAJM.683  | BetIAM      | PBetI      |         2.6  |               8.5 | 4100     | 2.7 |             310 | 300      |          0.9  |\n",
    "|  8 | Nar       | pAJM.661  | TtgRAM      | PTtg       |         0.46 |               3.4 |   95     | 1.9 |             140 |   2      |          0.2  |\n",
    "|  9 | DHBA      | pAJM.690  | PcaUAM      | P3B5B      |         1.6  |               4.5 |  370     | 1.8 |             360 |   9      |          1    |\n",
    "| 10 | Sal       | pAJM.771  | NahRAM      | PSalTTC    |         2.8  |               4.7 |   43     | 1.8 |             600 |   0.9    |          1.3  |\n",
    "| 11 | OHC14     | pAJM.1642 | CinRAM      | PCin       |         1.5  |               3   |    0.43  | 2.3 |             490 |   0.016  |          0.7  |\n",
    "| 12 | Acr       | pAJM.884  | AcuRAM      | PAcu       |         3.1  |              37   |  130     | 2.5 |              83 |  10      |          1.7  |\n",
    "| 13 | Ery       | pAJM.969  | MphRAM      | PMph       |         0.29 |               8   |   65     | 1.5 |              36 |   2      |          0.18 |"
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    "Create contracts for the sensor using this experimental data:"
   ]
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    "def create_sensor_contracts(sensor_input=\"AHL\", output=\"FP\", K=0.0, yleak=0.0,\n",
    "                            start=0.0, ymax_lin=0.0):\n",
    "    \"\"\"\n",
    "    Creates the contracts for a Marionette sensing subsystem\n",
    "    params:\n",
    "        * input (str): The inducer input to the sensor\n",
    "        * output (str): The output of the genetic construct.\n",
    "                        Inducer activates the production of this output\n",
    "        * K (float): The value of the Hill activation parameter K\n",
    "        * yleak (float): The minimum expression of output even\n",
    "                         in absence of inducer\n",
    "        * start (float): The value of inducer at which the induction starts\n",
    "        * ymax_lin (float): The maximum expression of output by the inducer\n",
    "                            before saturating (the end of linear regime)\n",
    "    \"\"\"\n",
    "    slope = (ymax_lin - yleak) / (K - start)\n",
    "    intercept = yleak - slope * start\n",
    "    contract_0 = PolyhedralIoContract.from_strings(\n",
    "        input_vars=[sensor_input],\n",
    "        output_vars=[output],\n",
    "        assumptions=[f\"{sensor_input} <= {start}\"],\n",
    "        guarantees=[f\"{output} <= {yleak}\"]\n",
    "    )\n",
    "    contract_lin = PolyhedralIoContract.from_strings(\n",
    "        input_vars=[sensor_input],\n",
    "        output_vars=[output],\n",
    "        assumptions=[\n",
    "            f\"{sensor_input} <= {K}\",\n",
    "            f\"-{sensor_input} <= {-1 * start}\"\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"-{output} + {slope}{sensor_input} <= {intercept}\",\n",
    "            f\"{output} - {slope}{sensor_input} <= {-1*intercept}\"\n",
    "        ]\n",
    "    )\n",
    "    contract_max = PolyhedralIoContract.from_strings(\n",
    "        input_vars=[sensor_input],\n",
    "        output_vars=[output],\n",
    "        assumptions=[\n",
    "            f\"-{sensor_input} <= {-1 * K}\"\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"-{output} <= {-1 * ymax_lin}\"\n",
    "        ]\n",
    "    )\n",
    "    return contract_0, contract_lin, contract_max"
   ]
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    "## Composition to find optimal sensor choices\n",
    "\n",
    "To speed up the experimental screening, we use _Pacti_ to choose a sensor from the library that meets the desired top-level behavior. Here, we have a tetracycline (aTc) sensor that expresses a dCas9 protein and the other sensor (the one we want to choose) that expresses the xRFP guide RNA. The xRFP guide RNA binds to dCas9 to repress the expression of red fluorescent protein (RFP), the output of the system. When either of the sensors are OFF, the output (RFP) is constitutively expressed (that is, output is ON). In this way, the system behavior is that of a NAND logic gate. We denote the contract for the tetracycline sensor as $C_{\\text{atc}}$ and the contract for dCas9 repression mechanism as $C_{\\text{dCas9}}$. For the top-level specification, we define the fold-change of the system as\n",
    "$F = \\frac{\\text{RFP}_{\\text{on}}}{\\text{RFP}_{\\text{off}}}.$\n",
    "\n",
    "To choose the sensor that achieves the highest fold-change for the system, we compose the contract for each of the available 13 sensors in the library with the tetracycline contract and the dCas9 repression contract to obtain the top-level system contract. The composition operation in _Pacti_ reports a design error for any sensor that is \"unsatisfiable in the given context\" to reject some of the choices while some other choices are rejected if they do not meet the set top-level system criteria."
   ]
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    "# Create all sensor contracts:\n",
    "sensor_names = [str(i) for i in df[\"Inducer\"]]\n",
    "sensor_library = {}\n",
    "sensor_library_params = {}\n",
    "for sensor in sensor_names:\n",
    "    sensor_params = {}\n",
    "    yleak_s = df.loc[df[\"Inducer\"] == sensor][\"ymin (RPUx10-3)\"].values[0]\n",
    "    yleak_s = yleak_s * 1e-3\n",
    "    s_start = df.loc[df[\"Inducer\"] == sensor][\"start\"].values[0]\n",
    "    s_K = df.loc[df[\"Inducer\"] == sensor][\"K (µM)\"].values[0]\n",
    "    ymax_s = df.loc[df[\"Inducer\"] == sensor][\"ymax Linear\"].values[0]\n",
    "    sensor_params = {\"leak\": yleak_s, \"start\": s_start,\n",
    "                     \"K\": s_K, \"ymax\": ymax_s}\n",
    "    s_output = \"dCas9\"\n",
    "    contract_s_0, contract_s_lin, contract_s_max = create_sensor_contracts(\n",
    "        sensor_input=sensor, output=\"xRFP\", start=s_start, K=s_K,\n",
    "        ymax_lin=ymax_s, yleak=yleak_s\n",
    "    )\n",
    "    sensor_library[sensor] = [contract_s_0, contract_s_lin, contract_s_max]\n",
    "    sensor_library_params[sensor] = sensor_params"
   ]
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      "text/plain": [
       "<Figure size 3200x2000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, all_ax = plt.subplots(nrows=3, ncols=5, figsize=(32, 20))\n",
    "index = 0\n",
    "for sensor in sensor_names:\n",
    "    ax = all_ax[index // 5][index % 5]\n",
    "    index += 1\n",
    "    yleak_s = df.loc[df[\"Inducer\"] == sensor][\"ymin (RPUx10-3)\"].values[0]\n",
    "    yleak_s = yleak_s * 1e-3\n",
    "    s_start = df.loc[df[\"Inducer\"] == sensor][\"start\"].values[0]\n",
    "    s_K = df.loc[df[\"Inducer\"] == sensor][\"K (µM)\"].values[0]\n",
    "    ymax_s = df.loc[df[\"Inducer\"] == sensor][\"ymax Linear\"].values[0]\n",
    "    ax = display_sensor_contracts(\n",
    "        sensor_input=sensor,\n",
    "        output=\"\",\n",
    "        leak=yleak_s,\n",
    "        start=s_start,\n",
    "        K=s_K,\n",
    "        ymax_lin=ymax_s,\n",
    "        xlim_min=10**-3,\n",
    "        xlim_max=10**4,\n",
    "        ylim_min=10**-3,\n",
    "        ylim_max=10**2,\n",
    "        ax=ax,\n",
    "        show=False,\n",
    "    )\n",
    "    ax.set_xlabel(sensor, fontsize=16)\n",
    "    ax.tick_params(axis=\"both\", which=\"major\", labelsize=16)\n",
    "    ax.set_ylabel(\"Output\", fontsize=16)\n",
    "    ax.legend(fontsize=16)\n",
    "all_ax[2][4].set_axis_off()\n",
    "# plt.savefig('all_sensors.svg')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6ffa0bf4",
   "metadata": {},
   "source": [
    "Write the contract for the dCas9 repression mechanism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4b0b247",
   "metadata": {},
   "outputs": [],
   "source": [
    "dCas9_contract_off = PolyhedralIoContract.from_strings(\n",
    "    # \"xRFP:OFF, dCas9:OFF, RFP:ON\",\n",
    "    input_vars=[\"xRFP\", \"dCas9\"],\n",
    "    output_vars=[\"RFP\"],\n",
    "    assumptions=[\n",
    "        \"xRFP <= 0.2\",\n",
    "        \"dCas9 <= 0.1\"\n",
    "    ],\n",
    "    guarantees=[\n",
    "        \"-RFP <= -10\"\n",
    "    ]\n",
    ")\n",
    "\n",
    "dCas9_contract_on = PolyhedralIoContract.from_strings(\n",
    "    # \"The dCas9 repression: xRFP:ON, dCas9:ON, RFP:OFF\",\n",
    "    input_vars=[\"xRFP\", \"dCas9\"],\n",
    "    output_vars=[\"RFP\"],\n",
    "    assumptions=[\n",
    "        \"-xRFP <= -0.3\",\n",
    "        \"xRFP <= 1\",\n",
    "        \"-dCas9 <= -0.1\",\n",
    "        \"dCas9 <= 0.8\"\n",
    "    ],\n",
    "    guarantees=[\n",
    "        \"RFP + 2xRFP + 0.1dCas9 <= 5\"\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9aa287e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our 2nd input is aTc that outputs \"dCas9\"\n",
    "# Create the contracts for this sensor\n",
    "# using our `create_sensor_contracts` function\n",
    "input2 = \"aTc\"\n",
    "in2_params = sensor_library_params[input2]\n",
    "c_atc_off, c_atc_on, c_atc_sat = create_sensor_contracts(sensor_input=\"aTc\",\n",
    "                                                         output=\"dCas9\",\n",
    "                                                         start=in2_params\n",
    "                                                         [\"start\"],\n",
    "                                                         K=in2_params[\"K\"],\n",
    "                                                         ymax_lin=in2_params\n",
    "                                                         [\"ymax\"],\n",
    "                                                         yleak=in2_params\n",
    "                                                         [\"leak\"])\n",
    "# Now we will go through all other sensors and\n",
    "# find out which are OK to use given the context\n",
    "sensor_ok = {}\n",
    "# We will store all sensor names that are not compatible in a dictionary:\n",
    "design_error = {}\n",
    "# We will store the fold-change for all the\n",
    "# sensors that are OK in this dictionary:\n",
    "fold_change = {}\n",
    "# Desired fold change: RFP_on/RFP_off\n",
    "desired_fold_change = 4\n",
    "# Go through all sensors in the library except aTc,\n",
    "# since that has been chosen for the 2nd subsystem\n",
    "for sensor in sensor_library:\n",
    "    if sensor == \"aTc\":\n",
    "        continue\n",
    "    in1_params = sensor_library_params[sensor]\n",
    "    # OFF condition\n",
    "    # (note that RFP will be ON for this, so we will calculate \"rfp_vals_on\")\n",
    "    c_sensor1 = sensor_library[sensor][0]\n",
    "    c_sensor2 = c_atc_off\n",
    "    # Compose the sensors together\n",
    "    # (if allowed! => Handle exceptions of the Pacti\n",
    "    # design error if composition is not allowed)\n",
    "    try:\n",
    "        sensor_comp = c_sensor2.compose(c_sensor1)\n",
    "        top_level_off = sensor_comp.compose(dCas9_contract_off)\n",
    "    except ValueError as e:\n",
    "        # If Pacti raises a ValueError saying that the composition was\n",
    "        # \"unsatisfiable in context\", then that\n",
    "        # means that this sensor will not work!\n",
    "        if \"unsatisfiable in context\" in str(e):\n",
    "            sensor_ok[sensor] = False\n",
    "    else:\n",
    "        sensor_ok[sensor] = True\n",
    "        # If the try block succeeds, this block runs\n",
    "        # Remove quantization errors that are introduced in solving equations\n",
    "        top_level_off = remove_quantization_errors(top_level_off)\n",
    "        # Get the guarantee term and evaluate it\n",
    "        sym_expr = gtp.PolyhedralTerm.to_symbolic(top_level_off.g.terms[0])\n",
    "        rfp_vals_on = {}\n",
    "        updated_sym = sym_expr.subs(sensor, 0.9 * in1_params[\"start\"])\n",
    "        updated_sym = updated_sym.subs(input2, 0.9 * in2_params[\"start\"])\n",
    "        subs_val = sympy.solve(updated_sym, \"RFP\")\n",
    "        rfp_vals_on[in1_params[\"start\"], in2_params[\"start\"]] = subs_val[0]\n",
    "\n",
    "    # ON condition\n",
    "    # (note that RFP will be OFF for this, so we will calculate \"rfp_vals_off\")\n",
    "    c_sensor1 = sensor_library[sensor][1]\n",
    "    c_sensor2 = c_atc_on\n",
    "    # Compose the sensors together\n",
    "    # (if allowed! => Handle exceptions of the Pacti\n",
    "    # design error if composition is not allowed)\n",
    "    try:\n",
    "        sensor_comp = c_sensor2.compose(c_sensor1)\n",
    "        top_level_on = sensor_comp.compose(dCas9_contract_on)\n",
    "    except ValueError as e:\n",
    "        # If Pacti raises a ValueError saying that the composition was\n",
    "        # \"unsatisfiable in context\", then that means\n",
    "        # that this sensor will not work!\n",
    "        if \"unsatisfiable in context\" in str(e):\n",
    "            design_error[sensor] = False\n",
    "    else:\n",
    "        design_error[sensor] = True\n",
    "        # If the try block succeeds, this block runs\n",
    "        # Remove quantization errors that are introduced in solving equations\n",
    "        top_level_on = remove_quantization_errors(top_level_on)\n",
    "        # Get the guarantee term and evaluate it\n",
    "        sym_expr = gtp.PolyhedralTerm.to_symbolic(top_level_on.g.terms[0])\n",
    "        rfp_vals_off = {}\n",
    "        updated_sym = sym_expr.subs(sensor, 0.9 * in1_params[\"K\"])\n",
    "        updated_sym = updated_sym.subs(input2, 0.9 * in2_params[\"K\"])\n",
    "        subs_val = sympy.solve(updated_sym, \"RFP\")\n",
    "        rfp_vals_off[in1_params[\"K\"], in2_params[\"K\"]] = subs_val[0]\n",
    "    # Now that we have rfp_vals_on and rfp_vals_off,\n",
    "    # we can compute the fold change:\n",
    "    if design_error[sensor]:\n",
    "        RFP_on = list(rfp_vals_on.values())[0]\n",
    "        RFP_off = list(rfp_vals_off.values())[0]\n",
    "        fold_change[sensor] = RFP_on / RFP_off\n",
    "        if fold_change[sensor] > desired_fold_change:\n",
    "            sensor_ok[sensor] = True\n",
    "        else:\n",
    "            sensor_ok[sensor] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eadac88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(fold_change.keys(), fold_change.values(), color=\"C1\", alpha=0.5)\n",
    "ax.set_xlabel(\"Sensor\", fontsize=14)\n",
    "ax.set_ylabel(\"Top-level fold-change\", fontsize=14)\n",
    "plt.xticks(rotation=45, fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "241ed934",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5748deb3",
   "metadata": {},
   "source": [
    "Choose subsystem 1 using the above exploration chart as the sensor that gives the highest-fold change:\n",
    "\n",
    "We choose \"Cuma\" with the contract:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43156395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Cuma]\n",
      "OutVars:[xRFP]\n",
      "A: [\n",
      "  Cuma <= 0.3\n",
      "]\n",
      "G: [\n",
      "  xRFP <= 0.0043\n",
      "]\n",
      "InVars: [Cuma]\n",
      "OutVars:[xRFP]\n",
      "A: [\n",
      "  Cuma <= 8.9\n",
      "  -Cuma <= -0.3\n",
      "]\n",
      "G: [\n",
      "  0.2321 Cuma - xRFP = -0.06532\n",
      "]\n",
      "InVars: [Cuma]\n",
      "OutVars:[xRFP]\n",
      "A: [\n",
      "  -Cuma <= -8.9\n",
      "]\n",
      "G: [\n",
      "  -xRFP <= -2\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "for contract in sensor_library[\"Cuma\"]:\n",
    "    print(contract)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "884b016d",
   "metadata": {},
   "source": [
    "Note that not all sensors were compatible. Some of the sensor choices led to errors in design automatically caught by _Pacti_ :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51784a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DAPG': 8.66785005774809,\n",
       " 'Cuma': 9.68674395539190,\n",
       " 'OC6': 2.52677363203287,\n",
       " 'Van': 7.26517302130202,\n",
       " 'IPTG': 4.72491416737617,\n",
       " 'Ara': 2.76605435260938,\n",
       " 'Cho': 3.30535361336978,\n",
       " 'DHBA': 3.32963257502971,\n",
       " 'Sal': 4.07634004092162,\n",
       " 'OHC14': 2.83319542816097,\n",
       " 'Acr': 6.97093280769728}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fold_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa4f1330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DAPG': True,\n",
       " 'Cuma': True,\n",
       " 'OC6': False,\n",
       " 'Van': True,\n",
       " 'IPTG': True,\n",
       " 'Ara': False,\n",
       " 'Cho': False,\n",
       " 'Nar': True,\n",
       " 'DHBA': False,\n",
       " 'Sal': True,\n",
       " 'OHC14': False,\n",
       " 'Acr': True,\n",
       " 'Ery': True}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensor_ok"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dddeceb4",
   "metadata": {},
   "source": [
    "## Finding specifications of missing parts\n",
    "\n",
    "For the NAND gate described above, if we write a desired top-level contract $C_{\\text{sys}}$ and assume that both sensors have been chosen (here we choose Sal and aTc) so that we have the contracts for both sensors. Then, we may use quotient operation in _Pacti_ to find the ideal specifications of the dCas9 mechanism. This can guide the design of the repression mechanism.\n",
    "\n",
    "\n",
    "### Contract for Sal sensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a32ca37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "yleak_sal = df.loc[df[\"Inducer\"] == \"Sal\"][\"ymin (RPUx10-3)\"].values[0] * 1e-3\n",
    "sal_start = df.loc[df[\"Inducer\"] == \"Sal\"][\"start\"].values[0]\n",
    "sal_K = df.loc[df[\"Inducer\"] == \"Sal\"][\"K (µM)\"].values[0]\n",
    "ymax_sal = df.loc[df[\"Inducer\"] == \"Sal\"][\"ymax Linear\"].values[0]\n",
    "\n",
    "sal_output = \"xRFP\"\n",
    "contract_sal_min, contract_sal_lin, contract_sal_max = create_sensor_contracts(\n",
    "    sensor_input=\"Sal\", output=sal_output, start=sal_start, K=sal_K,\n",
    "    ymax_lin=ymax_sal, yleak=yleak_sal\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c4fa979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Input 1', ylabel='xRFP'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_sensor_contracts(sensor_input=\"Input 1\", output=\"xRFP\", leak=yleak_sal,\n",
    "                         start=sal_start, K=sal_K, ymax_lin=ymax_sal, xlim_min=10**-3,\n",
    "                         xlim_max=10**3, ylim_min=10**-3, ylim_max=10**1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "61346684",
   "metadata": {},
   "source": [
    "### Contracts for aTc sensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ba82b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "yleak_atc = df.loc[df[\"Inducer\"] == \"aTc\"][\"ymin (RPUx10-3)\"].values[0] * 1e-3\n",
    "atc_start = df.loc[df[\"Inducer\"] == \"aTc\"][\"start\"].values[0]\n",
    "atc_K = df.loc[df[\"Inducer\"] == \"aTc\"][\"K (µM)\"].values[0]\n",
    "ymax_atc = df.loc[df[\"Inducer\"] == \"aTc\"][\"ymax Linear\"].values[0]\n",
    "atc_output = \"dCas9\"\n",
    "contract_atc_min, contract_atc_lin, contract_atc_max = create_sensor_contracts(\n",
    "    sensor_input=\"aTc\", output=atc_output, start=atc_start, K=atc_K,\n",
    "    ymax_lin=ymax_atc, yleak=yleak_atc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aea2005b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_sensor_contracts(\n",
    "    sensor_input=\"aTc\",\n",
    "    output=\"dCas9\",\n",
    "    leak=yleak_atc,\n",
    "    start=atc_start,\n",
    "    K=atc_K,\n",
    "    ymax_lin=ymax_atc,\n",
    "    xlim_min=10**-3,\n",
    "    xlim_max=10**0,\n",
    "    ylim_min=10**-3,\n",
    "    ylim_max=10**1,\n",
    ");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cc7f4348",
   "metadata": {},
   "source": [
    "### Top-level system contract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "547de0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "leak_RFP = np.max([yleak_atc, yleak_sal])\n",
    "max_RFP = np.max([ymax_atc, ymax_sal])\n",
    "\n",
    "\n",
    "def create_top_level_contracts(input1=\"u1\", input2=\"u2\", output=\"y\",\n",
    "                               input1_params=None, input2_params=None,\n",
    "                               output_params=None):\n",
    "    \"\"\"\n",
    "    Creates the top level system contracts\n",
    "    \"\"\"\n",
    "    u1_start = input1_params[\"start\"]\n",
    "    u1_K = input1_params[\"K\"]\n",
    "\n",
    "    u2_start = input2_params[\"start\"]\n",
    "    u2_K = input2_params[\"K\"]\n",
    "\n",
    "    y_max = output_params[\"max\"]\n",
    "    y_leak = output_params[\"leak\"]\n",
    "    all_contracts = []\n",
    "    contract_top_level_on1 = PolyhedralIoContract.from_strings(\n",
    "        # \"u1:OFF, u2:OFF, y:ON\",\n",
    "        input_vars=[\n",
    "            input1, input2\n",
    "        ],\n",
    "        output_vars=[\n",
    "            output\n",
    "        ],\n",
    "        assumptions=[\n",
    "            f\"{input1} <= {u1_start - u1_start*0.01}\",\n",
    "            f\"{input1} <= {u2_start - u2_start*0.01}\"\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"-{output}<={-1*y_max}\",\n",
    "        ]\n",
    "    )\n",
    "    all_contracts.append(contract_top_level_on1)\n",
    "    contract_top_level_on2 = PolyhedralIoContract.from_strings(\n",
    "        # \"u1:ON, u2:OFF, y:ON\",\n",
    "        input_vars=[\n",
    "            input1, input2\n",
    "        ],\n",
    "        output_vars=[\n",
    "            output\n",
    "        ],\n",
    "        assumptions=[\n",
    "            f\"-{input1} <= {-1*(u1_start + u2_start*0.01)}\",\n",
    "            f\"{input1} <= {u1_K - u1_K*0.01}\",\n",
    "            f\"{input2} <= {u2_start - u2_start*0.01}\"\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"-{output} <= {-1*y_max}\"\n",
    "        ]\n",
    "    )\n",
    "    all_contracts.append(contract_top_level_on2)\n",
    "    contract_top_level_on3 = PolyhedralIoContract.from_strings(\n",
    "        # \"u1:OFF, u2:ON, y:ON\",\n",
    "        input_vars=[\n",
    "            input1, input2\n",
    "        ],\n",
    "        output_vars=[\n",
    "            output\n",
    "        ],\n",
    "        assumptions=[\n",
    "            f\"{input1} <= {u1_start - u1_start*0.01}\",\n",
    "            f\"-{input2} <= {-1*(u2_start + u2_start*0.01)}\",\n",
    "            f\"{input2} <= {u2_K - u2_K*0.01}\",\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"-{output}<={-1*y_max}\",\n",
    "        ]\n",
    "    )\n",
    "    all_contracts.append(contract_top_level_on3)\n",
    "    contract_top_level_off = PolyhedralIoContract.from_strings(\n",
    "        # \"u1:ON, u2:ON, y:OFF\",\n",
    "        input_vars=[\n",
    "            input1, input2\n",
    "        ],\n",
    "        output_vars=[\n",
    "            output\n",
    "        ],\n",
    "        assumptions=[\n",
    "            f\"-{input1} <= {-1*(u1_start + u1_start*0.01)}\",\n",
    "            f\"{input1} <= {u1_K - u1_K*0.01}\",\n",
    "            f\"-{input2} <= {-1*(u2_start + u2_start*0.01)}\",\n",
    "            f\"{input2} <= {u2_K - u2_K*0.01}\",\n",
    "        ],\n",
    "        guarantees=[\n",
    "            f\"{output} <= {y_leak}\"\n",
    "        ]\n",
    "    )\n",
    "    all_contracts.append(contract_top_level_off)\n",
    "    return all_contracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15f6b6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sal_params = {\"start\": sal_start, \"K\": sal_K}\n",
    "atc_params = {\"start\": atc_start, \"K\": atc_K}\n",
    "RFP_params = {\"max\": max_RFP, \"leak\": leak_RFP}\n",
    "ctop_off_off, ctop_on_off, ctop_off_on, ctop_on_on = create_top_level_contracts(\n",
    "    input1=\"Sal\", input2=\"aTc\", output=\"RFP\",\n",
    "    input1_params=sal_params, input2_params=atc_params, output_params=RFP_params\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a7b65dac",
   "metadata": {},
   "source": [
    "### Print the contracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18ae9845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [aTc]\n",
      "OutVars:[dCas9]\n",
      "A: [\n",
      "  aTc <= 0.013\n",
      "  -aTc <= -0.0018\n",
      "]\n",
      "G: [\n",
      "  88.85 aTc - dCas9 = -0.155\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_atc_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "271819dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal]\n",
      "OutVars:[xRFP]\n",
      "A: [\n",
      "  Sal <= 43\n",
      "  -Sal <= -0.9\n",
      "]\n",
      "G: [\n",
      "  0.03077 Sal - xRFP = -0.02299\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_sal_lin)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "929fc96b",
   "metadata": {},
   "source": [
    "### Available composition \n",
    "(in the NAND gate ON condition => output is OFF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43fe393f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal, aTc]\n",
      "OutVars:[xRFP, dCas9]\n",
      "A: [\n",
      "  Sal <= 43\n",
      "  -Sal <= -0.9\n",
      "  aTc <= 0.013\n",
      "  -aTc <= -0.0018\n",
      "]\n",
      "G: [\n",
      "  0.03077 Sal - xRFP = -0.02299\n",
      "  88.85 aTc - dCas9 = -0.155\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_sal_lin.compose(contract_atc_lin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16346f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal, aTc]\n",
      "OutVars:[RFP]\n",
      "A: [\n",
      "  -Sal <= -0.909\n",
      "  Sal <= 42.57\n",
      "  -aTc <= -0.001818\n",
      "  aTc <= 0.01287\n",
      "]\n",
      "G: [\n",
      "  RFP <= 0.0049\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(ctop_on_on)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92b8099c",
   "metadata": {},
   "source": [
    "#### Take the quotient of the top-level contract in the ON condition from the available composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f59ce5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "csal_atc = contract_sal_lin.compose(contract_atc_lin)\n",
    "dCas_repression_lin = ctop_on_on.quotient(csal_atc)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ca9c63c8",
   "metadata": {},
   "source": [
    "## Result: We find the missing contract:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e9a3a15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [xRFP, dCas9]\n",
      "OutVars:[RFP]\n",
      "A: [\n",
      "  -xRFP <= -0.05096\n",
      "  xRFP <= 1.333\n",
      "  -dCas9 <= -0.3166\n",
      "  dCas9 <= 1.299\n",
      "]\n",
      "G: [\n",
      "  RFP <= 0.0049\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(dCas_repression_lin)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fd56a045",
   "metadata": {},
   "source": [
    "#### Find contract for the missing subsystem for other conditions of the NAND gate circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3bf8ef47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal]\n",
      "OutVars:[xRFP]\n",
      "A: [\n",
      "  Sal <= 0.9\n",
      "]\n",
      "G: [\n",
      "  xRFP <= 0.0047\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_sal_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9cfe3a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [aTc]\n",
      "OutVars:[dCas9]\n",
      "A: [\n",
      "  aTc <= 0.013\n",
      "  -aTc <= -0.0018\n",
      "]\n",
      "G: [\n",
      "  88.85 aTc - dCas9 = -0.155\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_atc_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "93870c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal, aTc]\n",
      "OutVars:[xRFP, dCas9]\n",
      "A: [\n",
      "  Sal <= 0.9\n",
      "  aTc <= 0.013\n",
      "  -aTc <= -0.0018\n",
      "]\n",
      "G: [\n",
      "  xRFP <= 0.0047\n",
      "  88.85 aTc - dCas9 = -0.155\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(contract_sal_min.compose(contract_atc_lin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2572a519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [Sal, aTc]\n",
      "OutVars:[RFP]\n",
      "A: [\n",
      "  Sal <= 0.891\n",
      "  -aTc <= -0.001818\n",
      "  aTc <= 0.01287\n",
      "]\n",
      "G: [\n",
      "  -RFP <= -1.3\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(ctop_off_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17c2fa9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "csal_atc = contract_sal_min.compose(contract_atc_lin)\n",
    "dCas_repression_off_on = ctop_off_on.quotient(csal_atc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f8c1e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InVars: [xRFP, dCas9]\n",
      "OutVars:[RFP]\n",
      "A: [\n",
      "  xRFP <= 0.0047\n",
      "  -dCas9 <= -0.3166\n",
      "  dCas9 <= 1.299\n",
      "]\n",
      "G: [\n",
      "  -RFP <= -1.3\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(dCas_repression_off_on)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7fb03328",
   "metadata": {},
   "source": [
    "## Write results to a JSON file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "92d526ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_json_contracts = write_contracts_to_file([dCas_repression_lin, dCas_repression_off_on],\n",
    "                                                names=[\"dCas_Linear\", \"dCas_off_on\"],\n",
    "                                                file_name=\"data/dCas9_repression.json\")"
   ]
  }
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