Biological_Computing_with_Synthetic_Minimal_Cells_and_Quorum_Sensing,_Sci_fair_2021.ipynb

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  "metadata": {
    "colab": {
      "name": "Biological Computing with Synthetic Minimal Cells and Quorum Sensing, Sci fair 2021",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2i-1JT84kf2",
        "outputId": "8edd6783-567a-41cc-cf89-cbf3709fa2bd"
      },
      "source": [
        "!pip install ddeint"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ddeint in /usr/local/lib/python3.7/dist-packages (0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from ddeint) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ddeint) (1.19.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbVaq0saa5sA"
      },
      "source": [
        "'''\r\n",
        "My science fair project! Take a look at https://github.com/dhrumilp15/Science-Fair/ for more docs!\r\n",
        "Author: Dhrumil Patel\r\n",
        "'''\r\n",
        "#import libraries\r\n",
        "import numpy as np\r\n",
        "from scipy.integrate import odeint\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "from itertools import chain\r\n",
        "from ddeint import ddeint"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbC5N5bXbBW7"
      },
      "source": [
        "# ODE\r\n",
        "alpha_A = 1.0564*(10**-7) #  AHL production rate\r\n",
        "alpha = 1.0564 * (10**-7)\r\n",
        "alpha_R = 1.0 * (10**-7)# Production of PpuR\r\n",
        "\r\n",
        "beta_A = 1.0564*(10**-6) # Feedback-regulated AHL production rate\r\n",
        "beta = 1.0564*(10**-6)\r\n",
        "\r\n",
        "x_0 = 5.4044 * (10**-7) # Initial value of AHL Concentration\r\n",
        "R_0 = 1.0 # Initial value of PpuR Concentration\r\n",
        "y_0 = 5.2 * (10**3) #Initial amount of Lactonase\r\n",
        "n_0 = 1.0 # Size of biomass\r\n",
        "s_0 = 1.0 # Amount of substrate\r\n",
        "\r\n",
        "n = 2.3 # Hill coefficient for x\r\n",
        "n_s = 2.5 # Hill Coefficient for substrate consumption process\r\n",
        "\r\n",
        "m = 2.5 # Hill coefficient\r\n",
        "\r\n",
        "gamma_A = 1.0 #Threshold of alpha_A\r\n",
        "gamma_R = 1.0 #Threshold of alpha_R\r\n",
        "gamma_S = 1.0 # Decay rate of substrate - Rate of substrate consumption\r\n",
        "gamma = 0.105 #AHL Decay rate\r\n",
        "\r\n",
        "pi_1_plus = 1e-7 # Rate at which AHL binds to PpuR\r\n",
        "pi_minus = 10.0 # Rate at which AHL and PpuR split up(disassociate)\r\n",
        "\r\n",
        "C_0 = 1.0 # Initial concentration of PpuR-AHL complex\r\n",
        "C_th = 3.597 * (10**-13) # Threshold of positive feedback for AHL concentration, x_th\r\n",
        "C_th2 = 1.0 #Threshold for lactonase activation\r\n",
        "\r\n",
        "D = 2.5 # Dilution Rate\r\n",
        "\r\n",
        "S = 1.0 # Concentration of Substrate\r\n",
        "\r\n",
        "K_m = 1.0 # Carrying capacity, consumption rate is reached\r\n",
        "\r\n",
        "tau = 2 # Delay in production of lactonase\r\n",
        "delta = 1.5*(10**-4) # Lactonase-dependent AHL degradation rate\r\n",
        "omega = 0.105 # Lactonase decay rate(w/ washout)\r\n",
        "rho = 5.0521 * (10**3) # Lactonase Production Rate\r\n",
        "\r\n",
        "N = 4.5929 * (10**11) #Cell density at equilibrium\r\n",
        "\r\n",
        "x_th = 3.597 * (10**-12) #Critical threshold for positive-feedback in x\r\n",
        "y_th = 3.597 * (10**-12) #Critical threshold for positive-feedback in y\r\n",
        "\r\n",
        "#spanish_gamma = (pi_1_plus*alpha_R)/(pi_minus*gamma_R)\r\n",
        "\r\n",
        "p = [alpha_A, beta_A, n, gamma_A, gamma_R, pi_1_plus, pi_minus, C_th, alpha_R]\r\n",
        "\r\n",
        "# ODE solver parameters\r\n",
        "abserr = 1.0e-8\r\n",
        "relerr = 1.0e-6\r\n",
        "stoptime = 10.0\r\n",
        "numpoints = 2000\r\n",
        "\r\n",
        "t = [stoptime * float(i) / (numpoints - 1) for i in range(numpoints)] # How long the graph will be\r\n",
        "\r\n",
        "def single_cell_model(w0,t,p, q): # Initial Values of Functions user gives\r\n",
        "  x_0, R_0, C_0 = w0 # We redefine the \"initial values\" of the function on each iteration in odeint\r\n",
        "  \r\n",
        "  alpha_A, beta_A, n, gamma_A, gamma_R, pi_1_plus, pi_minus, C_th, alpha_R = p # Parameters\r\n",
        "  q = q # So that q can be introduced in AHL Concentration function\r\n",
        "  f = [alpha_A + beta_A*((C_0**n)/(C_th**n + C_0**n))-gamma_A*x_0 - pi_1_plus*x_0*R_0 + pi_minus*C_0 - q*x_0,\r\n",
        "       alpha_R - pi_1_plus* x_0* R_0 + pi_minus*C_0 - gamma_R*R_0,\r\n",
        "       pi_1_plus*x_0*R_0 - pi_minus*C_0] # 1st Line - AHL Concentration, 2nd Line - PpuR Concentration, 3rd Line - PpuR-AHL Complex Concentration\r\n",
        "  return f # Put functions in f to be integrated in solver()\r\n",
        "\r\n",
        "  \"\"\"\r\n",
        "  1st line - Substrate concentration: D*s_0 - gamma_S * n_0 ((s_0**n_s)/((K_m ** n_s) + s_0 ** n_s)) - D*S\r\n",
        "  \r\n",
        "  2nd line - Biomass size: alpha_A * n_0 * ( (s_0 ** n_s) / ((K_m ** n_s) + s_0 ** n_s)) - D*n_0\r\n",
        "  \r\n",
        "  3rd  line - AHL Concentration: (alpha_A + beta_A*((C_0**n)/((C_th**n) + (C_0**n))))*n_0 - gamma_A*x_0 - pi_1_plus*R_0*x_0 + pi_1_minus*C_0 - D*x_0 - delta*x_0*y_0\r\n",
        "  \r\n",
        "  4th line - PpuR-AHL Concentration: pi_1_plus*R_0*x_0 - pi_1_minus*C_0\r\n",
        "  \r\n",
        "  5th line - PpuR Concentration: alpha_R + pi_1_minus*C_0 - pi_1_plus*R_0*x_0 - gamma_R*R_0\r\n",
        "  \r\n",
        "  Initial values of functions(will go to w1): s_0, n_0, C_0, R_0, x_0, y_0\r\n",
        "  \r\n",
        "  Variables(will go to p1): D, gamma_S, S, n_s, K_m, alpha_A, N, beta_A, n, C_th, gamma_A, pi_1_plus, pi_1_minus, delta, alpha_R, gamma_R\r\n",
        "    \r\n",
        "  Lactonase concentration: alpha_L*(C(t - tau)**m)/(C_th2**m + C(t - tau)**m)*n_0 - gamma_L * y_0 - D * y_0\r\n",
        "  \"\"\"\r\n",
        "\r\n",
        "# def single_cell_full_model(w1, t, p1, q):\r\n",
        "#   w1 = C_0, R_0, x_0, y_0, n_0, s_0\r\n",
        "#   print(p1)\r\n",
        "  \r\n",
        "#   D, gamma_S, S, n_s, m, K_m, alpha_A, beta_A, n, C_th, C_th2, gamma_A, pi_1_plus, pi_minus, delta, alpha_R, gamma_R = p1\r\n",
        "  \r\n",
        "#   fm = [D * S - gamma_S * n_0*((s_0 ** n_s)/((K_m ** n_s) + (s_0 ** n_s))) - D * s_0,\r\n",
        "#         alpha_A * n_0 * ( (s_0 ** n_s) / ((K_m ** n_s) + (s_0 ** n_s))) - D * n_0,\r\n",
        "#         (alpha_A + beta_A * ((C_0 ** n)/((C_th ** n) + (C_0 ** n)))) * n_0 - gamma_A * x_0 - pi_1_plus * R_0 * x_0 + pi_minus * C_0 - D * x_0 - delta * x_0 * y_0,\r\n",
        "#         pi_1_plus * R_0 * x_0 - pi_minus * C_0,\r\n",
        "#         alpha_R + pi_minus * C_0 - pi_1_plus * R_0 * x_0 - gamma_R * R_0,\r\n",
        "#        alpha_L*(C_0**m)/(C_th2**m + C_0**m)*n_0 - gamma_L * y_0 - D * y_0]\r\n",
        "#   return fm\r\n",
        "\r\n",
        "class Bacterium:\r\n",
        "  def __init__(self,x_0, R_0, C_0):\r\n",
        "    self.C_0 = C_0 # PpuR-AHL\r\n",
        "    self.x_0 = x_0 # AHL concentration\r\n",
        "    self.R_0 = R_0 # PpuR Concentration\r\n",
        "    #self.y_0 = y_0 # Lactonase concentration\r\n",
        "    #self.n_0 = n_0 # Size of biomass\r\n",
        "    #self.s_0 = s_0 # Substrate\r\n",
        "    self.w0 = [self.x_0, self.R_0, self.C_0]\r\n",
        "    #self.w1 = [self.x_0, self.R_0, self.C_0, self.n_0, self.s_0, self.y_0]\r\n",
        "  \r\n",
        "  def solver(self):\r\n",
        "    wsol = []\r\n",
        "    for q in np.linspace(0.0, 3.0, num=100):\r\n",
        "      wsol.append(odeint(single_cell_model, self.w0, t, args=(p, q), atol = abserr, rtol = relerr))\r\n",
        "      # Atol and rtol are tolerants\r\n",
        "    return wsol\r\n",
        "  \r\n",
        "  #def solver1(self):\r\n",
        "    #wsol1 = odeint(full_model, self.w1, t, args=(p1,), atol = abserr, rtol = relerr)\r\n",
        "    #return wsol1\r\n",
        "    \r\n",
        "  def nan_helper(self, A): # Takes care of NaNs\r\n",
        "      ok = ~np.isnan(A) #Are there NaNs\r\n",
        "      xp = ok.ravel().nonzero()[0] # x-coord of data values ravel for scaling up larger w/ multi-dim data\r\n",
        "      fp = A[~np.isnan(A)]# y-coord of data values\r\n",
        "      x  = np.isnan(A).ravel().nonzero()[0] # x-coord of interp values\r\n",
        "      A[np.isnan(A)] = np.interp(x, xp, fp)\r\n",
        "      return A\r\n",
        "  \r\n",
        "  def ranger(self,x, axis=1):\r\n",
        "    return np.max(x, axis=axis) - np.min(x, axis=axis)\r\n",
        "  \r\n",
        "  def plot(self): #Function to plot graph\r\n",
        "    \r\n",
        "    # Two subplots, the axes array is 1-d\r\n",
        "    N = 2 # Used only in this method\r\n",
        "    #f, axarr = plt.subplots(N, sharex=False)\r\n",
        "    x_new = []\r\n",
        "    for i in range(0,100):\r\n",
        "      x_new.append(self.solver()[i][:,0])\r\n",
        "    \r\n",
        "    ### Finding Double Difference between original AHl concentration and ones with AHL degrading enzyme\r\n",
        "    x_diff = []\r\n",
        "    x_new[0] = self.nan_helper(np.asarray(x_new[0]))\r\n",
        "    for i in range(1,100):\r\n",
        "      x_new[i] = self.nan_helper(np.asarray(x_new[i]))\r\n",
        "      x_diff.append(np.gradient(x_new[0] - x_new[i])) \r\n",
        "    x_range = []\r\n",
        "    \r\n",
        "    #find ranges of values - ideally, we want something with a small range\r\n",
        "    for i in range(len(x_diff)):\r\n",
        "      x_range.append(np.ptp(x_diff[i]))\r\n",
        "    \r\n",
        "    print (np.where(np.square(x_diff[1]) < 0.0025))\r\n",
        "    \r\n",
        "    #for i in range(N):\r\n",
        "      #axarr[i].plot(t, x_new[50*i], 'b')\r\n",
        "      #axarr[i].set_title('AHL Concentration')\r\n",
        "      #axarr[i].tick_params(\r\n",
        "      #axis='x',          # changes apply to the x-axis\r\n",
        "      #which='both',      # both major and minor ticks are affected\r\n",
        "      #bottom='off',      # ticks along the bottom edge are off\r\n",
        "      #labelbottom='off')\r\n",
        "    \r\n",
        "    # What the graph is going to look like\r\n",
        "    original = plt.plot(t,x_new[0],'g', label = 'Original') # Graph with q set to 0\r\n",
        "    q06 = plt.plot(t,x_new[20],'c', label = 'Degradation Rate = 0.61') # Graph with q set to 0.61\r\n",
        "    q15 = plt.plot(t,x_new[50],'b', label = 'Degradation Rate = 1.5') # Graph with q set to 1.5 - EXTRANEOUS; AHL hits 0 :(\r\n",
        "    q1 = plt.plot(t,x_new[99],'r', label = 'Degradation Rate = 3.0')# Graph with q set to 3  - EXTRANEOUS; AHL hits 0 :( \r\n",
        "    plt.legend(loc = 'upper right') # Put graph in upper right corner\r\n",
        "    plt.xlabel('Time (h)') # Label of x axis\r\n",
        "    plt.ylabel('AHL Concentration (M)') # Label of y axis\r\n",
        "    plt.title('Performance of Tunable Biological Resistor') # Title\r\n",
        "    plt.show() # Show graph"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "F1lxodsNfOZS",
        "outputId": "a6a8537d-5b32-4bdc-d389-9241e4f75faf"
      },
      "source": [
        "bob = Bacterium(x_0, R_0, C_0) # Initial Values assigned at beg. of code\r\n",
        "bob.plot() # Time to plot"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:65: RuntimeWarning: invalid value encountered in double_scalars\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(array([   0,    1,    2, ..., 1997, 1998, 1999]),)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "lqid8Cq04fgj",
        "outputId": "066f59c4-2a83-4d04-d125-b77b858ce40a"
      },
      "source": [
        "# DDE\r\n",
        "from ddeint import ddeint\r\n",
        "# Steady-State Params:\r\n",
        "\r\n",
        "abserr = 1.0e-8\r\n",
        "relerr = 1.0e-6\r\n",
        "stoptime = 10.0\r\n",
        "numpoints = 2000\r\n",
        "\r\n",
        "t = [stoptime * float(i) / (numpoints - 1) for i in range(2, numpoints)] # How long the graph will be\r\n",
        "\r\n",
        "N = 4.5929*(10**11)\r\n",
        "alpha = 1.0564 * (10**-7)\r\n",
        "gamma = 0.105\r\n",
        "delta = 1.5000 * (10**-4)\r\n",
        "beta = 1.0564*(10**-6)\r\n",
        "n = 2.3\r\n",
        "x_th = 3.597 * (10**-13)\r\n",
        "omega = 0.105\r\n",
        "rho = 5.0521 * (10**3)\r\n",
        "d = 2\r\n",
        "m = 2.5\r\n",
        "y_th = 3.597 * (10**-13)\r\n",
        "x_0 = 5.4044 * (10**-7)\r\n",
        "y_0 = 5.2 * (10**3)\r\n",
        "d = 2 # set parameter d(tau)\r\n",
        "\r\n",
        "def model(XY,t,d,q):\r\n",
        "    x, y = XY(t)\r\n",
        "    xd, yd = XY(t-d)\r\n",
        "    return [alpha - gamma*x - delta*x*y + beta*((x**n)/(x_th**n + x**n)) - q*x,\r\n",
        "            rho*((xd**m)/((y_th**m) + xd**m)) - omega*y]\r\n",
        "\r\n",
        "g = lambda t : np.array([x_0,y_0]) # 'history' at t<0\r\n",
        "\r\n",
        "spacing = 30\r\n",
        "\r\n",
        "def solve():\r\n",
        "  qq = []\r\n",
        "  for q in np.linspace(0,3.0,spacing):\r\n",
        "    qq.append(ddeint(model,g,t,fargs=(d,q)))\r\n",
        "  return qq\r\n",
        "\r\n",
        "def nan_helper(A): # Takes care of NaNs\r\n",
        "    ok = ~np.isnan(A) #Are there NaNs\r\n",
        "    xp = ok.ravel().nonzero()[0] # x-coord of data values ravel for scaling up larger w/ multi-dim data\r\n",
        "    fp = A[~np.isnan(A)]# y-coord of data values\r\n",
        "    x  = np.isnan(A).ravel().nonzero()[0] # x-coord of interp values\r\n",
        "    A[np.isnan(A)] = np.interp(x, xp, fp)\r\n",
        "    return A\r\n",
        "\r\n",
        "\r\n",
        "x_new = []\r\n",
        "for i in range(0,spacing):\r\n",
        "    x_new.append(solve()[i][:,0])\r\n",
        "\r\n",
        "### Finding Double Difference between original AHl concentration and ones with AHL degrading enzyme\r\n",
        "x_diff = []\r\n",
        "x_new[0] = nan_helper(np.asarray(x_new[0]))\r\n",
        "for i in range(1,spacing):\r\n",
        "  x_new[i] = nan_helper(np.asarray(x_new[i]))\r\n",
        "  x_diff.append(np.gradient(x_new[0] - x_new[i])) \r\n",
        "x_range = []\r\n",
        "\r\n",
        "#find ranges of values - ideally, we want something with a small range\r\n",
        "for i in range(len(x_diff)):\r\n",
        "  x_range.append(np.ptp(x_diff[i]))\r\n",
        "\r\n",
        "print (np.where(np.square(x_diff[1]) < 0.0025))\r\n",
        "\r\n",
        "def plot():\r\n",
        "    # f, axarr = plt.subplots(2, sharex=False)\r\n",
        "    original = plt.plot(t,x_new[0],'g', label = 'Original') # Graph with q set to 0\r\n",
        "    q06 = plt.plot(t,x_new[30 // spacing],'c', label = 'Degradation Rate = 0.61') # Graph with q set to 0.61\r\n",
        "    q15 = plt.plot(t,x_new[50 // spacing],'b', label = 'Degradation Rate = 1.5') # Graph with q set to 1.5 - EXTRANEOUS; AHL hits 0 :(\r\n",
        "    q1 = plt.plot(t,x_new[99 // spacing],'r', label = 'Degradation Rate = 3')# Graph with q set to 3  - EXTRANEOUS; AHL hits 0 :( \r\n",
        "    # axarr[0].set_title('AHL Concentration')\r\n",
        "    \r\n",
        "    plt.tick_params(\r\n",
        "    axis='x',          # changes apply to the x-axis\r\n",
        "    which='both',      # both major and minor ticks are affected\r\n",
        "    bottom='off',      # ticks along the bottom edge are off\r\n",
        "    labelbottom='off')\r\n",
        "    plt.legend(loc = 'upper right') # Put graph in upper right corner\r\n",
        "    #[original, q06, q15, q1], ['Degradation Rate = 0', 'Degradation Rate = 0.61', 'Degradation Rate = 1.5', 'Degradation Rate = 3'])\r\n",
        "    plt.xlabel('Time (h)') # Label of x axis\r\n",
        "    plt.ylabel('AHL Concentration (M)') # Label of y axis\r\n",
        "    # plt.title('Performance of Tunable Biological Resistor') # Title\r\n",
        "    plt.show() # Show graph\r\n",
        "\r\n",
        "    # axarr[1].plot(t, yy[:,1], 'g')\r\n",
        "    # axarr[1].set_title('Lactonase Concentration')\r\n",
        "\r\n",
        "plot()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([   0,    1,    2, ..., 1995, 1996, 1997]),)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1y72w3748Jut"
      },
      "source": [
        "We can see that the equilibrium values of the AHL concentration (The peak of each curve) vary proportionally to the degradation rate! The synell can emulate an op-amp!"
      ]
    }
  ]
}