{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fc8df5b",
   "metadata": {},
   "source": [
    "# OXT - OXTR binding model\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1b6df720",
   "metadata": {},
   "source": [
    "Author: Preeti Dubey\n",
    "Title: OXTR complex formation simulations done for myometrial cells when [OXT] = 10 nM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcefb9d",
   "metadata": {},
   "source": [
    "#### Here, we are defining the ODE model to perform simulation for surface level myometrial cells data provided by lab "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe331f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load packages\n",
    "\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b4bebfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OXT and OXTR binding code for wild type and it's standard error\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "\n",
    "\n",
    "def oxtmodel(x, t):\n",
    "    \n",
    "    kon = 6.8e+5  # per molar per min (from Phaneuf paper)\n",
    "    koff = 0.0011 # per min  (from Phaneuf paper)\n",
    "    Av = 6e+23\n",
    "    V = 1.4e-11 # litre It is given as 14047 cubic micro meter \n",
    "    Div = V*Av  # dividend of the oxtr copies \n",
    "    oxt = x[0]\n",
    "    oxtr = x[1]\n",
    "    oxr = x[2]\n",
    "   \n",
    "    \n",
    "    doxtdt = -kon*oxt*(oxtr) + koff*oxr\n",
    "    doxtrdt = -kon*oxt*(oxtr) + koff*oxr\n",
    "    doxrdt = kon*oxt*(oxtr) -koff*oxr\n",
    "\n",
    "    return(doxtrdt, doxtrdt, doxrdt)\n",
    "\n",
    "\n",
    "initial_t = 0\n",
    "end_t = 60*12\n",
    "num = 1000\n",
    "\n",
    "# oxtr conc is 2000 copies/cell\n",
    "# we need to get the molar concetration of oxtr in mol/litre\n",
    "# conc. = N/V = 2000/1.4e-11\n",
    "# conc = 1428e+11\n",
    "# molar concentration c = conc/NA = 1428e+11/6e+23 mol/L = 2.38e-10 mol/L\n",
    "\n",
    "\n",
    "\n",
    "# initial condition for wild type mean, upper bound and lower bound\n",
    "#x0_wt = [1e-8, 2.38678e-9, 0]\n",
    "x0_wt = [1e-8, 1.678e-9, 0]\n",
    "x0_wtub = [1e-8, 1.76e-9, 0]\n",
    "x0_wtlb = [1e-8, 1.6e-9, 0]\n",
    "# initial condition for mutant V281M\n",
    "x0_v281m = [1e-8, 7.4877e-10, 0]\n",
    "x0_v281mub = [1e-8, 8.38e-10, 0]\n",
    "x0_v281mlb = [1e-8, 6.59e-10, 0]\n",
    "# initial condition for mutant P108A\n",
    "x0_p108a = [1e-8, 2.658e-9, 0]\n",
    "x0_p108aub = [1e-8, 2.95e-9, 0]\n",
    "x0_p108alb = [1e-8, 2.36e-9, 0]\n",
    "# initial condition for mutant L206V\n",
    "x0_l206v = [1e-8, 3.044e-9, 0]\n",
    "x0_l206vub = [1e-8, 3.33e-9, 0]\n",
    "x0_l206vlb = [1e-8, 2.76e-9, 0]\n",
    "# initial condition for mutant V45L\n",
    "x0_v45l = [1e-8, 1.96e-9, 0]\n",
    "x0_v45lub = [1e-8, 2.13e-9, 0]\n",
    "x0_v45llb = [1e-8, 1.79e-9, 0]\n",
    "# initial condition for mutant E339K\n",
    "x0_e339k = [1e-8, 1.19e-9, 0]\n",
    "x0_e339kub = [1e-8, 1.25e-9, 0]\n",
    "x0_e339klb = [1e-8, 1.12e-9, 0]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# time span\n",
    "t = np.linspace(initial_t, end_t, num)\n",
    "\n",
    "# ode integration for all types \n",
    "x_wt = odeint(oxtmodel,x0_wt,t)   \n",
    "x_wtub = odeint(oxtmodel,x0_wtub,t)   \n",
    "x_wtlb = odeint(oxtmodel,x0_wtlb,t) \n",
    "\n",
    "x_v281m = odeint(oxtmodel,x0_v281m,t)  \n",
    "x_v281mub = odeint(oxtmodel,x0_v281mub,t)   \n",
    "x_v281mlb = odeint(oxtmodel,x0_v281mlb,t)  \n",
    "\n",
    "x_p108a = odeint(oxtmodel,x0_p108a,t)   \n",
    "x_p108aub = odeint(oxtmodel,x0_p108aub,t)   \n",
    "x_p108alb = odeint(oxtmodel,x0_p108alb,t)\n",
    "\n",
    "x_l206v = odeint(oxtmodel,x0_l206v,t)   \n",
    "x_l206vub = odeint(oxtmodel,x0_l206vub,t)   \n",
    "x_l206vlb = odeint(oxtmodel,x0_l206vlb,t)  \n",
    "\n",
    "x_v45l = odeint(oxtmodel,x0_v45l,t)   \n",
    "x_v45lub = odeint(oxtmodel,x0_v45lub,t)   \n",
    "x_v45llb = odeint(oxtmodel,x0_v45llb,t) \n",
    "\n",
    "x_e339k = odeint(oxtmodel,x0_e339k,t)   \n",
    "x_e339kub = odeint(oxtmodel,x0_e339kub,t)   \n",
    "x_e339klb = odeint(oxtmodel,x0_e339klb,t)   \n",
    "\n",
    "\n",
    "\n",
    "# Volume and avagadro's number \n",
    "Av = 6e+23\n",
    "V = 1.4e-11 # litre It is given as 14047 cubic micro meter \n",
    "Div = V*Av\n",
    "\n",
    "# solution extraction for wild type oxr complex\n",
    "oxt_wt = x_wt[:, 0]\n",
    "oxtr_wt = x_wt[:, 1]\n",
    "oxr_wt = x_wt[:, 2]\n",
    "\n",
    "oxt_wt_c = oxt_wt*Div\n",
    "oxtr_wt_c = oxtr_wt*Div\n",
    "oxr_wt_c = oxr_wt*Div\n",
    "\n",
    "# solution extraction for wt upper bound\n",
    "\n",
    "oxt_wtub = x_wtub[:, 0]\n",
    "oxtr_wtub = x_wtub[:, 1]\n",
    "oxr_wtub = x_wtub[:, 2]\n",
    "\n",
    "oxt_wtub_c = oxt_wtub*Div\n",
    "oxtr_wtub_c = oxtr_wtub*Div\n",
    "oxr_wtub_c = oxr_wtub*Div\n",
    "\n",
    "# solution extraction for wt lower bound\n",
    "\n",
    "oxt_wtlb = x_wtlb[:, 0]\n",
    "oxtr_wtlb = x_wtlb[:, 1]\n",
    "oxr_wtlb = x_wtlb[:, 2]\n",
    "\n",
    "oxt_wtlb_c = oxt_wtlb*Div\n",
    "oxtr_wtlb_c = oxtr_wtlb*Div\n",
    "oxr_wtlb_c = oxr_wtlb*Div\n",
    "\n",
    "# solution extraction for V281M \n",
    "oxt_v281m = x_v281m[:, 0]\n",
    "oxtr_v281m = x_v281m[:, 1]\n",
    "oxr_v281m = x_v281m[:, 2]\n",
    "\n",
    "oxt_v281m_c = oxt_v281m*Div\n",
    "oxtr_v281m_c = oxtr_v281m*Div\n",
    "oxr_v281m_c = oxr_v281m*Div\n",
    "\n",
    "# solution extraction for v281m upper bound\n",
    "\n",
    "oxt_v281mub = x_v281mub[:, 0]\n",
    "oxtr_v281mub = x_v281mub[:, 1]\n",
    "oxr_v281mub = x_v281mub[:, 2]\n",
    "\n",
    "oxt_v281mub_c = oxt_v281mub*Div\n",
    "oxtr_v281mub_c = oxtr_v281mub*Div\n",
    "oxr_v281mub_c = oxr_v281mub*Div\n",
    "\n",
    "# solution extraction for v281m lower bound\n",
    "\n",
    "oxt_v281mlb = x_v281mlb[:, 0]\n",
    "oxtr_v281mlb = x_v281mlb[:, 1]\n",
    "oxr_v281mlb = x_v281mlb[:, 2]\n",
    "\n",
    "oxt_v281mlb_c = oxt_v281mlb*Div\n",
    "oxtr_v281mlb_c = oxtr_v281mlb*Div\n",
    "oxr_v281mlb_c = oxr_v281mlb*Div \n",
    "\n",
    "\n",
    "# solution extraction for P108A \n",
    "oxt_p108a = x_p108a[:, 0]\n",
    "oxtr_p108a = x_p108a[:,1]\n",
    "oxr_p108a = x_p108a[:, 2]\n",
    "\n",
    "oxt_p108a_c = oxt_p108a*Div\n",
    "oxtr_p108a_c = oxtr_p108a*Div\n",
    "oxr_p108a_c = oxr_p108a*Div\n",
    "\n",
    "# solution extraction for p108a upper bound\n",
    "\n",
    "oxt_p108aub = x_p108aub[:, 0]\n",
    "oxtr_p108aub = x_p108aub[:,1]\n",
    "oxr_p108aub = x_p108aub[:, 2]\n",
    "\n",
    "oxt_p108aub_c = oxt_p108aub*Div\n",
    "oxtr_p108aub_c = oxtr_p108aub*Div\n",
    "oxr_p108aub_c = oxr_p108aub*Div\n",
    "\n",
    "# solution extraction for p108a lower bound\n",
    "\n",
    "oxt_p108alb = x_p108alb[:, 0]\n",
    "oxtr_p108alb = x_p108alb[:, 1]\n",
    "oxr_p108alb = x_p108alb[:, 2]\n",
    "\n",
    "oxt_p108alb_c = oxt_p108alb*Div\n",
    "oxtr_p108alb_c = oxtr_p108alb*Div\n",
    "oxr_p108alb_c = oxr_p108alb*Div\n",
    "\n",
    "# solution extraction for L206V \n",
    "oxt_l206v = x_l206v[:, 0]\n",
    "oxtr_l206v = x_l206v[:,1]\n",
    "oxr_l206v = x_l206v[:, 2]\n",
    "\n",
    "oxt_l206v_c = oxt_l206v*Div\n",
    "oxtr_l206v_c = oxtr_l206v*Div\n",
    "oxr_l206v_c = oxr_l206v*Div\n",
    "\n",
    "# solution extraction for l206v upper bound\n",
    "\n",
    "oxt_l206vub = x_l206vub[:, 0]\n",
    "oxtr_l206vub = x_l206vub[:,1]\n",
    "oxr_l206vub = x_l206vub[:, 2]\n",
    "\n",
    "oxt_l206vub_c = oxt_l206vub*Div\n",
    "oxtr_l206vub_c = oxtr_l206vub*Div\n",
    "oxr_l206vub_c = oxr_l206vub*Div\n",
    "\n",
    "# solution extraction for l206v lower bound\n",
    "\n",
    "oxt_l206vlb = x_l206vlb[:, 0]\n",
    "oxtr_l206vlb = x_l206vlb[:, 1]\n",
    "oxr_l206vlb = x_l206vlb[:, 2]\n",
    "\n",
    "oxt_l206vlb_c = oxt_l206vlb*Div\n",
    "oxtr_l206vlb_c = oxtr_l206vlb*Div\n",
    "oxr_l206vlb_c = oxr_l206vlb*Div\n",
    "\n",
    "\n",
    "# solution extraction for V45L \n",
    "oxt_v45l = x_v45l[:, 0]\n",
    "oxtr_v45l = x_v45l[:,1]\n",
    "oxr_v45l = x_v45l[:, 2]\n",
    "\n",
    "oxt_v45l_c = oxt_v45l*Div\n",
    "oxtr_v45l_c = oxtr_v45l*Div\n",
    "oxr_v45l_c = oxr_v45l*Div\n",
    "\n",
    "# solution extraction for v45l upper bound\n",
    "\n",
    "oxt_v45lub = x_v45lub[:, 0]\n",
    "oxtr_v45lub = x_v45lub[:,1]\n",
    "oxr_v45lub = x_v45lub[:, 2]\n",
    "\n",
    "oxt_v45lub_c = oxt_v45lub*Div\n",
    "oxtr_v45lub_c = oxtr_v45lub*Div\n",
    "oxr_v45lub_c = oxr_v45lub*Div\n",
    "\n",
    "# solution extraction for v45l lower bound\n",
    "\n",
    "oxt_v45llb = x_v45llb[:, 0]\n",
    "oxtr_v45llb = x_v45llb[:, 1]\n",
    "oxr_v45llb = x_v45llb[:, 2]\n",
    "\n",
    "oxt_v45llb_c = oxt_v45llb*Div\n",
    "oxtr_v45llb_c = oxtr_v45llb*Div\n",
    "oxr_v45llb_c = oxr_v45llb*Div\n",
    "\n",
    "# solution extraction for E339K \n",
    "oxt_e339k = x_e339k[:, 0]\n",
    "oxtr_e339k = x_e339k[:,1]\n",
    "oxr_e339k = x_e339k[:, 2]\n",
    "\n",
    "oxt_e339k_c = oxt_e339k*Div\n",
    "oxtr_e339k_c = oxtr_e339k*Div\n",
    "oxr_e339k_c = oxr_e339k*Div\n",
    "\n",
    "# solution extraction for e3312k upper bound\n",
    "\n",
    "oxt_e339kub = x_e339kub[:, 0]\n",
    "oxtr_e339kub = x_e339kub[:,1]\n",
    "oxr_e339kub = x_e339kub[:, 2]\n",
    "\n",
    "oxt_e339kub_c = oxt_e339kub*Div\n",
    "oxtr_e339kub_c = oxtr_e339kub*Div\n",
    "oxr_e339kub_c = oxr_e339kub*Div\n",
    "\n",
    "# solution extraction for e3312k lower bound\n",
    "\n",
    "oxt_e339klb = x_e339klb[:, 0]\n",
    "oxtr_e339klb = x_e339klb[:, 1]\n",
    "oxr_e339klb = x_e339klb[:, 2]\n",
    "\n",
    "oxt_e339klb_c = oxt_e339klb*Div\n",
    "oxtr_e339klb_c = oxtr_e339klb*Div\n",
    "oxr_e339klb_c = oxr_e339klb*Div\n",
    "\n",
    "\n",
    "# # Plots of all variants and their standard error (SE)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_figheight(9)\n",
    "fig.set_figwidth(12)\n",
    "fig.subplots_adjust(right=0.5)\n",
    "\n",
    "twin1 = ax.twinx()\n",
    "\n",
    "l1, = ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='L206V')\n",
    "l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='L206V')\n",
    "l3 = ax.fill_between(t, oxr_l206vub/1e-12, oxr_l206vlb/1e-12, color='lightblue', alpha=0.8)\n",
    "p1, = ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='P108A')\n",
    "p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='P108A')\n",
    "p3 = ax.fill_between(t, oxr_p108aub/1e-12, oxr_p108alb/1e-12,color='lightgreen', alpha=0.8)\n",
    "w1, = ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='WT')\n",
    "w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='Wild-type')\n",
    "w3 = ax.fill_between(t, oxr_wtub/1e-12, oxr_wtlb/1e-12, color='lightgray', alpha=0.8)\n",
    "v41, = ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='V45L')\n",
    "v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='V45L')\n",
    "v43 = ax.fill_between(t, oxr_v45lub/1e-12, oxr_v45llb/1e-12, color='thistle', alpha=0.8)\n",
    "e1,  = ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='E339K')\n",
    "e2,  = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='E339K')\n",
    "e3 = ax.fill_between(t, oxr_e339kub/1e-12, oxr_e339klb/1e-12, color= 'plum')\n",
    "v1, = ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='V281M')\n",
    "v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='V281M')\n",
    "v3 =  ax.fill_between(t, oxr_v281mub/1e-12, oxr_v281mlb/1e-12,color='lightpink', alpha=0.8)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Time (min)\", fontsize=18, fontweight='bold')\n",
    "ax.set_ylabel(\"[OXTR Complex] (pM)\", fontsize=18, fontweight='bold')\n",
    "twin1.set_ylabel(\"[OXTR Complex] (complexes/cell)\", fontsize=18, fontweight='bold')\n",
    "ax.set_xticks(np.arange(0,721,120))\n",
    "\n",
    "ax.set_yticks([0,  305,  610,  915, 1220, 1525, 1830, 2135, 2440, 2745, 3050, 3355, 3660, 3965])\n",
    "#ax.set_yticks([0,500,1000,1500,2000,2500,3050], fontsize=18, fontweight='bold')\n",
    "twin1.set_yticks([0,  2562,  5124,  7686, 10248, 12810, 15372, 17934, 20496, 23058, 25620, 28182, 30744, 33306])\n",
    "\n",
    "\n",
    "tkw = dict(size=4, width=1.5, labelsize=16)\n",
    "ax.tick_params(axis='both',  **tkw)\n",
    "twin1.tick_params(axis='both',  **tkw)\n",
    "# twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)\n",
    "# ax.tick_params(axis='x', **tkw)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"oxtrc_myo_oxt_nm_yaxis_pm.jpg\", dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b20604",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e559912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0030520053493014753\n",
      "25.636844934132395\n",
      "0.0041551219194458\n",
      "34.90302412334472\n",
      "0.005438230563641929\n",
      "45.6811367345922\n",
      "0.005037806373760024\n",
      "42.31757353958421\n",
      "0.006197000220672742\n",
      "52.054801853651036\n",
      "0.006520620219848545\n",
      "54.77320984672778\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "26460.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(oxr_v281m[-1]/1e-9)\n",
    "print(oxr_v281m_c[-1])\n",
    "print(oxr_e339k[-1]/1e-9)\n",
    "print(oxr_e339k_c[-1])\n",
    "print(oxr_v45l[-1]/1e-9)\n",
    "print(oxr_v45l_c[-1])\n",
    "print(oxr_wt[-1]/1e-9)\n",
    "print(oxr_wt_c[-1])\n",
    "print(oxr_p108a[-1]/1e-9)\n",
    "print(oxr_p108a_c[-1])\n",
    "print(oxr_l206v[-1]/1e-9)\n",
    "print(oxr_l206v_c[-1])\n",
    "3.15e-9*Div\n"
   ]
  }
 ],
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