{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fc8df5b",
   "metadata": {},
   "source": [
    "# OXT - OXTR model\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "21e666a1",
   "metadata": {},
   "source": [
    "Author: Preeti Dubey\n",
    "Title: OXTR complex formation simulations done for HEK293 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 HEK293 cells data provided by lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14aaa37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0166700b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Code to plot the OXTRC for wild type and variants in HEK cells using OXT as 10 nM\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 = 8.8e+6 # per molar per min (from gulliver thesis)\n",
    "    koff = 0.005 # per min (from gulliver thesis) \n",
    "    Av = 6e+23\n",
    "    V = 1e-12 # 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 = 10\n",
    "num = 1000\n",
    "Av = 6e+23\n",
    "V = 1e-12 # litre It is HEK293 cells volume given as 1046 cubic micro meter in the paper\n",
    "Div = V*Av\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, 2.54e-7, 0]\n",
    "x0_wtub = [1e-8, 2.89e-7, 0]\n",
    "x0_wtlb = [1e-8, 2.19e-7, 0]\n",
    "# initial condition for mutant V281M\n",
    "x0_v281m = [1e-8, 1.28e-7, 0]\n",
    "x0_v281mub = [1e-8, 1.46e-7, 0]\n",
    "x0_v281mlb = [1e-8, 1.11e-7, 0]\n",
    "# initial condition for mutant P108A\n",
    "x0_p108a = [1e-8, 3.1e-7, 0]\n",
    "x0_p108aub = [1e-8, 3.43e-7, 0]\n",
    "x0_p108alb = [1e-8, 2.75e-7, 0]\n",
    "# initial condition for mutant L206V\n",
    "x0_l206v = [1e-8, 3.55e-7, 0]\n",
    "x0_l206vub = [1e-8, 3.84e-7, 0]\n",
    "x0_l206vlb = [1e-8, 3.26e-7, 0]\n",
    "# initial condition for mutant V45L\n",
    "x0_v45l = [1e-8, 2.37e-7, 0]\n",
    "x0_v45lub = [1e-8, 2.64e-7, 0]\n",
    "x0_v45llb = [1e-8, 2.10e-7, 0]\n",
    "# initial condition for mutant E339K\n",
    "x0_e339k = [1e-8, 1.68e-7, 0]\n",
    "x0_e339kub = [1e-8, 1.91e-7, 0]\n",
    "x0_e339klb = [1e-8, 1.44e-7, 0]\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",
    "\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",
    "\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",
    "\n",
    "twin1 = ax.twinx()\n",
    "\n",
    "\n",
    "\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='Wild-type')\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_yticks([0,  50000,  100000,  150000, 200000, 250000, 300000, 350000, 400000])\n",
    "\n",
    "twin1.set_yticks([0,20000,40000,60000,80000,100000,120000,140000,160000,180000,200000,220000,242000])\n",
    "\n",
    "tkw = dict(size=4, width=1.5, labelsize=18)\n",
    "ax.tick_params(axis='both',  **tkw)\n",
    "twin1.tick_params(axis='both',  **tkw)\n",
    "\n",
    "# # # Create the inset axis\n",
    "inset1_ax = ax.inset_axes([0.22, 0.2, 0.58, 0.5])\n",
    "insettwin1 = inset1_ax.twinx()\n",
    "\n",
    "#inset1 plot for all \n",
    "\n",
    "l12, = inset1_ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='OXTRC (L206V)')\n",
    "lt1, = insettwin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OXTRC (L206V)')\n",
    "lt3 = inset1_ax.fill_between(t, oxr_l206vub/1e-12, oxr_l206vlb/1e-12, color='lightblue', alpha=0.8)\n",
    "p12, = inset1_ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='OXTRC (P108A)')\n",
    "pt1, = insettwin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OXTRC (P108A)')\n",
    "\n",
    "pt3 = inset1_ax.fill_between(t, oxr_p108aub/1e-12, oxr_p108alb/1e-12,color='lightgreen', alpha=0.8)\n",
    "\n",
    "w12, = inset1_ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='OXTRC (WT)')\n",
    "wt1, = insettwin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OXTRC (WT)')\n",
    "wt3 = inset1_ax.fill_between(t, oxr_wtub/1e-12, oxr_wtlb/1e-12, color='lightgray', alpha=0.8)\n",
    "\n",
    "v42, = inset1_ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='OXTRC (V45L)')\n",
    "vt42, = insettwin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OXTRC (V45L)')\n",
    "vt43 = inset1_ax.fill_between(t, oxr_v45lub/1e-12, oxr_v45llb/1e-12, color='thistle', alpha=0.8)\n",
    "\n",
    "e12,  = inset1_ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OXTRC(E339K)')\n",
    "et1,  = insettwin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OXTRC (E339K)')\n",
    "et3 = inset1_ax.fill_between(t, oxr_e339kub/1e-12, oxr_e339klb/1e-12, color= 'plum')\n",
    "\n",
    "v12, = inset1_ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='OXTRC (V281M)')\n",
    "vt1, = insettwin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OXTRC (V281M)')\n",
    "vt3 =  inset1_ax.fill_between(t, oxr_v281mub/1e-12, oxr_v281mlb/1e-12,color='lightpink', alpha=0.8)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # # Set the limits and formatting of the inset1 axis\n",
    "\n",
    "# Set properties for the ticks and tick labels using tkw2\n",
    "tkw2 = dict(size=8, width=2, labelsize=18)\n",
    "inset1_ax.tick_params(axis='both', **tkw2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "insettwin1.tick_params(axis='both',  **tkw2)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"oxtrc_hek_oxt_nm_yaxis_pm.jpg\", dpi=400, bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  }
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