{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fc8df5b",
   "metadata": {},
   "source": [
    "# OXT - OXTR model\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f05dfbbc",
   "metadata": {},
   "source": [
    "Author: Preeti Dubey\n",
    "Title: OXTR complex formation simulations done for HEK293 cells when [OXT] = 10 pM"
   ]
  },
  {
   "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": 1,
   "id": "0166700b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code to plot the OXTRC for wild type and variants in HEK cells using OXT as 10 pM\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 = 5\n",
    "num = 100\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-11, 2.54e-7, 0]\n",
    "x0_wtub = [1e-11, 2.89e-7, 0]\n",
    "x0_wtlb = [1e-11, 2.19e-7, 0]\n",
    "# initial condition for mutant V281M\n",
    "x0_v281m = [1e-11, 1.28e-7, 0]\n",
    "x0_v281mub = [1e-11, 1.46e-7, 0]\n",
    "x0_v281mlb = [1e-11, 1.11e-7, 0]\n",
    "# initial condition for mutant P108A\n",
    "x0_p108a = [1e-11, 3.1e-7, 0]\n",
    "x0_p108aub = [1e-11, 3.43e-7, 0]\n",
    "x0_p108alb = [1e-11, 2.75e-7, 0]\n",
    "# initial condition for mutant L206V\n",
    "x0_l206v = [1e-11, 3.55e-7, 0]\n",
    "x0_l206vub = [1e-11, 3.84e-7, 0]\n",
    "x0_l206vlb = [1e-11, 3.26e-7, 0]\n",
    "# initial condition for mutant V45L\n",
    "x0_v45l = [1e-11, 2.37e-7, 0]\n",
    "x0_v45lub = [1e-11, 2.64e-7, 0]\n",
    "x0_v45llb = [1e-11, 2.10e-7, 0]\n",
    "# initial condition for mutant E339K\n",
    "x0_e339k = [1e-11, 1.68e-7, 0]\n",
    "x0_e339kub = [1e-11, 1.91e-7, 0]\n",
    "x0_e339klb = [1e-11, 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(11)\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",
    "#ax.set_yticks([0,500,1000,1500,2000,2500,3050], fontsize=18, fontweight='bold')\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([0.2, 0.2, 0.65, 0.55])\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",
    "insettwin1.tick_params(axis='both',  **tkw2)\n",
    "\n",
    "ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='upper left', bbox_to_anchor=(1.3, 1.02))\n",
    "\n",
    "\n",
    "plt.savefig(\"oxtrc_hek_oxt_pm_yaxis_pm.jpg\", dpi=400, bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e559912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.637189919166063\n",
      "5352.3953209949295\n",
      "1.0063165092387647\n",
      "8453.058677605624\n",
      "1.6377291688544926\n",
      "13756.925018377739\n",
      "1.408476665430013\n",
      "11831.20398961211\n",
      "2.194353493181512\n",
      "18432.569342724702\n",
      "2.4950093092688683\n",
      "20958.078197858493\n"
     ]
    }
   ],
   "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",
    "\n"
   ]
  }
 ],
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