OXT-Binding-Model / python_scripts / oxt_dose_response_curve_myo.ipynb
oxt_dose_response_curve_myo.ipynb
Raw
{
 "cells": [
  {
   "cell_type": "raw",
   "id": "12ef7856",
   "metadata": {},
   "source": [
    "Author: Preeti Dubey\n",
    "Date: Dec 13, 2023\n",
    "Title: Dose response analysis of OXT-OXTR complex formation for myometrial cells"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0805c52f",
   "metadata": {},
   "source": [
    "Here we are developing a code for oxytocin doses range and its effect on OXTR complex formation in myometrial cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "642c4e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first thing first, let's inmport the python packages here\n",
    "\n",
    "import numpy as np   # for numbers and data\n",
    "from scipy.integrate import odeint # for integration of ode\n",
    "import matplotlib.pyplot as plt # for datavisualization or creating plots\n",
    "\n",
    "\n",
    "\n",
    "# This is the function for ode equation where oxt set to be variable\n",
    "def senana_mod(z, t, oxt):\n",
    "    oxtr,oxr = z\n",
    "    doxtrdt = (-kon*oxt*oxtr + koff*oxr)\n",
    "    doxrdt = (kon*oxt*oxtr -koff*oxr)\n",
    "\n",
    "    return[doxtrdt,doxrdt] # it will return oxtr conc. and oxr conc. over time\n",
    "\n",
    "\n",
    "# Set the initial conditions and parameters\n",
    "#oxt0 = 1e-9 # 10 nano molar\n",
    "oxtr0 = 1.678e-9 # 3 nano molar\n",
    "oxr0 = 0\n",
    "z0 = [oxtr0, oxr0]\n",
    "ts = np.linspace(0, 600, 1000) # time in minutes\n",
    "koff = 0.0011 # per minute\n",
    "kon = 6.8e+5\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  for myometrial cells\n",
    "Div = V*Av\n",
    "# Define the range of oxt values to plot\n",
    "start_value = 1e-12\n",
    "stop_value = 1e-6\n",
    "num_steps = 50\n",
    "\n",
    "# Use np.logspace to create an array with 7 equal steps in log space\n",
    "oxt_values = np.logspace(np.log10(start_value), np.log10(stop_value), num=num_steps)\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(16)\n",
    "fig.subplots_adjust(right=0.5)\n",
    "ax2 = ax1.twinx() # create a second Y-axis\n",
    "ax1.set_xlabel('[OXT] (M)', fontsize=18, fontweight='bold')\n",
    "ax1.set_ylabel('[OXTR Complex] (pM)', fontsize=18, fontweight='bold')\n",
    "ax2.set_ylabel('[OXTR Complex] (comlexes/cell)', fontsize=18, fontweight='bold')\n",
    "\n",
    "#ax2.tick_params(axis='both',  **tkw2)\n",
    "#plt.grid()\n",
    "for oxt in oxt_values:\n",
    "    sol = odeint(senana_mod, z0, ts, args=(oxt, ))\n",
    "    oxr = sol[:, 1]\n",
    "    oxr_c = oxr*Div# LR complex concentration\n",
    "    ax1.plot(oxt, oxr[-1]/1e-12, 'ko') # plot the Kd values at t=600\n",
    "    ax2.plot(oxt, oxr_c[-1], 'k-') # \n",
    "\n",
    "\n",
    "tkw2 = dict(size=4, width=1.5, labelsize=18)\n",
    "ax1.tick_params(axis='both',  **tkw2)\n",
    "ax2.tick_params(axis='both',  **tkw2)\n",
    "#ax1.set_xlim(-13, -5)\n",
    "ax1.set_xscale('log')\n",
    "# insettwin1.set_ylim(0, 60.48)\n",
    "# inset1_ax.set_xticks(np.arange(0,60*36,500))\n",
    "ax1.set_xticks([1e-12,1e-11,1e-10,1e-9,1e-8,1e-7,1e-6])\n",
    "\n",
    "#ax1.set_yticks([0,200,400,600,800,1000,1200, 1400, 1600, 1800])\n",
    "ax1.set_yticks(np.arange(0, 1805, 150))\n",
    "\n",
    "plt.savefig(\"oxt_dose_response_curve_myo\", dpi=400, bbox_inches='tight', format=\"jpg\")   \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5270816e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}