{ "cells": [ { "cell_type": "markdown", "id": "5fc8df5b", "metadata": {}, "source": [ "# OXT - OXTR binding model\n" ] }, { "cell_type": "raw", "id": "797d90cc", "metadata": {}, "source": [ "Author: Preeti Dubey\n", "Date: Dec 13, 2023\n", "Title: Internal model validation at [OXT] = Kd" ] }, { "cell_type": "markdown", "id": "9fcefb9d", "metadata": {}, "source": [ "#### Here, we are performing siumations for internal validation when OXT = Kd in myometrial cells for wild type. " ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "id": "9b4bebfb", "metadata": {}, "outputs": [ { "data": { "image/png": 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z1+Q9++yzHH300dTU1MTOd+jQgVdffZU999yzTDVzzhVhJPXozUjC+OTBzDVpjz76KCeddBKZQ0g22GADxo0bxxZbbFGmmjnnEkrcQzEJD2auyfrHP/7BmWeeWSeQbbHFFowfP56uXbvmyOmca0Jm4r0ZXWt1zz338D//8z91zm+zzTZ88MEHtG3r/+k61xxkrnDSULw3o2tyHn30Uc4555w653fccUc++ugjD2TOuTr8W8E1KS+88AInn3xynUeLu+++O2+++aYvoOlcCyHpm8A5wPZAZ+BIwsLMAG+a2aok5Xkwc03GmDFjOPzww+v0Whw8eDCjR4/2QOZcCyHpu8CthBgkwju11cAjhAWaTwQeT1Kmfzu4JuGjjz5i3333rTPP4qBBgzyQOdeCSNob+CO1gQwAM6sBnojOHZO0XP+GcGU3d+5c9txzzzoDovv378/YsWM9kDnXsvyYEHtWAc9mXHsn2u+WtFD/lnBltWrVKgYNGkTmsjubbropH374oXf2cK7lGUx4rHg58MuMazOj/cZJC/Vg5sqmpqaG3XbbjTlz5sTO9+jRg4kTJ9K+ffsy1cw514C6RfvxWa6lYlLnpIV6MHNlc9xxx/HBBx/EzlVWVjJu3Dg6d07837JzrnlYEO13ynJt32g/L2mhHsxcWVx11VU89dRTsXNt2rThtddeY9NNNy1TrZxzjeAtQieP64DzUicl/QK4hPAI8s2khSpzPE9rV1VVZZnvb1xpPfXUUwwfPrzO+UceeYQTTjihDDVyruFJWm5mVeWuR7lJOgT4D9mnuEp10z/EzF5KUq63zFyjmj59OieeeGKd87/85S89kDmXRtI+kh6V9IWkldH+eUlHZEk7RNKzkhZIWi5pvKSLJbXJU/7Zkt6WtFTSIkkjJR2VJ31HSddKmiSpWtJcSQ9J2jbJz2VmzwO/IQSu9MmHU59/kzSQgbfM6vCWWcOprq5m0003Zf78+bHzJ554Ig8//HCZauVc40jSMpN0JfB/hHdHzwBfABsBOwOvmNllaWmHA48C1cCDhHdSRwNbA4+Y2UlZyr8RuBSYTRio3B44FdgQGGFmt2Wk7wC8BAwFxgIvA5sBJxG62B9gZm8V9Q9RW+bhwLnAdtGpicDfzOzfScpZV54HszgPZg1nzz335O23346d69+/Px9//LGPJXMtXrHBTNJJwEPAi8DxZrYk43o7M1sdfe4KTCX0EBxqZmOj85WEgDMYOM3MHkjLPwR4HfgE2N3MFkbn+wLvAlXANmY2Iy3P5cD/IwS+U6IBzqlA+gQwARiYOl8O/g3iGsU111xTJ5B17tzZB0U7l0ZSBfBrYDlwemYgA0gFssiJQE/ggVQgi9JUA1dGh/+bUcT50f76VCCL8swA/gB0IMyZmKqT0vJclh6wzOxJ4DVC6yrVE7HQz7iRpB0lDcxybWB0rWcxZaXzbxHX4N555x2uu+662LmKigpee+01X5PMubghwJaEmTEWSjpS0k8kXSRpcJb0B0T7/2S5NooQFIdEjwmLyfPvjDQA/YDNgclmNr3IPPncDLwP3JDl2q+iazcWWdY6Pr2Ca1DV1dUcdNBBdWbBv+2229hpp53KUynnyqNS0sjUgZntlyXN7tH+S+A9INZ6kTQKONHMvopObR3tJ2cWZGZrJE0nzEq/FTBRUhXQB1hqZl9kuf+UaD8g7VzOe+TJk8/QaH9/lmsPAoenpSmat8xcgzrooINYvHhx7Nyhhx7K//5v5pMP5xzQK9qfD3QEDgK6ADsAzwHDgPTeUqnZNBblKC91foN6pq9vnnxSU1V9luXa5xlpiuYtM9dg/vznP/P666/HzvXo0YNnnnmmTDVyrqyqc7TG0qW60ovQAvtvdPyRpOMIraN9JQ02szFF3DPV3T1pT78k6ZPeI7U0xraETi7pUt38E3ck8ZaZaxDz5s3jBz/4QexcRUUFr776qk8e7FxuqQ4Z09ICGQBmtoLQOgPYI9qnWkXdyK5rRrpC6bO1wpLeo5BphAD4M0k7pk5GHUIuJwTFaUWWtY4HM9cgDjrooDpLuvziF79g++23L1ONnGsWJkX7r3NcTwW7jhnp67yvktSW0JlkDVFwMLNlhMd7nSVle5TXP9qnvx/LeY88efJJdTzpBbwrabKkSYR3hL0z0hTNg5kruZtvvpn//jf2RyXbbbcdl19+eZlq5FyzMYoQfPpLyrZsxA7Rfka0fznaH5Yl7TCgE/CGma1MO58vz+EZaSCMR5sJDJC0ZZF58rmZ2smG2xB6S36T2kesC4HfFlnWOh7MXEnNmzePn/zkJ7Fzbdu25aWXEs9O41yrY2bzCD36ugE/T78m6WDgUMLjvFTL5RHCLCGnStotLW0l8Ivo8PaM2/wp2l8hqXtanr7ABcBK4K60Ollant9EY+FSeYYD+xAGTb9a5M/4JSGQptYuS5/W6lPgcDObky1vPj4DSAafAWT97LXXXrz1VnxWm1tuuYURI0aUqUbONQ0JZgDpRZih45uEAclvA1sAxxHeJ51uZg+npT+WENSqgQcIrZ5jiKazAk62jC96STcRZqhPn87qFKAHuaezepkwDm4sYWqrzVm/6azaE3prbkcIZh8BL5rZqiTlrCvPg1mcB7P6e/TRR+tMIjxo0CDGjRtXngo514QknJtxQ8IMHscRxoUtAUYDvzSzOsujSBoKXEGYvqqSMMXVncAtZrY2M32U52zgB4RgUkN4Z3WDmWXtbiypI/BT4HRCIFsMjASuNrMJBX6eXwGPmdnb+dKtDw9mGTyY1c+aNWvo3r07S5cuXXeuTZs2zJw5k0022aSMNXOuaWjNS8BIqiG0Kj8HHo+2V0s5l6O/M3MlcdZZZ8UCGcBll13mgcw5B+Fd3MvANwitwReBLyX9NZqyK1tnl0S8ZZbBW2bJffjhhwwcGJ8zdOONN+bzzz/PkcO51qc1t8xSJG1AeJ93PHAwYYiBAUuBfwGPAf+OhhAkK9uDWZwHs+S23nprJk+ODzF588032XPPPctUI+eaHg9mcdE7uMOBE4AjCD04jdCb8nlCYHvKzL4upjx/zOjWyz/+8Y86geyYY47xQOacy8vMVpjZY2b2LcIyNocDfyMMPTiGMDzgwmLL85ZZBm+ZFa+mpobu3bvHJhJu3749CxcupFOnTmWsmXNNj7fMihOtn7Y3oSfnBDP7azH5fJI8V2+XXnppnRnxr7rqKg9kzrlEogDWH6g0s/GE8XWvJSrDW2Zx3jIrzoIFC+jVqxdr19YOYenVqxdffvllGWvlXNPlLbNA0qHAcGCxmf00moXkOWDXKMl7wEFmVuzExYC/M3P1dMopp8QCGcDdd99dnso455qTs4DvEWY4gfBebDdqp7XahTA4OxEPZi6xKVOm8OKL8WWIdtllFw4//PAcOZxzbp1don1qwtZDCb0YZwDLCAHtqKSFejBziZ122mmxY0k89thjZaqNc66ZSS3zkppoOLUu1GDg0uhzttn58/Jg5hJ56623ePfdd2PnjjjiCLbYYosy1cg518yk3hsul9Qb6AJ8Ec2mnxrn0y5pod6b0SVy5plnxo7btGnDvffeW6baOOeaoa8Js/MfT1grDWqD2IbRfgEJeTBzRXvmmWeYMmVK7NwZZ5zBhhtumCOHc87V8T5h6ZfvR8cGvBF9Tj3imZ20UO+an8G75ufWp0+f2HyL7du3Z9GiRVRWVpaxVs41D941P5B0OPAMtQtyLgV2MrNpkl4EDgBuNbOLkpTrLTNXlL///e91Jg6+6KKLPJA55xIxs39LOojwmHERcJ+ZTYsu/xt4gdqVtItWr5aZpB6hTpb4uWZT5y2z7DbeeGPmzKldybyqqorFixdTUeF9iJwrhrfMGlZRLTNJewCnAvsTulG2ic7XEJa6fhW4P9sKqK75e/DBB2OBDOCnP/2pBzLnXL1J6gacTIgpnQkDqftEl2dZwpZW3paZpOHA1cCg9NMZydIL+C9wjZk9laQSTYm3zOrKfFfmrTLnkvOWWS1JRwL3EZZ9ESGOdAS+InTdP9LMnktSZs5vI0kjCevJDKJ2mhEBa4C50U3XZFwbBDwe5XUtwKOPPlrnXdmPfvQjD2TOuXqRNBB4hNpABoCZrQKeJsSl4YnLzdUyix4hAiwBniSsAvqWmc3ISLcVsAdwZFSBzqFe1iZpZZoCb5nFbbbZZsyeXdtLtlOnTixZssSDmXMJecsskPRPwmsrA8YBOxPFDEnnA38E/mtmOycpN987s+nAr4F7zaw6V6KoF8o04AFJlcC5wI+SVMI1TU8++WQskAFcfPHFHsicc+tjP0Ig+wVhRen0pV5mRftNkhaar2XWxszWZr1YqND1yFtu3jKr1bdvXz799NN1x5WVlSxbtsyDmXP14C2zQNJKQkPqYGAlIZilWmYHE5aDWW1mHZKUm/NbaX2CUXMNZK7W6NGjY4EMYMSIER7InHPrK7Wib/8s13aP9guTFuozgGTwllkwaNAgxo8fv+64ffv2LFu2jLZtfZy9c/XhLbNA0nOEVtlXwF3AZYTHjt8Dbib0ZvyPmR2ZpNy830ySXk5YTzOzAxPmcU3MpEmTYoEM4Fvf+pYHMudcKdxJCGYbAT+mdnjXHdR2078zaaGFxpnVEB9HlrcsmnEvxhRvmcEBBxzAK6+8su64oqKCRYsW0blz5zLWyrnmzVtmtST9A0gtjJiKMalu+v80szOSllnsCxAVsbkWYN68eYwcOTJ27vDDD/dA5pwrpTOAC4B3gepoey86d2aefDkV+9zICC/t/kkYMO1aqBEjRpDZWv/Tn/5Upto451qiaKqq26OtJAo9ZnwH2DV1f2AV8DBhev53SlWJpqQ1P2asqamhY8eOrFq1at25vfbaizFjxpSxVs61DP6YMZB0vpnl/AtZUkfgd2b2vSTl5n3MaGa7A0OABwlTV3UAvgW8KWmMpNMlJV7e2jVNv/3tb2OBDODWW28tU22ccy3UHyU9ImmDzAuSdgDGAt9JWmjRXfMl9SY8zzwP6BWdNsJjx++a2dNJb94UteaWWeaEwptuuimzZs3Kk8M5VyxvmQVpHQtnAt8yszei8xcANwCV1KMzYdEjYM1sjpldBWwO/C1VL0JgSzSHlmt6xowZU2dC4csuu6xMtXHOtWBTCLFjC2CkpGskPQHcQghkAM8mLTRJyyz1iHEEsGPqNLAC+I6Z3Z/05k1Ra22ZDR06lDfeeGPdsU9d5VxpecssiN6J/Qb4PrXjyqA2nvzIzBJ3DCnYm1HSFtFNvw10p7Yb/qeE2Y3/amaJpx5xTcfixYvrdPI4/vjjPZA550rOzFYAIyR9AKQ6gghYDRxY30WeC80A8jhwFOFxZCqIvQzcCjxtZjW58rrm48orr6zTHf+mm24qU22ccy2dpO8TVmUxamNLW+AhSeeY2UuJyyxiBpCURcA9wIR8BZrZnxNXQtoUuA44DOgBfAE8AVybtNUnaR/gYkIvzA2BBcAHhK6eBZ/DtsbHjD169GDBggXrjgcNGsS4cePKVyHnWiB/zBhIegE4gNog9hDwTWCX6NiA28zsokTllnA6q1CLhD1QJPUD3iB0JHkS+Jiw2Of+wCRgqJnNL7KsK4H/A+YBzxCC4kaEDiqvmFnBHg2tLZiNHj2affbZJ3buscce47jjjitTjZxrmTyYBWmNpGXAD8zsnmiI16+Bi6jv1IhmlnMDaoC1aVtNgW1tvvJy3OM5QsAckXH+5uj8n4os56Qo/QtAlyzX2xVTTqdOnaw12W+//Sz6dzPAqqqqyl0l51okYJkV9102I/3/yYxtTo48Qwg9ABcAy4HxhCdUbfLc52zgbWAp4cnbSOCoPOk7AtcSGhnVhGFZDwHbFvNzpZVTQxhL1j/LtcOAL+sTSwq1zFL/qEUzsy2LTStpK+ATwi+vn6W9g5PUhdCyEtDLzHI2lyRVAFOBbwB9zeyrJHVO15paZmvWrKGyspK1a2uXnzvttNP45z//WcZaOdcyFdsyi753NwB+l+XyUjO7MSP9cOBRQoB5kBDQjga2Bh4xs5Oy3ONG4FJgNvAI0B44lfBqZoSZ3ZaRvgPwEjCUEIheBjYjNCJWAQeY2VuFfraorJuBn5jZ6hzXvwHcY2aHFVPeOkmjXyk3wihvA+7IcT3VajuwQDl7R+keJrxEPBL4CaHJOjhJnVpTy+zXv/51nb/8ZsyYUe5qOdcikaxlNqPItF0JLaSVwG5p5ysJr28MODUjz5Do/FSge9r5vsB8QlDsm5Hn8rTv2Iq088Oj8x+lny/HVu6+11tH+8k5rk+J9gMKlJNanfRLwszLzwC/Ivxl84akVyX1zJZR0sj0rdiKtwR//OMfY8d9+/Zliy22KFNtnHP1cCLQE3jAzMamTppZNXBldPi/GXnOj/bXW1oHOzObAfyBMG3hOanzkpSW5zJLe4JmZk8CrwHbAfsmqbikb0q6XtITkl6U1EHSsGhrn6QsSDADSEYlqiRtJmnzzC1hUd2i/aIc11PnNyhQTmp6rfMJz3UPAroAOxBad8MIf1G4yKeffsqnn34aOzdixIgy1cY5l6GDpDMk/UzSRZL2l5StQ8QB0f4/Wa6NIrw/GxI9Jiwmz78z0gD0I8z8NNnMpheZJy9J3yW05n4KHEPo8Lea8MjzFcLTtUSKXjo4is4/Jjy6650jmSUps5jbppWbT+qXLOBEM/tvdPyRpOMILb99JQ02s9joYDPbL/24qqoq0TvC5uqaa66JHbdt25YLL7ywPJVxrnWoTH/6k/ndk6E3cF/GuenRGKxX087lfLplZmskTQe2B7YCJkqqAvoQ3r19keW+2Z6GleoJGgCS9iZMuBFbC9PMaqJprb5DCHCPF1NeSpKW2fXALwn/yNkW5qzPIp2plle3HNe7ZqTLJdVUnpYWyIB1o82fiw73SFi/FuvJJ5+MHQ8ePJi2bUv5d4hzrp7uAg4kfNdWAQOBOwjvtP4taVBa2qRPt+rzNKxUT9BSfkyIPauoOwdjammx3Yosa50k315nkztY1Xel6UnRPldE7x/tc/1FkFnO1zmup4Jdx+Kq1bKNGzeOhQvjY9F9UmHnGlx1gdYYAGZ2bcapD4HzJS0l9EC8Bih2IGixT7fqVCNB2qT3GBylvZwQvI5IuzYz2m+c4P5AspbZhtT2ZulNGLdVkbElG+QWno0CHBJ1r18n6po/lDDxZKG5ukYR1lvrn+PF4Q7RfkbC+rVI114b/3+lY8eOHHXUUWWqjXOuSKl5DIelnUv6dKtQ+mytsFI9Qcu8x/gs11JxoHORZdXJWIxU6+guM5trZmvzpi6CmX0CPE9oPl+QcflaQhP7XovGmElqJ2mbaNaQ9HLmEcZXdAN+nn5N0sHAoYR/6GwvPFudF154IXZ8wAFFv7d1zpXP3GifPlYt59MtSW2BLQl/6E8DiL5LPwM6S8rW+sn2NKxUT9BSUnPn7ZTlWqpH5Lwiy1onSTD7PaE5eWjSmxTwfcIv6Zaoi+YvJb0M/JDwj3NFWto+wETC4L1MlxDGTVwhaZSkGyU9TOhpsxY4z8y+LnHdm50XXniBzEHhV199dZlq45xLYHC0n5Z27uVon22A8TCgE/CGma0sMs/hGWkgTGwxExggKdukGNny5PMWIZZcR1jsGQBJvyB8jxuFn8bVVeyANMI7s0mEwPAUYaqUszK3+gx2I4wkv4sw48cqwvIyvwc2zEjXN/pBsw4oJDwKvRmYHpUznzDf417F1qWlD5rOnL5qgw02KHeVnGsVKGLQNKHn4YZZzm9B6DVowM/SzncFvqIZDZoGDqHuVInpUyaupcBEGdm2JItzpiYdTl9MLZOZWbPuEteSp7OqqamhY8eOrFq1at25M888k3vvvbeMtXKudShmOitJ1xDGXr1C+KN8CWGc15GEAPUscJyZrUrLcyxhfFY18ADhMd4xRNNZASdbxhe9pJsIraD06axOIaxakms6q5cJgXAs4enY5tRjOquovF8BqV5nqbqlOpL82swuL7asdWXWM5jlYpa8E0iT0pKD2eOPP87xxx8fOzd16lT69euXI4dzrlSKDGb7EiZ/2JnarvlfA+MI487uywxMUb6hhFcygwlBbypwJ3CL5ejfIOls4AeE2TtqCLMn3WBmz+RI35EQaE8nBLLFhMmJrzazvEuD5SjvcODc6P4QXiH9zcz+nTtXnvISBLORFNH10sz2r09FmoqWHMz2339/Ro4cue64Z8+ezJ07N3cG51zJ+BIwDavoR4JWxPgI17S9+Wb8neoRRxyRI6VzzjUvzfr9liveCy+8QHV1dezc5ZcnfiztnHOJSJpWOFUdZmaJ3n/kDGaSuprZ4npUYr3yuoZx0003xY67d+/O1ltvnSO1c86VTF+SzyiSeI7cfOPMPpX0C0l9iq6BtGnUS+XTgoldo3rttddix4ccckiZauKca4Uy5/PNt9XvBrk6gKT1XjRgNPAvwmC3KYSxCCKM69qaMIHvkYTppwTQXHs1tsQOIK+//jp777137Nz777/PTjvtVJ4KOdcKtdYOIFEPzcQsvjpAQfnemf0TOI3Qetsn2gpJNQ//nqQSrmHdcMMNseMuXbp4IHPONYqkQam+cj5mNLMzgD0Jqzanxpfl22qAp4Hdzezshq22S+Lll+OzzPhcjM65cpPUUdJwST+KtmMldapveXl7M1pYhvsYSZsBJxMmgRxIWKYbwjQqHxAGzj1kZrPrWxHXMD766COWLFkSO/fjH/+4TLVxzjmQdCrwB+qugfa1pBFm9s/EZRY7aLq1aGnvzL7zne/wt7/9bd1xx44dWb58eRlr5Fzr1FrfmWWSdDTwBHU7e6SeANYAx5vZU0nKTTJrvmuG/v3v+Mwwu+++e5lq4pxzQFimKxXIVgEfR9vq6FwFcFXSQj2YtWCLFy/m888/j5379re/XabaOOccEBZLTi3z0tfMtjOz7Qjj0cZEabZPWqgHsxbs1ltvjR1XVFRw+umnl6k2zjkHhImTAX5lZnNSJ6PPv44OFyYt1INZC/bggw/Gjvv370/btj6DmXOurJ6I9l2zXOsW7R9NWqh/s7VQNTU1TJgQX5XhhBNOKFNtnHNunZ8Rxi3/UtJXwOvR+aHA9YSlaK5IWqj3ZszQUnozPvroo5x44omxc1999RUbbbRRmWrkXOvmvRkDSan11bLNwZhrXsaCCz97y6yFuuOOO2LHvXr18kDmnGsKUgEr22LPmatOF82DWQs1ZsyY2PGBBx5Yppo451wduYJVvScazrcEzMu5rhVphJl9tJ5luHqYMmUKS5cujZ374Q9/WKbaOOdczJYNUWi+ltl+1GNNGWqbkN0KJXQNI7NLfmVlpQ+Wds41CWbWIEuEFfOYsd7NPlcembN+7LjjjmWqiXPONY5igtmFhMmEiyFgfR9PuvVQU1PDtGnxVcpPOeWUMtXGOefqknQycBEwgLAuZqaCvRczFZP4PTN7o9gCJW/IldPjjz9OTU1N7Nx3v/vdMtXGOefiJF0M3JQ6LFW5+YLZU4R3X/MTllnffK4E7rrrrthxr1696Ny5c5lq45xzdVxIA7y+yhnMzOzY+hRY33yuNN54I96IHjZsWJlq4pxzWW1MaPA8SZjxY14pCi16bkZJhxeR5vL1q45bH3PnzmXhwvj8nP6I0TnXxEyN9n82s3fN7NNsW9JCk0w0/C9JN0qq05qT1FvSS8AvklbAlc7tt98eO27btq0PlnbONTW/IjxmPFNSySa7TzoDyA+BYZJOM7NPACQdCdwFbET9xqW5Enn88cdjxwMGDKCiwhdGcM41HWb2D0nbEiYcHibpHWqXhUlLZokWX0wSzBYQulDuBrwn6SJgZ+AHaWnGJbm5K62JEyfGjo855pgy1cQ557KTNAj4PqHx0wfYJEfSRMEsyZ/t2wHPRJ+7AH8jBLLUjB83AHslubkrnddff51Vq1bFzl1wwQVlqo1zzuX0W2CDtGNl2RIrumVmZnOBYyRdSghcqUqsBo40sxfrUwFXGn/5y19ix926dWPTTTctU22ccy6nPQgNoC+BxwjDuGry5ihCondmko4ALqF26n6Lyvi1pDPNbEK+/K7hjBw5Mna86667lqcizjmX39dAR+B8M3uqVIUm6Zr/V+BpoDchkL0BzI4+7wSMleRTs5dBTU0Ns2bNip07+eSTy1Qb55zL615C3NiilIUWvdK0pFQzcC2hC/7/AV0J786Oi66ZmbUpZQUbW3Ncafqpp55i+PDhsXPLli2jU6dOZaqRcy6TrzQdSDoYuBXYDPg9MAZYlJnOzEYlKTdp1/yZwJlm9lp0/DVwgqTzCXNtVSYsz5XAfffdFzvu2bOnBzLnXFP1HLWvqn6SI03qFVbRkvRmfBzYOS2Q1d7V7E/A7sCHSW7uSuP111+PHe+xxx5lqolzzhUtFdBybYkk6c14QoHrEyT5t2gjW7VqFXPmzImdO+2008pUG+ecK2gmDTDBRtLHjEjqBpwMbA90Br5HGPgGMCtXPtcwHn74YdLfe0ry9cucc02WmfVtiHKTds0/ErgP6EZt1/zvExbvrAKOJDwPdY3k/vvvjx1vvPHGtG2b+G8U55xr1pJ0zR8IPEJtIAPAzFYRuuxXAMOz53YN5e23344dDx06tEw1cc41FElnSrJo+06ONEMkPStpgaTlksZLulhSzh7mks6W9LakpZIWSRop6ag86TtKulbSJEnVkuZKeiiaazHpz3SCpKclTYm2pyUdn7SclCQdQC4HOkSf38+4NjraD65vRVxyS5cu5auvvoqdO+uss8pUG+dcQ5C0GaEr+9I8aYYDo4BhhM56fwDaE6aOeiBHnhuBuwnri/0F+DswEHha0g+ypO8AvAD8HFhM6Fb/ImFo1lhJeyb4mW4HHgKOALaKtiOAh6NryZlZURvwOWGM2bXAUML0I2uja0dGx18WW15T3Tp16mTNxR//+EcjPOo1wCoqKmzt2rXlrpZzLgtgmSX8PiI8BXsR+IQwjaAB38lI0xWYC6wEdks7X0mY3MKAUzPyDInOTwW6p53vS5heqhrom5Hn8ijPw0BF2vnh0fmP0s/n+ZlOSMWPaJ+5rQWOT/pvlaRl1iPav5rlWmqG2w0SlOfW0yOPPBI73myzzXzJF+dalguBA4BzgFyzOZwI9AQeMLOxqZNmVg1cGR3+b0ae86P99Wa2MC3PDEKrrkN0TwAkKS3PZWZWk5bnSeA1wmT0+xbxM6WvGPwIcHG0PUztHI3fK6KcmCTffIujff8s13aP9guzXHMN5P3340979923mP+OnHPNQfQe6lfA7y3/bBgHRPv/ZLk2ClgODIkeExaT598ZaQD6AZsDk81sepF5ctmF0JL7tZmdbGa3RNspwG8IrdFdiignJkkwey+6yXXA0amT0cvIn0aVezdpBVz9LF++nIUL4387nHHGGWWqjXOuCJVRB4uRkkbmSyipLaHn+EzCIpb5bB3tJ2deMLM1wHRCz/WtorKrCMOplprZF1nKmxLtBxRzjzx5cukW7bM95Uud61pEOTFJgtmd0X4j4MfUDnq7gzDeLD2Na2APP/xw7LiiooIDDzywTLVxzpXYzwmLH/+Pma0okDYVHOrMb5hxfoN6pq9vnlxSf4UflOVa6tzXRZQTk2QGkAclHQOkppdIBbNUN/37zezRpBVw9fPYY4/FjjfeeGN/X+Zc01ZtZvsVShTNpPQz4CYzG1OC+6a+o5POupEkfZJ7jAUOBy6R1J/a3vBDCU/9LEqTSNLRtWdENz6X8LIPYCJh5vw/Jb25q7933nknduzzMTrX/KU9XpwMXFVktlSrqFuO610z0hVKn60VlvQe+dxBCGYQgtfRaddSk3HcUUQ5MYn+lI96mN5uZrubWVW07RadK/lcWy67mpqaOvMxnnBC3qkznXPNQ2fCe6dtgeq0gdIGXB2l+Ut07nfR8aRoX+d9VRQctwTWANMAzGwZ8BnQWdLGWeqQ6uSX/n4s5z3y5MnKwoKct5F7cuHbrB6Ldvq8R83Qf/7zHzL/djjppJPKVBvnXAmtJDzpymYXwnu00YTgknoE+TLwLeAw4P6MPMOATsAoM1uZdv5l4Mwoz10ZeQ5PS5PyCaEzygBJW2bp0ZgtT05mdqGkF4GzgW0IgWwicLeZPV1MGZlyLs4pqahK1a2jNeteCM1hcc6zzjortoZZjx49mDdvXhlr5JwrZH0X55R0DaF1dp6Z/TXtfFdCsOkKDE2NNZNUSQgug4HTzOyBtDxDgNejfLunxppJ6kvolV4FbBONO0vluRz4f4SxYaekxppFs488AUwABqaPQWtM+Vpm+5H8BaA/amwEmeuX7bTTTuWpiHOu7MxssaTzCEFmpKQHgAXAMYQu9Y8AD2bkeUPSzcAlwHhJjxCmvzoF2BAYkR7IIjcDRxEGab8l6SXC2LOTCGPZzi0mkEnaCNgkVMM+yLg2kBBLvjCzr7Llz6XQO7N8C6et10Jqrv5mzpwZOz766KNzpHTOtQZm9gRh9o1RhOmiRgCrCcHq1Gx9GszsUuB/gDmEWTnOIkxJdbSZ3ZYl/UpC1/nrCF3wfwgcTGiV7W5mbxVZ3ZsJ8/vekOXar6JrNxZZ1jr5HjOenbQwADO7pz75moqm/phx7Nix7L777rFz8+fPZ8MNNyxTjZxzxVjfx4wthaRPCHNAnpsZLySdRZj8eJqZfTNJuTkfMzb3oNRSPfhg7GkBnTt39kDmnGtOUj0oP8ty7fOMNEWrV29GSYOo7Yo51czG1accl9zIkSNjxwMGFDN7jHPONRlro/22hBUB0qXWRUvciSTRODNJ+0iaSJin8cFoe1fSREk+y20jmDw5Poxj2LBhZaqJc87VyzRCP4ufSdoxdTLq/JFaZmZa0kKLbplFXTlfANpRt8PH1sBzkg40s9frZHYlsXz5chYvXhw7d+KJJ5apNs45Vy//ISwC2ovQGJpOCGBbAW2iz9lm888rScvsl4Sum6lAtiLaUtoD1yetgCve448/HjuuqKhg8GBf3Ns516zcTBg6ACF49QO+GX2GMBHxb5MWmiSY7UaImJMIq5lWRT1zdgM+jtLsniuzW3/PPPNM7Pgb3/iGTy7snGtWzOxLwswjqTFG6cO7PgUON7M52fLmk6QDyArCMtxXmdl7aRV7T9LPgYcIA+dcA3n77bdjxzvuuGOOlM4513SZ2VhJWxPGrW1HCGYfAS+a2ar6lJkkmD1PGB2ebWBa6tyz9amEK86sWbNix4cddliZauKcc+snClrPUqK4kXPQdJ2E0maEiS2/Bk40s4+j86npUqqAIfVpHjYlTXXQ9CeffMI3vxkfQ/jFF1/Qu3fvMtXIOZdEax00LamrmS0unHL98iZpmb0KdCTMqfWRpNRqod2j/QLgDSnW0dHMrF+Ce7gc7r8/Phl2ZWWlBzLnXHPwqaQ/ALebWbaB0nVI2hT4AfA9amNMXkmCWV/C40QjPN9Mn3bCouP0cz7xcAm9+GJ8bGHfvn3LUxHnnEumG2H82E8ljQb+BbwFTAHmUxtPtgb2AI4krDqdaM7fpDOA5CrcJxpuYBMmTIgd77XXXmWqiXPOJfJP4DRC7/l9oq2QVGPo78XeJEkw2z9BWldCNTU1ddYrO+aYY8pUG+ecK56ZnRGtiv1z4AgKDwlbS+gUcm16z/lCiu4A0lo0xQ4gb731Vp2W2IoVK6isrCxTjZxzSbXWDiDpoo6EJxOWqxkI9IwufQV8AIwEHjKz2UnLrtdEw65xPfHEE7Hjzp07eyBzzjU7ZjYLuCnaSipRMJO0J3AhMIDwwi7zXZn3XmwAo0ePjh1vueWWZaqJc841TUkmGj4duC9fErz3YoOYNGlS7HiPPfYoU02cc65pStIyuxLvtdjosnX+OPzww8tUG+eca5qSBLOtCC2vMYQZjeflT+5K4d133yWzk86RRx5Zpto451zTlCSYzSIEtP8zs+dKWYlotPd1hJmUewBfAE8QumYuzJM1X5lnAvdGh+eZ2V9LUNVGl7nsi3f+cM65upKsH3Ib4TFjSZ9xSeoHvAucA7xNaPVNAy4CxkjqUY8yNwNuBZaWsKpl4Z0/nHOusKJbZmb2+2hS4RFRr8bUpMOZ6a5LWIc/ElYcvdDMbk2dlHQz8EPCgp/nF1uYwuSQdxGmSXkM+FHC+jQpH3/8cezYO38451xdSXozbgEcSmid7RFt2RQdzCRtBRwCzAD+kHH5auC7wJmSLjWzYkcyXwgcAOwX7Zst7/zhnHPFSfLO7FZgS2onGs4madf8VLB53sxqYgWZLZH0OiHY7QW8VKgwSdsCvwJ+b2ajJDXrYOadP5xzzZ2kl9eziBFm9lGhREmC2X6EYLWcsBzMfKAmX4YibB3tJ+e4PoUQzAZQIJhJaksYBzcT+FmxFZA0Mv24U6dOxWZtcN75wznXAuxH/cYgp8YudysmcZJgVk1YgPM8M3ugHhXLJlXJRTmup85vUERZPwd2BvY2sxXrWa8m4fXXX48de+cP51wz1qDjlJMEs0cJ77A6NFBdskn98HmjuqQ9CK2xm8xsTJIbmNl+6cdVVVVNZhaTyZPjDdZdd921TDVxzrn1diFhMuFiCEj0eDJJMPsjYRmYmyR1IPRmrNOiMrOZCcpM5c/VjOyaka6OtMeLk4GrEty7ycvs/HHggQeWqSbOObfe3jOzN4pNHDqmFy9JMPsvtZ0/bs+RxhKWmZp0cECO6/2jfa53agCd0/JX5/gH+IukvxA6hlycoH5lM3PmTNasWRM7d9RRR5WpNs45V29PEWLD/IbMV58lYFKP4ZRxrj7PQ1+J9odIqkjv0SipC2Hp7BXAm3nKWAn8Lce1XQjv0UYTAmeiR5Dl9NRTT8WOO3TowAYbbFCeyjjnXD2Z2bGNkS9pMMsVsOr1Ys/MPpH0PKHH4gWE7v8p1xI6nNyRGmMmqR3QD1htZp9EZawAvpO1UtI1hGB2T3ObzurVV1+NHffu3btMNXHOudKRdLiZ/btAmsvN7JdJyi16Oiszqyhia5Pk5pHvA3OBWyQ9IemX0biEHxIeL16RlrYPMJEixpw1dx98EH9Puu2225apJs45V1L/knRj1N8hRlJvSS8Bv0haaJK5GRtE1MLaDbgb2BO4lND6ugUYbGZJn7O2CLNnx1cNHzp0aJlq4pxzJfdD4I1obl4AJB0JjCd0NExMmTNMFMwg7QacDWwTnfqY8BhvbH0q0NRUVVXZsmXFzpzVMFatWkWHDvEREO+//z477bRTeSrknFtvkpabWVW561FukuYBG0aHSwiTyu8M/CCVBHjfzBKNRUoUzCT9DPi/HJevMLNfJbl5U9QUgtmzzz4bm7ZKEmvWrKGiouwNaedcPXkwCyT1Av4KpLpnp3cqrAFuAq40s9VJyi362zGa5zD1HFNZtusl7Zfk5i67556LLxfXvXt3D2TOuRbBzOaa2THAj9NOC1gNHGZmP0kayCDZO7ML0246FvhdtL2dluaipBVwdb3zzjuxY5/GyjnXkkg6AriE2mFdqTHKv5a0XX3KTBLM9oxu+Bcz28PMLom2vYC/RBXaqz6VcHFTp06NHe+2225lqolzzpWWpL8CTwO9CXHjDWB29HknYKykHyYtN0kwS634/FiWa6lzG2a55hKaPz/egfOggw4qU02cc+Ug6deSXpI0S9IKSQskvS/pakk9cuQZIunZKO1ySeMlXSwp55ApSWdLelvSUkmLJI2UlHOqIUkdJV0raZKkaklzJT0ULb9VrHOpfT92LTAMGAQ8Hp2vBG5MUB6QLJgtifbZepikzi1NWgEXN2nSJGpq4ivrHHbYYWWqjXOuTH5ImDTiBeD3wD+ANcA1wHhJm6UnljQcGEUIDI8TFjtuD/wWyLrKiaQbCUOiNiY8Xfs7MBB4WtIPsqTvENXn58DiqF4vAscRWlN7Jvj5ZgIHmNm1ZlZjZl+b2QmEccf1W/XEzIraCDMY1xCWgvktcGK0/Ta6+Vrg5WLLa6pbp06drJxuvPFGIzzONcAqKyvLWh/nXGkAy6z479vKHOevj74b/ph2rith4omVwG7pZRAe4RlwakY5Q6LzU4Huaef7EuZCrAb6ZuS5PMrzMFCRdn54dP6j9PN5frZH0++Z5fp2wH+L/bdKbUlaZvdE+3aEziAPRtuF1C4Lc0+WfC6BN96ITyq98cYbl6kmzrlyMbPqHJceivb9086dCPQEHrC08b5RGVdGh/+bUc750f56M1uYlmcGoVXXATgndV5hBvdUnsssbR5dM3sSeI0QhPYt4mc7If2eWa5PAPYoVE6mJNNZ3QM8Sbw7Pmn7J6I0bj1MmDAhdrz11lvnSOmca4WOjvbj084dEO3/kyX9KGA5MCR6TFhMnn9npIEwK9PmwGQzm15knrwkdZN0nqTfSfqrpDaSNpe0ObCq2HJSkk40fAJhlHbmDCB3E6K5W0+fffZZ7HjPPZM8hnbONWGVkkamDixjYeBsJP2IsMxVN8K0f3sTAln6BBWpv3jrLJVlZmskTQe2B7YCJkqqIsxzu9TMvshy2ynRPn1prpz3yJMnp2jqqvsIP1eqa/73CYt3VgFHAs/lLCCLRMEsalreEm2uxGpqaliyZEnsnPdkdK5V+xHwjbTj/wD/Y2ZfpZ1LLW6caxHj1PkN6pm+vnmykjQQeITQQWXdiitmtkrS08DphPdwiYJZwceMkrpGW9bAJ6ltKk2SG7u63n333Trn9trLh+4510JUm9l+qa2YDGbW28xEGJN1PKF19b6kXRLcNxUwkk3Emyx9kntcTm0/i/czro2O9oMT3BsoEMyi8QYLgS8JvVyy6UvoSbMg3/gEV9jzzz8fO66qqqJt2/qsn+qca0nM7Esze5yw9mMP4N60y6lWUbc6GYOuGekKpc/WCkt6j3z2IwS9X1A7s1TKrGi/SRHlxBRqmZ1CiLiPm9nUbAmi849EZZ2StAKu1ptvxhfU3mSTxL9P51wLZmafAhOA7SVtFJ2eFO3rvK+KnqhtSRijNi0qYxnwGdBZUrbu0qmekunvx3LeI0+eXFKDvl/Nci3V8WODIsqJKRTMdiVE0LyrgqZdTzRlv4ubNGlS7NgX5HTOZZH6K3dttH852mebXWEY0Al4w8xWpp3Pl+fwjDQAnxAGOg+QlG2y2Gx5clkc7ftnubZ7tM/ZdT+XQsGsT7SflTdViPLp6V09fP7557Fjf1/mXOsjaRtJvbOcr5B0PdCLEJxSX/iPAPOAU6P1JlPpK6ld6eT2jOL+FO2vkNQ9LU9f4ALCAOy7UuejQd+pPL+RVJGWZziwD6HFmK21lek9whO/66gdaoCk7wA/JTSg6nYgKKDQC5nU9VzPSVNSz0v9BU89rVmzhsx11A4++OAy1cY5V0aHATdIGkVoEc0n9Gjcl9ABZA5wXiqxmS2WdB4hqI2U9ACwADiG0KX+EcIEF6TleUPSzYSZ68dLSvUuPIUwx+6IaAB1upsJa5CdCLwl6SXC2LOTCGPZzk0fTJ3HncDBwEaEZWBSnUbuoLab/p1FlBOTd3HOaHzC5sDfzOy7edL9GfgO8KmZNev1Ssq1OOfo0aPZZ5991h37gpzOtSzFLs4paQfCjB1DgU0J74+WEd5H/Qu4xcwWZMk3FLiC0BOwkjBV1Z1R+rWZ6aM8ZxPGDm9HmK7wPeAGM3smR/qOhNbT6YTYsBgYCVwdzdxRFEn/AE6LDtMX5wT4p5mdUWxZKYVaUm8DWwDnSHrBzB7OUqkTCNOeGPG1zVwCmT0ZO3fu7IHMuVbIzD4kPOpLmu914IiEee4hwTSEZrYCuDra1scZhG745xICKcBE4G/UPs5MpFAw+yehCVkBPCDpu8DzhGZvD+CgaEs1Df9Rn0q4ugtybrrppmWqiXPONazoHdzt1H2XV2+FgtlThAkkU8+/DqDu3FupQDbKzJ4qVcVam8yejNttV6/FVp1zrlXKG8zMzCSdRGiN7ZglSeoZ53h8jNl6mTNnTux4yJAhZaqJc86VjqRiuutnMjM7MEmGgr0PzWyupMHAxcBZ1E44CWEg3d3A7/MsWeAKWLVqFStWxNejO/TQQ8tUG+ecK6n9SD41VtKpt/L3ZsyaQepE6F3ztZktT3rDpq4cvRlfeOEFDjnkkHXHkuqsNu2ca96K7c3Y0kiqz5eZmVmbJBkSjwuLAliLC2Ll9PLL8VZ4t26FhvU551yzcU7hJOvPBzk3Ae+9917s2HsyOudaisZatNmDWRMwdWp8DudtttkmR0rnnGs5JA2ido7GqWY2rr5leTBrAr788svY8e67754jpXPONX+S9gH+TMYs/JImA+ebWTFzPMb4FBNlVlNTU2dOxv33379MtXHOuYYlaQjwAiGQKWPbGngumporEQ9mZTZu3Lg653bd1VfScc61WL8kTGqcGqe8ItpS2gPXJy20pMEsmiDTJfDKK6/Ejjt16uRzMjrnWrLdCOPIJgG7mVlVNGRhN+DjKE3idy0l+daUNEjSY8D7pSivNcmck/Eb3/hGmWrinHONItUKu8rM1nXljj7/PDpMPPyrYDCTtIOkP0l6VtKfJe2cdm0bSU8Qlg04tpjyXNyECfFVE/r161emmjjnXKNILRGSbcaO1LlnkxaatzejpO2AN4D0UetnSjqQsM7OPcSffbqEPvvss9jxzjvvnCOlc861CD8BhgHXSvrIzD4GkLQ1cA0wI0qTSKGu+T8GOhOiZSpgtQd+S1iDpkNa2pnAr5NWoLVbtGhR7HjvvfcuU02cc65RvAp0BDYBPpK0MDrfPdovAN6QYm0kM7O8j60KrTQ9hbBMdw3wn+j0oUAbaoPbdELPk3vNbE2xP01T1ZhzM37++ef06dMndm7RokV07dq1Ue7vnGs8rXVuxkzRXI2Zq0unZAtIooi5Ggu940p90/7UzI42s6OBy6md1fgeYFszu7MlBLLG9uKLL8aO27Vr54HMOdcapMaV5TqvPGmyKvSYsZIQtMamnUv//CMzW1XszVzcmDFjYsc9evQoU02cc67RNMisEMVOZ5UesNa1wMxsfmmr07p88MEHsePNN9+8TDVxzrnGUZ+pqopRbDAbnfEyDkCS1macMzPz+R6LNGPGjNjx9ttvX56KOOdcM1ds4Mn1ks675K+HefPmxY732muvMtXEOecaj6Q9gQsJ8zNuSJYYU6j3YqZiglmul3RuPVRXV7Ny5crYuYMPPrhMtXHOucYh6XTgvnxJyN6rMa9CwaxRVghtjV577bXYcUVFBVtuuWWZauOcc43mShqgQZQ3mDXWCqGt0ahRo2LHXbp0KVNNnHOuUW1FaHmNIUzAMS9/8uIUms5qWnTTk9InhHTr7/3343Myb7zxxmWqiXPONapZhID2f2b2XKkKLTRoum+0VZbqhi745JNPYsff/OY3y1QT55xrVLcRHjMeXspCvRt9mcyZMyd2vNNOO5WnIs4514jM7PfRpMIjol6NY4Cvs6S7Lkm5HszKZPHixbHjwYMHl6kmzjnXeCRtQZjjV8Ae0ZZNgwSzcyUdVEzCpNG0NZo7dy41NTWxc8OGDStTbZxzrlHdCmxJfDWWTCXvmp+SpIu+B7MCRo4cGTtu27YtnTt3Lk9lnHOuce1HCFbLCcvBzCeszLJe6jsDSC6Jo2lr9NZbb8WOu3XrVqaaOOdco6smLPh8npk9UKpCiw1mc4CVBVO5omROMJy5pplzzrVgjwLfJb6483orNpidaGZvlPLGrdn06dNjx/379y9TTZxzrtH9kbAMzE2SOhB6My7KTGRmM5MU6r0Zy+DLL7+MHXu3fOdcK/Jfajt/3J4jjZEwPnkwK4OlS5fGjvfee+8y1cQ558om2+or+Xo45uXBrJHNnDkTs3g/GV/6xTnXyuQKWPWegLhQMDuAECk/LKYwSVVmtqy+lWkNMrvlt2/fnspKny3MOdc6mFmhaRTrpVChVwKfmNniAumQtA/hWajLI7Nbfvfu3ctUE+dcUyOph6TvSHpc0lRJKyQtkjRa0rclZf3OljRE0rOSFkhaLmm8pIsltclzr7MlvS1paXSPkZKOypO+o6RrJU2SVC1prqSHJG1bip99fRUKZgcA4yWdmSuBpHaSfgO8TBjV7fKYMGFC7HjTTTctU02cc03QScBfgD2Bt4DfEbqy7wD8FXhIUuxRnKThwChgGPA48AegPWF5lazjuCTdCNwNbBzd7+/AQOBpST/Ikr4D8ALwc2Ax8HvgReA4YGw0x2IiknaTdKukF6LtVkm7JS1nHTPLuRFGZa+NtseAjTKu7wSMj67XAGvzldcctk6dOllD6tu3rxEe3Rpgp59+eoPezznXNADLrMD3D6EBcTRQkXG+NzAz+t44Ie18V2AuYRzwbmnnK4E3ovSnZpQ1JDo/Feiedr4vYTaOaqBvRp7LozwPp9cNGB6d/yizzgV+zp+lxZbM7afFlpO+FWqZvU3tC7nhwIeSjlFwJfAmsH1amj8XKK/V++qrr2LHu+yyS5lq4pxraszsZTN72sxqMs7PAf4UHe6XdulEoCfwgJmNTUtfTXhNBPC/Gbc5P9pfb2YL0/LMILTqOpA2hWHUEkzluSy9bmb2JPAasB2wbzE/o6QDgF+kDrNs10vaL2vmPAoFsyGEZuWa6Ca9CM3Y6cC1hKasgC+Bo8ws8x/NpampqWH58uWxcz7BsHOtRmX0XmqkpJH1yL862q9JO3dAtP9PlvSjCPMfDokeExaT598ZaQD6AZsDk81set0sWfPkc2G0FzCW8Cj1d4TGU8pFRZa1Tt5gZmY1ZvYLYC9CMzJVgc2jvREeP+5gZs8mvXlrM2nSpDrd8nfeeecy1cY511xIagucFR2mB6Gto/3kzDxmtobQ8GhLWNkZSVVAH2CpmX2R5VZTov2AYu6RJ08+exJix1/MbA8zuyTa9iK8vxMh5iRSVBdJM3sf+D6witr3PRD+oX5gZvOT3rg1GjVqVOy4srKStm19qJ9zrUS1me2X2hLm/RWhE8izZvZc2vnULOV1poPKOL9BPdPXN08+PaL9Y1mupc5tWGRZ6xQMZpLaSLoWeAlolzpNCGhbEno7npT0xq3R2LFjY8c9evTIkdI55wJJFwKXAh8DOXuW58oe7ZOuaJIkfdJ7LIn2u2a5ljq3NMu1vPI2C6LxA/cCu1Bb4dmE7p4jCC8KNwIekHQ88P30F4ouLrNb/mabbVammjjnmgNJFxC6wU8ADjSzBRlJUq2iXOtIdc1IVyh9tlZY0nsU8l9CJ5afS+oJvB6dH0roaGLUY8xyoZbZe8QD2T+AgWZ2GbAb8H5a2pOB+NomLmbmzPgk0Ntss02ZauKca+okXQzcRpiBaf+oR2OmSdG+zvuq6D3bloQOI9MALMzQ9BnQWdLGWcpLLeGR/n4s5z3y5MnnnmjfjtAZ5MFou5DaZWHuyZIvr0LBrAMhkM0jLANzppktAjCzjwgv8q4njA0QYQCey2H+/PirRe+W75zLRtJPCIOexxEC2dwcSV+O9odluTYM6AS8YWbp61Hmy3N4RhqATwhj3AZIyjYxRrY8OZnZPcCTxLvjk7Z/IkqTSDEdQJ4mtMbqvKwzszVmdhWheTipTk63Tk1NDStWrIidGzx4cJlq45xrqiRdRejw8S7h0eK8PMkfITQ2Tk2fPUNSJbVjuTKXWUmNV7tCUve0PH2BCwgDsO9KnY8GfKfy/CZ9Sq1o9pF9CI9BXy3yRwQ4AbiY8HRvBWGg9vuELvn16oOhzK7isYvSt83sb0UVFP7xfmlmP6xPRZqKqqoqW7as9HMlT5kyhQED4q301atXe29G51oJScvNrKpAmrMJ00ytBW4l+3uoGWZ2d1qeYwlBrZrQn2EBcAyhS/0jwMmW8UUv6SbgEkIfiEcIY4ZPIfQ0HGFmt2Wk70BoeQ0hjA17iTBE6yRCL/cDzCw+8WwjyxvMYglDNN4LGER4EbiIMJXVm2a2tsFq2MgaKpjdddddnHvuueuOO3ToQHV1dcnv45xrmooMZtcAVxco6tXMrv2ShgJXAIMJU1lNBe4Ebsn1/RwFzh8QZu+oIfSRuMHMnsmRviPwU+B0QiBbDIwErjazCdnyZORPdRRZHo2By7zelvBYFCticvs6+YsJZpKOA24m/ACZZgKXZnsM2Rw1VDC78MILufXWW9cdf+Mb32DOnGzvc51zLVExwaylimbjf5LQihtoZlOzpPkmobNLW+DYXEE1l2LGmX2P0AxNzfoRuwxsATws6fzMvK7WxIkTY8ebbLJJmWrinHON7hRCvHg8WyADiM4/QohLpyS9Qd5gJmkrwhgHiPc4Se+Bklrm+ndRZE1M0qaS7pT0uaSVkmZI+l36y8kC+eu1BlBj+vTTT2PH/fr1K1NNnHOu0e1KiBX/LpAudT3bgOq8Cn3J/4DwYhDC89STCMsEdIz2JxF63EAYM/D9pBWQ1C8q4xzCRJO/JYyJuAgYI6mYaTISrwHU2ObOjfesHThwYJlq4pxzja5PtJ9VIN1nGemLVqgr3YGEaPoacFDGS7uZwExJTxB6uewTpU/qj4TZ+C80s3UvlSTdDPyQMI6t0CPMyYTeO/9KX55A0s8IAfIE4HhCgCuLJUuWxI733DPxWnbOOddcpWJNrllEUlKdRBJ38y7UMtsi2t+WrfcJQNRTJhWE+ia5efQY8xBgBmEdnXRXA8uAM6OZnnOqxxpAjWrBggXU1MSqxtChQ8tUG+eca3SpR1NHFkh3VEb6ohUKZpXRvtCs+Kn5wjrkTVVXav2b57MEoiWEObs6UY/lANJkWwOoUY0ePTp23KZNGzp37lym2jjnXKNLLfR8Tq6J6SWdQHjdZMTXNitKoabcfMJy3UOBV/KkGxLtMyfBLKSYdXIOIcwJ9lLCsvOtAZSeZmT6cadOnZLepqB33nkndtylS5eS38M555qwfxL6NlQQJqb/LvA8Icb0AA6KttSKLP9IeoNCwew9QrPwMknvm9m/MhNIOgK4LKrAewnvX+p1cjLlWgOoUX300Uex42984xtlqolzzpXFU4S+F/tExwdQd2XqVCAbZWZPJb1BoWD2ICGYVQFPSRpPmMpkLqHTxq6EGUFSlXggaQUKqO9aPEWvAZQ5kr6qqirxvQr55JNPYsd9+/Yt9S2cc67JMjOLHi8+D+yYJUnqu3489RhjBoWD2f2E7vl7RMc7ZqlIqhJjo/RJlHqdnFChwmsANaovvoivTr7ddtuVqSbOOVceZjZX0mDCBMNnUfuaCcJE9XcDvzezes3zlzeYmdnaaBLLfwE7U9sCS0kFsnGE6UeSztFY6nVyUmsA/ZYwLcqBeZZOaDSLFsVj8a67Jh4P6JxzzZ6ZrQB+CfxSUifCK6SvzWz5+pZd7NyM7Qi9TE4htMzSJxp+ELjLzFbnLiFnuf0IE2LOAPpljBHrAnxBeGHYM1pUrlB5PyG8JxsHHFxg6YSsSj0346pVq+jQId7J89NPP2XzzbNNc+mca6la89yMjaGogWlRoPpztJWMmX0i6XlCj8ULqB2vBnAt4V3dHalAFgXVfsBqM4u9iIrWALqOMJvIIeV+tJjy7rvvxo4leSBzzrkSawqLaX0feAO4RdKBwETCtFT7Ex4vXpGWtk90/VPSBmhHSxlcR1gD6DXgwiyzV8XWAGosY8aMiR03RNd/55xr7XIGM0kLCWvcHG5mRQ9gS5ovap3tRghGhwFHEB4v3gJcW2QLK7WUdxvCy8VsXiW8YGxU//3vf2PHG220UWNXwTnnWrx8LbNuhM4eSVtvifOZ2SzCO7lC6WZQdxkazOwa4Jpi79eYpkyZEjvedNNNy1QT55xruYoJOOdKOqjBa9JCzZ49O3Y8YECujpvOOefqq5hgVrDF5HJbsCD+lHTnnXcuU02cc655kLSDmX2YJE+hiYZVj82lWb48Pnxijz32yJHSOedaN0mDJD0GvJ80b76W2bX1rxIQ1jtr1ebMmUPmOD5vmTnnWiNJOxBmlNocmA3cbmbvR9e2IYwRPpq6k3MUJWcwM7P1DWat3ltvvRU7btu2Le3bt8+R2jnnWiZJ2xGGYKUPGj8zGo61KXAP0J71eLrXFMaZtViZA6Z96RfnXCv1Y6AzocWVCljtCVMPbkd8LcyZwK+T3sCDWQOaOHFi7Lhnz55lqolzzpXV3oRAVkPt2pKHArtRG9ymA9cD95pZ4sWUC3UAceth+vTpsePNNtusTDVxzrmy6hPtf2pmR5vZ0cDl1L4fuwfY1szurE8gAw9mDWrOnDmx4/79++dI6ZxzLVpltB+bdi7984/MbNX63MCDWQPKHGM2cODAMtXEOeeahPSAta4FZmbz17dgf2fWgKqr42vM+Rgz51wrNzrLJPCSlLkWpplZovjkwayBzJ49u84Ys5122qk8lXHOuaYhM5JZjvOJeTBrIG+++WbsuF27drRt6//czrlWK1vAKtmsUf7t2kDefz8+G4uPMXPOtWINPsevB7MG8vHHH8eOe/XqVaaaOOdceZnZPQ19D+/N2EB8jJlzzgWSpkn6RNIuDXUPb5k1EB9j5pxz6/QldPaoLJCu3rxl1kC+/vrr2PGgQYPKUxHnnGsFPJg1kBUrVsSOfYyZc841HH/M2AA+/fTTOud22GGHMtTEOeealHMlHVRMQjO7LknBHswagI8xc865rJJ00U8UzPwxYwMYN25c7Lhr167lqYhzrtmRdKKkWyW9JmmxJJP09wJ5hkh6VtICScsljZd0saQ2efKcLeltSUslLZI0UtJRedJ3lHStpEmSqiXNlfSQpG2T/HhFbol5c6EBZK5j5mPMnHMJXAkMApYCs4Ft8iWWNBx4FKgGHgQWAEcTFr4cCpyUJc+NwKVR+X8hLJR5KvC0pBFmdltG+g7AC1F5Y4HfA5tFZR8p6QAze6uIn20OsLKIdIl5MGsAM2bMiB1vvvnm5amIc645+iEhyEwF9gVeyZVQUldCMFoL7GdmY6PzVwEvAydKOtXMHkjLM4QQyD4BdjezhdH5G4B3gRslPWNmM9JudQkhkD0CnGJmNVGeB4EngDslDUydz+NEM3ujqH+FhPwxYwP48ssvY8cDBgwoU02cc82Nmb1iZlMsc6by7E4EegIPpAJZVEY1oYUH8L8Zec6P9tenAlmUZwbwB6ADae+2FKa5T+W5LD1gmdmTwGvAdoTAWzYezBpA5hizHXfcsTwVcc61dAdE+/9kuTYKWA4MiR4TFpPn3xlpAPoBmwOTzWx63SxZ8zQ6D2YNYOXK+CPh3XbbrUw1cc41IZVRJ4uRkkaWqMyto/3kzAtmtgaYTnidtBWApCqgD7DUzL7IUt6UaJ/+OCnnPfLkaXT+zqzEPv/88zrrmPkYM+dcA+kW7RfluJ46v0E909c3T6YDCNNZfZgnzTqSqsxsWTFpU7xlVmJjx46NHbdt29bHmDnnAKrNbL/U1kj3THVzL+b9W7ok6Yu5x5XAJ2a2uGBh0j7AfxPcH/BgVnLjx4+PHVdVVZWpJs65ViDVKuqW43rXjHSF0mdrhSW9RzYHAOMlnZkrgaR2kn5D6IW5ZZ6ysvJgVmKZ65j16NGjTDVxzrUCk6J9nfdVktoSgsIaYBpA9OjuM6CzpI2zlJda3iP9/VjOe+TJk0034G5Jj0naKKOuOxGGBVwK5BzonY8HsxLLnJdx442z/ffinHMl8XK0PyzLtWFAJ+ANM0vvlZYvz+EZaSCMR5sJDJCUrcWULU+mt6l9HDkc+FDSMQquBN4Etk9L8+c8ZWXlwazEPv/889hx3759y1MR51xr8AgwDzhV0rpu05IqgV9Eh7dn5PlTtL9CUve0PH2BCwgzdNyVOh+Nd0vl+Y2kirQ8w4F9gAnAq3nqOQT4OaGVKKAX8Diht+W1hBlIBHwJHGVmmWPjCvKeCSW2YMGC2LEPmHbOJSHpWODY6LB3tB8s6e7o8zwz+xGAmS2WdB4hqI2U9ABhOqtjCF3qHyFMcbWOmb0h6WbCrB7jJT1CCCanABsCIzJm/wC4GTiKMEj7LUkvEcaenUQYy3Zuvtk/omu/kPQv4F5qW2Gp6ZGMENy+Z2bzC/wTZaXiBpm3HlVVVbZsWaIeoTHt2rVjzZo1646feOIJhg8fXoqqOeeaMUnLzaxgjzBJ1wBX50nyqZn1zcgzFLgCGExYzXkqcCdwi5mtzXGfs4EfEGbvqAHeA24ws2dypO8I/BQ4nRCEFgMjgavNbEKhnyutnH0I8zy2S50ivNPb28zmFFtOnXI9mMWtTzBbs2YN7dq1i52bNWsWm266aSmq5pxrxooNZi1VNIP/z4HLqe3kIWq79M8HLjCzh+tTvr8zK6EJE+J/nEjyQOaca/WiZWLeJIw3a0sIYp8BNwKrouONgAck3Z/+Lq9YHsxK6N13340dd+jQIUdK55xrVd4DdqG2t+I/gIFmdhmwG/B+WtqTgQ+S3sCDWQl9+GF8phZflNM554AwE78IPS9PNLMzzWwRgJl9BOwJXE9YykZA4jFNHsxKaOrUqbHjnj17lqkmzjnX5DxNaI09lnnBzNaY2VWENdMm1clZBA9mJTRz5szYsb8vc845AM4zs+Fm9mW+RGb2DrAzcEvSG/g4sxKaO3du7Lhfv35lqolzzjUdZva31Odo0PVewCDCFFeLgPHAm2a2NlpY9IdJ7+HBrIQyF+Xcfvvty1MR55xrgiQdRxiAvXmWyzMlXZrtMWQx/DFjCa1YsSJ2PGjQoDLVxDnnmhZJ3yPMSLI5tb0a110GtgAelnR+vcr3QdNx9R00vWDBgjoz5C9btoxOnTqVqmrOuWasNQ+alrQVYf7GdtQNZCkWXVsF7GBmU3Oky8pbZiWSOcasTZs2Hsiccy74AWH+Rwhjzk4C+gIdo/1JhCVgIAS87ye9gb8zK5HMRTk9kDnn3DoHElperwEHmdmatGszCe/LniAsI7NPlD4Rb5mVyMSJE2PH3bsnno3FOedaqi2i/W0ZgWydaELkW6PDvklv4MGsRKZNmxY77t27d46UzjnX6lRG+0LLu6TW0Eo8F6AHsxL57LPPYsebb56t56lzzrVKqSA2tEC6IdF+Qd5UWXgwK5F58+bFjn1RTuecW+c9Qk/FyyQdmS2BpCOAywjv1t5LegPvAFIiS5cujR3vsMMOZaqJc841OQ8CRwJVwFOSxgNjgblAL2BXwowgqfXNHkh6Ax9nlqG+48yk+NCJjz/+mK233rpU1XLONXOtfJxZG+B1YI/oVLbAk/oSfQcYkmuF7Fz8MWMJzJ49u865/v37l6EmzjnX9ESB6Vhq1y3LNgMIwDjg2KSBDDyYlcR778Uf77Zt25aKCv+ndc65FDObQ5hg+HzgFUInj7XR/pXo/J5m9kV9yvd3ZiUwYcKE2LEPmHbOubrMbDXw52grKW8+lMDkyZNjxz5g2jnnGpcHsxLIXJSzV69eZaqJc841LZIWSpovaY/Cqeufzx8zlsDnn38eO/YVpp1zbp1uhN6LSeNNonwezEpg/vz4DC2+wrRzztVxrqSDGqpwD2YlsGTJktixjy9zzrk6zmnIwj2YlUB1dXXs2FeYds65mFwLcpaMB7P19PXXX5M5i8rAgQPLVBvnnGtyrl3P/DMLJ/Fgtt7GjRsXO66oqKCysjJ7Yueca2XMbH2DWVG8a/56+vDDD2PHHTt2LFNNnHOu9fJgtp4mTZoUO+7atWuZauKcc62XB7P1NH369Nhxz549y1QT55xrvTyYrafMFaY32WSTMtXEOedaLw9m6+mrr76KHW+55ZZlqolzzrVeHszW06JFi2LHAwYMKFNNnHOu9fJgtp5WrFgRO/YxZs451/g8mK2HVatWsXZtfEHUnXfeuUy1cc651suD2XrIHGMmiQ033LBMtXHOudbLg9l6+OCDD2LHHTp0KFNNnHOudWsSwUzSppLulPS5pJWSZkj6naRESzaXqpxiTZgwIXbcpUuXhriNc66Vaezvspag7HMzSuoHvAH0Ap4EPgb2AC4CDpM01Mzm5ymipOUkMW3atNhxjx49Slm8c64VKsd3WUvQFFpmfyT80i40s2PN7KdmdgDwW2Br4PpGLqdos2bNih337t271LdwzrU+jf5d1hIoc/mSRr25tBXwCTAD6GdmNWnXugBfENbB6WVmyxq6HICqqipbtixvknW22mqr2HRWZ599NnfffXdReZ1zrYuk5WZWVSBNyb7LWptyt8wOiPbPp//SAMxsCfA60AnYq5HKSWThwoWx4/79+5eyeOdc61OW77KWoNzBbOtoPznH9SnRvtC0GvUuR9LI9K3AfWIyW3DbbbddkuzOudalsojvmlJ9J7Y65Q5m3aL9ohzXU+c3aKRyilZTU8Pq1atj53bZZZdSFe+ca50a/buspSh7b8YCFO3X98VeznLMbL/046qqqqLutXTpUrbZZhvmz5/PkiVLWL16NZttttl6VtM514JVZ37f1EOpvhNbnHIHs9RfGd1yXO+aka6hyyla165dmThxYqmKc845KMN3WUtR7seMqWWacz3/TfWoyPX8uNTlOOdcOfl3WT2Vu2t+P2Aq+buhVgA9C3TNL0k5kKxrvnPOFavIrvkl+y5rbcraMjOzT4Dngb7ABRmXrwWqgHtTvzRJ7SRtE/3C612Oc841Rf5dVn9lbZlB1qlbJgJ7AvsTmtJDUlO3SOoLTAc+NbO+9S0nH2+ZOecaQjEtsyhdSb7LWpuyBzMASZsB1wGHAT0ITekngGvNbEFaur7kCGZJysnHg5lzriEUG8yitOv9XdbaNIlg1pR4MHPONYQkwcwlV+7ejM4559x682DmnHOu2fNg5pxzrtnzYOacc67Z82DmnHOu2fNg5pxzrtnzYOacc67Z82DmnHOu2fNg5pxzrtnzGUAySKoBViTIUhntqxugOq7+/PfS9LT230lHM/MGRAPxYLaeJI2EuitWu/Ly30vT478T15D8rwTnnHPNngcz55xzzZ4/ZnTOOdfsecvMOedcs+fBzDnnXLPnwcw551yz58HMOedcs+fBrJ4kbSrpTkmfS1opaYak30nqXu66tVSSekj6jqTHJU2VtELSIkmjJX1bUtb/niUNkfSspAWSlksaL+liSW0a+2doLSSdKcmi7Ts50vjvxZWM92asB0n9gDeAXsCTwMfAHsD+wCRgqJnNL18NWyZJ5wO3A18ArwAzgW8AxwPdgEeBkyztP2pJw6Pz1cCDwALgaGBr4BEzO6kxf4bWQNJmwAdAG6AzcJ6Z/TUjjf9eXGmZmW8JN+A5wIARGedvjs7/qdx1bIkbcADhC68i43xvQmAz4IS0812BucBKYLe085WEP0YMOLXcP1dL2gABLwKfADdE/8bfyUjjvxffSr75Y8aEJG0FHALMAP6QcflqYBlwpqSqRq5ai2dmL5vZ02ZWk3F+DvCn6HC/tEsnAj2BB8xsbFr6auDK6PB/G67GrdKFhD86ziH8v5CN/15cyXkwS+6AaP98li/VJcDrQCdgr8auWCu3OtqvSTuX+l39J0v6UcByYIikDg1ZsdZC0rbAr4Dfm9moPEn99+JKzoNZcltH+8k5rk+J9gMaoS4OkNQWOCs6TP+CzPm7MrM1wHSgLbBVg1awFYh+B/cRHvf+rEBy/724kmtb7go0Q92i/aIc11PnN2j4qrjIr4AdgGfN7Lm08/67ajw/B3YG9jazQkso+e/FlZy3zEpP0d67iTYCSRcClxJ6lJ6ZNHu099/VepC0B6E1dpOZjSlFkdHefy+uaB7Mkkv91dgtx/WuGelcA5F0AfB7YAKwv5ktyEjiv6sGlvZ4cTJwVZHZ/PfiSs6DWXKTon2ud2L9o32ud2quBCRdDNwGfEgIZHOyJMv5u4q+hLckdBiZ1kDVbA06E/59twWq0wZKG6F3L8BfonO/i4799+JKzt+ZJfdKtD9EUkV6j0ZJXYChwArgzXJUrjWQ9BPCe7JxwMFmNi9H0peBbwGHAfdnXBtG6HU6ysxWNlBVW4OVwN9yXNuF8B5tNCGApR5B+u/FlV65B7o1xw0fNF3Of/uron/jscCGBdJ2Bb7CB+eW63d1DbkHTfvvxbeSbt4yq5/vE/6nu0XSgcBEYE/CdFaTgSvKWLcWS9LZwHXAWuA14EJJmclmmNndAGa2WNJ5wCPASEkPEKZNOoZo2iTCVEquEfnvxTUED2b1YGafSNqN8MV6GHAEYb7AW4BrrW5HBFcaW0b7NsDFOdK8CtydOjCzJyTtS/gD4wTCX/9TgUuAW8zMe8yVgf9eXKn5RMPOOeeaPe/N6JxzrtnzYOacc67Z82DmnHOu2fNg5pxzrtnzYOacc67Z82DmnHOu2fNg5pxzrtnzYOaaPEl9MyawbRYkfSet3geknb8m7fzdZahXd0mLo/vf29j3d64heDBzjUrSjPTAVMS2X7nrXB+SqggzxAC8aWYvl7M+6cxsIfDH6PAMSbuWsz7OlYIHM9ccfAHsk7Y1B98DNo4+31bOiuTwB8KEvqL4dcica7J8bkbX2E4kzMOXci5wTvR5DnBSRvoPLCwFMroR6lYSCrMfnx8dLgeebOD7dTazpUnymNksSa8DewNHSepjZp81TA2da3jeMnONyszGmtno1AbMTLu8Mv1atC3K984s45HkjpJul/SVpCWSno7yStJFkqZIWilpoqRvZaufpFMkPS9pnqRVkr6QdL+kHRP8mLtSu0jrqEKBRtLukl6QtFTS15IelNQrI83ItJ/zHEkXRz/HKuAXUZq9JD0Z1Xl19F5sqqRHc/y8/4r2baj7R4RzzYq3zFxL8jDx1YuPArYnrD93ftr5bYC/S5pmZmMAJFUA9wGnZ5TZGzgVOE7SSWb2dBH12Dft81sF0g4GTgPap507GehGWJEhm8upDZYASNoGGAl0SDvdJdr6AVXAPzLKSa/b/sDvCtTVuSbLW2auJekJfJuwivGy6NyWhEB2G2GpnjfS0l+Y9vl71AayecAFwMGEVo8RgsR9kroXUY+BaZ+nFEg7gLBszTHUdhgBOFTS1jny9AeeAo4DjgVeIATuVCB7mNqlib5HCGLZVuOenPY5ScvTuSbHW2auJbnCzO4EiB6rHRGdf9vMRkTnq4Ah0fn0Vty30z7fBYyPPj8HHAnsTGgtnQzcUaAePdM+zy+Qdh4w3MxWAE9LOpnQckzVb1KWPO+a2fD0E5I2STucSVgwdla0Ltifc9w7fd29XjnSONcseMvMtSTpra70IDIm7XN6C2XDtM/bpX3+MWEl69S2c9q17Yuoh3J8zmZMFMhS0uu9YWbiyGNZzj1J6EADcCnwKbBM0nuSbpS0WYF6Npvxe85l48HMtSSL0j7XpH3+Okf6QoEmmy5FpJmb9jlXQErJXJV8TdrnXPX7IvOEmc0FdgF+TnjsOJPQa3RnQnB7TVLXjGzpdfuqQD2da9I8mDkXTEz7/D0zU+YGdAS+W0RZH6R9zvXea33UaUVJkpl9YWb/Z2aHmNkWhGCVapVuQe3j1Wx1+wDnmjF/Z+Zc8DdCywbgJkk9gXcIvQw3A4YSOmnsCMwoUNbItM97lLSWuZ0k6RLC48ZphNbhJoQOMCmVGXnS6/Zqw1bPuYblwcy54E+EAcSnAZ2Jxm7V03uEnoIDgGH1GdRcDxXAntGWzWzgpYxzR0b7tcBDDVQv5xqFP2Z0DjCzGjM7ndBb8T+Ed0hrCB1GxhN6MB4BzCqiLKO2x2NHQhf6hvYWcCPhseIcYBWwEphKCNSDzWxJKrGkzal97PgvMyv4cznXlCn8f+ecK6VoCMAUwvyMb5nZXmWuUoyk3xB6bRqwu5m9W+YqObdevGXmXAMws2XUTuC7p6QDy1mfdJI2oHZGlH94IHMtgbfMnHPONXveMnPOOdfseTBzzjnX7Hkwc8451+x5MHPOOdfseTBzzjnX7Hkwc8451+x5MHPOOdfseTBzzjnX7P1/cLGQXODQBtQAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 360x540 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 = koff/kon\n", " oxtr = x[0]\n", " oxr = x[1]\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, doxrdt)\n", "\n", "\n", "initial_t = 0\n", "end_t = 60*50\n", "num = 1000\n", "\n", "\n", "# initial condition for wild type mean, upper bound and lower bound\n", "\n", "x0_wt = [1.678e-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", "\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", "\n", "oxtr_wt = x_wt[:, 0]\n", "oxr_wt = x_wt[:, 1]\n", "\n", "\n", "oxtr_wt_c = oxtr_wt*Div\n", "oxr_wt_c = oxr_wt*Div\n", "\n", "\n", "\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_figheight(7.5)\n", "fig.set_figwidth(5)\n", "#fig.subplots_adjust(right=0.5)\n", "\n", "\n", "twin1 = ax.twinx()\n", "\n", "w1, = ax.plot(t/60, oxr_wt/1e-9, '-', linewidth=4, color='black', label='WT')\n", "w2, = twin1.plot(t/60, oxr_wt_c, '-', linewidth=4, color='black', label='Wild-type')\n", "\n", "\n", "ax.set_xlabel(\"Time (hrs)\", fontsize=18, fontweight='bold')\n", "ax.set_ylabel(\"[OXTR Complex] (nM)\", fontsize=18, fontweight='bold')\n", "twin1.set_ylabel(\"[OXTR Complex] (complexes/cell)\", fontsize=18, fontweight='bold')\n", "\n", "# ax.set_yticks([0,0.1, 0.2, 0.3, 0.4, 0.5,0.6, 0.7, 0.83])\n", "# twin1.set_yticks([0, 1000, 2000, 3000, 4000, 5000, 6000, 7000])\n", "\n", "\n", "tkw = dict(size=4, width=1.5, labelsize=20)\n", "ax.tick_params(axis='both', **tkw)\n", "twin1.tick_params(axis='both', **tkw)\n", "\n", "plt.savefig(\"oxtrc_myo_oxt_equal_kd.jpg\", dpi=400, bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "252cb1ba", "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 }