{
"cells": [
{
"cell_type": "markdown",
"id": "5fc8df5b",
"metadata": {},
"source": [
"# OXT - OXTR binding model\n"
]
},
{
"cell_type": "raw",
"id": "73e78113",
"metadata": {},
"source": [
"Author: Preeti Dubey\n",
"Title: OXTR complex formation simulations done for myometrial cells when [OXT] = 1 microM"
]
},
{
"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": 2,
"id": "9b4bebfb",
"metadata": {},
"outputs": [],
"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 = 10\n",
"num = 100\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-6, 1.678e-9, 0]\n",
"x0_wtub = [1e-6, 1.76e-9, 0]\n",
"x0_wtlb = [1e-6, 1.6e-9, 0]\n",
"# initial condition for mutant V281M\n",
"x0_v281m = [1e-6, 7.4877e-10, 0]\n",
"x0_v281mub = [1e-6, 8.38e-10, 0]\n",
"x0_v281mlb = [1e-6, 6.59e-10, 0]\n",
"# initial condition for mutant P108A\n",
"x0_p108a = [1e-6, 2.658e-9, 0]\n",
"x0_p108aub = [1e-6, 2.95e-9, 0]\n",
"x0_p108alb = [1e-6, 2.36e-9, 0]\n",
"# initial condition for mutant L206V\n",
"x0_l206v = [1e-6, 3.044e-9, 0]\n",
"x0_l206vub = [1e-6, 3.33e-9, 0]\n",
"x0_l206vlb = [1e-6, 2.76e-9, 0]\n",
"# initial condition for mutant V45L\n",
"x0_v45l = [1e-6, 1.96e-9, 0]\n",
"x0_v45lub = [1e-6, 2.13e-9, 0]\n",
"x0_v45llb = [1e-6, 1.79e-9, 0]\n",
"# initial condition for mutant E339K\n",
"x0_e339k = [1e-6, 1.19e-9, 0]\n",
"x0_e339kub = [1e-6, 1.25e-9, 0]\n",
"x0_e339klb = [1e-6, 1.12e-9, 0]\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 E339k 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 E339k 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 = plt.figure()\n",
"# fig.set_figheight(15)\n",
"# fig.set_figwidth(30)\n",
"\n",
"\n",
"\n",
"# plt.subplot(2,3,1)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_wt/1e-9, '-', linewidth=4, color='black', label=' Wild type')\n",
"\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_wtub/1e-9, oxr_wtlb/1e-9,color='lightgray', alpha=0.8)\n",
"\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid()\n",
"# #plt.savefig(\"oxt_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"# plt.subplot(2,3,2)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_v281m/1e-9, '-', linewidth=4, color='red', label='V281M')\n",
"\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_v281mub/1e-9, oxr_v281mlb/1e-9,color='lightpink', alpha=0.8)\n",
"\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid()\n",
"# #plt.title(\"Oxytocin-Oxytocin Receptor Binding in Wild type and Mutants\", fontsize=18, fontweight='bold')\n",
"# #plt.savefig(\"oxtr_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"# plt.subplot(2,3,3)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_p108a/1e-9, '-', linewidth=4, color='green', label='P108A')\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_p108aub/1e-9, oxr_p108alb/1e-9,color='lightgreen', alpha=0.8)\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# #plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid(linewidth=4)\n",
"# #plt.savefig(\"oxrcomp_conc_alltype_order1\", dpi=400, format=\"jpg\")\n",
"\n",
"# plt.subplot(2,3,4)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_l206v/1e-9, '-', linewidth=4, color='blue', label='L206V')\n",
"\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_l206vub/1e-9, oxr_l206vlb/1e-9,color='lightblue', alpha=0.8)\n",
"\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid()\n",
"# #plt.savefig(\"oxt_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"# plt.subplot(2,3,5)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_v45l/1e-9, '-', linewidth=4, color='purple', label='V45L')\n",
"\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_v45lub/1e-9, oxr_v45llb/1e-9,color='thistle', alpha=0.8)\n",
"\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid()\n",
"# plt.savefig(\"oxt_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"# plt.subplot(2,3,6)\n",
"# #plot mean line \n",
"# plt.plot(t/60, oxr_e339k/1e-9, '-', linewidth=4, color='magenta', label='E339K')\n",
"\n",
"# # plot upper bound and lower bound\n",
"# plt.fill_between(t/60, oxr_e339kub/1e-9, oxr_e339klb/1e-9,color='plum', alpha=0.8)\n",
"\n",
"# plt.ylabel(\"[OXTRC] (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend(fontsize = 18, loc='lower right')\n",
"# #plt.grid()\n",
"# #plt.title(\"Oxytocin-Oxytocin Receptor Binding in Wild type and Mutants\", fontsize=18, fontweight='bold')\n",
"# #plt.savefig(\"oxtr_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"# # plt.subplot(1,3,3)\n",
"# # #plot mean line \n",
"# # plt.plot(t, oxr_p108a/1e-9, '-', linewidth=4, color='blue', label='Mean OXT (WT)')\n",
"# # # plot upper bound and lower bound\n",
"# # plt.fill_between(t, oxr_p108aub/1e-9, oxr_p108alb/1e-9,color='lightblue', alpha=0.8, label='SE OXT (WT)')\n",
"# # plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# # plt.ylabel(\"Oxytocin Receptor Complex Conc.(nM)\", fontsize=18, fontweight='bold')\n",
"# # plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# # plt.xticks(fontsize = 18, fontweight='bold')\n",
"# # plt.yticks(fontsize = 18, fontweight='bold')\n",
"# # plt.legend(fontsize = 18)\n",
"# # #plt.grid(linewidth=4)\n",
"# plt.savefig(\"oxrcomp_conc_all_myo_se\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"\n",
"# # # Plots of all variants and their standard error (SE)\n",
"\n",
"\n",
"# fig, ax = plt.subplots()\n",
"# fig.set_figheight(12)\n",
"# fig.set_figwidth(20)\n",
"# fig.subplots_adjust(right=0.5)\n",
"\n",
"\n",
"\n",
"# twin1 = ax.twinx()\n",
"\n",
"\n",
"# # l1, = ax.plot(t, oxr_l206v/1e-9, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# # l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# # p1, = ax.plot(t, oxr_p108a/1e-9, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# # p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# # w1, = ax.plot(t, oxr_wt/1e-9, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# # w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# # v41, = ax.plot(t, oxr_v45l/1e-9, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# # v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# # e1, = ax.plot(t, oxr_e339k/1e-9, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# # e2, = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# # v1, = ax.plot(t, oxr_v281m/1e-9, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"# # v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"\n",
"\n",
"# l1, = ax.plot(t, oxr_l206v/1e-6, ':', 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-6, oxr_l206vlb/1e-6, color='lightblue', alpha=0.8)\n",
"# p1, = ax.plot(t, oxr_p108a/1e-6, '-.', 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-6, oxr_p108alb/1e-6,color='lightgreen', alpha=0.8)\n",
"# w1, = ax.plot(t, oxr_wt/1e-6, '-', linewidth=4, color='black', label='OxR (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-6, oxr_wtlb/1e-6, color='lightgray', alpha=0.8)\n",
"# v41, = ax.plot(t, oxr_v45l/1e-6, '.', 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-6, oxr_v45llb/1e-6, color='thistle', alpha=0.8)\n",
"# e1, = ax.plot(t, oxr_e339k/1e-6, '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-6, oxr_e339klb/1e-6, color= 'plum')\n",
"# v1, = ax.plot(t, oxr_v281m/1e-6, '--', 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-6, oxr_v281mlb/1e-6,color='lightpink', alpha=0.8)\n",
"# # ax.set_xlim(0.0e+00, 12)\n",
"# ax.set_ylim(0, 0.004)\n",
"# # # ax.set_xlim(0, 12)\n",
"# twin1.set_ylim(0, 33600.0)\n",
"\n",
"\n",
"# ax.set_xlabel(\"Time (min)\", fontsize=18, fontweight='bold')\n",
"# ax.set_ylabel(\"[OXTRC] ($\\mu$M)\", fontsize=18, fontweight='bold')\n",
"# twin1.set_ylabel(\"[OXTRC] (complexes/cell)\", fontsize=18, fontweight='bold')\n",
"# # #ax.set_title(\"(A): Myometrial Cells\", fontsize=18, fontweight='bold')\n",
"# # #twin2.set_ylabel(\"OXTR Conc. (copies/cell)\", fontsize=14)\n",
"\n",
"\n",
"# # #ax.yaxis.label.set_color(p1.get_color('black'))\n",
"# # #twin1.yaxis.label.set_color(p2.get_color('black'))\n",
"# # #twin2.yaxis.label.set_color(p3.get_color())\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",
"# # # twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)\n",
"# # # ax.tick_params(axis='x', **tkw)\n",
"\n",
"# # #ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='center left', bbox_to_anchor=(1.3, 0.8))\n",
"# ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='best')\n",
"# # #plt.savefig(\"oxt_com_conc_myo_2min.jpg\", dpi=400, bbox_inches='tight')\n",
"# # #plt.savefig(\"oxt_com_conc_myo_5min.jpg\", dpi=400, bbox_inches='tight')\n",
"# # #plt.savefig(\"oxt_com_conc_myo_60min.jpg\", dpi=400, bbox_inches='tight')\n",
"# plt.savefig(\"oxtrc_myo_SE.jpg\", dpi=400, bbox_inches='tight')\n",
"# plt.show()\n",
"\n",
"\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0166700b",
"metadata": {},
"outputs": [],
"source": [
"# OXT and OXTR ligand binding code for wild type and variants. Here we consider that OXT binds to 7-transmembrane and \n",
"def oxtmodel(x, t):\n",
" \n",
" #kon = 1.2e+4 # per molar per sec\n",
" #koff = 1.92e-5 # per sec\n",
" kon = 6.8e+5 # per molar per min (from Phaneuf paper)\n",
" koff = 0.0011 # per min (from Phaneuf paper)\n",
" #kon = 5.28e+8 # per molar per hr (from gulliver thesis)\n",
" #koff = 0.3 # per hr (from gulliver thesis)\n",
"# kon = 4.3e+7 # per molar per hour\n",
"# koff = 0.0693 # /hour\n",
" #kon = 4.3e+7 # per molar per hour\n",
" #koff = 0.0693 # /hour\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 = 10\n",
"num = 100\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 \n",
"#x0_wt = [1e-8, 2.38678e-9, 0]\n",
"x0_wt = [1e-6, 1.678e-9, 0]\n",
"# initial condition for mutants V281M\n",
"x0_v281m = [1e-6, 7.4877e-10, 0]\n",
"# initial condition for mutants P108A\n",
"x0_p108a = [1e-6, 2.658e-9, 0]\n",
"# initial condition for mutants L206V\n",
"x0_l206v = [1e-6, 3.044e-9, 0]\n",
"# initial condition for mutants V45L\n",
"x0_v45l = [1e-6, 1.96e-9, 0]\n",
"# initial condition for mutants E339K\n",
"x0_e339k = [1e-6, 1.19e-9, 0]\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_v281m = odeint(oxtmodel,x0_v281m,t) \n",
"x_p108a = odeint(oxtmodel,x0_p108a,t) \n",
"x_l206v = odeint(oxtmodel,x0_l206v,t) \n",
"x_v45l = odeint(oxtmodel,x0_v45l,t) \n",
"x_e339k = odeint(oxtmodel,x0_e339k,t) \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\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 mutant V281M\n",
"\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 mutant P108A\n",
"\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",
"\n",
"# solution extraction for mutant L206V\n",
"\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 mutant V45L\n",
"\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",
"# solution extraction for mutant E339K\n",
"\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",
"# Combine the arrays into a list of rows\n",
"rows = list(zip(t, oxr_wt, oxr_v281m, oxr_e339k, oxr_v45l, oxr_p108a, oxr_l206v))\n",
"\n",
"# Open a new CSV file for writing\n",
"with open('variants_com_myo.csv', 'w', newline='') as file:\n",
" writer = csv.writer(file)\n",
"\n",
" # Write the header row\n",
" writer.writerow(['Time', 'WT', 'V281M', 'E339K', 'V45L', 'P108A', 'L206V'])\n",
"\n",
" # Write the data rows\n",
" writer.writerows(rows)\n",
"\n",
"# fig = plt.figure()\n",
"# fig.set_figheight(15)\n",
"# fig.set_figwidth(30)\n",
"\n",
"# plt.subplot(2,3,1)\n",
"# plt.plot(t, oxt_wt/1e-12, '-', linewidth=4, color='black', label='OXT (WT)')\n",
"# plt.plot(t, oxt_v281m/1e-12, '--', linewidth=4, color='red', label='OXT (V281M)')\n",
"# plt.plot(t, oxt_p108a/1e-12, '-.', linewidth=4, color='green', label='OXT (P108A)')\n",
"# plt.plot(t, oxt_l206v/1e-12, ':', linewidth=4, color='blue', label='OXT (L206V)')\n",
"# plt.plot(t, oxt_v45l/1e-12, '.', linewidth=4, color='purple', label='OXT (V45L)')\n",
"# plt.plot(t, oxt_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OXT (E339K)')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"Oxytocin Conc. (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# plt.legend()\n",
"# plt.grid()\n",
"# plt.savefig(\"oxt_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"# plt.subplot(2,3,2)\n",
"# plt.plot(t, oxtr_wt/1e-12, '-', linewidth=4, color='black', label='OXTR (WT)')\n",
"# plt.plot(t, oxtr_v281m/1e-12, '--', linewidth=4, color='red', label='OXTR (V281M)')\n",
"# plt.plot(t, oxtr_p108a/1e-12, '-.', linewidth=4, color='green', label='OXTR (P108A)')\n",
"# plt.plot(t, oxtr_l206v/1e-12, ':', linewidth=4, color='blue', label='OXTR (L206V)')\n",
"# plt.plot(t, oxtr_v45l/1e-12, '.', linewidth=4, color='purple', label='OXTR (V45L)')\n",
"# plt.plot(t, oxtr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OXTR (E339K)')\n",
"# plt.ylabel(\"Oxytocin Receptor Conc. (nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# #plt.ylim([0,3.5])\n",
"# plt.legend()\n",
"# plt.grid()\n",
"# plt.title(\"Oxytocin-Oxytocin Receptor Binding in Wild type and Mutants\", fontsize=18, fontweight='bold')\n",
"# plt.savefig(\"oxtr_conc\", dpi=400, format=\"jpg\")\n",
"\n",
"# plt.subplot(2,3,3)\n",
"# plt.plot(t, oxr_wt/1e-12, '-', linewidth=3, color='black', label='OxR (WT)')\n",
"# plt.plot(t, oxr_v281m/1e-12, '--', linewidth=3, color='red', label='OxR (V281M)')\n",
"# plt.plot(t, oxr_p108a/1e-12, '-.', linewidth=3, color='green', label='OxR (P108A)')\n",
"# plt.plot(t, oxr_l206v/1e-12, ':', linewidth=3, color='blue', label='OxR (L206V)')\n",
"# plt.plot(t, oxr_v45l/1e-12, '.', linewidth=3, color='purple', label='OxR (V45L)')\n",
"# plt.plot(t, oxr_e339k/1e-12, 'o', linewidth=3, color='magenta', label='OxR (E339K)')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.ylabel(\"Oxytocin Receptor Complex Conc.(nM)\", fontsize=18, fontweight='bold')\n",
"# plt.xlabel(\"Time (hr)\", fontsize=18, fontweight='bold')\n",
"# plt.xticks(fontsize = 18, fontweight='bold')\n",
"# plt.yticks(fontsize = 18, fontweight='bold')\n",
"# #plt.xlim([6,14])\n",
"# #plt.ylim([0,3.5])\n",
"# plt.legend(fontsize = 18)\n",
"# plt.grid(linewidth=4)\n",
"# plt.savefig(\"oxrcomp_conc_alltype_order1\", dpi=400, format=\"jpg\")\n",
"\n",
"\n",
"\n",
"\n",
"# plt.show()\n",
"\n",
"\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "7a174923",
"metadata": {},
"source": [
"### Bound OXTRC plots for all variants when OXT concentration is given as 1 micro Molar "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "191f7856",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x900 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# bound OXTRC plots for all variants when OXT concentration is given as 1 micro Molar \n",
"\n",
"def oxtmodel(x, t):\n",
" \n",
" #kon = 1.2e+4 # per molar per sec\n",
" #koff = 1.92e-5 # per sec\n",
" kon = 6.8e+5 # per molar per min (from Phaneuf paper)\n",
" koff = 0.0011 # per min (from Phaneuf paper)\n",
" #kon = 5.28e+8 # per molar per hr (from gulliver thesis)\n",
" #koff = 0.3 # per hr (from gulliver thesis)\n",
"# kon = 4.3e+7 # per molar per hour\n",
"# koff = 0.0693 # /hour\n",
" #kon = 4.3e+7 # per molar per hour\n",
" #koff = 0.0693 # /hour\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 = 10\n",
"num = 100\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 \n",
"\n",
"x0_wt = [1e-6, 1.678e-9, 0]\n",
"# initial condition for mutants V281M\n",
"x0_v281m = [1e-6, 7.4877e-10, 0]\n",
"# initial condition for mutants P108A\n",
"x0_p108a = [1e-6, 2.658e-9, 0]\n",
"# initial condition for mutants L206V\n",
"x0_l206v = [1e-6, 3.044e-9, 0]\n",
"# initial condition for mutants V45L\n",
"x0_v45l = [1e-6, 1.96e-9, 0]\n",
"# initial condition for mutants E339K\n",
"x0_e339k = [1e-6, 1.19e-9, 0]\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_v281m = odeint(oxtmodel,x0_v281m,t) \n",
"x_p108a = odeint(oxtmodel,x0_p108a,t) \n",
"x_l206v = odeint(oxtmodel,x0_l206v,t) \n",
"x_v45l = odeint(oxtmodel,x0_v45l,t) \n",
"x_e339k = odeint(oxtmodel,x0_e339k,t) \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\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 mutant V281M\n",
"\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 mutant P108A\n",
"\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",
"\n",
"# solution extraction for mutant L206V\n",
"\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 mutant V45L\n",
"\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",
"# solution extraction for mutant E339K\n",
"\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",
"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",
"# l1, = ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# p1, = ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# w1, = ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# v41, = ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# e1, = ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# e2, = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# v1, = ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"# v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OxR (V281M)')\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='OxR (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",
"# l1, = ax.plot(t, oxr_l206v/1e-6, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# p1, = ax.plot(t, oxr_p108a/1e-6, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# w1, = ax.plot(t, oxr_wt/1e-6, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# v41, = ax.plot(t, oxr_v45l/1e-6, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# e1, = ax.plot(t, oxr_e339k/1e-6, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# e2, = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# v1, = ax.plot(t, oxr_v281m/1e-6, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"# v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"\n",
"\n",
"# l1, = ax.plot(t/60, oxr_l206v/1e-9, ':', linewidth=4, color='blue', label='L206V')\n",
"# l2, = twin1.plot(t/60, oxr_l206v_c, ':', linewidth=4, color='blue', label='L206V')\n",
"# p1, = ax.plot(t/60, oxr_p108a/1e-9, '-.', linewidth=4, color='green', label='P108A')\n",
"# p2, = twin1.plot(t/60, oxr_p108a_c, '-.', linewidth=4, color='green', label='P108A')\n",
"# w1, = ax.plot(t/60, oxr_wt/1e-9, '-', linewidth=4, color='black', label='Wild-type')\n",
"# w2, = twin1.plot(t/60, oxr_wt_c, '-', linewidth=4, color='black', label='Wild-type')\n",
"# v41, = ax.plot(t/60, oxr_v45l/1e-9, '.', linewidth=4, color='purple', label='V45L')\n",
"# v42, = twin1.plot(t/60, oxr_v45l_c, '.', linewidth=4, color='purple', label='V45L')\n",
"# e1, = ax.plot(t/60, oxr_e339k/1e-9, 'o', linewidth=4, color='magenta', label='E339K')\n",
"# e2, = twin1.plot(t/60, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='E339K')\n",
"# v1, = ax.plot(t/60, oxr_v281m/1e-9, '--', linewidth=4, color='red', label='V281M')\n",
"# v2, = twin1.plot(t/60, oxr_v281m_c, '--', linewidth=4, color='red', label='V281M')\n",
"\n",
"#ax.set_xlim(0, 35)\n",
"#ax.set_ylim(0, 0.022)\n",
"#ax.set_xlim(0, 12)\n",
"#twin1.set_ylim(60000, 80000)\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, 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",
"#twin1.set_yticks([0,5000,10000,15000,20000,25620], fontsize=18, fontweight='bold')\n",
"#ax.set_yticks(np.arange(0, 3571.5, 500))\n",
"#twin1.set_yticks(np.arange(0, 30001, 2000))\n",
"\n",
"#ax.yaxis.label.set_color(p1.get_color('black'))\n",
"#twin1.yaxis.label.set_color(p2.get_color('black'))\n",
"#twin2.yaxis.label.set_color(p3.get_color())\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",
"#ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='center left', bbox_to_anchor=(1.3, 0.8))\n",
"\n",
"#ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='best')\n",
"#plt.savefig(\"oxt_com_conc_myo_2min.jpg\", dpi=400, bbox_inches='tight')\n",
"#plt.savefig(\"oxt_com_conc_myo_5min.jpg\", dpi=400, bbox_inches='tight')\n",
"#plt.savefig(\"oxt_com_conc_myo_60min.jpg\", dpi=400, bbox_inches='tight')\n",
"plt.savefig(\"oxtrc_myo_oxt_mircom_yaxis_pm.jpg\", dpi=400, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4c2b3979",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Here we are plotting the same as above removing right Y-axis \n",
"def oxtmodel(x, t):\n",
" \n",
" #kon = 1.2e+4 # per molar per sec\n",
" #koff = 1.92e-5 # per sec\n",
" kon = 6.8e+5 # per molar per min (from Phaneuf paper)\n",
" koff = 0.0011 # per min (from Phaneuf paper)\n",
" #kon = 5.28e+8 # per molar per hr (from gulliver thesis)\n",
" #koff = 0.3 # per hr (from gulliver thesis)\n",
"# kon = 4.3e+7 # per molar per hour\n",
"# koff = 0.0693 # /hour\n",
" #kon = 4.3e+7 # per molar per hour\n",
" #koff = 0.0693 # /hour\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 = 10\n",
"num = 100\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 \n",
"\n",
"x0_wt = [1e-6, 1.678e-9, 0]\n",
"# initial condition for mutants V281M\n",
"x0_v281m = [1e-6, 7.4877e-10, 0]\n",
"# initial condition for mutants P108A\n",
"x0_p108a = [1e-6, 2.658e-9, 0]\n",
"# initial condition for mutants L206V\n",
"x0_l206v = [1e-6, 3.044e-9, 0]\n",
"# initial condition for mutants V45L\n",
"x0_v45l = [1e-6, 1.96e-9, 0]\n",
"# initial condition for mutants E339K\n",
"x0_e339k = [1e-6, 1.19e-9, 0]\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_v281m = odeint(oxtmodel,x0_v281m,t) \n",
"x_p108a = odeint(oxtmodel,x0_p108a,t) \n",
"x_l206v = odeint(oxtmodel,x0_l206v,t) \n",
"x_v45l = odeint(oxtmodel,x0_v45l,t) \n",
"x_e339k = odeint(oxtmodel,x0_e339k,t) \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\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 mutant V281M\n",
"\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 mutant P108A\n",
"\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",
"\n",
"# solution extraction for mutant L206V\n",
"\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 mutant V45L\n",
"\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",
"# solution extraction for mutant E339K\n",
"\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",
"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",
"# l1, = ax.plot(t, oxr_l206v/1e-12, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# p1, = ax.plot(t, oxr_p108a/1e-12, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# w1, = ax.plot(t, oxr_wt/1e-12, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# v41, = ax.plot(t, oxr_v45l/1e-12, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# e1, = ax.plot(t, oxr_e339k/1e-12, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# e2, = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# v1, = ax.plot(t, oxr_v281m/1e-12, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"# v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OxR (V281M)')\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='OxR (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",
"# l1, = ax.plot(t, oxr_l206v/1e-6, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# l2, = twin1.plot(t, oxr_l206v_c, ':', linewidth=4, color='blue', label='OxR (L206V)')\n",
"# p1, = ax.plot(t, oxr_p108a/1e-6, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# p2, = twin1.plot(t, oxr_p108a_c, '-.', linewidth=4, color='green', label='OxR (P108A)')\n",
"# w1, = ax.plot(t, oxr_wt/1e-6, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# w2, = twin1.plot(t, oxr_wt_c, '-', linewidth=4, color='black', label='OxR (WT)')\n",
"# v41, = ax.plot(t, oxr_v45l/1e-6, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# v42, = twin1.plot(t, oxr_v45l_c, '.', linewidth=4, color='purple', label='OxR (V45L)')\n",
"# e1, = ax.plot(t, oxr_e339k/1e-6, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# e2, = twin1.plot(t, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='OxR (E339K)')\n",
"# v1, = ax.plot(t, oxr_v281m/1e-6, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"# v2, = twin1.plot(t, oxr_v281m_c, '--', linewidth=4, color='red', label='OxR (V281M)')\n",
"\n",
"\n",
"# l1, = ax.plot(t/60, oxr_l206v/1e-9, ':', linewidth=4, color='blue', label='L206V')\n",
"# l2, = twin1.plot(t/60, oxr_l206v_c, ':', linewidth=4, color='blue', label='L206V')\n",
"# p1, = ax.plot(t/60, oxr_p108a/1e-9, '-.', linewidth=4, color='green', label='P108A')\n",
"# p2, = twin1.plot(t/60, oxr_p108a_c, '-.', linewidth=4, color='green', label='P108A')\n",
"# w1, = ax.plot(t/60, oxr_wt/1e-9, '-', linewidth=4, color='black', label='Wild-type')\n",
"# w2, = twin1.plot(t/60, oxr_wt_c, '-', linewidth=4, color='black', label='Wild-type')\n",
"# v41, = ax.plot(t/60, oxr_v45l/1e-9, '.', linewidth=4, color='purple', label='V45L')\n",
"# v42, = twin1.plot(t/60, oxr_v45l_c, '.', linewidth=4, color='purple', label='V45L')\n",
"# e1, = ax.plot(t/60, oxr_e339k/1e-9, 'o', linewidth=4, color='magenta', label='E339K')\n",
"# e2, = twin1.plot(t/60, oxr_e339k_c, 'o', linewidth=4, color='magenta', label='E339K')\n",
"# v1, = ax.plot(t/60, oxr_v281m/1e-9, '--', linewidth=4, color='red', label='V281M')\n",
"# v2, = twin1.plot(t/60, oxr_v281m_c, '--', linewidth=4, color='red', label='V281M')\n",
"\n",
"#ax.set_xlim(0, 35)\n",
"#ax.set_ylim(0, 0.022)\n",
"#ax.set_xlim(0, 12)\n",
"#twin1.set_ylim(60000, 80000)\n",
"\n",
"\n",
"ax.set_xlabel(\"Time (min)\", fontsize=18, fontweight='bold')\n",
"ax.set_ylabel(\"[Bound OXTR Complex] (pM)\", fontsize=18, fontweight='bold')\n",
"#twin1.set_ylabel(\"[Bound OXTR Complex] (complexes/cell)\", fontsize=18, fontweight='bold')\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",
"#twin1.set_yticks([0,5000,10000,15000,20000,25620], fontsize=18, fontweight='bold')\n",
"#ax.set_yticks(np.arange(0, 3571.5, 500))\n",
"#twin1.set_yticks(np.arange(0, 30001, 2000))\n",
"\n",
"#ax.yaxis.label.set_color(p1.get_color('black'))\n",
"#twin1.yaxis.label.set_color(p2.get_color('black'))\n",
"#twin2.yaxis.label.set_color(p3.get_color())\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",
"#ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='center left', bbox_to_anchor=(1.3, 0.8))\n",
"\n",
"#ax.legend(handles=[l1,p1, w1, v41, e1, v1], fontsize=18, loc='best')\n",
"#plt.savefig(\"oxt_com_conc_myo_2min.jpg\", dpi=400, bbox_inches='tight')\n",
"#plt.savefig(\"oxt_com_conc_myo_5min.jpg\", dpi=400, bbox_inches='tight')\n",
"#plt.savefig(\"oxt_com_conc_myo_60min.jpg\", dpi=400, bbox_inches='tight')\n",
"plt.savefig(\"oxtrc_myo_oxt_mircom_yaxis_pm_v1.jpg\", dpi=400, bbox_inches='tight')\n",
"plt.show()"
]
}
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