ICD_extend_fullset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import maboss\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")\n",
    "colormap_scale = sns.light_palette(\"black\", as_cmap=True)\n",
    "colormap_ratios = sns.diverging_palette(240, 0, center='light', as_cmap=True)\n",
    "def save_figure(figure, title):\n",
    "    #figure.savefig(\"%s.png\" % title, bbox_inches=\"tight\")\n",
    "    figure.savefig(\"%s.pdf\" % title, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bnd = 'ICD_ExtendDy.bnd'\n",
    "cfg = 'ICD_ExtendDy.cfg'\n",
    "upp = 'ICD_ExtendDy.upp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node Tumor {\n",
      "\n",
      "\n",
      "\trate_up = 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "Node ChemoT {\n",
      "\n",
      "\n",
      "\trate_up = 0.0;\n",
      "\trate_down = $degChemo;\n",
      "}\n",
      "\n",
      "Node DyingTumor {\n",
      "\n",
      "\n",
      "\trate_up = (Tumor & ChemoT) ? $effectChemo : 0.0 ;\n",
      "\trate_down = 0.0 ;\n",
      "}\n",
      "\n",
      "Node DC {\n",
      "\n",
      "\n",
      "\trate_up = 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node cd4 {\n",
      "\n",
      "\n",
      "\trate_up = 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node cd8 {\n",
      "\n",
      "\n",
      "\trate_up = 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node aDC {\n",
      "\n",
      "\tlogic = (DC &  P2X7R);\n",
      "\trate_up = @logic ? $u_aDC : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node cd4_Act {\n",
      "\n",
      "\tlogic = (( IL2_rec | IL4_rec | (CD28 & TCR_II)) & cd4);\n",
      "\trate_up = @logic ? $u_cd4_Act : 0.0;\n",
      "\trate_down = @logic ? 0.0 : 0.0;\n",
      "}\n",
      "\n",
      "Node P2Y2R {\n",
      "\n",
      "\tlogic = (DC);\n",
      "\trate_up = @logic ? $u_P2Y2R : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node P2X7R {\n",
      "\n",
      "\tlogic = (DC & P2Y2R);\n",
      "\trate_up = @logic ? $u_P2X7R : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node FPR1 {\n",
      "\n",
      "\tlogic = (aDC);\n",
      "\trate_up = @logic ? $u_FPR1 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node LRP1 {\n",
      "\n",
      "\tlogic = (DC & FPR1);\n",
      "\trate_up = @logic ? $u_LRP1 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node TLR4 {\n",
      "\n",
      "\tlogic = (aDC & LRP1 & FPR1);\n",
      "\trate_up = @logic ? $u_TLR4 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node IFN1_rec {\n",
      "\n",
      "\tlogic = (Tumor);\n",
      "\trate_up = @logic ? $u_IFN1_rec : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node IL1B_rec {\n",
      "\n",
      "\tlogic = (cd4);\n",
      "\trate_up = @logic ? $u_IL1B_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : 0.0;\n",
      "}\n",
      "\n",
      "Node IL2_rec {\n",
      "\n",
      "\tlogic = (cd8) | (cd4);\n",
      "\trate_up = @logic ? $u_IL2_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : 0.0;\n",
      "}\n",
      "\n",
      "Node IL4_rec {\n",
      "\n",
      "\tlogic = cd4 | Th2;\n",
      "\trate_up = @logic ? $u_IL4_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL4_rec;\n",
      "}\n",
      "\n",
      "Node IL6_rec {\n",
      "\n",
      "\tlogic = cd4 | Th17;\n",
      "\trate_up = @logic ? $u_IL6_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL6_rec;\n",
      "}\n",
      "\n",
      "Node IL12_rec {\n",
      "\n",
      "\tlogic = (cd4 & !cd8) | (Th1 & !cd8);\n",
      "\trate_up = @logic ? $u_IL12_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL12_rec;\n",
      "}\n",
      "\n",
      "Node IFNg_rec {\n",
      "\n",
      "\tlogic = ((cd8 & !cd4)|(!cd8 & cd4));\n",
      "\trate_up = @logic ? $u_IFNg_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IFNg_rec;\n",
      "}\n",
      "\n",
      "Node IL17_rec {\n",
      "\n",
      "\tlogic = (cd4 | CTL);\n",
      "\trate_up = @logic ? $u_IL17_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL17_rec;\n",
      "}\n",
      "\n",
      "Node TGFb_rec {\n",
      "\n",
      "\tlogic = (cd4);\n",
      "\trate_up = @logic ? $u_TGFb_rec : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_TGFb_rec;\n",
      "}\n",
      "\n",
      "Node CXCR3 {\n",
      "\n",
      "\tlogic = ((!cd4 & cd8) | (cd4 & !cd8)) | (CXCR3);\n",
      "\trate_up = @logic ? $u_CXCR3 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node CD28 {\n",
      "\n",
      "\tlogic = (((!cd4 & cd8) | (cd4 & !cd8)) & CXCR3) | (CD28);\n",
      "\trate_up = @logic ? $u_CD28 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node TCR_I {\n",
      "\n",
      "\tlogic = (CD28 & cd8);\n",
      "\trate_up = @logic ? $u_TCR_I : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node TCR_II {\n",
      "\n",
      "\tlogic = (cd4 & CD28);\n",
      "\trate_up = @logic ? $u_TCR_II : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node Th1 {\n",
      "\n",
      "\tlogic = (cd4 & CD28 & TCR_II & IL12_rec) | Th1;\n",
      "\trate_up = @logic ? $u_Th1 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node Th2 {\n",
      "\n",
      "\tlogic = (cd4 & CD28 & TCR_II & (IL4_rec | IL2_rec) & !(Th1 | Th17 | Treg)) | Th2;\n",
      "\trate_up = @logic ? $u_Th2 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node Th17 {\n",
      "\n",
      "\tlogic = (cd4 & CD28 & TCR_II) & (IL6_rec | IL1B_rec | TGFb_rec) & !(Th1 | Th2 | Treg) | Th17;\n",
      "\trate_up = @logic ? $u_Th17 : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node Treg {\n",
      "\n",
      "\tlogic = (Treg) | ((cd4 & CD28 & TCR_II) & (TGFb_rec | IL2_rec) & !(Th1 | Th2 | Th17));\n",
      "\trate_up = @logic ? $u_Treg : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node CALR_o {\n",
      "\n",
      "\tlogic = (DyingTumor);\n",
      "\trate_up = @logic ? $u_CALR_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_CALR_o;\n",
      "}\n",
      "\n",
      "Node ATP_o {\n",
      "\n",
      "\tlogic = (DyingTumor);\n",
      "\trate_up = @logic ? $u_ATP_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_ATP_o;\n",
      "}\n",
      "\n",
      "Node ANXA1_o {\n",
      "\n",
      "\tlogic = (DyingTumor);\n",
      "\trate_up = @logic ? $u_ANXA1_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_ANXA1_o;\n",
      "}\n",
      "\n",
      "Node HMGB1_o {\n",
      "\n",
      "\tlogic = (DyingTumor & ANXA1_o);\n",
      "\trate_up = @logic ? $u_HMGB1_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_HMGB1_o;\n",
      "}\n",
      "\n",
      "Node CXCL10_o {\n",
      "\n",
      "\tlogic = (Tumor & IFN1_rec);\n",
      "\trate_up = @logic ? $u_CXCL10_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_CXCL10_o;\n",
      "}\n",
      "\n",
      "Node CD80_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4 & LRP1);\n",
      "\trate_up = @logic ? $u_CD80_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_CD80_o;\n",
      "}\n",
      "\n",
      "Node MHC_I_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4 & LRP1);\n",
      "\trate_up = @logic ? $u_MHC_I_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_MHC_I_o;\n",
      "}\n",
      "\n",
      "Node MHC_II_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4 & LRP1);\n",
      "\trate_up = @logic ? $u_MHC_II_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_MHC_II_o;\n",
      "}\n",
      "\n",
      "Node IL1B_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4 & LRP1);\n",
      "\trate_up = @logic ? $u_IL1B_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL1B_o;\n",
      "}\n",
      "\n",
      "Node IL2_o {\n",
      "\n",
      "\tlogic = ((cd4 & CD28 & TCR_II) & cd4_Act) | (Th1 & cd4_Act) | (DC & TLR4 & (TCR_II | TCR_I)) | (Th2 & cd4_Act);\n",
      "\trate_up = @logic ? $u_IL2_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL2_o;\n",
      "}\n",
      "\n",
      "Node IL4_o {\n",
      "\n",
      "\tlogic = (Th2 & cd4_Act) | ((cd4 & CD28 & TCR_II) & cd4_Act) | (preCTL & cd4_Act);\n",
      "\trate_up = @logic ? $u_IL4_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL4_o;\n",
      "}\n",
      "\n",
      "Node IL6_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4 & TCR_II);\n",
      "\trate_up = @logic ? $u_IL6_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL6_o;\n",
      "}\n",
      "\n",
      "Node IL12_o {\n",
      "\n",
      "\tlogic = (aDC & TLR4);\n",
      "\trate_up = @logic ? $u_IL12_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL12_o;\n",
      "}\n",
      "\n",
      "Node IL17_o {\n",
      "\n",
      "\tlogic = (IL1B_rec & Th17 & cd4_Act);\n",
      "\trate_up = @logic ? $u_IL17_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IL17_o;\n",
      "}\n",
      "\n",
      "Node IFNg_o {\n",
      "\n",
      "\tlogic = (IL12_rec & CTL) | (IFNg_rec & Th1 & cd4_Act) | (IL12_rec & CTL) | ((cd4 & CD28 & TCR_II) & IL12_rec & cd4_Act ) | (IL12_rec & Th1 & cd4_Act);\n",
      "\trate_up = @logic ? $u_IFNg_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IFNg_o;\n",
      "}\n",
      "\n",
      "Node TGFb_o {\n",
      "\n",
      "\tlogic = (cd4 & cd4_Act) | (Treg & cd4_Act) | Tumor;\n",
      "\trate_up = @logic ? $u_TGFb_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_TGFb_o;\n",
      "}\n",
      "\n",
      "Node Perforin_o {\n",
      "\n",
      "\tlogic = CTL;\n",
      "\trate_up = @logic ? $u_Perforin_o : 0.0;\n",
      "\trate_down = (-log(1-$pop_Tumor))/$PerforinRecycled;\n",
      "}\n",
      "\n",
      "Node preCTL {\n",
      "\n",
      "\tlogic = (IL2_rec & cd8 & TCR_I);\n",
      "\trate_up = @logic ? $u_preCTL : 0.0;\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node CTL {\n",
      "\n",
      "\tlogic = (!IL2_rec & IFNg_rec & preCTL) | (IL2_rec & preCTL);\n",
      "\trate_up = @logic ? $u_CTL : 0.0;\n",
      "\trate_down = 0 ;\n",
      "}\n",
      "\n",
      "Node IFN1_o {\n",
      "\n",
      "\tlogic = (Tumor & TLR4) | (DC & TLR4);\n",
      "\trate_up = @logic ? $u_IFN1_o : 0.0;\n",
      "\trate_down = @logic ? 0.0 : $d_IFN1_o;\n",
      "}\n",
      "\n",
      "Node Division {\n",
      "\n",
      "\n",
      "\trate_up = (Tumor? $growth_tumor : 0.0) + ((CTL & !(Perforin_o)) ? $clonal_exp_rate : 0.0);\n",
      "\trate_down = 0.0;\n",
      "}\n",
      "\n",
      "Node Death {\n",
      "\n",
      "\n",
      "\trate_up = (Tumor ? ((-log(1-$rate_perforin)*$aggressivity)+$ChemoEffect) : 0.0) + (Perforin_o ? $PerforinDeath : 0.0);\n",
      "\trate_down = 0.0;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "sim = maboss.load(bnd,cfg)\n",
    "sim.print_bnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "initDC=0.1\n",
    "initCd4=0.05\n",
    "initCd8=0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$nb_mutable = 0;\n",
      "$nb_mutable = 0;\n",
      "$rate_ATP_i = 0.0;\n",
      "$rate_CALR_i = 0.0;\n",
      "$rate_HMGB1_i = 0.0;\n",
      "$rate_ANXA1_i = 0.0;\n",
      "$rate_IFN1_i = 0.0;\n",
      "$rate_CXCL10_i = 0.0;\n",
      "$rate_CD80_i = 0.0;\n",
      "$rate_MHC_II_i = 0.0;\n",
      "$rate_IL1B_i = 0.0;\n",
      "$rate_IL2_i = 0.0;\n",
      "$rate_IL4_i = 0.0;\n",
      "$rate_IL6_i = 0.0;\n",
      "$rate_IL12_i = 0.0;\n",
      "$rate_IL17_i = 0.0;\n",
      "$rate_IFNg_i = 0.0;\n",
      "$rate_TGFb_i = 0.0;\n",
      "$rate_MHC_I_i = 0.0;\n",
      "$rate_perforin = 0.0;\n",
      "$pop_Tumor = 0.0;\n",
      "$clonal_exp_rate = 0.05;\n",
      "$growth_tumor = 0.00001;\n",
      "$ChemoEffect = 0.00002;\n",
      "$PerforinDeath = 1/240;\n",
      "$PerforinRecycled = 6;\n",
      "$degChemo = 0.006944444444444444;\n",
      "$effectChemo = 1;\n",
      "$u_Tumor = 50;\n",
      "$d_Tumor = 50;\n",
      "$u_DC = 50;\n",
      "$d_DC = 50;\n",
      "$u_aDC = 50;\n",
      "$d_aDC = 50;\n",
      "$u_cd4 = 50;\n",
      "$d_cd4 = 50;\n",
      "$u_cd8 = 50;\n",
      "$d_cd8 = 50;\n",
      "$u_Th1 = 50;\n",
      "$d_Th1 = 50;\n",
      "$u_Th2 = 50;\n",
      "$d_Th2 = 50;\n",
      "$u_Th17 = 50;\n",
      "$d_Th17 = 50;\n",
      "$u_Treg = 50;\n",
      "$d_Treg = 50;\n",
      "$u_preCTL = 50;\n",
      "$d_preCTL = 50;\n",
      "$u_CTL = 5/12;\n",
      "$d_CTL = 50;\n",
      "$u_CD28 = $rate_CD80_i/6;\n",
      "$d_CD28 = 50;\n",
      "$u_CXCR3 = $rate_CXCL10_i/6;\n",
      "$d_CXCR3 = 50;\n",
      "$u_FPR1 = $rate_ANXA1_i/6;\n",
      "$d_FPR1 = 50;\n",
      "$u_IFN1_rec = $rate_IFN1_i/6;\n",
      "$u_IFNg_rec = $rate_IFNg_i/6;\n",
      "$d_IFNg_rec = 50;\n",
      "$u_IL12_rec = $rate_IL12_i/6;\n",
      "$d_IL12_rec = 50;\n",
      "$u_IL17_rec = $rate_IL17_i/6;\n",
      "$d_IL17_rec = 50;\n",
      "$u_IL1B_rec = $rate_IL1B_i/6;\n",
      "$u_IL2_rec = $rate_IL2_i/6;\n",
      "$u_IL4_rec = $rate_IL4_i/6;\n",
      "$d_IL4_rec = 50;\n",
      "$u_IL6_rec = $rate_IL6_i/6;\n",
      "$d_IL6_rec = 50;\n",
      "$u_LRP1 = $rate_CALR_i/6;\n",
      "$d_LRP1 = 50;\n",
      "$u_P2X7R = $rate_ATP_i/6;\n",
      "$d_P2X7R = 50;\n",
      "$u_P2Y2R = $rate_ATP_i/6;\n",
      "$d_P2Y2R = 50;\n",
      "$u_TCR_I = $rate_MHC_I_i/6;\n",
      "$d_TCR_I = 50;\n",
      "$u_TCR_II = $rate_MHC_II_i/6;\n",
      "$d_TCR_II = 50;\n",
      "$u_TGFb_rec = $rate_TGFb_i/6;\n",
      "$d_TGFb_rec = 50;\n",
      "$u_TLR4 = $rate_HMGB1_i/4;\n",
      "$d_TLR4 = 50;\n",
      "$u_ANXA1_o = 1/6 ;\n",
      "$d_ANXA1_o = 50;\n",
      "$u_ATP_o = 1/6;\n",
      "$d_ATP_o = 50;\n",
      "$u_CALR_o = (1/6);\n",
      "$d_CALR_o = 50;\n",
      "$u_HMGB1_o = 1/6;\n",
      "$d_HMGB1_o = 50;\n",
      "$u_IFN1_o = 1/6;\n",
      "$d_IFN1_o = 50;\n",
      "$u_CD80_o = 1/6;\n",
      "$d_CD80_o = 50;\n",
      "$u_CXCL10_o = 1/6;\n",
      "$d_CXCL10_o = 50;\n",
      "$u_MHC_I_o = 1/6;\n",
      "$d_MHC_I_o = 50;\n",
      "$u_MHC_II_o = 1/6;\n",
      "$d_MHC_II_o = 50;\n",
      "$u_IL1B_o = 1/6;\n",
      "$d_IL1B_o = 50;\n",
      "$u_IL2_o = 1/6;\n",
      "$d_IL2_o = 50;\n",
      "$u_IL4_o = 1/6;\n",
      "$d_IL4_o = 50;\n",
      "$u_IL6_o = 1/6;\n",
      "$d_IL6_o = 50;\n",
      "$u_IL12_o = 1/6;\n",
      "$d_IL12_o = 50;\n",
      "$u_IL17_o = 1/6;\n",
      "$d_IL17_o = 50;\n",
      "$u_IFNg_o = 1/6;\n",
      "$d_IFNg_o = 50;\n",
      "$u_TGFb_o = 1/6;\n",
      "$d_TGFb_o = 50;\n",
      "$u_Perforin_o = 1/6;\n",
      "$u_cd4_Act = 50;\n",
      "$aggressivity = 1.0;\n",
      "$InitDC = 0.1;\n",
      "$InitCD4 = 0.05;\n",
      "$InitCD8 = 0.05;\n",
      "[Tumor].istate = 0.2[0] , 0.7999999999999999[1];\n",
      "[ChemoT].istate = 0.2[0] , 0.7999999999999999[1];\n",
      "[DyingTumor].istate = 1.0[0] , 0.0[1];\n",
      "[DC].istate = 0.9[0] , 0.1[1];\n",
      "[cd4].istate = 0.95[0] , 0.05[1];\n",
      "[cd8].istate = 0.95[0] , 0.05[1];\n",
      "[aDC].istate = 1.0[0] , 0.0[1];\n",
      "[cd4_Act].istate = 1.0[0] , 0.0[1];\n",
      "[P2Y2R].istate = 1.0[0] , 0.0[1];\n",
      "[P2X7R].istate = 1.0[0] , 0.0[1];\n",
      "[FPR1].istate = 1.0[0] , 0.0[1];\n",
      "[LRP1].istate = 1.0[0] , 0.0[1];\n",
      "[TLR4].istate = 1.0[0] , 0.0[1];\n",
      "[IFN1_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL1B_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL2_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL4_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL6_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL12_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IFNg_rec].istate = 1.0[0] , 0.0[1];\n",
      "[IL17_rec].istate = 1.0[0] , 0.0[1];\n",
      "[TGFb_rec].istate = 1.0[0] , 0.0[1];\n",
      "[CXCR3].istate = 1.0[0] , 0.0[1];\n",
      "[CD28].istate = 1.0[0] , 0.0[1];\n",
      "[TCR_I].istate = 1.0[0] , 0.0[1];\n",
      "[TCR_II].istate = 1.0[0] , 0.0[1];\n",
      "[Th1].istate = 1.0[0] , 0.0[1];\n",
      "[Th2].istate = 1.0[0] , 0.0[1];\n",
      "[Th17].istate = 1.0[0] , 0.0[1];\n",
      "[Treg].istate = 1.0[0] , 0.0[1];\n",
      "[CALR_o].istate = 1.0[0] , 0.0[1];\n",
      "[ATP_o].istate = 1.0[0] , 0.0[1];\n",
      "[ANXA1_o].istate = 1.0[0] , 0.0[1];\n",
      "[HMGB1_o].istate = 1.0[0] , 0.0[1];\n",
      "[CXCL10_o].istate = 1.0[0] , 0.0[1];\n",
      "[CD80_o].istate = 1.0[0] , 0.0[1];\n",
      "[MHC_I_o].istate = 1.0[0] , 0.0[1];\n",
      "[MHC_II_o].istate = 1.0[0] , 0.0[1];\n",
      "[IL1B_o].istate = 0.5[0] , 0.5[1];\n",
      "[IL2_o].istate = 1.0[0] , 0.0[1];\n",
      "[IL4_o].istate = 1.0[0] , 0.0[1];\n",
      "[IL6_o].istate = 1.0[0] , 0.0[1];\n",
      "[IL12_o].istate = 1.0[0] , 0.0[1];\n",
      "[IL17_o].istate = 1.0[0] , 0.0[1];\n",
      "[IFNg_o].istate = 1.0[0] , 0.0[1];\n",
      "[TGFb_o].istate = 1.0[0] , 0.0[1];\n",
      "[Perforin_o].istate = 1.0[0] , 0.0[1];\n",
      "[preCTL].istate = 1.0[0] , 0.0[1];\n",
      "[CTL].istate = 1.0[0] , 0.0[1];\n",
      "[IFN1_o].istate = 1.0[0] , 0.0[1];\n",
      "[Division].istate = 1.0[0] , 0.0[1];\n",
      "[Death].istate = 1.0[0] , 0.0[1];\n",
      "\n",
      "time_tick = 0.1;\n",
      "max_time = 4;\n",
      "sample_count = 10000;\n",
      "discrete_time = 0;\n",
      "use_physrandgen = 1;\n",
      "seed_pseudorandom = 0;\n",
      "display_traj = 0;\n",
      "statdist_traj_count = 0;\n",
      "statdist_cluster_threshold = 1;\n",
      "thread_count = 1;\n",
      "statdist_similarity_cache_max_size = 20000;\n",
      "Tumor.is_internal = False;\n",
      "ChemoT.is_internal = False;\n",
      "DyingTumor.is_internal = False;\n",
      "DC.is_internal = False;\n",
      "cd4.is_internal = False;\n",
      "cd8.is_internal = False;\n",
      "aDC.is_internal = False;\n",
      "cd4_Act.is_internal = False;\n",
      "P2Y2R.is_internal = False;\n",
      "P2X7R.is_internal = False;\n",
      "FPR1.is_internal = False;\n",
      "LRP1.is_internal = False;\n",
      "TLR4.is_internal = False;\n",
      "IFN1_rec.is_internal = False;\n",
      "IL1B_rec.is_internal = False;\n",
      "IL2_rec.is_internal = False;\n",
      "IL4_rec.is_internal = False;\n",
      "IL6_rec.is_internal = False;\n",
      "IL12_rec.is_internal = False;\n",
      "IFNg_rec.is_internal = False;\n",
      "IL17_rec.is_internal = False;\n",
      "TGFb_rec.is_internal = False;\n",
      "CXCR3.is_internal = False;\n",
      "CD28.is_internal = False;\n",
      "TCR_I.is_internal = False;\n",
      "TCR_II.is_internal = False;\n",
      "Th1.is_internal = False;\n",
      "Th2.is_internal = False;\n",
      "Th17.is_internal = False;\n",
      "Treg.is_internal = False;\n",
      "CALR_o.is_internal = False;\n",
      "ATP_o.is_internal = False;\n",
      "ANXA1_o.is_internal = False;\n",
      "HMGB1_o.is_internal = False;\n",
      "CXCL10_o.is_internal = False;\n",
      "CD80_o.is_internal = False;\n",
      "MHC_I_o.is_internal = False;\n",
      "MHC_II_o.is_internal = False;\n",
      "IL1B_o.is_internal = False;\n",
      "IL2_o.is_internal = False;\n",
      "IL4_o.is_internal = False;\n",
      "IL6_o.is_internal = False;\n",
      "IL12_o.is_internal = False;\n",
      "IL17_o.is_internal = False;\n",
      "IFNg_o.is_internal = False;\n",
      "TGFb_o.is_internal = False;\n",
      "Perforin_o.is_internal = False;\n",
      "preCTL.is_internal = False;\n",
      "CTL.is_internal = False;\n",
      "IFN1_o.is_internal = False;\n",
      "Division.is_internal = False;\n",
      "Death.is_internal = False;\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sim.network.set_istate('Tumor',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "sim.network.set_istate('ChemoT',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "sim.network.set_istate('DC',[1-initDC,initDC])\n",
    "sim.network.set_istate('cd4',[1-initCd4,initCd4])\n",
    "sim.network.set_istate('cd8',[1-initCd8,initCd8])\n",
    "sim.param['$degChemo'] = 1/(24*6)\n",
    "sim.print_cfg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "WT = maboss.UpdatePopulation(sim,upp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  1.2642828305562337  mins\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "WT_res = WT.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes=('Tumor','DC','aDC','cd4','cd8','Th1','Th2','Th17','Treg','preCTL','CTL','CALR_o','HMGB1_o','Perforin_o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "WT_table = WT_res.get_nodes_stepwise_probability_distribution(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adc = plt.figure()\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Tumor'],'-g',label=\"Tumor\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['CALR_o'],'-',label=\"CALR_o\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['aDC'],'-r',label=\"aDC\")\n",
    "plt.ylabel(\"Rel.size\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.legend()\n",
    "save_figure(adc,\"aDC_Tumor_CALR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tcells = plt.figure()\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Th1'],label=\"Th1\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Th2'],label=\"Th2\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Th17'],label=\"Th17\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['CTL'],label=\"CTL\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Perforin_o'],label=\"Perforin_o\")\n",
    "plt.ylabel(\"Rel.size\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.legend()\n",
    "save_figure(Tcells,\"Tcells\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NTcells = plt.figure()\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Treg'],label=\"Treg\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['preCTL'],label=\"preCTL\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['CTL'],label=\"CTL\")\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Perforin_o'],label=\"Perforin_o\")\n",
    "plt.ylabel(\"Rel.size\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.legend()\n",
    "save_figure(NTcells,\"other_Tcells\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " __Higher initial DC population__  (i.e. $\\text{initial pop}\\cdot 1.5$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "HighDC_sim = maboss.load(bnd,cfg)\n",
    "HighDC_sim.param['$InitDC'] = 0.15\n",
    "initDC=0.15\n",
    "initCd4=0.05\n",
    "initCd8=0.05\n",
    "\n",
    "HighDC_sim.network.set_istate('Tumor',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "HighDC_sim.network.set_istate('ChemoT',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "HighDC_sim.network.set_istate('DC',[1-initDC,initDC])\n",
    "HighDC_sim.network.set_istate('cd4',[1-initCd4,initCd4])\n",
    "HighDC_sim.network.set_istate('cd8',[1-initCd8,initCd8])\n",
    "\n",
    "HighDC = maboss.UpdatePopulation(HighDC_sim,upp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  1.450850248336792  mins\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "HighDC_res = HighDC.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "HighDC_table = HighDC_res.get_nodes_stepwise_probability_distribution(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Low DC population__ (i.e. $\\text{initial pop}/ 1.5$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "LowDC_sim = maboss.load(bnd,cfg)\n",
    "LowDC_sim.param['$InitDC'] = 0.067\n",
    "\n",
    "initDC=0.067\n",
    "initCd4=0.05\n",
    "initCd8=0.05\n",
    "\n",
    "LowDC_sim.network.set_istate('Tumor',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "LowDC_sim.network.set_istate('ChemoT',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "LowDC_sim.network.set_istate('DC',[1-initDC,initDC])\n",
    "LowDC_sim.network.set_istate('cd4',[1-initCd4,initCd4])\n",
    "LowDC_sim.network.set_istate('cd8',[1-initCd8,initCd8])\n",
    "LowDC = maboss.UpdatePopulation(LowDC_sim,upp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  1.508527441819509  mins\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "LowDC_res = HighDC.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "LowDC_table = LowDC_res.get_nodes_stepwise_probability_distribution(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Faster Clonal expansion__ (i.e. $\\text{initial pop}\\cdot 1.5$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "FastClonal_sim = maboss.load(bnd,cfg)\n",
    "FastClonal_sim.param['$clonal_exp_rate'] = 0.075\n",
    "\n",
    "\n",
    "initDC=0.1\n",
    "initCd4=0.05\n",
    "initCd8=0.05\n",
    "\n",
    "FastClonal_sim.network.set_istate('Tumor',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "FastClonal_sim.network.set_istate('ChemoT',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "FastClonal_sim.network.set_istate('DC',[1-initDC,initDC])\n",
    "FastClonal_sim.network.set_istate('cd4',[1-initCd4,initCd4])\n",
    "FastClonal_sim.network.set_istate('cd8',[1-initCd8,initCd8])\n",
    "\n",
    "\n",
    "FastClonal = maboss.UpdatePopulation(FastClonal_sim,upp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  1.48511616786321  mins\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "FastClonal_res = FastClonal.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "FastClonal_table = FastClonal_res.get_nodes_stepwise_probability_distribution(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Slower Clonal expansion__ (i.e. $\\text{initial pop} / 1.5$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "SlowClonal_sim = maboss.load(bnd,cfg)\n",
    "SlowClonal_sim.param['$clonal_exp_rate'] = 0.033\n",
    "\n",
    "initDC=0.1\n",
    "initCd4=0.05\n",
    "initCd8=0.05\n",
    "\n",
    "SlowClonal_sim.network.set_istate('Tumor',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "SlowClonal_sim.network.set_istate('ChemoT',[initDC+initCd4+initCd8,1-initDC-initCd4-initCd8])\n",
    "SlowClonal_sim.network.set_istate('DC',[1-initDC,initDC])\n",
    "SlowClonal_sim.network.set_istate('cd4',[1-initCd4,initCd4])\n",
    "SlowClonal_sim.network.set_istate('cd8',[1-initCd8,initCd8])\n",
    "                                  \n",
    "SlowClonal = maboss.UpdatePopulation(SlowClonal_sim,upp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  1.4711871981620788  mins\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "SlowClonal_res = SlowClonal.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "SlowClonal_table = SlowClonal_res.get_nodes_stepwise_probability_distribution(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutants_vs_WT = plt.figure()\n",
    "plt.plot(WT_table['PopRatio']*WT_table['Tumor'],\"sg\",label=\"WT\")\n",
    "plt.plot(HighDC_table['PopRatio']*HighDC_table['Tumor'],\"^r\",label=\"$HighDC\")\n",
    "plt.plot(LowDC_table['PopRatio']*LowDC_table['Tumor'],\"*r\",label=\"$LowDC\")\n",
    "plt.plot(FastClonal_table['PopRatio']*FastClonal_table['Tumor'],\"^b\",label=\"$FastClonal\")\n",
    "plt.plot(SlowClonal_table['PopRatio']*SlowClonal_table['Tumor'],\"*b\",label=\"$SlowClonal\")\n",
    "\n",
    "plt.ylabel(\"Rel.size\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.xlim(left=0, right=120)\n",
    "plt.legend()\n",
    "save_figure(mutants_vs_WT,\"mutants_vs_WT\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}