{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44c7d61c",
   "metadata": {},
   "source": [
    "# EGF-TNFα signaling\n",
    "\n",
    "This notebook reproduces the analysis of the EGF-TNFα signaling model of the [MaBoSS website](https://maboss.curie.fr).\n",
    "\n",
    "This model was used as an example for the SBML-qual standard format\n",
    "([Chaouiya et al., 2013](https://doi.org/10.1186/1752-0509-7-135)), and can be found in the\n",
    "[BioModels](https://www.ebi.ac.uk/biomodels/BIOMD0000000562) database (BIOMD0000000562). The MaBoSS files\n",
    "were exported from [GINsim](http://ginsim.org), with `egf` and `tnfa` as inputs.\n",
    "\n",
    "**Requirements:** [pyMaBoSS](https://pymaboss.readthedocs.io/), and the files `chaouiya_maboss.bnd` and\n",
    "`chaouiya_maboss.cfg`, in the same folder as this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74999d61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T21:24:11.188491Z",
     "iopub.status.busy": "2026-10-07T21:24:11.188332Z",
     "iopub.status.idle": "2026-10-07T21:24:12.706475Z",
     "shell.execute_reply": "2026-10-07T21:24:12.705690Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipylab module is not installed, menus and toolbar are disabled.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import maboss\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Common style for the figures of the MaBoSS website notebooks\n",
    "COLORS = [\"#FF7F00\", \"#1F77B4\", \"#2CA02C\", \"#D62728\", \"#9467BD\",\n",
    "          \"#8C564B\", \"#E377C2\", \"#17BECF\", \"#BCBD22\", \"#7F7F7F\"]\n",
    "mpl.rcParams.update({\n",
    "    \"figure.figsize\": (7, 4),\n",
    "    \"figure.dpi\": 100,\n",
    "    \"savefig.dpi\": 150,\n",
    "    \"savefig.bbox\": \"tight\",\n",
    "    \"axes.prop_cycle\": mpl.cycler(color=COLORS),\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.grid\": True,\n",
    "    \"grid.alpha\": 0.3,\n",
    "    \"font.size\": 11,\n",
    "    \"axes.titlesize\": 12,\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"legend.frameon\": False,\n",
    "    \"lines.linewidth\": 2,\n",
    "})\n",
    "\n",
    "FIG_DIR = \"figures\"\n",
    "os.makedirs(FIG_DIR, exist_ok=True)\n",
    "\n",
    "def save(fig, name):\n",
    "    \"\"\"Save a figure in the figures folder.\"\"\"\n",
    "    fig.savefig(os.path.join(FIG_DIR, name + \".png\"))\n",
    "\n",
    "def state_label(state):\n",
    "    \"\"\"Readable label for a MaBoSS state.\"\"\"\n",
    "    return \"none active\" if state == \"<nil>\" else state.replace(\" -- \", \", \")\n",
    "\n",
    "def pie(probas, ax, title=None, min_proba=0.01, labels=None):\n",
    "    \"\"\"Pie chart of a distribution; probabilities below min_proba are grouped as 'others'.\"\"\"\n",
    "    probas = pd.Series(probas).sort_values(ascending=False)\n",
    "    small = probas[probas < min_proba]\n",
    "    probas = probas[probas >= min_proba]\n",
    "    if len(small) > 0:\n",
    "        probas[\"others\"] = small.sum()\n",
    "    names = [labels.get(s, state_label(s)) if labels else state_label(s) for s in probas.index]\n",
    "    colors = [COLORS[i % len(COLORS)] if s != \"others\" else \"#D9D9D9\" for i, s in enumerate(probas.index)]\n",
    "    wedges, _ = ax.pie(probas.values, colors=colors, startangle=90, counterclock=False,\n",
    "                       wedgeprops={\"linewidth\": 1, \"edgecolor\": \"white\"})\n",
    "    ax.legend(wedges, [f\"{n} ({p:.1%})\" for n, p in zip(names, probas.values)],\n",
    "              loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
    "    ax.set_aspect(\"equal\")\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "\n",
    "def set_refstate_all(sim, value=0):\n",
    "    \"\"\"Give a reference state to every node.\n",
    "\n",
    "    With MaBoSS 2.6.6, the states of the stationary distributions only include the nodes\n",
    "    having a reference state; this makes them include all the nodes, as in previous versions.\n",
    "    \"\"\"\n",
    "    for node in sim.network:\n",
    "        if node not in sim.refstate:\n",
    "            sim.refstate[node] = value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a1f806",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "In the configuration file, the initial state of every node is random, and `ras`, `egf` and `akt` are the\n",
    "output nodes. We set the following conditions:\n",
    "\n",
    "* no TNFα (`tnfa` is initially inactive), EGF being present in half of the cells;\n",
    "* `erk` is also an output node, to follow its activity;\n",
    "* the simulation runs until time 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60df2443",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T21:24:12.708652Z",
     "iopub.status.busy": "2026-10-07T21:24:12.708420Z",
     "iopub.status.idle": "2026-10-07T21:24:13.462291Z",
     "shell.execute_reply": "2026-10-07T21:24:13.461442Z"
    }
   },
   "outputs": [],
   "source": [
    "sim = maboss.load(\"chaouiya_maboss.bnd\", \"chaouiya_maboss.cfg\")\n",
    "sim.network.set_istate(\"tnfa\", [1, 0])\n",
    "sim.network[\"erk\"].is_internal = False\n",
    "sim.update_parameters(max_time=50, time_tick=0.1)\n",
    "result = sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843d3905",
   "metadata": {},
   "source": [
    "## Fixed points\n",
    "\n",
    "The simulation reaches three fixed points. They correspond to the stable states of the published model\n",
    "for the wild type: with EGF, AKT is active, while without EGF, GSK3 is active (with or without PH)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21048115",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T21:24:13.464706Z",
     "iopub.status.busy": "2026-10-07T21:24:13.464580Z",
     "iopub.status.idle": "2026-10-07T21:24:13.835445Z",
     "shell.execute_reply": "2026-10-07T21:24:13.834567Z"
    }
   },
   "outputs": [
    {
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wYEGle421a9dyrVq1hMefP3/mXbt2cdeuXXncuHGixI6OjmZ7e3tOS0vj1atXs5WVFevo6PCwYcOEv80ZPn36xElJSaLE/dr8+fO5R48ewuMBAwZwmTJlePny5Wxvb8+LFi2SJG5SUhLr6OgIfzdu377N5ubmPGHCBPb19WVbW1tJ4qamprKHhwfv3buXmdPP76pVq/K7d+/Y29ubiYjLlCmTbWVsblWqVIkPHjzIzOmNC0lJSbx06VI2MzNjExMTXrFihSTXsfDwcCYipUru6dOnc4ECBRS2p6SkKO2nKkhmJfJ1UrF37152cXFhQ0ND7t+/Pzds2JC7d+8uWjw/Pz/evn27QvyMFpxq1aoJ5Xnz5g1bWloKF/wf9fz5c65YsaLwOCYmhidOnJhlK0ePHj0Uas/EkpaWxr/88gtfu3ZN6bnHjx9zs2bN2MLCQvTafrlcnm3iFBwczA0aNGAiUrgZ/xGrV6/mihUrclpaGu/YsYOrVKkiPHf16lWuUKECy2Qy7tmzp1A7KpaoqCgeMWIE6+rqcsmSJfnIkSPMzFykSBGeOHEid+3alc3MzNjQ0JCbNWuWZU+EH7Vr1y5u0aIFu7m5cYECBZiImIjY1NSUvb29uW3btjx16lRRYl26dIl9fX2Vtjdo0IBHjRolPL527RoTkWg1/WlpaVyqVCkuWLAgz5o1i9+8ecOmpqZZ7jty5EiuUKGCKHEzPH36lFu3bs1ExHXr1uV//vmHw8LCWEtLi5ctW8ZNmzZlAwMDtrCwYB8fH37+/LkocVNTU4WeDWZmZvznn39ySkoKd+zYkX/99VceOnQoOzs7s5aWFletWlWhlvhHderUSeEG9Pz58+zp6anUyvHgwQO2tbVVaKnPq0ePHrGTk5NShY9cLudWrVqxjo4Onz9//ofjZOfy5ctcvnx54Xrx7t073r17N5coUYLnzJkj3PA7OzvzsGHDRL9BSUtL45IlSwq9TJiZly9fzrq6ukxEXLFixSyv53nl5eWV5bV6y5YtTETZtp79qOPHj7OWlpbQO2vGjBk8efJkpf2io6PZ0tKSN2/eLGp8f39/dnBwYENDQ542bRrHxcXx2LFjuXHjxjxu3DguXbo0ExF7enry/PnzRYu7cuVK1tfX51OnTjFzesJeunRpXrlypbDPs2fPWEdHR7hB/1EJCQlcpEgRNjU15T/++INDQ0PZw8NDaT+5XM7btm1jOzs7Ub/3+/fvc/369ZmIuFWrVhwaGsq3bt1iExMTXrJkCdevX591dXXZxsaGe/furdTTKa8CAwO5QYMGwuOoqCgePnw46+rqsoWFhUKS07p1a16+fLkocb82a9Ys9vT05JCQECGRzUgwBw8ezLNmzZIkbkxMDOvo6PDWrVv51KlTbGlpKVxXnj9/ziYmJqLESUpKUmgcWrlypfC5JyUlsZ2dHV+8eJGZ0/+GWFtb87Bhw0Sp6E5MTGQi4nv37vHnz5/ZxsaGHzx4wMzpFRQDBw5kbW1tLlu2LJ89e/aH42V2584dJiKOjY1V2P7s2TMmIj5x4oSwbfHixdynTx9R4+cUklkJhISEcI0aNZS2JyYm8p9//snVq1fnrl27Kp0cPyIuLi7LWt0dO3awvb09y2Qy7tGjB7dt25YnTZokWlxmzrLm6dmzZ9y2bVuhlWPRokVcpEgR/vLli6ix09LSuEqVKmxra8tr167Ndr/MLV1iOXPmDBMRN23alB89epTlPseOHROty2JoaCgbGRlx1apV2cHBQelmTy6X88aNG9nGxoYLFSokWqKRWUhICDdp0oSJiH/55Rd2d3cX/jgfPHhQsq7tCQkJ3KVLFx4yZAgvXryY9+/fz8uWLWN9fX3RKma+599//2U3Nzel79PQ0JCDgoJEixMbG8uTJk1iAwMDdnJyYplMptCClmHbtm1KXfnEcvbsWS5btixra2vzgAED2NDQkF1dXXn06NF88eJFyVqyIiIieMiQIayjo8OlS5fmpk2bspGREbdp04Y3btwo2g1gZlFRUUoVXWlpabxq1Sq2tLQUWjnq1q0raotpdpVrMTExXLZsWba0tOQXL16IFu9raWlpvH79ei5cuDAXKlSI+/fvz1paWlylShWePXs237t3T7LYzMwbN27k4sWLc0pKCqekpHCZMmV43759vHv3bnZycsryb2hefasic/To0UxEoieSGQIDA7lWrVpMROzk5MTVqlXLsuW5cuXKvG7dOtHjx8XF8dSpU9nQ0JAdHBy4U6dOrK+vz40bN+YVK1YotNqKJSYmhqtUqcJExCVLlmQnJyc2NjZWunZaWVmJWmmTOZFzcXFhMzMzjo6OznLf2NjYbJ/7Efv37+dixYqxnp4eDx06lGUyGXt4ePCkSZP45s2bovU6SEtL4+nTp3N8fHyW92CZ/1a7u7vz3LlzuWjRopK1nL1+/ZpLlizJRMQVKlTgd+/eMXN6RaWrq6tS7yIxTZkyhWUyGRsaGvKGDRuE7atWreL27duLEmPx4sXcokUL4fH58+eFis5Dhw6xm5ub8Ny///7LxYoVEyVuhsaNG7OZmRnXr1+f+/fvr/T8vXv3uF69ekxEorWCr1ixgm/fvs0tW7bMsjKwbNmyQoXQx48f2crKikNCQkSJnVtIZiVw7949LliwoKRjBnMj8x+zIkWKiJpEf8+5c+eE8bQbN24U/f2Tk5N59uzZbGRkxAUKFOCNGzeK3kUtO0uXLmVTU1MmItbV1eURI0ZIOi6EOb11yNnZmWUyGffu3TvLblJfvnz5ZmKfV2/evOE5c+Ywc3qS7urqyjKZjIcPHy75cWclNTWVHRwc+Pfff1dZzK9vxgIDA7lw4cKSjFd+/vw5t2/fnolIqfJCLpdzkyZNJDn2jN+f1NRUXrFiBVtYWLCRkZHQWqoKwcHB3LBhQ6GySOoxQdnJfHPs5uamsuN//vw5W1pasqenp+RzH3z58oXHjBnDenp67OTkJFpPku9JTk5mR0dH3rBhAy9dupTr168vPJeQkCBJkpWVtLQ0btq0Kevr6/PNmzcli/PXX3+xk5MTExH7+PgoJNj//vsvm5qaCgnAj/jnn3+4fPnySjefYWFhwji7ypUrSzJuNLPExETesWMHL168mO/evcsFChRQuPe4fv06FylSRJLx6Q8fPhQSuUqVKmVb2SyVxMRE/v3337lQoUJsYWHBmzdvFv2+ZPv27VytWrXv7nf06FF2dXVlIlIYPiKF1NRUfvz4scK976BBg7h169aSxmVOHwKSuUHnn3/+4cKFC/M///wjyvu7uLgIY9CXLl2qMMxlyZIlXLZsWaGSd+zYsTx48GBR4mZITExkX19fJiL28PDItlfg3r17+dmzZz8c78mTJ2xlZSW0rmeVN0ycOJGLFy/OzMz9+vXjkSNH/nDcvEIy+4PkcrnSBBIpKSmsr6+vthqKrMjlcq5cuTJv3bpV9Pf+3hgMf39/Ll++vKRJ5tu3b7lbt24sk8m4cuXKkt6UMDPPnTuXK1SowNevX+fg4GCeNWsWGxsbs5WVFa9Zs0ayioz379+zpaUlL1++nO3s7NjY2JgXLFgg2TiYzPz8/HjixInC4+TkZF68eDGbmppKftzZ+e2339jR0VGylsJvfa4Z3dvnzZsnadwLFy6wl5cXy2QyoZtz9erVuX79+qLfCKakpCglyJGRkTxs2DDW0dHhMmXKiD5RzbccPHiQixcvznp6ejx27FjRe3Ywf//6lZyczMWLFxe6TKoq9oULF1hXV1fhd05KoaGh3KJFCyYibtSokdCNTWyZj3n58uXs5OTEVlZWfP/+fUniZRX3a9HR0Vy6dGl2dXWVNHZ8fDzPnDmTCxYsyGZmZty7d28ePHgw29jYsL+/vyixHj58yHXr1mU/Pz++c+eO0o3t5cuXuVy5cqKPG/2W5ORkNjY25iZNmnBAQACfOXOGXVxcFLqZS+HIkSPs6urKurq6PHLkSEkqXW/evMl//fVXlkN73r17xz169GCZTCZ61/k6derwrl27mDm91fBb3bVXrlzJ9erVEy12Zk+ePOG9e/cq3e9GRUVx06ZNuVGjRpJM6BYVFcV//PEH+/n58Zo1axRa2Tdv3swODg587Ngx0eJljMMNDg5ma2trhfv+4OBg1tPT4ypVqnCbNm24cOHCkvQGbNOmDY8ePZp/+eUXJiJu3769JD3wmJknT57MQ4YMYeb0Vuhp06Yp7ZMxzGrPnj1sa2urlkaNDEhmf9DWrVvZ1NSUlyxZolBr7+3tzTt37hQeJycn8+nTp3nYsGGiXtByatu2bVypUiXRE8qIiAieMmVKts9nTKST1eyZPyqrVpKbN29ylSpVWCaTsa+vr+hjR5nTJ3EoUKCAUmvRkydPuEiRIsKYr6dPn4oem5mFroexsbE8efJkNjAwyHbCGjEVK1Ysywvnx48fuX///mxubi5Jl989e/Zw2bJl2djYmMuVK8dLliwRxi1++PCB9fT0JBlfuH//fj59+nSWz8XFxXH//v25ZcuWoifwycnJPGLECIVtaWlpvGbNGrayshLGs0pRgXHq1Cl2d3fP8rl///2XGzduLFnFVFJSUpY3PUlJSTxv3jw2NjYWfTwQM/P48eO/2d1wwYIFCt3LxBIWFvbdipB169ZJklQ+ePCA+/Tpw40aNeLBgwcrVP6dOnWK3dzceNCgQaLHvXPnjkI3wISEBG7atKnorRhZ+V6M0NBQXrFihehxDx8+zMePH1fY9vr1a/7111+ZiFhbW5u3bdsmetzk5GRu1qwZGxgY8MSJExVaVjJ3MRerx9Tr1695wYIFvHz58iwndfrrr7/Y0NCQiYhlMlmWY4elkJycLMyabWVlJVqvpdTUVO7ataswd0PBggUVxg9mFhAQwNWqVeOmTZuKEps5ff6G2rVr840bN9jS0lKYXPRrUVFRbGNjI8mwgVWrVrGuri6bmZkJvWgyt1iK1Sr6tefPn7ODgwM7ODhw6dKlWSaTsYWFhZDQv3v3TvR7v507d7KBgQHr6+vzsGHDlJ6/du0ad+rUibt27SrZbNKvX78W7nsPHz4szMI/adIk0XtcTp8+XZik1tPTM8tW/bS0NLa2tmYDAwNJhkjkBpLZHxQVFcV+fn6so6PDpUqVEmqCevbsyQMGDOBt27Zxx44d2djYmAsWLCjZsgufPn3ikydP8q1bt7K8uT59+nSW4+5+1I4dO77Z1SUqKorXrFkjetxHjx6xvb09r1u3Tul45XI5b926lYsWLSpKd4uvXb16lfX19bP8nNesWcNVqlThsmXLsoODg6gzNWaXKL548ULojtqlSxfR4mWQy+U8fvx4Lly48DdnkBXzWDMcOXKErayseOXKlbx7927u06cPy2QyhRuSadOmSZLYdejQIdtJUTZt2sRz586VpEX46tWrXKRIkSyf+/z5M48YMYLr1Kkjelzm9AmgdHR0vjmuSorvee/evWxubs5ExHXq1MmyC5UUcZmZHRwcvjlh2d69ezk0NFT0uEuXLuWWLVuK/r7fExQUxGZmZty9e3ceOXIku7m5KY0ZTUlJkaQ1ZcyYMVkmyVJ3337z5g1ra2uLMnlXbnXp0kUYnvG1a9euccWKFUVrlf1aYmIiz5kzh42MjLhIkSK8detWhYqoL1++iFIZd+bMGTY2NmYTExMmInZ0dMyyy/THjx/5yJEjkvw+Maf/rbp9+zafPHlSqcU5PDyc+/Xrx23atBEl1vjx47lGjRr86NEjvn//PtetW/e7LftitlyFhIQI3Yfr1KmT7bn94cMHSYZ43b59my0sLITJRb29vdna2pp1dHTYz89Psus1c3oiP2nSJOFcfvz4MTdo0IC1tbWFSZiksGrVKmHyybZt20pyf5kbycnJvHDhQjYxMWE7OztRyxMdHc1NmzZlImJLS8tsh0AMHDiQy5Urp/ZhlUhmRfLw4UOh6b9p06Y8aNAgJiK2t7fnAQMG8LFjxyQbeL9u3To2MDAQagidnZ2F6cKlFhgYyEZGRiobp5ohMjKShwwZ8s0lcKTs6qutrZ3ljKr+/v7cqVMnjoyMZAsLC161apVoccuVK/fNdXwvXLjAf//9t2jxMrx69YqLFi3KRMQ1a9YUdfmI7ylbtizv379feHzw4EEuU6YMp6SkcL9+/USb9j4r06ZNU8s6alFRUUxE35zsSIxz+/Xr10pdw+VyOZuYmGRZ8ZWYmChJl8SnT5+ymZkZb9iwgffv3y+MlW3fvr2kEyBlaNy4scJSJapy+vRpdnR0VHncunXrKsxoevv2bbaxseEvX77wwoULlZbzENOmTZtyNM5PCqampnzr1i2Vx501axZ37Ngx2+dV8bcz8zCcKlWqiDoM5+3bt1y4cGHes2cPM6e3Qtrb2/OMGTM4NTWVnz59Kvla78zpyWrFihWF+yBtbW3u0aOH0vksxrXz/v37bGlpqXA9fPv2LZuZmfGECRPYyMiICxcuLElFfmbnzp1jS0tLLliwIDs7OwvfgdTkcjlXrVpVGLa2Z88e9vT05NjYWGGogqWlpSQts0+fPmUjIyOlCuy0tDSuWbMmN2vWTPSYGSZOnMinT5/mI0eOCK2iX/d6kEJycjIfPnyYz549m+X5++HDB/7tt99Ev5ZERkayiYkJ29vbc6FChXjevHlZfu5iLlmXV0hmRZZxkmcs66CKCYFMTU354MGDHBUVxRcvXuRGjRoxEYneZerGjRv822+/KdQAJicns56ensJabhmePn3Kr1+/FrUMX8u8BE6nTp2y7WojtowxhHPmzBEmaPny5YtC9/IGDRqIttxBVFQUN2/enIlIWMdXiplds5O5ht/W1laSCS2+lrG+WcZxJiUlcfHixYUxm1WrVhWtJnLp0qVKE9/s37+fy5Qpo7RvWloa37hxQ5QbtBcvXvDo0aOVrhNOTk5ZjtF8+/ataK0amzdvZi0tLfb09FRoCa1ZsyavX7+emf+/Vr9NmzZsbW0tSeXBxIkTlWZYP3z4MJcoUYINDAx48uTJoq1ZPGbMGKWZp8eNG5dlj5mEhATRkp+TJ08qDUX5ep3mzB4+fCjJ73dkZCTLZDKF8V7Vq1cXuv7WrFlTtN8pf39/pevEtyo/AwMDRWkNjo6O5iFDhijdYNWuXTvLLqYREREcHBz8w3GZ08cAHzhwQGFbRnfAr6WlpfHNmzdVNqkYs+IwnNWrV4vynr6+vgprvDOnrzn6yy+/sIODAxMR29nZibrmelY6dOjAnTt35rCwMA4LC+P58+dzoUKFuHTp0qLfh9WvX19pje9Xr14xEXHr1q1506ZNwt9rKVsK9+3bxwcOHODXr1+zj48Py2QyrlOnjqiz62dl69atXKVKFZbL5SyXy7lkyZLCMKenT5+yvr4+T5s2TZLJvS5dusT6+vpZNg5t2bIl22EyYsvcfT2rXg9iiYmJYU9PT9bW1mYi4qJFi6qs0iI4OJjnz5+vMIlssWLFlK5xPwMksxLI3PRvbW3Na9eulayVcPTo0TxjxgyFbXK5nIcPH86GhoaitqKtX7+eDQwMlGoAPT09effu3ZyWlsZXr17l8ePHs5ubGzs6Oop2E/o9e/bsYRMTEzY0NOSpU6dKchHNLDU1lceMGcPa2tpsYmLClStXZhMTE27WrBnL5XJOTExkCwuLbFtRcyM2Npbd3d25V69evGvXLp48eTJbW1srreOrCm/fvuXu3buzTCbjSpUqSTrdfkREBBORMMnFvHnzhLGLr1+/Zmtra9GOvUOHDkoT3zx//lzomhgTE8N79+7lHj16sJWVlWhrqR09elQYy5W5lbRVq1ZCRUhgYCDPnDmTK1SowMbGxqLMeJohKCiIa9euzUTE7dq142fPnvHQoUO5YsWKwlqjLi4uPHToUEm+67dv37KPjw8fPXpU6bnk5GResGABu7q6ijLxU0REBHt6erKWlhb369dPSBZ37tzJ5cqVY+b0Xhfr16/nVq1acYECBYSk/kctXLhQGIqSefyknZ0dnzt3jlNSUvj8+fM8cuRILlGiBLu7u0vSmhUWFsZExFevXmXm9GEiGV3EoqKi2MLCQrRr9qhRo5QmvklKSmJdXV1+/PgxJyYm8vHjx3nQoEHs4OCgsFbmjwgICGBbW1s2MjLiOXPmCDe9fn5+QhfnR48e8YIFC7hWrVqsp6cnWgtS586dmYi4QYMGwoRWr169Yi0tLY6NjeXY2Fjet28f9+zZk62trblHjx6ixM0sMTGR//jjDx44cCDv2bNHKVnOWGdVjIqpmzdvsq2trdKY89GjR7ONjQ3v2LGDjx8/zl5eXmxrayvpkl6mpqZKXW0fPXrEZmZmok6iFhcXx4MHD1b6XMeMGaMwa69cLudy5cp9c04RsV2/fp0rVarEWlpa3L9/f8kqvENCQoQKoKtXr7Ktra3w3MePH0Vb2zUrkZGRbGBgwEOHDlVKHqdNm8Y9e/aULHZWwsPDuW/fvqyjoyNJJcKIESO4Y8eOHB8fz6Ghoezr68symYxr164teaXF116+fCnMiN6gQQPRKgHFgGRWBHFxcXz+/Hml8YQZJ7mWlpYkYxbu3bvHnTt3zrIrS1JSEhsaGmY7IUFePX/+nNu1a8dExLVr1+bAwEDu3r07lylThq2srFhPT48bNWrEy5Ytk6RV9luTKm3YsIEdHR25QYMGktSQ3b59m2fMmMEzZ84UapmfPHnCc+fO5eHDh/O2bds4JSWF5XI59+/fn319fUWJO23aNG7Xrp3Ctk+fPglrirm6uko2qdjFixd56tSpPGfOHL5z546w/datW1y1alXW09OTZJKtDA0aNGArKys+cuQIW1hYCD0Afv31Vx49erSosU6ePMmlS5dmHR0dHjJkiHCDVKVKFdbX12dLS0vu1q0b//3336JW0mS+Tnh6egozBxYtWpQdHBxYW1uba9asyfPnzxelVVYulyu1VOzZs4ednZ1ZX1+fy5cvz97e3jxnzhxJZ5iVy+VcunRpNjAwyHLdvAxiVgSmpaXx6tWr2crKik1MTHjRokX8zz//sJ6eHleuXJllMhkXLVqU/fz8+OzZs6JeR74eihISEsLNmjVjDw8PNjMzY0NDQ27evDmvXbtWshlmM1pRvLy8OCwsjB0cHIRr2ZQpU0S7ZmXIuE4QEf/666/86tUr9vT05PLly3PBggXZ2NiYO3TowFu3bhV1jF1MTAyPHz+e9fX12cXFhffu3cubNm1iW1tbLlmyJMtkMi5fvjxPnz5dtBvCwYMH8507d/j06dPs5ubG2traPGjQIP706RNbWFhw5cqVWV9fny0sLNjX15d3794tybJLXbp0YScnJ65VqxZra2uzm5ubJDNxM6ffg3xdEfXp0ye2srJS6MocGhrKRCTJGMOUlBTes2cPOzg4ZPn8tGnTRF2zOCtyuZxbtmyp1BugTZs2oq35+bWPHz/y2bNnlZawksvlvHnzZra1tRX9/o+Z+c8//xSWqGFOn9jL2tpaqHCcOXMmd+3aVfS4X5eBiLhhw4Z89uxZfv/+Pe/cuZNNTU2/Oa9HXl25coVbtmzJbdu2zbZVUqohT/b29kr30jdu3BAqLb6eLFIsiYmJfOHChSzvATJmRFfVJG45gWT2BwUGBrKtrS3r6uoyEXH16tUVftGZ02d0k6L1rEyZMsKC2F//UUxMTGQjIyPJbkbPnz8vtHI4Oztzs2bN+O+//5Zk0pAMMTExbGpqynXr1s2yJv3SpUuSjZdYsWIFFyhQgOvUqcOlSpViIuKqVasqdXVJTk7m1q1b8+DBg0UbI121atUsx95++vSJDQwMuHr16qynp8fnzp0TJV6GiRMncqFChbhevXrs4uLCRMStWrUSns+YbENKL1++5GLFijERsbe3N+/du5c7dOjAXl5ekpxrKSkpvHTpUjYzM2Nzc3O2trbmUaNG8ZUrV0RPqr5OTAMDA4VW0mLFinH16tV569atSkt//aitW7eypaUl79ixQ2F7QkICz5o1iwsWLMh2dna8fft2UZO5rNYOvXr1KleoUEFYe1PKipHMoqKieMSIEayrq8vFihVjKysr/v333yVbjiazjKEourq67OjoyB07duRDhw6prBfLxYsX2dDQkAsUKMDVq1fnN2/e8IIFC9jW1laSG7KMlkA7OzsuUKAAFylShPv27cunTp2SvFfJ06dPuXXr1sLvlLu7O69evVr0ZTMCAwPZ3t5e+A6TkpJ42bJlbG5uzmZmZmxtbc0jR47ky5cvSzp+9PLly1y8eHHh2ph5uaWWLVtKNvFSZvfu3VMaXvPu3TvW0dGRZGyhv7+/ME7262saM/OMGTO4U6dOosTKajhVdmJjY9nc3DxXr8mpgwcPcsGCBVlXV5dlMhm3bdtWaXUFKSY7+/z5M1tZWSkk7eHh4WxlZcXOzs7coEEDNjc3l2y5mMx2797Njo6OwndvbGwsyVwxwcHBbGpqyt26deMmTZqwlpYWV69eXZj4Skq7du1iCwuLLIciZFRaZJ7/QCwhISHs5OQk5DUVKlRQmpQxLS1NJUtC5hSS2R8QFxfHLi4uvHjxYk5LS+NBgwaxsbGxsCyMFOtMZfbq1Sthen83NzchmUlJSeGRI0eKOg18VjK3cpiamvIff/wh6vifN2/eKNWaZ8z+qK2tzf3791eY4Xfo0KGS1BS9ePGCLSwsFGr8unXrxj169OD3799LOiaGmbl58+YKSWRmjo6O/OzZM27Tpg1XrFhRtJg3b95kOzs74Y+WXC7nevXq8eTJk/nx48eidJ/OqS9fvvCUKVPY09OTy5Qpw2PHjpW00oQ5vaJg0KBBrK2tzWXLlhV9SZgdO3awrq6u0pquzOl/pJ2dndnQ0JCnTJki+g3gx48feeDAgbx9+3besmWLUiXImzdvhPFXVatWFW1Jh0WLFrGhoaHSFP5yuZw3bNjAhQsXZmNjY54/f77K/kiGhIRwkyZNFFpLVeHroShZzcoupaCgIG7QoAFraWkJs6E+evRI9DiZuzlmXkpM6slqvl7//ezZs1y2bFmF1lIxTZw4kfv168fM6S1VGS3cERERPGTIENbR0WE3N7dsl/oSy/Tp03nu3LlK20+dOsVlypRhPT09lXdNZE6fkyC7v2E/Si6X85YtW7hIkSKsra3NkydPFoZXBQcHs62trShj36Ojo9nExIRr1qyZo2vyoEGDuFu3bj8c92vPnj1jc3NzPnToEKelpXGdOnXY2NhY0rW4Mxw9ejTL7/HZs2c8YsQIHjBggCTX0OjoaO7Tp4/S8J7U1FS+desWnz59+pvLq/2IDh068B9//CE8vn37NlevXp1lMhn37NlTsgrYe/fusY6ODhOR0lBCKSUnJ7ObmxtPnz6dU1NTecKECWxsbCwMRVJFRUVeIJn9AVOmTGEfHx9mTr8BtLS05KdPn/KoUaOEdcf++usvycuRuXUjoya4YcOGks1K+fUMo1FRUTx8+HDW1dXlUqVKifbHcsKECaylpZXlzW9GlzFjY2MeOHAg9+jRg21sbCS5sMyePVthyZunT5+ypaUlv337lu/cuSPZH+kMp06dYplMxoMGDVJovTl9+jQXLVqUmZkPHTrEVlZWosUcOHCgwqQe169fZ3t7e46NjeWDBw+qZF1IVYuNjVVa/uj+/ftcv359JiJu06aNaLXdKSkpvGTJEt6/fz8vWbKEV61apdBi83UrqRTrqzKz0GqV1TID169f56pVq/Ldu3dFiRUfH88zZ87kixcv8vTp05VaUb58+cJjxoxhPT09Ll68uGg13+Hh4Tx8+HBu06aNwhqnmR07doxdXV1ZV1dXaTKqH+Hv789t2rThoUOHZtnqmbmLube3tyQtZ6mpqXz48GHevXu30g1fbGysZDe/crmcGzZsqLT9xYsXwhj12rVrS9KlesuWLbxlyxaFbampqbxixQq2sLBgMzMzhXXgf9S6devY0NCQFy9ezE5OTkqTHQUHBwuzdLds2VKS7sWrV6/mOnXq8LRp07J8PiUlRdRjzqlHjx6xra2tZGuOZoiJieEJEyawvr4+6+rqsp2dHZubm4t6zFeuXOGxY8dySEgI9+nTJ8v7jS9fvvDw4cO5Vq1aknzPbdq0ESosrly5wg4ODvzp0ydu1aoVExHb2NiIOlt1hosXL3KLFi24Zs2aor/39/zyyy88ZswYoadQSkoK37x5U/JKbeb0pduyupf29/dnBwcH9vLykix25qEpzZs3l6SV/2uLFi3i5s2bM3N6ZZy1tTXfu3ePZ86cyUTEBgYG2f4dVScks3n0/Plzhe4W/fv3Zz8/P2ZO/0WztbVlHx8fyRZP/ppcLueNGzeyjY0NFyhQgOfOnSt668anT5+4evXq3KNHjyxv6kNCQrh9+/ai3ZykpaXxunXr+MWLF9y6dWtetGiRQre0mJgYnjdvHlepUoWbNGkiWY3z4MGDFbovt2rVSlg3cNWqVTxw4EBJ4ma2du1aYbxVjx492NfXl42MjIRuNX/88YeoXaxbtWoljGOUy+VcqVIlYRr+SZMm8ezZs0WL9TM4e/YslypVKtsZPvft2ye0vIht1KhRrK2tLYyXzez169fs6+sr6UQL+/bt46JFi6psmQG5XC5MIla9enWlruqhoaHcoUMHUcbcv3z5kq2trblcuXLCsIzsktXk5GRevHgxL1y48IfjMqdfNywtLblOnTpsYGDAtra22VYwBgYGcrt27US/OUtLS+OmTZuyvr4+a2trs5GREc+aNUsl663Gx8eztrZ2tjPMX7x4kTt37ixJt9tBgwZlO5YsMjKShw0bJsyKLga5XM6zZs1iXV1dNjEx4TNnzmS538GDB0WbPO5rQ4cOZZlMxiYmJpJVfuXW/v372c3NjQ8ePKiymBk9lYiIa9WqJUkSferUKbaxscmyJ8mLFy947dq1kpzXZ86c4RIlSgjxqlevLlT2v3//nmUyGQ8ZMkSSVTQ2bNggrLGa3brJUjh58iR7enoKiey7d+/Yy8uLiYgLFy4s2Vqv8fHxXK9ePTY3N892uF5cXJwkQ1NWr16tMA9Kxozoenp6PHr0aMlaoT98+MBWVlZCpeq4ceO4e/fuzPz/8y107txZLUucfQ+S2Tx69+6dMBV5cnIyFyxYUKF7RenSpSVJrj5//sxdu3Zlc3Nz9vDw4JUrVyp07f3y5QuPHTuW9fT0uFixYqL+EWnQoIFCEvPp0yeeP38+79u3T7QYWUlOTubRo0ezrq4uu7q68rFjxySN97X169ezTCbjM2fO8JkzZ9jFxYUTExM5OTmZPTw8sr1x+VEBAQG8f/9+IZF59uwZjxo1imvVqsWtWrUSZkV9+/YtOzs785UrV0SLPX36dDYwMOB79+7xpk2buHLlyiyXy/nLly9sZ2ensu6YqvD69Wu2tbVVaAm8ffs2z549W9IJkDK7d++eMKGXOrryZF56ScplBjLLGDKgpaXFvXr1Er1XhVwu52rVqvGsWbOEbbNmzWIbGxtOSEjgw4cP8/nz5yU5zr///puLFSsmdGd99uwZ29jY8Pbt2/nevXu8Z88epV4AUvD39+fKlStzTEwMR0REqKybb4bSpUurZRmHNWvWcJ06dVQaMywsjC0tLYVu661bt/7mhIVSCAgI4KpVq7JMJmMfHx/Jhzqlpqby9u3buW/fvjx69GhhluwMd+/eVRonryrnzp0TupYPHDhQ9K7lme+1SpQoobS0mxT+/fdf4e/8o0ePuECBAkJim5iYyAYGBqLN1ZGVsLAwYcburCoipTBq1CihYurTp09cpkwZHjRoEIeGhrKXlxcvWLBAkrjv378XllcqU6aM5PMpPHz4kFNSUvj169dsaWmp1AMyY3Z/Y2NjLlKkiCSt0hEREQrjjq2trRXmAKpatarS2NmfBZJZEbx584aJSEg6bty4wfb29qLXzGV022ratCnv27dPWE+2dOnSSrPWPXnyhFu2bMlFixYVZZKNgIAAdnFxEcZ1PXnyhO3t7dnR0ZG1tLREW8LiWx4/fszNmjUTxrdJMcYrK4mJiVyuXDk2MDBgR0dH/vvvvzkxMZF79uzJjRo1Ev1mOC4ujhs3bsx6enpsaGjIRMTly5dXmliMOb17pLu7u1KXuh8VGRnJzs7ObGpqytbW1nz9+nWOjo7mZs2aZbkmpyabNGkSDxs2THi8Y8cONjAwYDs7Oy5YsKDSHxUp7d27l11cXNjAwIAnTZokeSvp1zIvvbR582bJ42XuUWJsbMwLFiwQbVKgTZs2ccWKFRV+P798+cKmpqbs6uoqTBzSuHFjUXuxxMfHs6Ojo1Ire79+/bhy5coKE5Z8vY/YfH19lSobX7x4we3btxfGyopx8/vx40euUqWKUkVjly5dePr06QrbUlNT+dKlS6K1jI4YMYLHjBmj0Fpx8+ZNNjMzU9o3LCyM169fL8kN/4cPH4TK4+PHj3OpUqVYX1+fx48fr5LukJlt375duH5J2RLfsWNHNjMz4/r167OdnR0TEbdv317lx5ud1NRUXrlyJVtaWkp2PXvy5InQxbdRo0aS9cbbuXOnQoX1yZMn2djYWBgmsH79etGWt/qeK1eucPny5SWriMxs3rx5bGtry0uXLuXSpUsrzHzfuXPnLNeO/lGZh3NlXuXAz89P1FnXM6SmprKjoyO7ublx/fr1v7mc04cPH3jbtm2ilyEwMFDhPj42NpaJSDjnHjx4wFZWViqbqDC3kMyKQC6Xc/ny5blw4cLs4+PDJiYmkoyV3bx5M1esWJGTk5M5Pj6enZyceMOGDezu7s5ExM2aNVMacyXWOrNr167lWrVqMXP6DYGzs7PQSjt+/HjJpmJPTEzkLVu2KFwsM24UdHV1eeTIkZJ0qflaZGQkd+nShWUyGZuamrKRkRE3b95cku4eI0eO5FatWgnvffDgQdbW1ubAwEClaeeTkpJEXUs4s1evXgnjNSwtLdnQ0JC7d+/+U81gJ4amTZvy4sWLmTm9y625uTlfu3aNU1NT2c3NTWULlGfI3EqaXbdnqd2+fVul6xdnbuUoWbLkD69pGxsby0WKFFFasurSpUusr6/Pu3fv5vj4eN68eTPr6OgIXejFMHPmTKWltJiZK1WqxK1bt+YPHz7ws2fPuFKlSuzh4SFa3KwMHjw42xk+L1y4INqEeWFhYUKLZJMmTYTr1Ny5c7l169YcHR3Nf/31F/v4+LC5uTkXKFBAtPHBixcv5kKFCnHhwoV5/fr1nJaWJnRxfv78OQcEBPDUqVPZ29ubiYjHjh0rStzvSUlJ4T/++INNTU3Z1tZWWC9bVTJPuCXFjKfHjx9nV1dX4eY+NTWVN23aJPxt/JlERUWJOsFaVg0VGUsySbEMUHJyMtvZ2SkMN4mKimJra2suUaIEd+7cmQsVKiTa/AY5kZaWxuvXrxfue6WSkJDAvXv35qJFi/KMGTOEysmXL1+yhYWFJD1cunTpopDYZcxxYWZmxpaWlkpzXIghJCSEa9SoIdzLfz07NXN6paGYk6xm1qpVK6WKgbp167K5uTn7+PiwhYWFJBUHYkEyK5JPnz7x6NGjuVWrVpLc/MbExLCdnZ3QFXL27Nncpk0bZk6vadfT02NHR0fJuuAGBgayjo4ON2zYkK2srBRugkaMGMFTp04VPWZkZCSXLVuWx4wZo1QbljG+zdTUlAsXLizJempZCQsL4+PHj0vWzTYxMZELFiyo0D1t5MiR3LdvX2Zmrl27dpYXOSmFhoby8ePHf9pZ7H7Ub7/9xkZGRtysWTM2NjYWZqeWy+VcrFgxld4gZPb27VtJl/H4GYWGhnL9+vW5YMGC2Y61zAm5XJ5lt/s6derw0qVLFbY1btxY1OvX8+fPlSZ7yhjz9PUEbgUKFBAtbmaRkZHcrl07NjY25pIlS6qsu+vRo0eFibSGDx/Ou3btEibkKVy4MPfq1Yv3798v+sQ479+/5169egkTaV26dIlLlSrFhQoVYh0dHa5Tpw4vXrxYsqVpkpOTs+3G+vHjRx4wYACvWLFCktjf8+LFC0kqIIcMGcJLlixR2n7q1CkmIrXMmiylpKQkHjp0KBcqVIi1tLS4UqVKShVFqampSmvNiuH69etcvXp1pe2hoaHct29f7tChg1IXb1WJjo7md+/eqTTm+/fv2cPDg5ctWybae/7555988uRJvnr1Kjs6OmbZAvnp0yceMGAAW1lZSTLBqo+PD48cOVIYH/v17NRNmzZVmhBVDGlpaWxiYqLUgyM6OponTZrELVq0EL33n9iQzGqI69ev86hRo5g5/YJZuHBhhW6nxYsXl3y2wHPnznH//v0VWp0fP37MVlZWkoyLGTBgwHdnzQ0PD+cOHTqwgYGByi+oUnj8+DETkdASHRISwpaWlvzhwwdOS0tje3t7yWapzq/kcjkvW7aMBw0apDBGds2aNVylShU1lix/yuhyJeYEPczpf7A3bdqkVEFQo0YN9vf3FzXW1+7fv6/USnzgwAEuV66cJPFatGjB1atX52HDhgnru86YMUMlXcQyVzQaGhqyq6srX716VfIx2MzMd+7cEVo3ChYsyPPmzZOkW2BmN27cYAcHB2GJPLHXZ/4ZrVu3jmvVqsUTJ07M8nk7Ozvev3+/agslsSFDhrCXlxdv2bJFOH4iknwCyAcPHrCPjw+XL19e0jiaIjw8nN3c3HjlypWivm/btm2F4R9ZVdJkJtUETBl/m5KTk3nRokVsYmLCNjY2vGjRIp4+fTqXKVNG9B5TCQkJPHLkSDY0NJTsuFQByawGykh4Mmq337x5w6amppIO/M/K3bt3uUSJEpLV2JiammZbu/vrr78KP2fUKv0XaoJjY2NZX1+fx40bx8zMTZo0EWZXPXr0KFetWlWdxfvPiIqK+mYL94YNG9jOzk7lE7hA+tAICwsLSddLzPDgwQO2tLRUyxi/Zs2a8Z9//in6+96/f5+LFi0q/D3IvMyTo6Ojyrq7hoeHc79+/VhLS4u9vLwkX487s507d7KDgwMXKFCAp0+fLlkSHxcXx3Z2dvz777/zmTNnuEePHqylpcVVqlT5KWf8FEvGjX+BAgX40qVLCs99+PCBDQ0Ns1yKSlNFRkayqampUuv7ihUrmIh49+7dksXeunUr6+vrMxFluS45iGfEiBGsra3Nenp6SuPwmdMbdFQ5AVJ4eDj7+fmxqakpOzk5SbKqQWBgIBcuXFgY7/6zjon9HiSzGig6OpoLFCjAHTt25AMHDnClSpW+OWA8rz5//sy9e/dmFxcXrlevnsJEIklJSdy3b18+deqU6HGZ07vbElGW66UlJyezg4OD8Pj58+fs7u7+n6kNHz16tHBhyZiG/+XLl+zo6KjSJQ7+y2bNmpXtIvP//vsv9+jR44e6ucKPUcVMv1++fJFk8rSc+PPPP7lixYqiV0AmJyezv79/lsu/vH79mrt27coymUyYCV0VgoKCuHbt2kxEkvydyk58fDxPmzaNDQ0N2dHRUZIW2u3btwvDfTLcvXuXa9asyTKZjLt37y7p5DjqdOjQIS5RogTr6Ohwv379+MyZM3zy5EmuXLmyJMOO1OnSpUtcoUKFLJ/r2LEjd+jQQdL4mZcbqlu3Lt+7d0/SePnVli1b+ObNmwpD2DKPwy9ZsqTKhrSpUnR0NI8ePVpY410VM3OLDcmshjp8+DA7ODiwTCbjHj16iD4oXC6Xc926dblx48a8ceNGrlSpkrBuW2BgoKixsuPu7s6NGjVSuuHz9/dXmmRBqkHx6pCamsp+fn4sk8nY1taWW7Rowebm5pJNQZ/fJCUl8cSJE1lLS0tYZH7jxo3/mcoQ+L5//vmH3d3dRe+q9j1yuZwHDBjADRs2lCTJmT9/PltaWnLZsmWzHW8tRQ+W1NRU/v3337lJkyY8bty4LCuC9uzZk2Xl5I+6ceMGd+jQgTt16iQsl5dZWFiYJC3g4eHhPHPmTJ43b16Wz+/atYtLlCgh2Tjdn0FSUhLPnz+fjY2NmYjYwMBA1HGMP4Pdu3ezpaUlGxgYZNmbZ8qUKdyxY0eVlEXq5Ybg/2XuWVKiRAn28vLiTp06qbtYourdu7fCjPqPHz8WliNq3LixZDNzSwHJrAaTy+WSzTi6bt06rlq1KqekpHBsbCzb29vzsmXL2M7OjrW0tLhv374cHh4uSewMp0+fZl1dXfb29ubdu3fzv//+y+vXr2dTU1PJl7T4GYSEhPDixYt5wYIFSrMYQ9516dKFW7ZsydevX+ezZ8+yj48Py2QyrlChgtom0QDxrVu3jqtXr84eHh7cv39/paW8Pnz4IEnc0NBQ/vXXX9nNzY0bNWrEW7ZsUZhJNTw8XLKKk/v373P9+vWZiLhBgwb87NkzSeJ8bcKECVy8eHHu1asX29rasoGBAU+ePFnypaVCQ0PZ3Nycu3TpwnXq1GEi4ho1avCdO3ckjcuc3tVWT0+Pa9asmW1lan6pIMs8AVe5cuX48uXL6i6SaDIvZVW0aFGFCpnPnz9ziRIlJJt4Myupqam8YsUKtrCw4KZNm6osbn4VEhLC48aN4ylTpmhsF9zM9u7dy+Hh4d+c6CpjOSIrKyuVD1/MKySzoOTz589sa2srTCg1ZcoU7tKlCzOnd6GSyWRsZWXFZ8+elbwsly9fZk9PT2FtRgsLC0nHp8B/27Vr19ja2lrpAr1//36hpbZ3796SrcsIqjFv3jwuXLgwjxw5kgcNGsRWVlasp6cnyUyQmb17946trKy4devWPHHiRKF7be3atSWfiCizffv2cdGiRdnAwIAnTpwo6Zjgz58/C5PUMSuO0bWzs5N0QqQBAwYIS8QxK65/2bt3b8kqLJj/fy33jB5LDx48kCyWpsg8AVfnzp3/U9fRCxcuCPciNWrU4O7du7OjoyOPGTNGLeWJjIxU+coGoNkSExPZ0dGRTUxM2MXFhXfs2JHtvikpKRo1Dw2SWVBy/PhxHjFiBDOnn/ympqYKs7xaWlqqZXmYu3fvakwtEfycFi9eLKyX/DVfX19u1qwZm5iYcIsWLVRcMhBLbGwsm5ubK7TExsfHc+/evVlLS+uH17D9ljFjxvCgQYMUtp06dYotLCwkH1f3tczrFRcpUkSyiZ8CAgK4SZMmStszj9GVqhKhevXqSi3umde/dHNzkyRuZhnri+ro6PDQoUMx2zynDwXq1q2buoshurS0NF69ejVbWVkxEbGvr69K1+MG+FFfvnwR1gR3dXXlo0ePKu1z9+5djRvrj2QWvun27dtsYGAg1Kw/f/6cLS0t8936l/DfcODAATYwMMiy2/bw4cN50aJFfOnSJSYiTLKhoa5du8aVK1dW2i6Xy7lmzZrcs2dPyWJXq1Ytyy6WBw4cYJlMppYxbm/fvuVu3bpJNub+7Nmz7OTklO1apjdu3JCke967d++4YcOG2Sbp0dHRKuluzJze9XPZsmVsbm7OFhYWSuuPwn9LVFQUDx8+nHV1dblUqVIqnVAN4EcNHjyYd+/eLfQsadKkCYeEhDBzeiVoyZIlRV8aT2pIZuGbwsLCWFtbm4cPH87nzp3jypUr86xZs9RdLIA8SUlJ4XLlyrGNjQ0fOXJE2P7ixQu2sbERklwjIyOVLiUC4rh48SKXL1+ezc3N+fPnz0rPT5kyhdu3by96XLlczi1atGAHBwdevHix0vPR0dFMRPzixQvRY6tLSkoKN23aVBgCUrt2bZWN0d23b5+wXImRkRFv3bpVJXG/JyIiggcPHoyx9/nEw4cPhVYuVS15BSCmU6dOsZubG+vq6nK3bt24adOm3LZtW3UXK9e0COAbHBwcaNWqVbRx40aqV68elShRgiZMmKDuYgHkiY6ODp04cYLc3NyoefPmVKJECapfvz6VKVOGunfvTqVKlaJHjx6RlpYWVahQQd3FhVzS0tKiuLg4ioyMpHbt2tGnT5+E51JTU+nEiRPUvHlz0eMmJiaSjY0NvXnzhqZMmUIHDx5UeP7w4cNUtmxZcnJyEj22uixfvpwiIiLo6tWrtHnzZnr79i2VLl2aJk6cSLGxsZLF/fz5M/Xr149WrVpFFy9epNatW1P37t2pevXqdPv2bcni5oS5uTktX76cqlWrptZygGqUKlWKjh07RidPnqTWrVuruzgAudawYUMKCgqi1atX0+vXr8nY2Jg2btyo7mLlmoyZWd2FgJ9fYmIiJSQkkJmZmbqLAiCKs2fP0tGjRykhIYEaN25MrVu3puTkZPrll1+oWbNmNGrUKHUXEfIgJSWFli9fTjNnziQiIl9fXypatCgdOnSIjIyM6ODBgySTySSJHRQURH5+fnTp0iWqW7cuNWnShD59+kRbtmyhI0eO/KcqSDw9PWnz5s3k7e1NRETJycm0dOlS+u2338jIyIjmzZtHPj4+osfdtGkTXbhwgbZs2SJsCwgIID8/P7px4wZ1796d5s+fT1ZWVqLHBgCAnw+SWQAAInr//j21bNmSWrZsSZMnT1Z3ceAHffz4kSZPnkzr168nuVxOPXr0oPXr15O2trbksXfv3k1jx46lly9fUuHChenUqVPk4eEheVxVOXHiBA0cOJDu3LlD5ubmCs99+PCBJk6cSHp6erRq1SpR4z58+JDWrVtHRkZGQmVFBmamHTt20OzZs+nChQtUuHBhUWMDAMDPCcksAMD/yOVy0tLC6AtNEx0dTTt27KCEhARq1qwZlSpVSnjun3/+IT8/P7p48SK1b9+eFi5cKGp333PnztGNGzfI1dWVWrZsSbq6ukRElJCQQAsWLKB58+aRubk5zZs3j3799VfR4qrL27dvycXFhZKTk2ns2LE0b968LPdjZtFbwH19fWn79u3k5ORE169fJ1tbW5XEBQCAnxfu2gAA/geJrOZ5//49lS9fnv78809as2YNlSlThnx9fent27dElN4d9sKFC7R7924KCAigUqVK0YkTJ0SJPW3aNGrdujUdPXqUfv31VypVqhTt27ePiIgMDQ1p6tSp9OjRI6pZsyZ17dqVWrRoIUpcdSpSpAjdu3ePmjRpQvPnz6dmzZrR48ePlfaTIqHcsGEDzZs3jyIjI8nb25s2bdpEX9fHI5EFAMhfcOcGAAAaq3///tSsWTOhC2qRIkXo7NmzVLJkSfrtt98oMTGRiIg6dOhAISEhNG3aNKpSpcoPx7106RKtW7eO7t+/T1euXCEvLy8yNzendu3aUb169ejevXtERGRvb0/+/v509epV8vX1/eG4PwNXV1c6duwYHT16lJ4+fUru7u40atQoio6OljSunp4ejR07lkJDQ6lZs2bUp08fqlSpEl29elXSuAAA8PNCN2MAANBIJ0+eJD8/P7p//z5pa2tTuXLlaNKkSVSjRg2qUKECvX37lqpWrUrXrl0TNa5cLqdy5crRtGnTqE2bNrRlyxZatWoVXb9+ncaNG0cLFiwgbW1tOn/+PNWsWVPU2D+blJQUWrZsGc2cOZP09PQoKCiIihQpopLYd+7cIT8/P7p69SrNnz+fxowZo5K4AADw80AyCwAAGoeZyd3dnRYuXEhNmjShPXv20Jw5c+ju3bskk8lo+vTp9PDhQxo0aBDVrl1b1NgbNmwgf39/Onv2LMnlcipWrBitW7eOGjRoQLGxsWRiYkJbtmyhLl26qGTCqZ/Bx48fyd/fn/z8/FQee+fOneTh4UFubm4qjw0AAOqFZBYAADTSw4cPqXTp0kRE1L59e6pUqRKNHTuWiIgmTZpEOjo6NGPGDNHjxsbG0ufPn8ne3p5u3LhBLVu2pPDwcCIiioiIoKJFi9Lnz58xfhMAAEBiGDMLAAAaKSORJSKKiYmhwMBAYmZ6/fo1bdq0iTp06CBJXCMjI7K3txfixsTEUGhoKBERzZo1i9q1a4dEFgAAQAXQMgsAABrv0KFD1LZtW7KxsaHo6GgaOnQozZkzR/K4SUlJVKVKFXr48CHZ2NhQWloa3blzh6ytrSWPDQAAkN8hmQUAAI3BzHTy5En6+PEjNWrUiAoXLiw8FxwcTIcPH6ayZctS8+bNRY/96tUrOnPmDBUrVoxq1aolbI+Pj6edO3fSly9fqFu3bmRhYSF6bAAAAFCGZBYAADRGz549aefOnUSUPqvwoEGDaPr06WRqaipp3Nu3b1OtWrVIT0+PoqOjycvLi5YsWSL65FIAAACQcxgzCwAAGuHixYt07do1evPmDUVGRtL8+fNp69atVKJECVq1ahWlpaVJFnvgwIG0YsUK+vz5M926dYtMTU2pTp061L59e3rx4oVkcQEAACB7SGYBAEAjnDx5koYOHUoWFhZUoEABGj58OIWGhlL79u1p6NChVK5cOfr06ZPocSMjI+njx4/Us2dPIiKqWLEinT9/nvbs2UN37tyh0qVL08aNG0WPCwAAAN+GZBYAADSCiYkJpaamKmyzsLCgVatWUWBgIDVo0IAsLS1Fj2tgYEC6urokl8sVtrdv354ePnxIkyZNwhqnAAAAaoAxswAA8FOLj4+nwYMH0/79+8nAwICOHj1K5cuXV0nsAwcO0Pjx4yk0NJRGjBhB8+bNI21tbZXEBgAAgG9DyywAAPzURo4cSUFBQdS/f38yNTWlKlWq0NChQykyMlLSuA8ePKDu3btTw4YNqV27drR06VLy9vamc+fOSRoXAAAAcgYtswAA8NMKDw8nNzc3Cg0NJVNTU5LL5bR27VqaMmUKyeVymjlzJg0YMECS1tJevXqRm5sbjRo1ioiIHj58SCNGjKCTJ09SmzZtaNGiReTi4iJ6XAAAAMgZtMwCAMBPiZkpODiYKlasKCy9o6WlRQMGDKDQ0FDy9fWl4cOH06ZNm0SPHR8fT48ePaImTZoI20qXLk0nTpygQ4cOUXBwMDVt2pRQHwwAAKA+aJkFAICf0vbt28nPz4/09PQoNDSUjIyMlPZ59OgRFS1alHR1dUWLm5SURG5ubhQREUG///47DRgwQGmf5ORkev78Obm6uooWFwAAAHIHLbMAAPBTqlWrFjVo0IDev39PNWvWpKCgIKV9XF1dRU1kiYh0dXVp7NixpKurS8OGDaOFCxdSSkqKwj56enpIZAEAANQMySwAAPyUHB0d6a+//qJLly6RTCaj8uXLU79+/ejjx4+SxtXS0qJ+/fpRaGgoDRkyhCZOnEgeHh50/PhxSeMCAABA7iCZBQCAn1rNmjXp9u3btHr1ajp48CCVKFGCli1bJnlcExMTWrx4Md2/f59cXFyoadOm1KxZM3r9+rXksQEAAOD7kMwCAMBPT0tLi/r27UuPHz+m3r1706tXr1QW29XVlY4dO0ZHjx6liIgIMjAwUFlsAAAAyB4mgAIAAAAAAACNg5ZZAAAAAAAA0DhIZgEAAAAAAEDjIJkFAAAAAAAAjYNkFgAAAAAAADQOklkAAAAAAADQOEhmAQAAAAAAQOMgmQUAAAAAAACNg2QWAAAAAAAANA6SWQAAAAAAANA4SGYBAAAAAABA4yCZBQAAAAAAAI2DZBYAAAAAAAA0zv8BdG5wBTb6HTQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1100x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixed_points = result.get_fptable().sort_values(\"Proba\", ascending=False).reset_index(drop=True)\n",
    "nodes = sorted(fixed_points.columns[3:], key=str.lower)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 2.4))\n",
    "ax.imshow(fixed_points[nodes].values, cmap=mpl.colors.ListedColormap([\"#EEEEEE\", COLORS[0]]),\n",
    "          vmin=0, vmax=1, aspect=\"auto\")\n",
    "ax.set_xticks(range(len(nodes)), nodes, rotation=60, ha=\"right\")\n",
    "ax.set_yticks(range(len(fixed_points)), [f\"{p:.1%}\" for p in fixed_points[\"Proba\"]])\n",
    "ax.set_xticks(np.arange(-0.5, len(nodes)), minor=True)\n",
    "ax.set_yticks(np.arange(-0.5, len(fixed_points)), minor=True)\n",
    "ax.grid(which=\"minor\", color=\"white\", linewidth=2)\n",
    "ax.grid(which=\"major\", visible=False)\n",
    "ax.tick_params(which=\"both\", length=0)\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "ax.set_ylabel(\"Probability\")\n",
    "ax.set_title(\"Fixed points (active nodes in orange)\")\n",
    "save(fig, \"egf_tnf_fixed_points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267d23cc",
   "metadata": {},
   "source": [
    "## Trajectories\n",
    "\n",
    "The activities of ERK and RAS are transient, while AKT remains active when EGF is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8557e0c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T21:24:13.837499Z",
     "iopub.status.busy": "2026-10-07T21:24:13.837362Z",
     "iopub.status.idle": "2026-10-07T21:24:13.998602Z",
     "shell.execute_reply": "2026-10-07T21:24:13.997908Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trajectories = result.get_nodes_probtraj()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for node in [\"erk\", \"akt\", \"ras\"]:\n",
    "    ax.plot(trajectories.index, trajectories[node], label=node.upper())\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Probability\")\n",
    "ax.set_title(\"Activity of ERK, AKT and RAS\")\n",
    "ax.legend()\n",
    "save(fig, \"egf_tnf_trajectories\")"
   ]
  }
 ],
 "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
