Bladder_ActoNet.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "This notebook has been executed using the docker image `colomoto/colomoto-docker:2020-07-01`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import actonet\n",
    "import biolqm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Downloading http://ginsim.org/sites/default/files/Bladder_Model.zginml"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lqm = biolqm.load(\"http://ginsim.org/sites/default/files/Bladder_Model.zginml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {\n",
    "    \"GrowthInhibitors\":1,\n",
    "    \"EGFR_stimulus\":1,\n",
    "    \"FGFR3_stimulus\":1\n",
    "}\n",
    "target = {\"Apoptosis_b1\": 1, \"RB1\": 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "an = actonet.ActoNet(lqm, inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[FromCondition('input', PermanentPerturbation(ATM_b1=1, p16INK4a=0)),\n",
       " FromCondition('input', PermanentPerturbation(DNAdamage=1, p16INK4a=0)),\n",
       " FromCondition('input', PermanentPerturbation(RB1=1, TP53=1)),\n",
       " FromCondition('input', PermanentPerturbation(Apoptosis_b1=1, RB1=1)),\n",
       " FromCondition('input', PermanentPerturbation(ATM_b1=1, RB1=1)),\n",
       " FromCondition('input', PermanentPerturbation(DNAdamage=1, RB1=1))]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = an.reprogramming_fixpoints(target, maxsize=2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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    "s.as_graph()"
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       EGFR_stimulus  FGFR3_stimulus  GrowthInhibitors\n",
       "input              1               1                 1"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "s.aliases"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row3_col3\" class=\"data row3 col3\" >1</td>\n",
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       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row4_col5\" class=\"data row4 col5\" >0</td>\n",
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       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row5_col0\" class=\"data row5 col0\" >1</td>\n",
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       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row5_col3\" class=\"data row5 col3\" >1</td>\n",
       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row5_col4\" class=\"data row5 col4\" ></td>\n",
       "                        <td id=\"T_903d3296_bc39_11ea_992d_0242ac110002row5_col5\" class=\"data row5 col5\" ></td>\n",
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   "outputs": [],
   "source": [
    "from colomoto.minibn import BooleanNetwork\n",
    "from colomoto_jupyter import tabulate\n",
    "import cabean # for attractor computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = biolqm.to_minibn(lqm, ensure_boolean=True)\n",
    "for n, v in inputs.items():\n",
    "    test[n] = v\n",
    "test[\"RB1\"] = 1\n",
    "test[\"TP53\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cabean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AKT</th>\n",
       "      <th>ATM_b1</th>\n",
       "      <th>ATM_b2</th>\n",
       "      <th>Apoptosis_b1</th>\n",
       "      <th>Apoptosis_b2</th>\n",
       "      <th>CDC25A</th>\n",
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       "      <th>E2F3_b1</th>\n",
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       "      <th>FGFR3</th>\n",
       "      <th>FGFR3_stimulus</th>\n",
       "      <th>GRB2</th>\n",
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       "      <th>Growth_Arrest</th>\n",
       "      <th>MDM2</th>\n",
       "      <th>PI3K</th>\n",
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       "      <th>SPRY</th>\n",
       "      <th>TP53</th>\n",
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       "      <th>p16INK4a</th>\n",
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      ],
      "text/plain": [
       "   AKT  ATM_b1  ATM_b2  Apoptosis_b1  Apoptosis_b2  CDC25A  CHEK1_2_b1  \\\n",
       "0    0       0       0             1             0       0           0   \n",
       "1    0       1       0             1             0       0           1   \n",
       "\n",
       "   CHEK1_2_b2  CyclinA  CyclinD1  CyclinE1  DNAdamage  E2F1_b1  E2F1_b2  \\\n",
       "0           0        0         0         0          0        0        0   \n",
       "1           0        0         0         0          1        0        0   \n",
       "\n",
       "   E2F3_b1  E2F3_b2  EGFR  EGFR_stimulus  FGFR3  FGFR3_stimulus  GRB2  \\\n",
       "0        0        0     0              1      1               1     0   \n",
       "1        0        0     0              1      1               1     0   \n",
       "\n",
       "   GrowthInhibitors  Growth_Arrest  MDM2  PI3K  PTEN  Proliferation  RAS  RB1  \\\n",
       "0                 1              1     0     0     1              0    1    1   \n",
       "1                 1              1     0     0     1              0    1    1   \n",
       "\n",
       "   RBL2  SPRY  TP53  p14ARF  p16INK4a  p21CIP  \n",
       "0     1     1     1       0         0       1  \n",
       "1     1     1     1       0         0       1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabulate(cabean.attractors(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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