pancreatic_cancer.ipynb

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   "source": [
    "# Pancreatic cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df35ee11-c299-46d3-b5f4-bfe8a16329a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "# import booleanNetwork module from ./src\n",
    "from BNMPy import booleanNetwork as bn\n",
    "from BNMPy import BMatrix  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41addb18-e052-4b9f-8f7a-4acebe045a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'input_files/pancreatic_vundavilli_2020_fig3.txt' # Source from PMID: 30281473 DOI: 10.1109/TCBB.2018.2872573\n",
    "\n",
    "equations = BMatrix.get_equations(file)\n",
    "gene_dict = BMatrix.get_gene_dict(equations)\n",
    "upstream_genes = BMatrix.get_upstream_genes(equations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21952092-1e84-4d19-bf50-fc08baba5ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity_matrix = BMatrix.get_connectivity_matrix(equations, upstream_genes, gene_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7902036b-0ccf-488d-8f57-87d44907e9b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "truth_table = BMatrix.get_truth_table(equations, upstream_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b4646772-1a94-41e0-a452-f37dd1002334",
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   "outputs": [],
   "source": [
    "ngenes = len(equations)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "id": "a3852318-77e3-457a-b0a8-df436db234fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial state\n",
    "x0 = np.random.randint(2, size=ngenes) #random inital state \n",
    "x0 = np.array(x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "148935b1-0303-4d92-a0c7-8eb1812eab2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.09 s, sys: 40.1 ms, total: 8.13 s\n",
      "Wall time: 8.19 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "network = bn.BooleanNetwork( ngenes , connectivity_matrix, truth_table, x0  ) # create a Boolean network object\n",
    "noise_level = 0.05 # noise\n",
    "y = network.update_noise ( noise_level  , 200000) # simulation with noise"
   ]
  }
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