{
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  {
   "cell_type": "markdown",
   "id": "f24fd591-7d50-440e-bae0-85b3ce29cde3",
   "metadata": {},
   "source": [
    "# Reproducing Montagud Prostate Cancer results with pyMaBoSS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d087d4-f710-4636-8c91-536eeb96e2bc",
   "metadata": {},
   "source": [
    "First we import MaBoSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a454b683-7242-4edb-9782-89d60dc5a6af",
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     "iopub.status.idle": "2024-08-02T11:34:27.924229Z",
     "shell.execute_reply": "2024-08-02T11:34:27.923690Z",
     "shell.execute_reply.started": "2024-08-02T11:34:26.986195Z"
    }
   },
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      ],
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       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "    var mymodule_jsapi = {  };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-6236873\", \"mymodule\", {\"name\": \"MaBoSS\", \"sub-menu\": [{\"name\": \"Load MaBoSS file\", \"snippet\": [\"masim = maboss.load(\\\"filename.bnd\\\", \\\"filename.cfg\\\")\"]}, {\"name\": \"Network\", \"sub-menu\": [{\"name\": \"Set istate\", \"snippet\": [\"maboss.wg_set_istate(masim)\"]}, {\"name\": \"Get initial state\", \"snippet\": [\"masim.get_initial_state()\"]}]}, {\"name\": \"Simulation\", \"sub-menu\": [{\"name\": \"Create mutant\", \"snippet\": [\"maboss.wg_make_mutant(masim)\"]}, {\"name\": \"Run\", \"snippet\": [\"simres = masim.run()\"]}, {\"name\": \"Set initial states\", \"snippet\": [\"masim.network.set_istate([\\\"mygenelist\\\"],{})\"]}, {\"name\": \"Set output\", \"snippet\": [\"maboss.wg_set_output(masim)\"]}]}, {\"name\": \"Results\", \"sub-menu\": [{\"name\": \"Save results\", \"snippet\": [\"simres.save(\\\"filename\\\")\"]}, {\"name\": \"Plot piechart\", \"snippet\": [\"simres.plot_piechart()\"]}, {\"name\": \"Plot trajectory\", \"snippet\": [\"simres.plot_trajectory()\"]}]}, {\"name\": \"Conversion\", \"sub-menu\": [{\"name\": \"Convert to biolqm\", \"snippet\": [\"biolqm_model = maboss.to_biolqm(masim)\"]}, {\"name\": \"Convert to minibn\", \"snippet\": [\"minibn_model = maboss.to_minibn(masim)\"]}]}, \"---\", {\"name\": \"Documentation\", \"external-link\": \"http://pymaboss.readthedocs.io\"}]}, null, mymodule_jsapi);\n",
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   ],
   "source": [
    "import maboss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9930ecc2-1273-4109-a4b0-0c0dd6edbb04",
   "metadata": {},
   "source": [
    "Link to the article : https://elifesciences.org/articles/72626"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82000d3b-00d7-408d-bbdd-104102603837",
   "metadata": {},
   "source": [
    "The model can be considered as a model of healthy prostate cells when no mutants (or fused genes) are present. We refer to this model as the wild type model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d741861d-1dc3-42ea-8a7e-292d921910bb",
   "metadata": {},
   "source": [
    "### Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea654770-dae5-4fca-9c7d-18047216cccc",
   "metadata": {
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     "shell.execute_reply.started": "2024-08-02T11:34:27.925201Z"
    }
   },
   "outputs": [],
   "source": [
    "MODEL_BND = \"models/Montagud2022_Prostate_Cancer.bnd\"\n",
    "MODEL_CFG = \"models/Montagud2022_Prostate_Cancer.cfg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484c3580-7b70-4e8f-a425-d6d0b2dcb782",
   "metadata": {},
   "source": [
    "First, we need to load the model to create a Simulation. Here is pyMaBoSS' documentation regarding the loading a model : https://pymaboss.readthedocs.io/en/latest/api/load.html\n",
    "\n",
    "Fill the next block to store the simulation in the variable model_prostate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73ecc55a-7a14-4446-b9fc-bd59ca2c4d0c",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-08-02T11:34:28.223790Z",
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     "shell.execute_reply.started": "2024-08-02T11:34:27.928078Z"
    }
   },
   "outputs": [],
   "source": [
    "model_prostate = maboss.load(MODEL_BND, MODEL_CFG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b52b224-3a11-411a-896a-04fbfeb1b839",
   "metadata": {},
   "source": [
    "The Simulation object has several objects or methods that you can use. Here is the documentation : https://pymaboss.readthedocs.io/en/latest/api/simulation.html\n",
    "\n",
    "You can also use python help() command to get the same information (without proper formatting unfortunately)"
   ]
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     "shell.execute_reply": "2024-08-02T11:34:28.227068Z",
     "shell.execute_reply.started": "2024-08-02T11:34:28.224321Z"
    }
   },
   "outputs": [],
   "source": [
    "# help(model_prostate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c39c8a-048a-4670-82ae-1606c103419c",
   "metadata": {},
   "source": [
    "### Run the default simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8f05ba-3ee7-4985-b52f-67c62c4ccf29",
   "metadata": {},
   "source": [
    "Now that the simulation is created, we can already simulate the default case, without changing any settings. \n",
    "\n",
    "Here is the documentation of the function to run a simulation : https://pymaboss.readthedocs.io/en/latest/api/simulation.html#maboss.simulation.Simulation.run\n",
    "\n",
    "Fill the next block to run the simulation and store the result in the res_prostate variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "393048b0-c2ee-47be-a640-f98e88f5f817",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:28.228418Z",
     "iopub.status.busy": "2024-08-02T11:34:28.228224Z",
     "iopub.status.idle": "2024-08-02T11:34:29.226818Z",
     "shell.execute_reply": "2024-08-02T11:34:29.226333Z",
     "shell.execute_reply.started": "2024-08-02T11:34:28.228401Z"
    }
   },
   "outputs": [],
   "source": [
    "res_prostate = model_prostate.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ff08c8-e0be-4c3f-b00a-e8e2ca4a96ac",
   "metadata": {},
   "source": [
    "The Result object has many options to access the results, or visualize them. Here is the documentation : https://pymaboss.readthedocs.io/en/latest/api/result.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2319956-e03a-4620-beb7-10f4f59bd723",
   "metadata": {},
   "source": [
    "### Print results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d0b840-d4f4-4360-81ad-a4a3e2cbe589",
   "metadata": {},
   "source": [
    "Now that we simulated the model, we can visualize the results. The simplest kind of result that we often look is the states probability distribution of last time point. Fill the next block to visualize it :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7795b78-a5e5-4864-a9be-22eac6810ee1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.227303Z",
     "iopub.status.busy": "2024-08-02T11:34:29.227181Z",
     "iopub.status.idle": "2024-08-02T11:34:29.311012Z",
     "shell.execute_reply": "2024-08-02T11:34:29.310393Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.227293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_prostate.plot_piechart()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2b22b1-9a39-4e6c-b8e6-35991935e48b",
   "metadata": {},
   "source": [
    "We can also plot not only the last state, but the whole trajectory of the states probability distribution. Fill the next block to visualize it : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c92d148-de54-4624-a92d-ef9371609209",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.312251Z",
     "iopub.status.busy": "2024-08-02T11:34:29.312137Z",
     "iopub.status.idle": "2024-08-02T11:34:29.437520Z",
     "shell.execute_reply": "2024-08-02T11:34:29.436938Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.312241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_prostate.plot_trajectory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fb289e-ee60-42af-89d0-d33a0f4786ba",
   "metadata": {},
   "source": [
    "### Printing the list of nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fac0a2-3baf-4161-a277-10e827e3eb58",
   "metadata": {},
   "source": [
    "One important variable of the Simulation is the Network, which stores all of the information on the Boolean network. Here is the documentation related to the Network object : https://pymaboss.readthedocs.io/en/latest/api/network.html. It allows to modify initial states, or to set output nodes (which nodes will be part of the results), and can be accessed by using Simulation.Network, for example : model_prostate.network.  \n",
    "\n",
    "If we want to perform modifications on the model (changing output nodes, initial values, or simulating mutants), one important information is the list of the nodes of the model. You can print them with the following command :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05981c00-8278-430c-bc9c-5a1aa71f85ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.438248Z",
     "iopub.status.busy": "2024-08-02T11:34:29.438103Z",
     "iopub.status.idle": "2024-08-02T11:34:29.440765Z",
     "shell.execute_reply": "2024-08-02T11:34:29.440384Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.438236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Acidosis', 'AKT', 'AMPK', 'AMP_ATP', 'Androgen', 'APAF1', 'Apoptosis', 'AR', 'AR_ERG', 'ATM', 'ATR', 'AXIN1', 'BAD', 'Bak', 'BAX', 'BCL2', 'Bcl_XL', 'beta_catenin', 'BIRC5', 'BMP2', 'BRCA1', 'BRCA2', 'Carcinogen', 'Caspase3', 'Caspase8', 'Caspase9', 'CDH2', 'cFLAR', 'CHK1_2', 'COX4I2', 'CyclinB', 'CyclinD', 'CytoC', 'DAXX', 'DNA_Damage', 'DNA_Repair', 'Dsh', 'E2F1', 'eEF2', 'eEF2K', 'EGF', 'EGFR', 'EMT', 'EP300', 'ERG', 'ERK', 'ETS1', 'ETV1', 'EZH2', 'E_cadherin', 'FADD', 'FGF', 'FGFR3', 'FOS', 'FOXA1', 'FOXO', 'FRS2', 'fused_event', 'GADD45', 'GLI', 'GLUT1', 'GSH', 'GSK3', 'HIF1', 'HSPs', 'Hypoxia', 'IDH1', 'IKK', 'Invasion', 'JNK', 'JUN', 'Lactic_acid', 'LDHA', 'MAP3K1_3', 'MDM2', 'MED12', 'MEK1_2', 'Metastasis', 'Migration', 'mTORC1', 'mTORC2', 'MXI1', 'MYC', 'MYC_MAX', 'NCOA3', 'NCOR1', 'NCOR2', 'NF1', 'NF_kB', 'NKX3_1', 'Nutrients', 'p14ARF', 'p15', 'p21', 'p38', 'p53', 'p70S6kab', 'p90RSK', 'PDK1', 'PHDs', 'PI3K', 'PIP3', 'PKC', 'Proliferation', 'PTCH1', 'PTEN', 'RAF', 'RAGS', 'RAS', 'RB1', 'Rheb', 'ROS', 'RTK', 'RUNX2', 'SHH', 'Slug', 'SMAD', 'SMO', 'Snail', 'SPOP', 'TAK1', 'TCF', 'TERT', 'TGFb', 'TGFBR', 'TNFalpha', 'TSC1_2', 'TWIST1', 'VEGF', 'VHL', 'WNT', 'YWHAZ', 'ZBTB17']\n"
     ]
    }
   ],
   "source": [
    "print(model_prostate.network.names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b71e60c-285f-4097-81e9-9b667d0e2ddc",
   "metadata": {},
   "source": [
    "### Simulating the Wild Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81017947-74a6-4a9f-97dc-d58b4161aad8",
   "metadata": {},
   "source": [
    "The default simulation use all the possible combinations of input nodes, in order to show all the possible phenotypes. Now suppose we want to simulate the healthy condition for this model. It is defined in the paper as : \n",
    "\n",
    "*These healthy cells mostly exhibit quiescence (neither proliferation nor apoptosis) in the absence of any input (Figure 3A)*\n",
    "\n",
    "So in this case, we need to inactivate all the inputs. We provide here the list of inputs, created by listing all the inputs nodes in the interaction graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7da7070-3d3a-4e79-8270-943800877258",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.441348Z",
     "iopub.status.busy": "2024-08-02T11:34:29.441166Z",
     "iopub.status.idle": "2024-08-02T11:34:29.443776Z",
     "shell.execute_reply": "2024-08-02T11:34:29.443359Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.441338Z"
    }
   },
   "outputs": [],
   "source": [
    "input_nodes = ['EGF', 'FGF', 'Androgen', 'TGFb', 'Hypoxia', 'Nutrients', 'Carcinogen', 'fused_event', 'Acidosis', 'SPOP', 'TNFalpha']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4502dd-a49e-47a2-8098-fcc77f5b6bd4",
   "metadata": {},
   "source": [
    "Here is the documentation of the function to set new initial values : https://pymaboss.readthedocs.io/en/latest/api/network.html#maboss.network.Network.set_istate\n",
    "\n",
    "Complete the next block to set the initial states of all the nodes to zero : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b581a7eb-15aa-4c2e-8514-483daa23799a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.444264Z",
     "iopub.status.busy": "2024-08-02T11:34:29.444155Z",
     "iopub.status.idle": "2024-08-02T11:34:29.456978Z",
     "shell.execute_reply": "2024-08-02T11:34:29.456387Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.444255Z"
    }
   },
   "outputs": [],
   "source": [
    "model_prostate_zero = model_prostate.copy()\n",
    "for node in input_nodes:\n",
    "    model_prostate_zero.network.set_istate(node, [1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ee021f-a374-40c2-9477-22225c946e85",
   "metadata": {},
   "source": [
    "With all nodes initial values to zero, we can now simulate the model and print the trajectories of the node probabilities to reproduce Figure 3A of the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26c14d1b-92bc-432e-b94c-4833d4abb1a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:29.457889Z",
     "iopub.status.busy": "2024-08-02T11:34:29.457558Z",
     "iopub.status.idle": "2024-08-02T11:34:30.788203Z",
     "shell.execute_reply": "2024-08-02T11:34:30.787797Z",
     "shell.execute_reply.started": "2024-08-02T11:34:29.457873Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_prostate_zero = model_prostate_zero.run()\n",
    "res_prostate_zero.plot_node_trajectory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aaf2750-8422-4d9d-84ad-6a26767d22ee",
   "metadata": {},
   "source": [
    "### Simulating the growth condition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79f3e60-66cd-419e-8724-a0f91e139598",
   "metadata": {},
   "source": [
    "Now we want to simulate a case where cells are proliferating. To do this, we need to activate the input nodes which will trigger this phenotype. It is defined in the paper as : \n",
    "\n",
    "*When Nutrients and growth factors (EGF or FGF) are present, Proliferation is activated *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8deddccc-ea7b-439f-ac29-0d12f09098b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:30.788685Z",
     "iopub.status.busy": "2024-08-02T11:34:30.788569Z",
     "iopub.status.idle": "2024-08-02T11:34:30.800547Z",
     "shell.execute_reply": "2024-08-02T11:34:30.800107Z",
     "shell.execute_reply.started": "2024-08-02T11:34:30.788674Z"
    }
   },
   "outputs": [],
   "source": [
    "prostate_growth = model_prostate_zero.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc991930-5dfe-4bbd-87d6-2dd6658d8045",
   "metadata": {},
   "source": [
    "Using the same function as previously, complete the next block to set new initial values : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de8169a4-b5c9-476a-85c6-dda8ce883db6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:30.800987Z",
     "iopub.status.busy": "2024-08-02T11:34:30.800883Z",
     "iopub.status.idle": "2024-08-02T11:34:30.803859Z",
     "shell.execute_reply": "2024-08-02T11:34:30.803010Z",
     "shell.execute_reply.started": "2024-08-02T11:34:30.800977Z"
    }
   },
   "outputs": [],
   "source": [
    "prostate_growth.network.set_istate('EGF', [0, 1])\n",
    "prostate_growth.network.set_istate('Nutrients', [0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132f23ef-20ff-47f4-a2ef-0d845b859345",
   "metadata": {},
   "source": [
    "Then simulate the model, and plot the state probability distributions of the last time point to verify that we obtain a proliferative phenotype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88ff8ce8-8f56-49f1-9a8a-4e5f46d72ac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:30.804793Z",
     "iopub.status.busy": "2024-08-02T11:34:30.804604Z",
     "iopub.status.idle": "2024-08-02T11:34:31.574416Z",
     "shell.execute_reply": "2024-08-02T11:34:31.573940Z",
     "shell.execute_reply.started": "2024-08-02T11:34:30.804777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_prostate_growth = prostate_growth.run()\n",
    "res_prostate_growth.plot_piechart()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2fa625-b4cc-4fab-8ddf-3ec9638229fd",
   "metadata": {},
   "source": [
    "### Simulating MYC_MAX mutant on proliferative phenotype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f793d53c-7ca9-4603-97ec-3ecee0f9431c",
   "metadata": {},
   "source": [
    "Now we want to reproduce one result on this model : MYC_MAX inhibition will inhibit the proliferative phenotype (Appendix 1—figure 34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ea6eeb6-79d4-48eb-a6f0-cb4dfb67a371",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:31.574915Z",
     "iopub.status.busy": "2024-08-02T11:34:31.574796Z",
     "iopub.status.idle": "2024-08-02T11:34:31.584618Z",
     "shell.execute_reply": "2024-08-02T11:34:31.584299Z",
     "shell.execute_reply.started": "2024-08-02T11:34:31.574905Z"
    }
   },
   "outputs": [],
   "source": [
    "mutant_prostate_growth = prostate_growth.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22157295-7018-4210-aa03-99ef328dcddf",
   "metadata": {},
   "source": [
    "To simulate the inhibition of a node, we need to use the mutate function : https://pymaboss.readthedocs.io/en/latest/api/simulation.html#maboss.simulation.Simulation.mutate\n",
    "\n",
    "Fill the next block to modify the simulation mutant_prostate_growth to simulate an inhibition on the MYC_MAX node : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c70e160-ef8a-43e0-8086-703703001f20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:31.585210Z",
     "iopub.status.busy": "2024-08-02T11:34:31.585084Z",
     "iopub.status.idle": "2024-08-02T11:34:31.587662Z",
     "shell.execute_reply": "2024-08-02T11:34:31.587171Z",
     "shell.execute_reply.started": "2024-08-02T11:34:31.585199Z"
    }
   },
   "outputs": [],
   "source": [
    "mutant_prostate_growth.mutate(\"MYC_MAX\", \"OFF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870aab07-79da-4dd6-9949-d0ab4e0e0e77",
   "metadata": {},
   "source": [
    "Then run the simulation, and plot the state probability distribution of the final state : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfba244c-71d8-4584-8f8e-5405fd3c0f18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:31.588475Z",
     "iopub.status.busy": "2024-08-02T11:34:31.588294Z",
     "iopub.status.idle": "2024-08-02T11:34:32.366091Z",
     "shell.execute_reply": "2024-08-02T11:34:32.365634Z",
     "shell.execute_reply.started": "2024-08-02T11:34:31.588460Z"
    }
   },
   "outputs": [],
   "source": [
    "res_mutant_prostate_growth = mutant_prostate_growth.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46dc507f-9db3-44a0-9f41-cd041a210bca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:32.366813Z",
     "iopub.status.busy": "2024-08-02T11:34:32.366670Z",
     "iopub.status.idle": "2024-08-02T11:34:32.397570Z",
     "shell.execute_reply": "2024-08-02T11:34:32.396983Z",
     "shell.execute_reply.started": "2024-08-02T11:34:32.366802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_mutant_prostate_growth.plot_piechart()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ac0e04-0d64-46ba-abc5-a8d4ba2c1a99",
   "metadata": {},
   "source": [
    "### Checking multiple single node mutants "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13af5ff6-5ec0-49bb-a91b-e4747160ad6e",
   "metadata": {},
   "source": [
    "Now we want to test multiple mutants, to try to find other interesting mutations which would reduce or block proliferation. pyMaBoSS propose methods to help with this : https://pymaboss.readthedocs.io/en/latest/api/sensitivity_api.html\n",
    "\n",
    "First, we import these functions : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e7a67021-4bbc-4f3d-8ee8-fe0c601818a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:32.398095Z",
     "iopub.status.busy": "2024-08-02T11:34:32.397986Z",
     "iopub.status.idle": "2024-08-02T11:34:32.400185Z",
     "shell.execute_reply": "2024-08-02T11:34:32.399619Z",
     "shell.execute_reply.started": "2024-08-02T11:34:32.398085Z"
    }
   },
   "outputs": [],
   "source": [
    "from maboss.pipelines import simulate_single_mutants, filter_sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c8218db-12da-464c-b378-44aed542e980",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:32.400722Z",
     "iopub.status.busy": "2024-08-02T11:34:32.400599Z",
     "iopub.status.idle": "2024-08-02T11:34:32.403115Z",
     "shell.execute_reply": "2024-08-02T11:34:32.402785Z",
     "shell.execute_reply.started": "2024-08-02T11:34:32.400712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function simulate_single_mutants in module maboss.pipelines:\n",
      "\n",
      "simulate_single_mutants(model, list_nodes=[], sign='BOTH', cmaboss=False)\n",
      "    Simulates a batch of single mutants and return an array of results\n",
      "\n",
      "    :param model: the model on which to perform the simulations\n",
      "    :param list_nodes: the node(s) which are to be mutated (default: all nodes)\n",
      "    :param sign: which mutations to perform. \"ON\", \"OFF\", or \"BOTH\" (default)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(simulate_single_mutants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a81757ef-6ffa-47ce-9c60-42dbe95b450c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:32.403760Z",
     "iopub.status.busy": "2024-08-02T11:34:32.403601Z",
     "iopub.status.idle": "2024-08-02T11:34:32.406537Z",
     "shell.execute_reply": "2024-08-02T11:34:32.406243Z",
     "shell.execute_reply.started": "2024-08-02T11:34:32.403747Z"
    }
   },
   "outputs": [],
   "source": [
    "candidates_nodes = [\"AKT\", \"AR\", \"EGFR\", \"ERK\", \"MYC_MAX\", \"ROS\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c0d682-ff10-4916-a788-1f95c3df17a0",
   "metadata": {},
   "source": [
    "Fill the next block to simulate the inhibition of all these candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ef4c332-ebe0-44ab-9b43-0d5c42a1af57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:32.409088Z",
     "iopub.status.busy": "2024-08-02T11:34:32.408791Z",
     "iopub.status.idle": "2024-08-02T11:34:36.821279Z",
     "shell.execute_reply": "2024-08-02T11:34:36.820672Z",
     "shell.execute_reply.started": "2024-08-02T11:34:32.409073Z"
    }
   },
   "outputs": [],
   "source": [
    "simulations = simulate_single_mutants(prostate_growth, candidates_nodes , \"OFF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e51dcee-8feb-47d5-a04e-2ca6a19fddee",
   "metadata": {},
   "source": [
    "This function returns a dictionnary with an identifier for the mutant as key, and the simulation results as a value : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32f10191-4b6c-4289-adb2-c0442e8a4fa9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:36.821739Z",
     "iopub.status.busy": "2024-08-02T11:34:36.821635Z",
     "iopub.status.idle": "2024-08-02T11:34:36.824270Z",
     "shell.execute_reply": "2024-08-02T11:34:36.823927Z",
     "shell.execute_reply.started": "2024-08-02T11:34:36.821729Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('AKT', 'OFF'): <maboss.result.Result object at 0x71c81144c350>, ('AR', 'OFF'): <maboss.result.Result object at 0x71c8115aecf0>, ('EGFR', 'OFF'): <maboss.result.Result object at 0x71c8091f20f0>, ('ERK', 'OFF'): <maboss.result.Result object at 0x71c809176120>, ('MYC_MAX', 'OFF'): <maboss.result.Result object at 0x71c8113b0d40>, ('ROS', 'OFF'): <maboss.result.Result object at 0x71c83f544c80>}\n"
     ]
    }
   ],
   "source": [
    "print(simulations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9108652-11fb-433d-a72e-7f3adaf025bb",
   "metadata": {},
   "source": [
    "Now finish writing the next block with a code which plots the piechart for all of these simulations, with the identifier of the simulation as a title. \n",
    "\n",
    "To set the title, call plt.title('example_title') just after the piechart plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e5082953-423e-4d6a-bbe1-a60269a9f924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:36.824760Z",
     "iopub.status.busy": "2024-08-02T11:34:36.824658Z",
     "iopub.status.idle": "2024-08-02T11:34:37.114558Z",
     "shell.execute_reply": "2024-08-02T11:34:37.114259Z",
     "shell.execute_reply.started": "2024-08-02T11:34:36.824751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for simulation, result in simulations.items():\n",
    "    result.plot_piechart()\n",
    "    plt.title(simulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6965a90-3b2a-4d26-a9b0-b0e858d6e308",
   "metadata": {},
   "source": [
    "### Filter set of results which are not proliferative"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a927c66-8164-4418-80af-d6735ed72637",
   "metadata": {},
   "source": [
    "Now we can already see which results are interesting, but if we have a lot of simulation it might get handy to be able to filter the results according to their phenotypes : this is what the **filter_sensitivity** function is for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2702b672-095f-42ac-825d-a6a0f71756a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:37.115110Z",
     "iopub.status.busy": "2024-08-02T11:34:37.114987Z",
     "iopub.status.idle": "2024-08-02T11:34:37.117863Z",
     "shell.execute_reply": "2024-08-02T11:34:37.117524Z",
     "shell.execute_reply.started": "2024-08-02T11:34:37.115099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function filter_sensitivity in module maboss.pipelines:\n",
      "\n",
      "filter_sensitivity(results, state=None, node=None, minimum=None, maximum=None)\n",
      "    Filter a list of results by state of nodes value\n",
      "\n",
      "    :param results: the list of results to filter\n",
      "    :param state: the state on which to apply the filter (default None)\n",
      "    :param node: the state on which to apply the filter (default None)\n",
      "    :param minumum: the minimal value of the node (default None)\n",
      "    :param maximum: the maximal value of the node (default None)\n",
      "\n",
      "    Example :\n",
      "\n",
      "    Filtering results showing more than 50% for Proliferation node\n",
      "    >>> res_ensemble = filter_sensitivity(results, node='Proliferation', maximum=0.5)\n",
      "\n",
      "    Filtering results showing more than 10% for Apoptosis -- NonACD state\n",
      "    >>> res_ensemble = filter_sensitivity(results, state='Apoptosis -- NonACD', minimum=0.1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(filter_sensitivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4a637d-645b-4a16-85ad-eb712ca343e7",
   "metadata": {},
   "source": [
    "In the next block, filter the previous results to only select those without any proliferation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3f9d2383-6d24-48d8-a6aa-460b704dd6b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:37.118387Z",
     "iopub.status.busy": "2024-08-02T11:34:37.118255Z",
     "iopub.status.idle": "2024-08-02T11:34:37.120733Z",
     "shell.execute_reply": "2024-08-02T11:34:37.120324Z",
     "shell.execute_reply.started": "2024-08-02T11:34:37.118375Z"
    }
   },
   "outputs": [],
   "source": [
    "res_filtered = filter_sensitivity(simulations, state='Proliferation', maximum=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38eae367-fa1f-4a7f-9558-d5c9f544f186",
   "metadata": {},
   "source": [
    "You can reuse the previous code which plots the piechart for all the filtered simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d6210eb8-292a-43ea-8706-08ff6646fd77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:37.121344Z",
     "iopub.status.busy": "2024-08-02T11:34:37.121215Z",
     "iopub.status.idle": "2024-08-02T11:34:37.232451Z",
     "shell.execute_reply": "2024-08-02T11:34:37.232003Z",
     "shell.execute_reply.started": "2024-08-02T11:34:37.121333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u/oyARWFBm6iordCDnz2oZbnLTEcB/MbIziP1xIVPMK8FAYnCgla3v2y/7l52t7YXbTcdBfA7mXGZev6S59UpqpPpKIBXUVjQqr7M/1L3Zd/HtYCANhQXGqdnsp7RoORBpqMAXmM3HQD+Y+Guhbp96e2UFaCNFVUX6falt2vRrkWmowBewwgLWsW8TfP01JdPySN+nQBvscmm3w/+vW7qdZPpKECbo7DgjM38aqZe2fCK6RhAwJrYZ6J+M+A3pmMAbYrCghZzup2a9sU0LdixwHQUIOBd3f1qPXL+IwqyB5mOArQJCgtapMpZpSmfTtGn+z41HQXAf2WlZmnG8Blc8Rl+icKCZiutKdVdn9yldQXrTEcBcIJzk87VCyNfULuQdqajAK2KwoJmKa4u1h1L79DGwo2mowBoRJ+EPnp59MuKDok2HQVoNRQWNFlxdbF+seQX2nxks+koAE6jV3wv/W303xQTGmM6CtAqWIcFTVJcXaxJSyZRVgAfsfnIZk1aMknF1cWmowCtgsKC06obWdlyZIvpKACaYcuRLfrFkl+opKbEdBTgjFFY8INKa0p1x9I7GFkBfNTmI5t1x5I7VFpTajoKcEYoLGhURW2FJn88mQm2gI/bULhBkz+erEpnpekoQItRWHBKTrdTv/30t/ru0HemowBoBd8d+k6/zf6tnG6n6ShAi1BYcBKPx6OHVz6slftXmo4CoBV9vv9zPbzyYXFyKHwRhQUneWbtM/pg1wemYwBoAx/s+kB/+eovpmMAzUZhQQOvbXhNczfNNR0DQBt6beNrmruR1zl8C4UF9T7YyTsvIFA8s/YZfbCTkVT4Dla6hSRpVd4q3fXxXXJ6mJAHBIpge7BeGPmChnUcZjoKcFoUFmhPyR7dsPAG1mkAAlB0SLTeHPOmukR3MR0F+EEcEgpwpTWlunvZ3ZQVIECV1JTo7mV3q6ymzHQU4AdRWAKY2+PW71b8TruLd5uOAsCg3cW79bsVv5Pb4zYdBWgUhSWAzfxqpj7f/7npGAAs4LP9n+nZr541HQNoFIUlQH2w8wO9tvE10zEAWMirG1/lzCFYFpNuA9D6Q+t1y79vUY27xnQUABYTGhSqVy99VX3b9zUdBWiAEZYAU1xdrPs+vY+yAuCUql3Vuu/T+1RcXWw6CtAAhSXAPLTyIR0oP2A6BgALO1B+QH9c+UfTMYAGKCwB5I1Nbyg7N9t0DAA+YHnucs3bNM90DKAec1gCxMbCjbp50c2qddeajgLARzjsDr1xxRs6O+Fs01EARlgCQVlNme7/9H7KCoBmqXXX6v5P72dROVgChSUATPtimnJLc03HAOCDcktzNe2LaaZjABQWf/fPbf/Uv3P+bToGAB/275x/673t75mOgQDHHBY/lleWp2v+dY3Ka8tNRwHg4yIdkfp/P/1/SolKMR0FAYoRFj/2yKpHKCsAWkV5bbkeWfWI6RgIYBQWP/XO1ne0+sBq0zEA+JEvDnyh+dvmm46BAMUhIT/EoSAAbSXSEan3fvqeOkZ1NB0FAYYRFj/j8Xj08KqHKSsA2gSHhmAKhcXPzN82X2sOrDEdA4AfW31gtd7Z+o7pGAgwHBLyI/nl+bpywZWqcFaYjgLAz0UER+hfV/1LHSI7mI6CAMEIix+Z8eUMygoAr6hwVmjG2hmmYyCAUFj8xOoDq7VkzxLTMQAEkI9yPuJsRHgNhcUP1Lpr9cSaJ0zHABCAnlzzJNcpg1dQWPzAG5ve0O7i3aZjAAhAu4p3ad6meaZjIABQWHzcwfKDevnbl03HABDA/vfb/9XB8oOmY8DPUVh83Iy1TLQFYFaFs0JPr33adAz4OQqLD/sy/0t9lPOR6RgAoH/n/Ftr89eajgE/RmHxYc9+9azpCABQb+bXM01HgB+jsPioT/Z8ou8Of2c6BgDU++7Qd/pk7yemY8BPUVh8kMvt0nPrnjMdAwBO8vzXz8vldpmOAT9EYfFB7+98n9OYAVjSruJd+tfOf5mOAT9EYfEx1a5qvfjNi6ZjAECjXvjmBVW7qk3HgJ+hsPiYNze/qYMVrHcAwLoOVhzUPzb/w3QM+BkKiw8prSnV7PWzTccAgNOavWG2ymrKTMeAH6Gw+JC3t76tkpoS0zEA4LSKq4v11ta3TMeAH6Gw+IgqZ5Xe2PSG6RgA0GTzNs1jLgtaDYXFR7y3/T0dqTpiOgYANFlhVaHe2/6e6RjwExQWH+B0OzV341zTMQCg2V7b8JqcbqfpGPADFBYfsGj3IuWV55mOAQDNlleep8W7F5uOAT9AYbE4j8ejV9a/YjoGALTYnPVz5PF4TMeAj6OwWNyy3GXaWbzTdAwAaLGdxTu1PHe56RjwcRQWi2PuCgB/wL4MZ4rCYmGbCzdrXcE60zEA4Ix9XfC1thzZYjoGfBiFxcL+sYWlrQH4D/ZpOBMUFosqri7Wot2LTMcAgFazaNciFVcXm44BH0VhsagFOxawQiQAv1LlqtKCHQtMx4CPorBYkMfj0bvb3jUdAwBaHfs2tBSFxYLW5K9RTkmO6RgA0OpySnK05sAa0zHggygsFsQ7EAD+bP62+aYjwAdRWCympKZEy/eywBIA/7V873KV1pSajgEfQ2GxmKU5S1XjrjEdAwDaTI27Rkv3LDUdAz6GwmIxH+760HQEAGhz7OvQXBQWC8kvz9dXB78yHQMA2tza/LXKL883HQM+hMJiIQt3LZRHXNEUgP/zyMPimGgWCouFMEQKIJCwz0NzUFgsYuuRrdpxdIfpGADgNduLtmtb0TbTMeAjKCwWwdAogEC0aBf7PjQNhcUilu1dZjoCAHjdslz2fWgaCosF7CnZw1L8AALS7uLd2luy13QM+AAKiwVk52abjgAAxizPZXVvnB6FxQIoLAAC2af7PjUdAT6AwmJYcXWxvin4xnQMADBm3cF1Kq4uNh0DFkdhMeyz/Z/J6XGajgEAxjg9Tn2+/3PTMWBxFBbDOBwEAOwLcXoUFoNcbpdW7V9lOgYAGLdy/0q53C7TMWBhFBaDNhVuUmltqekYAGBcaW2pNh/ZbDoGLIzCYtCXB780HQEALOPLfPaJaByFxSBenADwPfaJ+CEUFkOcbqfWFawzHQMALGNdwTrmsaBRFBZDNhVuUnltuekYAGAZZbVl2lS4yXQMWBSFxRCGPgHgZMztQ2MoLIbwogSAk/FmDo2hsBjg9rhZjh8ATuGbgm/k8XhMx4AFUVgMyCnJYf4KAJxCWW2ZckpyTMeABVFYDGBSGQA0jn0kToXCYgAvRgBoHPtInAqFxQBejADQOPaROBUKi5d5PB5tObLFdAwAsKwtR7Yw8RYnobB4GRNuAeCHldWWaU/JHtMxYDEUFi9jqBMATo99JU5EYfEyDgcBwOmxr8SJKCxetrt4t+kIAGB57CtxIgqLl3FcFgBOb08p+0o0RGHxIpfbpX1l+0zHAADL21e6Ty63y3QMWAiFxYv2l+2X0+00HQMALK/WXau8sjzTMWAhFBYv4voYANB07DNxPAqLFzF/BQCajn0mjkdh8SJefADQdIyw4HgUFi/aW7LXdAQA8BnsM3E8CosXHaw4aDoCAPiMgooC0xFgIRQWLzpUcch0BADwGQWVFBZ8j8LiJVXOKpXWlpqOAQA+o7SmVNWuatMxYBEUFi85VMnoCgA0FyPTqENh8ZLDlYdNRwAAn8O+E3UoLF7C5DEAaD72nahDYfES3iUAQPNxOB11KCxewnFYAGg+3uyhDoXFS0pqSkxHAACfU1LNvhPHUFi8pMJZYToCAPgc9p2oQ2HxkvLactMRAMDnsO9EHQqLl1TU8i4BAJqLfSfqUFi8hBcdADQfh4RQh8LiJeVOhjUBoLk4JIQ6FBYvYYQFAJqPERbUobB4CYUFAJqPERbUobB4CVccBYDmq3HVmI4Ai6CweInb4zYdAQB8jsvjMh0BFkFh8RK3KCwA0Fwej8d0BFgEhcVLGGEBgOZj34k6FBYAgGV5xAgLjqGweIlNNtMRAMDn2G38mcIx/CZ4SZAtyHQEAPA5FBbU4TfBS2w2RlgAoLns/JnCf/Gb4CXB9mDTEQDA5wTZGZ3GMRQWLwkPDjcdAQB8TkRwhOkIsAgKi5dEOiJNRwAAn8O+E3UoLF7Ciw4Amo99J+pQWLyEYU0AaD4Op6MOhcVLIhwUFgBoLkZYUIfC4iW86ACg+dh3og6FxUs4JAQAzcfoNOpQWLyEdwkA0HzsO1GHwuIlCeEJpiMAgM9JCGPfiWMoLF7SPry96QgA4HPaR7DvxDEUFi/hRQcAzcebPdShsHgJLzoAaD72nahDYfESXnQA0HyMTqMOhcVLYsNi5bA7TMcAAJ8RYg9RTGiM6RiwCAqLFyWGJ5qOAAA+g30mjkdh8aKkiCTTEQDAZ3A4CMejsHhRp6hOpiMAgM9IbZdqOgIshMLiRenR6aYjAIDP6BLdxXQEWAiFxYt48QFA0/EmD8ejsHgRhQUAmq5zdGfTEWAhFBYvorAAQNMxwoLjUVi8KCokigt5AUATJIYncqVmNEBh8TJGWQDg9NhX4kQUFi/rGtPVdAQAsDwOB+FEFBYv6xHXw3QEALA89pU4EYXFy3on9DYdAQAs7+yEs01HgMVQWLysZ1xP2W382AGgMUG2IPWM72k6BiyGv5xeFuGIUNdo5rEAQGO6xnRVeHC46RiwGAqLAb0SepmOAACW1SuefSRORmExgHksANA49pE4FQqLAbwYAaBx7CNxKhQWA3rF92LiLQCcgt1m11nxZ5mOAQvir6YBEY4I9YhljQEAOFGP2B6KcESYjgELorAYMjB5oOkIAGA5g5IHmY4Ai6KwGMKLEgBOxr4RjaGwGDKww0DZZDMdAwAsw26za0CHAaZjwKIoLIbEhMYoMy7TdAwAsIzMuEzFhMaYjgGLorAYxNAnAHxvYAfm9qFxFBaDKCwA8L3ByYNNR4CFUVgMGtBhAOuxAID+O38lmfkraBx/LQ2KCY1Rn4Q+pmMAgHF9EvsoOiTadAxYGIXFsOFpw01HAADjslKzTEeAxVFYDMtKyzIdAQCMY1+I06GwGJYZl6lOUZ1MxwAAYzpFdVKPOC5Xgh9GYbGA4akcFgIQuBhdQVNQWCyAFyuAQMY+EE1BYbGAgckDFeWIMh0DALyunaMdy/GjSSgsFuCwO3RhpwtNxwAAr7ug0wVy2B2mY8AHUFgsYnT6aNMRAMDrLk2/1HQE+AgKi0UMTx2udiHtTMcAAK9pF9JOF6debDoGfASFxSJCgkI0qsso0zEAwGtGdxmtkKAQ0zHgIygsFvLjbj82HQEAvGZMtzGmI8CHUFgsZGCHgUqOTDYdAwDaXEpkigZ2GGg6BnwIhcVCbDabLu96uekYANDmLu96uWw2m+kY8CEUFovhsBCAQMC+Ds1FYbGYzLhMZcZlmo4BAG2mZ1xPrh2EZqOwWNDYHmNNRwCANjM2k30cmo/CYkE/yfiJwoPDTccAgFYXHhyun3T7iekY8EEUFgtqF9JOl6VfZjoGALS6y7terqgQrp2G5qOwWNT4nuNNRwCAVjcuc5zpCPBRFBaLOjvxbPVN7Gs6BgC0mnMSz9HZiWebjgEfRWGxsBvOusF0BABoNTf0Yp+GlqOwWNhl6ZcpPizedAwAOGMJYQm6tAtXZkbLUVgszBHkYC4LAL8wvud4OYIcpmPAh1FYLO6mXjdxijMAnxYRHKEbe91oOgZ8HIXF4mJCY3Rt5rWmYwBAi12bea1iQmNMx4CPo7D4gJ/3/rkcdoZSAfgeh92hCb0nmI4BP0Bh8QEdIjvoJxmsDAnA9/w046fqENnBdAz4AQqLj7j17Ftlt/G/C4DvsNvsurXPraZjwE/wF9BHpMek60edf2Q6BgA02aguo9QluovpGPATFBYfMqnvJNMRAKDJJvaZaDoC/AiFxYf0SuilUV1GmY4BAKc1qsso9UroZToG/AiFxcfcfe7dCrIFmY4BAI0KtgXr1+f+2nQM+BkKi4/pGtNVV3W/ynQMAGjUVT2uUnpMuukY8DMUFh90Z/87FRYUZjoGAJwkLChMv+z3S9Mx4IcoLD4oKSKJq54CsKQbe92opIgk0zHghygsPmpin4lqF9LOdAwAqBcdEq2JfTkzCG2DwuKjYkJjOGUQgKVM7DtR0SHRpmPAT1FYfNjPev9MqVGppmMAgFKjUnVTr5tMx4Afo7D4sNCgUP1+8O9NxwAAPTjkQYUGhZqOAT8WbDoAzkxWWpaGpw7Xp/s+NR0FQIDKSs3SxakXm47RZlwul2pra03H8DsOh0NBQU1fV8zm8Xg8bZgHXpBbmqur379a1a5q01EABJjQoFAtuHKBUtv53+Fpj8ej/Px8HT161HQUvxUbG6vk5GTZbLbTbssIix9Ia5em2/rcppe+fcl0FAABZmKfiX5ZViTVl5WkpCRFREQ06Y8qmsbj8aiiokIFBQWSpJSUlNPehxEWP1HtqtaVC67U/rL9pqMACBCpUalacNUCv5y74nK5tG3bNiUlJSkhIcF0HL9VWFiogoICZWZmnvbwEJNu/URoUKgeGPyA6RgAAsgDgx/wy7IiqX7OSkREhOEk/q3u59uUOUIUFj+SlZbF1ZwBeMWoLqM0PG246RhtjsNAbas5P18Ki5+ZOmSq4kLjTMcA4MfiQuM0dchU0zEQYCgsfiYhPEF/GPIH0zEA+LE/DP2DEsKZ1xEIXnvtNcXGxtZ//uijj6p///5GsnCWkB+6rOtlWrJniZbuWWo6CgA/M6rLKF2WfpnpGEb1ndvXq99v/c/Xe/X7HW/8+PG64oorjH3/4zHC4qceGvqQ4sPiTccA4Efiw+L10NCHTMfAGSoqKlJZWVmTtg0PD1dSUuNX3z506JCqqqpaK9oPorD4qfiweA4NAWhVU4dM5Y2Qj3I6nVq4cKHGjRunlJQU7dy5Uzk5ObLZbHrvvfc0YsQIRUREqF+/fvriiy/q73fiIaETLVq0SCkpKZo8eXKD+7UFDgn5sUvTL9WSnCVasmeJ6Sg4A+Vby3V40WFV7qmU86hTne/urOgB318R1+PxqGBBgYo+LZKr3KXwbuHqOKGjwjqF1W/jrnUr/618Fa8plrvGrajeUeo4oaMc8Y4f/N6FnxTq8OLDch51KrRTqFJuTFFkz8j62w8vPqxDiw9JktqPaa/ESxPrb6vYWaG81/OU8UiGbHbOtPB1l6ZfqtHpo03HQDOtX79ec+fO1bx581RbW6tx48Zp+fLl6tevn3JyciRJU6dO1dNPP60ePXpo6tSpuuGGG7Rjxw4FB5++Itx0001KTEzU66+/rksuuUSdO3fWhAkTNGHCBKWlpbXqc2GExc89fP7DSo5MNh0DZ8Bd7VZY5zCl/OzUK0EeXnRYhR8VKuVnKcp4JEOOGIdyZuTIVemq3yb/zXyVfF2itF+mqdvUbnJXu7Vn5h553I2vG1m8plj5b+ar/U/aK+NPGYrMjNSev+xRTWGNJKkqt0oH/99BpU1OU9rkNB1896Cq9h0bGvY4Pcqbm6eOP+9IWfEDKZEp+uPQP5qOgSYqLCzU888/r/POO08DBw7Ujh079OKLL+rAgQN66aWXdP755zfYfsqUKRozZowyMzM1bdo07dmzRzt27GjS9woODtaYMWP09ttvKz8/X/fff78++ugjde3aVT/60Y/0xhtvqLKyslWeF4XFz8WExuipi59SsI3BNF/V7px26jC2g2IGxpx0m8fjUeGSQrX/SXvFDIxRWGqYOv2ik9zVbhWvLpYkuSpcKlpRpJTrUxR1dpTCu4Qr9fZUVe2rUtnGxo9jH/7osOIujlP88HiFdQxTyk0pcsQ7dGTZEUlS9YFqhaWGKap3lKJ6RyksLUzVeceuZ3Vo8SFF9oxURDcW3fJ1wbZgPXXxU4oJPfn3D9Y0a9Ys3XPPPYqKitKOHTu0YMECXXPNNQoJCTnl9uecc079f9ctkV+3ZH5zxMTEaNKkSVqxYoVWrVql3bt3a8KECfroo49a9kROQGEJAOcmnas7+99pOgbaQO2hWjmLnYrqE1X/NbvDrsizIlWxo0KSVJlTKY/L02AbR5xDYalh9ducyO10qzKnssF9JCmqT1T9fUJTQ1VzsEY1hTWqOVyj6vxqhaaGqvpgtY5+flRJ1zQ+UQ++465z71L/pP6mY6AZbr/9dj322GPKz89X7969dcstt+iTTz6R2+0+5fYOx/eHhusWcmts2x9SVVWl+fPn66c//akuvPBCJSYm6oUXXtDIkSNb9kROQGEJEJP6TtKwjsNMx0ArcxY7JUnB0Q1H0IKjg+tvcxY7ZQu2KSiy4XU6gqKD6rc5kavUJblPftzj7xPWMUwdxnZQzowc5Tydo+RrkxXWMUx5c/OUPC5ZZRvKtH3qdu14eIfKt5a3yvOFdw3rOEwT+0w0HQPN1LFjR02dOlXbtm3TRx99pNDQUI0dO1ZdunTRAw88oI0bN7ba9/J4PPrss890++23Kzk5Wffee6969+6t7777TmvWrNGdd96pdu3atcr3orAECJvNpicufEKJ4Ymn3xi+58RpIq11SdPTPG78JfHKnJ6pzOmZir8kXkWfFckeZldE9wjtf2W/Ot/dWck3JCv3pVy5a5v/jg3mtA9vrycufIKl6X3csGHD9PLLLys/P18zZszQt99+q379+mn9+tZZ22XevHm69NJLVV5ernfeeUd79+7V9OnTddZZZ7XK4x+PiQ0BJCE8QdMvmq7bl94ut4c/Hv4gOObYS9hZ7JQj9vthXWeps/624JhgeZweucpdDUZZXCUuBXc/9S4gqF2QZNdJIzCuUlf9457IWepUwb8K1O3BbqrYVaHQ5ND6D4/Lo5r8GoWlhZ3yvrAWu82u6RdNZzVbPxIWFqbrr79e119/vfLy8hQVFaUjR46c8eOOHDlS+fn5io6OPv3GZ4jCEmCGpAzR5HMm68VvXzQdBa3A0d6h4JhglW0sU3iXcEnH5p+UbylX8rhjZ4eFp4fLFmRT2cYyxQw+NnGy9mitqvZVqcO4Dqd8XHuwXeHp4SrbWNbgFOqyjWVqd+6ph3cPvHlAiaMT5Yh3qHL3sXkzdTwuzw+ekQRrmXzOZA1OGWw6hmWZXHm2NXTs2FGSFB0dLY+n4esyNja2wdduueUW3XLLLfWfP/roo3r00UdPeixvoLAEoMn9JmvLkS1alrvMdBQ0gavKpZqDNfWf1xyuUeWeSgVFBSkkIUQJoxN06INDCu0QqpAOITr04SHZQ+2KGXqsnARFBCnu4jgdeOuAgqKCFBQZpPy38o+d4XP295Nqd/95t6IHRCvhR8feVSdemqh9/7dP4enhCu8erqLsItUW1ip+xMkLh5VtKFPNwRql/iJVkhTeLVzVB6pV+l2pao/Uyma3KTQltC1/TGglIzuP1OR+k03HAE5CYQlANptNT170pH62+GfaXrTddBycRuXuSuX8Oaf+8/x/5EuSYi+IVeovUpV4RaLcNW7lvZ53bOG4jHClT0lXUPj3h3+Sb0iW7FLuC8fmkkT1ilKn33RqsEZKTUGNnKXfHwKKGRIjZ5lTBe8XyFl8bOG4Lr/topDEhqdGumvcypuXp7RfptU/niPOoZSfpWj/7P2yOWxKnZQqewhT5qwuMy6TeSuwLJvnxPEgBIz9Zft1w4c3qKi6yHQUAIbFhcbpHz/+hzpFdTIdxRKqqqq0e/dude3aVWFhzL1qK835OfOWJ4B1iuqkZ7KeYVE5IMAF24P1TNYzlBVYGoUlwA1KHqTfD/696RgADHpg0AMalDzIdAxL4iBE22rOz5fCAl1/1vUalznOdAwABozLHKfxZ403HcNy6lZ/rag49WrQaB11P9/jV9ttDMcCIEl6cMiDyq/I14p9K0xHAeAlw1OH68EhD5qOYUlBQUGKjY2tv6ZOREQEk5FbkcfjUUVFhQoKChQbG6ugoKDT3odJt6hX6azUL5b8Qt8e+tZ0FABtrF/7fpo9erbCgplQ2hiPx6P8/HwdPXrUdBS/FRsbq+Tk5CaVQQoLGiiuLtaExRO0q3iX6SgA2khGTIbmXj6XKzA3kcvlUm1trekYfsfhcDRpZKUOhQUnyS/P188W/UwHKw6ajgKglXWI6KB5V8xTcmSy6ShAszDpFidJjkzWy6NeVnRI218bAoD3xITG6OVRL1NW4JMoLDiljNgMvTDyBYUFcXwb8AfhweH66yV/VUZshukoQItQWNCo/kn99eyIZxViDzn9xgAsK8QeoplZM9U/qb/pKECLUVjwgy7odIFmjpgph/3058gDsB6H3aFnRzyrCzpdYDoKcEYoLDiti1Mv1l+y/kJpAXyMw+7QzKyZuij1ItNRgDNGYUGTZKVlUVoAH1JXVoanDTcdBWgVFBY0WVZaFnNaAB8QYg/RsyOepazAr7AOC5pt5f6Vumf5Pap2VZuOAuAEoUGhem7Ec8xZgd+hsKBF1uav1a+X/VqltaWmowD4r3Yh7TTrklka0GGA6ShAq6OwoMW2FW3T5KWTdajykOkoQMBLCk/SS6NeUmZcpukoQJugsOCM7C/br8lLJyunJMd0FCBgpUen6+VRL6tjVEfTUYA2Q2HBGSuqKtKdH9+pDYUbTEcBAk7fxL56YeQLiguLMx0FaFOcJYQzFhcWpzmXztEFHZnkB3jTBZ0u0OzRsykrCAgUFrSKCEeEZo2cpSszrjQdBQgIV2ZcqVmXzFKEI8J0FMArOCSEVjd341zN/GqmXB6X6SiA3wmyBeneAffq52f/3HQUwKsoLGgTK/ev1P0r7ldpDac9A62lXUg7PX3x0xrWaZjpKIDXUVjQZnKKc3T3srs5gwhoBV1jumrWJbPUJbqL6SiAERQWtKnSmlLdv+J+rdy/0nQUwGdd2OlCPXXxU2oX0s50FMAYCgvanNvj1rNfPatXN75qOgrgc249+1b9ZsBvZLdxjgQCG4UFXpOdm62HVj6k4upi01EAy4sNjdVjFzzGBQyB/6KwwKvyy/P1uxW/07qCdaajAJZ1XtJ5+vPFf1ZyZLLpKIBlUFjgdU63Uy9884LmrJ8jj/j1A+rYbXZN7DNRd/W/S0H2INNxAEuhsMCYVXmr9IfP/qDCqkLTUQDjEsIS9MRFT2hYR05ZBk6FwgKjDlce1tTPp2pV3irTUQBjhnUcpscvfFyJ4YmmowCWRWGBJby77V09vfZpldeWm44CeE2kI1JTBk7RtZnXmo4CWB6FBZZxoOyAHl71sFYfWG06CtDmzk85X9OGTVNKVIrpKIBPoLDAct7Z+o7+8tVfGG2BX4p0ROq+gffpuszrTEcBfAqFBZaUV5anh1c9rDUH1piOArSaoSlDNW3YNHWM6mg6CuBzKCywLI/HowU7FujZr5/VkaojpuMALRYfFq/fnPcbXd3jatNRAJ9FYYHlldSUaNbXszR/23y5PC7TcYAmC7IFaVzPcfrVub9SdEi06TiAT6OwwGdsObJFj69+XN8c+sZ0FOC0+rfvr6lDp+qs+LNMRwH8AoUFPsXj8ej9ne9r5lczOUwES4oPi9e9A+7VlRlXymazmY4D+A0KC3xSSU2J/vfb/9XbW95WjbvGdBxAIfYQXX/W9bqj3x0c/gHaAIUFPu1A2QG98M0L+mDXB3J73KbjIADZbXb9pNtPdFf/u1hTBWhDFBb4hR1FO/TcuueUnZttOgoCSFZalu459x51j+tuOgrg9ygs8CvrCtbp2a+e1dcFX5uOAj92XtJ5unfAveqf1N90FCBgUFjgl1bsW6HZ62drXcE601HgR85NOleT+k7SxakXm44CBBwKC/zaVwe/0uz1s/X5/s9NR4EPu7DThZrUd5IGdBhgOgoQsCgsCAhbjmzRnPVztGTPEibnoknsNrtGdxmtiX0nspYKYAEUFgSUvSV79cqGV/TBzg84HRqnFBoUqh93+7Fu63ObOkd3Nh0HwH9RWBCQiqqK9M/t/9Q7W9/RgfIDpuPAAlIiUzSu5ziN7TFWcWFxpuMAOAGFBQHN5XYpOzdbb299W6sPrJZHvBwCiU02DU0ZqvE9xysrLUtB9iDTkQA0gsIC/FduSa7mb5+v93e8z7L/fi4+LF5Xdr9S1/W4TmnRaabjAGgCCgtwglp3rVbuX6kPd32oT3M/VZWrynQktIKwoDBlpWVpTLcxuqDTBXLYHaYjAWgGCgvwA8pry7V0z1J9uOtDfZn/JWcY+Ri7za7ByYM1ptsYjeoySpGOSNORALQQhQVoooKKAi3evViLdy/WpsJNzHexKJts6p3QW5d3vVyXd71cSRFJpiMBaAUUFqAFCioKlJ2brezcbP0n/z+qdlWbjhTQQoNCNTh5sLLSspSVlkVJAfwQhQU4QxW1FfriwBfKzs3Win0rmLDrJfFh8bo49WJlpWXp/JTzFeGIMB0JQBuisACtyO1xa1PhJv0n/z/6Mv9LrStYp/LactOx/EKkI1LnJp2rQcmDNDh5sHon9JbdZjcdC4CXUFiANuRyu7SpcJO+PPglBaaZji8ogzoMUu+E3qyTAgQwCgvgRS63S9uKtmlT4ab6j21F2wL+MgEh9hBlxmWqd0Lv+o/MuEwKCoB6FBbAsFp3rXYe3VlfYLYe2aqckhwdrT5qOlqbiA2NVXp0unrG96wvJxmxGayLAuAHUVgAiyquLlZOSY72lOxRTvGxf/eU7FFuaa4qnBWm4/2giOAIpbVLU5foLuoS3UXpMenH/o1OV0xojOl4AHwQhQXwQWU1ZTpUeUiHKw+roKJAhysP61DFIRVUFqiwslClNaWqcFaovLZcFbUVqnRWtnjdGJtsCg8OV4QjQpGOSEUER6hdSDslhCcoKTxJ7SPaKzE8UUkRSUoMT1T78PaKColq5WcMINBRWIAA4PF4VOmsVHltucpry1XrrpXb45ZHnvrVe+02u2yyyW6zy2F3KNIRqUhHpMKDw2Wz2Qw/AwCBjsICAAAsj0UMAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5VFYAACA5f1/v+7ADD6uHo4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for simulation, result in res_filtered.items():\n",
    "    result.plot_piechart()\n",
    "    plt.title(simulation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19167bd0-1b9a-4b42-aab2-1e3ad25dc0db",
   "metadata": {},
   "source": [
    "### Working with personalized models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336afc52-d6d9-426b-bbcd-ff55e4723ae5",
   "metadata": {},
   "source": [
    "In this article, Montagud et al. generated multiple personalized versions of the model, corresponding to patients or cell lines. Here, we're going to work with a subset of them for simplicity. \n",
    "In the next block, we build a dictionnary of models, with an identifier as key, and the Simulation object as value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1c7e4391-9a85-4ecc-978d-49972facf6be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:37.233046Z",
     "iopub.status.busy": "2024-08-02T11:34:37.232911Z",
     "iopub.status.idle": "2024-08-02T11:34:40.958766Z",
     "shell.execute_reply": "2024-08-02T11:34:40.958382Z",
     "shell.execute_reply.started": "2024-08-02T11:34:37.233034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_0': <maboss.simulation.Simulation at 0x71c8083b9c40>,\n",
       " 'model_1': <maboss.simulation.Simulation at 0x71c80825a450>,\n",
       " 'model_2': <maboss.simulation.Simulation at 0x71c81153a360>,\n",
       " 'model_3': <maboss.simulation.Simulation at 0x71c8087f4410>,\n",
       " 'model_4': <maboss.simulation.Simulation at 0x71c808382240>,\n",
       " 'model_5': <maboss.simulation.Simulation at 0x71c8080b5790>,\n",
       " 'model_6': <maboss.simulation.Simulation at 0x71c808351b20>,\n",
       " 'model_7': <maboss.simulation.Simulation at 0x71c808332660>,\n",
       " 'model_8': <maboss.simulation.Simulation at 0x71c8085a0ec0>,\n",
       " 'model_9': <maboss.simulation.Simulation at 0x71c8080b6360>}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_models = {}\n",
    "for i in range(10):\n",
    "    model_i = maboss.load(\n",
    "        \"models/prostate_models/PC_20191219_TCGA_mutCNA_RNA_%d.bnd\" % i,\n",
    "        \"models/prostate_models/PC_20191219_TCGA_mutCNA_RNA_%d.cfg\" % i\n",
    "    )\n",
    "    patients_models.update({'model_%d'%i : model_i})\n",
    "patients_models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "878ea60c-9829-4ba8-8ef0-92b46ae1b906",
   "metadata": {},
   "source": [
    "Now in the next block, simulate all of these models, and build a panda DataFrame with the state probability distribution of the last time point, for all the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "65aeec3b-0af3-4612-948f-2c4240392d94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:34:40.959288Z",
     "iopub.status.busy": "2024-08-02T11:34:40.959177Z",
     "iopub.status.idle": "2024-08-02T11:35:08.474869Z",
     "shell.execute_reply": "2024-08-02T11:35:08.474471Z",
     "shell.execute_reply.started": "2024-08-02T11:34:40.959279Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "last_states = pandas.DataFrame()\n",
    "for name, model in patients_models.items():\n",
    "    res = model.run()\n",
    "    last_state = res.get_last_states_probtraj()\n",
    "    \n",
    "    last_state.index = [name]\n",
    "    last_states = pandas.concat([last_states, last_state])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db88b952-3f58-4294-9e7a-fadd06223e83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:35:08.475363Z",
     "iopub.status.busy": "2024-08-02T11:35:08.475258Z",
     "iopub.status.idle": "2024-08-02T11:35:08.481935Z",
     "shell.execute_reply": "2024-08-02T11:35:08.481683Z",
     "shell.execute_reply.started": "2024-08-02T11:35:08.475354Z"
    }
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>&lt;nil&gt;</th>\n",
       "      <th>Apoptosis</th>\n",
       "      <th>Apoptosis -- Proliferation</th>\n",
       "      <th>Apoptosis -- Quiescence</th>\n",
       "      <th>Proliferation</th>\n",
       "      <th>Proliferation -- Quiescence</th>\n",
       "      <th>Quiescence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_0</th>\n",
       "      <td>0.001810</td>\n",
       "      <td>0.212600</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.319948</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.464333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_1</th>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.135201</td>\n",
       "      <td>0.009929</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>0.341369</td>\n",
       "      <td>0.001346</td>\n",
       "      <td>0.503132</td>\n",
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       "    <tr>\n",
       "      <th>model_2</th>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.383903</td>\n",
       "      <td>0.019466</td>\n",
       "      <td>0.007040</td>\n",
       "      <td>0.048759</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>0.527323</td>\n",
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       "    <tr>\n",
       "      <th>model_3</th>\n",
       "      <td>0.005770</td>\n",
       "      <td>0.025800</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.289956</td>\n",
       "      <td>0.005082</td>\n",
       "      <td>0.661392</td>\n",
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       "    <tr>\n",
       "      <th>model_4</th>\n",
       "      <td>0.001738</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014528</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.979426</td>\n",
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       "    <tr>\n",
       "      <th>model_5</th>\n",
       "      <td>0.009011</td>\n",
       "      <td>0.198506</td>\n",
       "      <td>0.012265</td>\n",
       "      <td>0.003346</td>\n",
       "      <td>0.049208</td>\n",
       "      <td>0.002824</td>\n",
       "      <td>0.724840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_6</th>\n",
       "      <td>0.007439</td>\n",
       "      <td>0.404426</td>\n",
       "      <td>0.080145</td>\n",
       "      <td>0.002196</td>\n",
       "      <td>0.107413</td>\n",
       "      <td>0.003245</td>\n",
       "      <td>0.395137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_7</th>\n",
       "      <td>0.007127</td>\n",
       "      <td>0.119243</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.193702</td>\n",
       "      <td>0.002600</td>\n",
       "      <td>0.672728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_8</th>\n",
       "      <td>0.006714</td>\n",
       "      <td>0.183271</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.079311</td>\n",
       "      <td>0.004214</td>\n",
       "      <td>0.718695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_9</th>\n",
       "      <td>0.009955</td>\n",
       "      <td>0.291753</td>\n",
       "      <td>0.008096</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>0.172686</td>\n",
       "      <td>0.003192</td>\n",
       "      <td>0.502776</td>\n",
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      ],
      "text/plain": [
       "            <nil>  Apoptosis  Apoptosis -- Proliferation  \\\n",
       "model_0  0.001810   0.212600                    0.000200   \n",
       "model_1  0.002977   0.135201                    0.009929   \n",
       "model_2  0.011158   0.383903                    0.019466   \n",
       "model_3  0.005770   0.025800                    0.012000   \n",
       "model_4  0.001738   0.000400                         NaN   \n",
       "model_5  0.009011   0.198506                    0.012265   \n",
       "model_6  0.007439   0.404426                    0.080145   \n",
       "model_7  0.007127   0.119243                    0.003200   \n",
       "model_8  0.006714   0.183271                    0.003600   \n",
       "model_9  0.009955   0.291753                    0.008096   \n",
       "\n",
       "         Apoptosis -- Quiescence  Proliferation  Proliferation -- Quiescence  \\\n",
       "model_0                 0.000400       0.319948                     0.000709   \n",
       "model_1                 0.006046       0.341369                     0.001346   \n",
       "model_2                 0.007040       0.048759                     0.002351   \n",
       "model_3                      NaN       0.289956                     0.005082   \n",
       "model_4                      NaN       0.014528                     0.003907   \n",
       "model_5                 0.003346       0.049208                     0.002824   \n",
       "model_6                 0.002196       0.107413                     0.003245   \n",
       "model_7                 0.001400       0.193702                     0.002600   \n",
       "model_8                 0.004195       0.079311                     0.004214   \n",
       "model_9                 0.011543       0.172686                     0.003192   \n",
       "\n",
       "         Quiescence  \n",
       "model_0    0.464333  \n",
       "model_1    0.503132  \n",
       "model_2    0.527323  \n",
       "model_3    0.661392  \n",
       "model_4    0.979426  \n",
       "model_5    0.724840  \n",
       "model_6    0.395137  \n",
       "model_7    0.672728  \n",
       "model_8    0.718695  \n",
       "model_9    0.502776  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_states"
   ]
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  {
   "cell_type": "markdown",
   "id": "45fb7032-191d-4a1b-9eb8-a56330fb5f11",
   "metadata": {},
   "source": [
    "We can observe a lot of variations in the obtained phenotypes.\n",
    "\n",
    "Now let's go back to our initial problem : the inhibition of the proliferative phenotype. Fill the next block by building a similar array, but this time simulating the proliferative phenotype with a MYC_MAX inhibition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d52a9f48-261a-4b3e-bd0f-69de6ee14ebd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:35:08.482392Z",
     "iopub.status.busy": "2024-08-02T11:35:08.482282Z",
     "iopub.status.idle": "2024-08-02T11:35:32.700779Z",
     "shell.execute_reply": "2024-08-02T11:35:32.700231Z",
     "shell.execute_reply.started": "2024-08-02T11:35:08.482383Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "last_states = pandas.DataFrame()\n",
    "for name, model in patients_models.items():\n",
    "    t_model = model.copy()\n",
    "    t_model.network.set_istate('EGF', [0, 1])\n",
    "    t_model.network.set_istate('Nutrients', [0, 1])\n",
    "    t_model.mutate('MYC_MAX', 'OFF')\n",
    "    res = t_model.run()\n",
    "    last_state = res.get_last_states_probtraj()\n",
    "    \n",
    "    last_state.index = [name]\n",
    "    last_states = pandas.concat([last_states, last_state])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0a519fb9-89b8-483f-abe5-79e1a255609c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:35:32.701274Z",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>&lt;nil&gt;</th>\n",
       "      <th>Apoptosis</th>\n",
       "      <th>Apoptosis -- Proliferation -- Quiescence</th>\n",
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       "      <th>Proliferation -- Quiescence</th>\n",
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       "      <th>Apoptosis -- Quiescence</th>\n",
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       "    <tr>\n",
       "      <th>model_0</th>\n",
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       "      <td>0.331844</td>\n",
       "      <td>0.000600</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>model_1</th>\n",
       "      <td>0.003166</td>\n",
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       "      <th>model_2</th>\n",
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       "      <td>0.001800</td>\n",
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       "      <td>0.005326</td>\n",
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       "      <th>model_3</th>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.035200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027279</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.929572</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>model_4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>model_5</th>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.204660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.780929</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.003800</td>\n",
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       "    <tr>\n",
       "      <th>model_6</th>\n",
       "      <td>0.006431</td>\n",
       "      <td>0.386516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114596</td>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.401473</td>\n",
       "      <td>0.084846</td>\n",
       "      <td>0.003021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_7</th>\n",
       "      <td>0.003320</td>\n",
       "      <td>0.108234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.224400</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.658653</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.000966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_8</th>\n",
       "      <td>0.004131</td>\n",
       "      <td>0.173463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073723</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.734484</td>\n",
       "      <td>0.005315</td>\n",
       "      <td>0.005137</td>\n",
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       "    <tr>\n",
       "      <th>model_9</th>\n",
       "      <td>0.007383</td>\n",
       "      <td>0.286068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.172343</td>\n",
       "      <td>0.002136</td>\n",
       "      <td>0.514402</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.012667</td>\n",
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      "text/plain": [
       "            <nil>  Apoptosis  Apoptosis -- Proliferation -- Quiescence  \\\n",
       "model_0  0.000956   0.212600                                    0.0002   \n",
       "model_1  0.003166   0.149669                                       NaN   \n",
       "model_2  0.013235   0.408120                                       NaN   \n",
       "model_3  0.003931   0.035200                                       NaN   \n",
       "model_4       NaN        NaN                                       NaN   \n",
       "model_5  0.003573   0.204660                                       NaN   \n",
       "model_6  0.006431   0.386516                                       NaN   \n",
       "model_7  0.003320   0.108234                                       NaN   \n",
       "model_8  0.004131   0.173463                                       NaN   \n",
       "model_9  0.007383   0.286068                                       NaN   \n",
       "\n",
       "         Proliferation  Proliferation -- Quiescence  Quiescence  \\\n",
       "model_0       0.331844                     0.000600    0.453800   \n",
       "model_1       0.255045                     0.001273    0.585616   \n",
       "model_2       0.024174                     0.001800    0.541747   \n",
       "model_3       0.027279                     0.003218    0.929572   \n",
       "model_4            NaN                          NaN    1.000000   \n",
       "model_5       0.005602                     0.001198    0.780929   \n",
       "model_6       0.114596                     0.003117    0.401473   \n",
       "model_7       0.224400                     0.002827    0.658653   \n",
       "model_8       0.073723                     0.003746    0.734484   \n",
       "model_9       0.172343                     0.002136    0.514402   \n",
       "\n",
       "         Apoptosis -- Proliferation  Apoptosis -- Quiescence  \n",
       "model_0                         NaN                      NaN  \n",
       "model_1                    0.001600                 0.003631  \n",
       "model_2                    0.005598                 0.005326  \n",
       "model_3                    0.000800                      NaN  \n",
       "model_4                         NaN                      NaN  \n",
       "model_5                    0.000239                 0.003800  \n",
       "model_6                    0.084846                 0.003021  \n",
       "model_7                    0.001600                 0.000966  \n",
       "model_8                    0.005315                 0.005137  \n",
       "model_9                    0.005000                 0.012667  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36311cff-b15f-4e55-ac28-63a26677effe",
   "metadata": {},
   "source": [
    "Now filter this dataframe to only show the personalized models which show less than 10% of Proliferation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c53efb54-1467-46ef-ad56-40e2f9fd0a51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-02T11:35:32.707991Z",
     "iopub.status.busy": "2024-08-02T11:35:32.707740Z",
     "iopub.status.idle": "2024-08-02T11:35:32.713319Z",
     "shell.execute_reply": "2024-08-02T11:35:32.713012Z",
     "shell.execute_reply.started": "2024-08-02T11:35:32.707978Z"
    }
   },
   "outputs": [
    {
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       "      <td>0.541747</td>\n",
       "      <td>0.005598</td>\n",
       "      <td>0.005326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_3</th>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.035200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027279</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.929572</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_5</th>\n",
       "      <td>0.003573</td>\n",
       "      <td>0.204660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.780929</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.003800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_8</th>\n",
       "      <td>0.004131</td>\n",
       "      <td>0.173463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073723</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.734484</td>\n",
       "      <td>0.005315</td>\n",
       "      <td>0.005137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            <nil>  Apoptosis  Apoptosis -- Proliferation -- Quiescence  \\\n",
       "model_2  0.013235   0.408120                                       NaN   \n",
       "model_3  0.003931   0.035200                                       NaN   \n",
       "model_5  0.003573   0.204660                                       NaN   \n",
       "model_8  0.004131   0.173463                                       NaN   \n",
       "\n",
       "         Proliferation  Proliferation -- Quiescence  Quiescence  \\\n",
       "model_2       0.024174                     0.001800    0.541747   \n",
       "model_3       0.027279                     0.003218    0.929572   \n",
       "model_5       0.005602                     0.001198    0.780929   \n",
       "model_8       0.073723                     0.003746    0.734484   \n",
       "\n",
       "         Apoptosis -- Proliferation  Apoptosis -- Quiescence  \n",
       "model_2                    0.005598                 0.005326  \n",
       "model_3                    0.000800                      NaN  \n",
       "model_5                    0.000239                 0.003800  \n",
       "model_8                    0.005315                 0.005137  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_states[last_states['Proliferation'] < 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b7fefa3-b20c-4cc2-9e0d-74adbc4d7abd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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