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   "source": [
    "# UPMaBoSS notebook for the cell fate model"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter notebook implements all the analyses presented in the `UPMaBoSS: a tool for dynamic cell population\n",
    "modeling` manuscript by Stoll et al., which will be available on [bioRxiv.org](https://www.biorxiv.org/search/uppmaboss) in the coming days (as of 2019-05-24).\n",
    "\n",
    "There are two options to run this notebook.\n",
    "\n",
    "1) With the corresponding docker image\n",
    "\n",
    "All necessary software and files are available in the\n",
    "[docker image for the CoLoMoTo notebook](http://www.colomoto.org/notebook)\n",
    "starting with version `2019-05-24`. Further information and usage instructions are available\n",
    "in the documentation of the CoLoMoTo notebook.\n",
    "Launch the docker application on your desktop using the *colomoto-docker* wrapper with the following command:\n",
    "\n",
    "    colomoto-docker -V 2019-05-24\n",
    "    \n",
    "This notebook can then be found in the folder: `usecases/Population-level Cell Fate with UpPMaBoSS`.\n",
    "\n",
    "\n",
    "2) By creating a conda environment locally\n",
    "\n",
    "We suggest to download miniconda3 and create an environment (here named named *umb*) with the following command:\n",
    "\n",
    "     conda create -n umb -c colomoto -c potassco pymaboss notebook seaborn ginsim-python\n",
    "     \n",
    "To launch this environment, you need to activate it with the following command before launching the jupyter notebook:\n",
    "\n",
    "    conda activate umb\n",
    "    jupyter notebook"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UPMaBoSS computes the evolution and the dynamics of a population of cells taking into account both their intracellular and intercellular regulations.\n",
    "\n",
    "Simulations with UPMaBoSS are based on a logical model describing the intracellular regulations (logical regulatory graph complemented with logical rules), taking into account cell death, cell division, and intercellular communications. \n",
    "\n",
    "The logical model of an individual cell is described as a MaBoSS model. At predefined intervals, the simulation of the cell population is stopped and the status of each cell of the population is updated according to the value of the variables representing cell death and cell division, and the value of the variables accounting for the activation or inhibition of signals coming from other cells. The simulation is then continued with the updated conditions until the maximum simulation time is reached. Since MaBoSS results are probabilistic, the simulation outputs of UpPMaBoSS can be interpreted as the behaviour of a cell population. \n",
    "\n",
    "To run UPMaBoSS, three files are needed: a model file (in BND format), a configuration file with the transition rates, the simulation parameters and the initial conditions (in CFG format), and a file defining the update parameters (in UPP format).\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook presented illustrate the use of UPMaBoSS with the analysis of a modified version of the cell fate model previously published by Calzone et al. (2010). Three files related to the logical model used for the analysis are provided together with this notebook:\n",
    "- the model files: *CellFateModel_uppmaboss.bnd* and *CellFateModel_uppmaboss.cfg*\n",
    "- the update configuration file for UPMaBoSS: *CellFateModel_uppmaboss.upp* \n",
    "\n",
    "The code included in the following cell sets the working environment, and particular load different libraries required to run the notebook."
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Set up working environment\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "import maboss\n",
    "import ginsim\n",
    "import time\n",
    "\n",
    "# Set seaborn style and create color maps: white-black scale and blue-light-red for ratios\n",
    "sns.set_style(\"white\")\n",
    "colormap_scale = sns.light_palette(\"black\", as_cmap=True)\n",
    "colormap_ratios = sns.diverging_palette(240, 0, center='light', as_cmap=True)\n",
    "\n",
    "# Shortcut to save figures with a common pattern and format\n",
    "def save_figure(figure, title):\n",
    "    figure.savefig(\"figure_%s.pdf\" % title, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original cell fate model (Calzone et al., 2010) was defined to study the cell fate decision process in response to the engagement of the death receptors TNFR and Fas.  Depending to the presence of the corresponding  ligands, several pathways can be activated: survival through NFkB activation, death through necrosis (NonACD), or apoptosis. \n",
    "This model has been extended to account for a documented activation of TNFalpha by NFkB (bold arrow), as well as by the addition of two output nodes DIVISION and DEATH, as shown in the regulatory graph using GINsim with the following command line. "
   ]
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  <g id="Apoptosis" class="node node_outputs">
    <ellipse id="Apoptosis_shape" class="shape" cx="842.0" cy="736.5" rx="45.0" ry="12.5"/>
    <text  id="Apoptosis_text" x="842" y="739" text-anchor="middle">Apoptosis</text>
  </g>
  <g id="Survival" class="node node_outputs">
    <ellipse id="Survival_shape" class="shape" cx="199.0" cy="734.5" rx="45.0" ry="12.5"/>
    <text  id="Survival_text" x="199" y="737" text-anchor="middle">Survival</text>
  </g>
  <g id="DIVISION" class="node node_outputs">
    <ellipse id="DIVISION_shape" class="shape" cx="192.0" cy="809.5" rx="45.0" ry="12.5"/>
    <text  id="DIVISION_text" x="192" y="812" text-anchor="middle">DIVISION</text>
  </g>
  <g id="DEATH" class="node node_outputs">
    <ellipse id="DEATH_shape" class="shape" cx="722.0" cy="809.5" rx="45.0" ry="12.5"/>
    <text  id="DEATH_text" x="722" y="812" text-anchor="middle">DEATH</text>
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrg = ginsim.load(\"http://ginsim.org/sites/default/files/CellFate_multiscale.zginml\")\n",
    "ginsim.show(lrg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The BND file lists all the logical rules for each of the variables.\n",
    "The CFG file defines all the parameters for the simulations (transition rates, simulation parameters and initial conditions).\n",
    "The UPP file informs on the intercellular conditions and population updates.\n",
    "These files are loaded with the code contained in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the required files\n",
    "bnd_file =\"CellFateModel_uppmaboss.bnd\"\n",
    "cfg_WT = \"CellFateModel_uppmaboss.cfg\"\n",
    "upp_file = \"CellFateModel_uppmaboss.upp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MaBoSS simulation of the wild type model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running UPMaBoSS, we strongly suggest to run an initial simulation of the wild type model with MaBoSS for the whole simulation time. This enables the user to verify how the population behaves in a normal MaBoSS run without taking into account cell death or cell division.  Here, the total (virtual) simulation time is 48 hours. \n",
    "The output results are restricted to the three model nodes \"Death\", \"Division\" and \"NFkB\". Other nodes can be considered in the output, but the most important is to select all the nodes that are used for population updating, as shown in the UPP file below. By considering these nodes in the output, MaBoSS computes explicitly the values of these nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "death = Death;\n",
      "division = Division ;\n",
      "$TNF_induc u= $ProdTNF_NFkB*p[(NFkB,Death) = (1,0)];\n",
      "steps = 48;\n",
      "MaBoSS = MaBoSS\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ufile = open(upp_file, 'r') \n",
    "print(ufile.read())\n",
    "ufile.close()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Note that the variable \"Death\" monitors death by apoptosis or by non-apoptotic cell death, while the variable \"Division\" reports the activity of NFkB.\n",
    "\n",
    "The following command lines load the wildtype model and set the maximal global simulation time to 48 hours (and run it for measuring the executation time, to be compared with UPMaBoSS below):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  0.47714616854985553  mins\n"
     ]
    }
   ],
   "source": [
    "# Load the Wild-Type model\n",
    "model_WT = maboss.load(bnd_file, cfg_WT)\n",
    "\n",
    "# Set some parameters in the cfg file: \"max_time\" \n",
    "# and select the variables considered to be internal (not appearing in the resulting csv file)\n",
    "# here, only the two outputs will appear: Death and Division in the figures\n",
    "model_48h = maboss.copy_and_update_parameters(model_WT, {'max_time':48})\n",
    "start = time.time()\n",
    "run_48h = model_48h.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")\n",
    "for name in \"mcIAP mXIAP mROS NonACD Apoptosis Survival TNF ATP FADD cIAP FASL TNFR DISC_TNF DISC_FAS RIP1 RIP1ub RIP1K IKK CASP8 BAX BCL2 ROS MPT MOMP SMAC Cyt_c XIAP apoptosome CASP3 cFLIP\".split(' '):\n",
    "    model_48h.network[name].is_internal = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the nodes considered in the output and launch the simulation with the following command lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_48h.network.set_output(('Apoptosis','NonACD','Survival'))\n",
    "run_48h = model_48h.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the simulation can be displayed with the following command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results of the WT simulation\n",
    "figure = plt.figure(figsize = (6,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "outPlot = run_48h.get_nodes_probtraj().plot(ax=plt.gca(),fontsize=15)\n",
    "outPlot.set_xlabel(\"hours\",fontsize=15)\n",
    "outPlot.set_ylabel(\"probability\",fontsize=15)\n",
    "save_figure(figure, 'MaBoSS_phenotypes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the mean temporal profiles of the nodes Apoptosis, Division, NonACD and Survival computed for 100,000 trajectories (as specified in the BND file) simulations over 48h. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UPMaBoSS simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now wish to explore the population dynamics for the cell fate model considering cell death, cell division and intercellular events. \n",
    "To do so, we divide the 48 hours into 48 runs (the variable \"steps\" is set to 48 in CellFateModel_uppmaboss.upp file) of 1 for each time steps (the variable \"max_time\" is set to 1 in CellFateModel_uppmaboss.cfg file). This choice of the value 1 is based on the analysis of the profile of the MaBoSS simulation above, as such a time-step enables to capture transient changes of activities of the output nodes Death, Division and NFkB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wild Type simulations with and without the feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider two conditions: when the cells evolve without inter-cellular communication (model referred to as \"NoFB\") and when the presence of NFkB affects that status of other cells (model referred to as \"WT\"). The model with the feedback considers that NFkB activates TNFR in other cells. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell run UpPMaBoSS on the WT model, calling iteratively 48 times MaBoSS and updating the input variables between each of these calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exec. time:  2.3992390831311545  mins\n"
     ]
    }
   ],
   "source": [
    "# Run UPMaBoSS on the WT model and collect population ratios\n",
    "# A folder named \"WT\" \"is created with the 48 runs\n",
    "uppModel_WT = maboss.UpdatePopulation(model_WT, upp_file)\n",
    "start = time.time()\n",
    "run_WT = uppModel_WT.run()\n",
    "stop = time.time()\n",
    "duration = (stop-start)/60\n",
    "print(\"exec. time: \",duration,\" mins\")\n",
    "pop_ratios_WT = run_WT.get_population_ratios('NFkB -> TNF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a model variant and launch a 48h UPMaBoSS simulation for the NoFB model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a variant without the population feedback NFkB -> TNF\n",
    "model_NoFB = maboss.copy_and_update_parameters(model_WT, {'$ProdTNF_NFkB': 0})\n",
    "\n",
    "# Run UPMaBoSS for the model without population feedback\n",
    "# A folder named \"NoFB\" \"is created with the 48 runs\n",
    "uppModel_NoFB = maboss.UpdatePopulation(model_NoFB, upp_file)\n",
    "run_NoFB = uppModel_NoFB.run('NoFB')\n",
    "pop_ratios_NoFB = run_NoFB.get_population_ratios('No NFkB -> TNF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now display the results of the two previous UPMaBoSS simulation in a single graph with the following command lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot population ratio with/without population feedback\n",
    "# A figure is saved in the folder with the name: \"figure_ratios.pdf\"\n",
    "figure = plt.figure(figsize = (6,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "pop_ratios_WT.plot(style='.-k', legend=True,fontsize=15)\n",
    "outPlot = pop_ratios_NoFB.plot(style='.-b', legend=True)\n",
    "outPlot.set_xlabel(\"hours\",fontsize=15)\n",
    "outPlot.set_ylabel(\"probability\",fontsize=15)\n",
    "save_figure(figure, 'ratios')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, comparing the curves obtained after a pulse of TNF at t = 0 in the absence (blue curve) or in the presence (black curve) of the feedback from NFκB to TNFα, we see that the TNF paracrine loop leads to a significant decrease of the population size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparative simulations of different TNF treatments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we simulate the effect of several scenarios for TNF treatments on the wild type cell fate model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first run a simulation for 48 hours without TNF treatment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model with no TNF (TNF OFF)\n",
    "model_noTNF = model_WT.copy()\n",
    "model_noTNF.network.set_istate('TNF', [1,0])\n",
    "\n",
    "# Run UPMaBoSS for the model with no TNF\n",
    "# A folder named \"noTNF\" \"is created with the 48 runs\n",
    "uppModel_noTNF = maboss.UpdatePopulation(model_noTNF, upp_file)\n",
    "run_noTNF = uppModel_noTNF.run('noTNF')\n",
    "pop_ratios_noTNF = run_noTNF.get_population_ratios('noTNF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then simulate a constant treatment for another 48h on a population of cells that received an initial TNF treatment (reTNF) and compare this simulation with the treatment of a population that was not initially treated with TNF (reTNF_noTNF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model for a second treatment of TNF\n",
    "model_reTNF = maboss.copy_and_update_parameters(model_WT, {'$TNF_induc': 20})\n",
    "\n",
    "# Run UPMaBoSS for the second treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_reTNF\" is created with the 48 runs\n",
    "uppModel_TNF_reTNF = maboss.UpdatePopulation(model_reTNF, upp_file, previous_run=run_WT)\n",
    "run_TNF_reTNF = uppModel_TNF_reTNF.run('TNF_reTNF')\n",
    "pop_ratios_TNF_reTNF = run_TNF_reTNF.get_population_ratios('TNF')\n",
    "\n",
    "# Run UPMaBoSS for a treatment of TNF after a run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_reTNF\" \"is created with the 48 runs\n",
    "uppModel_noTNF_reTNF = maboss.UpdatePopulation(model_reTNF, upp_file, previous_run=run_noTNF)\n",
    "run_noTNF_reTNF = uppModel_noTNF_reTNF.run('noTNF_reTNF')\n",
    "pop_ratios_noTNF_reTNF = run_noTNF_reTNF.get_population_ratios('TNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_TNF_noTNF = maboss.UpdatePopulation(model_WT, upp_file, previous_run=run_WT)\n",
    "# note that model_WT has TNF initially ON, but this initial conditition is replaced by previous_run=run_WT\n",
    "run_TNF_noTNF = uppModel_TNF_noTNF.run('TNF_noTNF')\n",
    "pop_ratios_TNF_noTNF = run_TNF_noTNF.get_population_ratios('noTNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_noTNF_noTNF = maboss.UpdatePopulation(model_WT, upp_file, previous_run=run_noTNF)\n",
    "run_noTNF_noTNF = uppModel_noTNF_noTNF.run('noTNF_noTNF')\n",
    "pop_ratios_noTNF_noTNF = run_noTNF_noTNF.get_population_ratios('noTNF')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the population ratios computed for the two protocols: \n",
    "1. (Pulse of TNF) + TNF, \n",
    "2. (no pulse of TNF) + TNF. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot population ratios for a second treatment of TNF with or without the initial TNF stimulation\n",
    "# A figure \"figure_two_steps_ratios.pdf\" is saved\n",
    "figure = plt.figure(figsize = (12,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "pop_ratios_WT.name = \"TNFPulse\"\n",
    "pop_ratios_WT.plot(style='.-k', legend=True,fontsize=20)\n",
    "pop_ratios_noTNF.plot(style='.-b', legend=True)\n",
    "pop_ratios_TNF_reTNF.plot(style='.-r', legend=True)\n",
    "pop_ratios_noTNF_reTNF.plot(style='.-g', legend=True)\n",
    "save_figure(figure, 'two_steps_ratios')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot population ratios for a second treatment of TNF with or without the initial TNF stimulation, starting at time=48\n",
    "# Plot the control in dashed lines\n",
    "# A figure \"figure_second_steps_ratios.pdf\" is saved\n",
    "figure = plt.figure(figsize = (12,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "plt.axis((48,96,0,1.7))\n",
    "norm_pop_ratios_TNF_reTNF = pd.core.series.Series(index=pop_ratios_TNF_reTNF.index,data=pop_ratios_TNF_reTNF.values/pop_ratios_TNF_reTNF.values[0])\n",
    "norm_pop_ratios_TNF_reTNF.name = \"TNFPulse_TNF\"\n",
    "outPlot = norm_pop_ratios_TNF_reTNF.plot(style='.-r', legend=True,fontsize=20)\n",
    "\n",
    "norm_pop_ratios_TNF_noTNF = pd.core.series.Series(index=pop_ratios_TNF_noTNF.index,data=pop_ratios_TNF_noTNF.values/pop_ratios_TNF_noTNF.values[0])\n",
    "norm_pop_ratios_TNF_noTNF.name = \"TNFPulse_noTNF\"\n",
    "norm_pop_ratios_TNF_noTNF.plot(style='--r', legend=True)\n",
    "\n",
    "norm_pop_ratios_noTNF_reTNF = pd.core.series.Series(index=pop_ratios_noTNF_reTNF.index,data=pop_ratios_noTNF_reTNF.values/pop_ratios_noTNF_reTNF.values[0])\n",
    "norm_pop_ratios_noTNF_reTNF.name = \"noTNF_TNF\"\n",
    "norm_pop_ratios_noTNF_reTNF.plot(style='.-g', legend=True)\n",
    "\n",
    "norm_pop_ratios_noTNF_noTNF = pd.core.series.Series(index=pop_ratios_noTNF_noTNF.index,data=pop_ratios_noTNF_noTNF.values/pop_ratios_noTNF_noTNF.values[0])\n",
    "norm_pop_ratios_noTNF_noTNF.name = \"noTNF_noTNF\"\n",
    "outPlot = norm_pop_ratios_noTNF_noTNF.plot(style='--g', legend=True)\n",
    "outPlot.set_xlabel(\"hours\",fontsize=15)\n",
    "\n",
    "outPlot.set_ylabel(\"population ratio\",fontsize=15)\n",
    "save_figure(figure, 'second_steps_norm_ratios')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these results, we conclude that the population that has received an initial treatment of TNF shows a resistance to the second TNF treatment, as the corresponding population ratio (in red) barely decreases compared to that of the population not submitted to an initial TNF pulse (in green). \n",
    "\n",
    "In conclusion, the cells that have not been treated previously  (in blue and green) respond better to the TNF treatment compared to those that have been previously treated (in black and red)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Study TNF resistance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we study the conditions for TNF resistance by simulating single mutants of all 26 internal variables of the model. Each model mutant is defined by fixing the value of the corresponding variable to 0 (deletion) whatever its logical rule. \n",
    "\n",
    "The four scenarios combining initial and secondary TNF treatments are simulated for each of the mutant:\n",
    "- no pulse of TNF followed by no TNF, \n",
    "- no pulse of TNF followed by TNF, \n",
    "- pulse of TNF followed by no TNF, \n",
    "- pulse of TNF followed by TNF.\n",
    "\n",
    "A folder \"TNF_sensitivity\" is created in the working folder, which includes both WT and mutant simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='Figure6.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = 'TNF_sensitivity'\n",
    "\n",
    "def tnf_effect(workdir, model, name, df=None):\n",
    "    \"explore the effect of TNF in a given model variant\"\n",
    "    \n",
    "    if df is None:\n",
    "        df = pd.DataFrame()\n",
    "    \n",
    "    # Configuration of the model variants corresponding to combinations \n",
    "    # of (no) initial and secondary treatments\n",
    "    model_pulse = model\n",
    "    model_nopulse = model.copy()\n",
    "    model_nopulse.network.set_istate('TNF', [1,0])\n",
    "    \n",
    "    model_tnf = maboss.copy_and_update_parameters(model, {'$TNF_induc':20})\n",
    "    model_notnf = model\n",
    "    \n",
    "    # storage of all runs\n",
    "    runs = {}\n",
    "    \n",
    "    # Simulations of no initial TNF pulse, then with or without constitutive TNF\n",
    "    uppModel_no_pulse = maboss.UpdatePopulation(model_nopulse, upp_file)\n",
    "    run_no_pulse = uppModel_no_pulse.run(os.path.join(workdir, name, 'nopulse'))\n",
    "    \n",
    "    uppModel_notnf = maboss.UpdatePopulation(model_notnf, upp_file, previous_run=run_no_pulse)\n",
    "    runs['No pulse TNF, no TNF'] = uppModel_notnf.run(os.path.join(workdir, name, 'noPulse_noTNF'))\n",
    "    uppModel_tnf = maboss.UpdatePopulation(model_tnf, upp_file, previous_run=run_no_pulse)\n",
    "    runs['No pulse TNF, TNF'] = uppModel_tnf.run(os.path.join(workdir, name, 'noPulse_TNF'))\n",
    "    \n",
    "    # Simulations of initial TNF pulse, then with or without constitutive TNF\n",
    "    uppModel_pulse = maboss.UpdatePopulation(model_pulse, upp_file)\n",
    "    run_pulse = uppModel_pulse.run(os.path.join(workdir, name, 'pulse'))\n",
    "    \n",
    "    uppModel_notnf = maboss.UpdatePopulation(model_notnf, upp_file, previous_run=run_pulse)\n",
    "    runs['Pulse TNF, no TNF'] = uppModel_notnf.run(os.path.join(workdir, name, 'pulse_noTNF'))\n",
    "    uppModel_tnf = maboss.UpdatePopulation(model_tnf, upp_file, previous_run=run_pulse)\n",
    "    runs['Pulse TNF, TNF'] = uppModel_tnf.run(os.path.join(workdir, name, 'pulse_TNF'))\n",
    "\n",
    "    # Collection of population ratios\n",
    "    for key in runs:\n",
    "        pop_ratios = runs[key].get_population_ratios(key)\n",
    "        if key not in df.columns:\n",
    "            df[key] = pd.Series(name=name, dtype=np.float)\n",
    "        df.at[name, key] = pop_ratios.iloc[-1]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code launches the simulations for all 26 mutants and for the four protocols considered.\n",
    "\n",
    "Note that this could take a while, depending on your computer, it might take several hours. The user may skip the next three cells fo code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATP BAX BCL2 CASP3 CASP8 Cyt_c DISC_FAS DISC_TNF FADD FASL IKK MOMP MPT NFkB RIP1 RIP1K RIP1ub ROS SMAC TNFR XIAP cFLIP cIAP mROS mXIAP mcIAP "
     ]
    }
   ],
   "source": [
    "# TNF sensitivity for the WT model\n",
    "ratios = tnf_effect(workdir, model_WT, 'WT')\n",
    "\n",
    "# List of all mutations to test\n",
    "# Here all species will be mutated except for the output nodes\n",
    "excluded = set( ( 'NonACD', 'Apoptosis', 'Survival', 'Division', 'Death', 'TNF', 'apoptosome') )\n",
    "nodes = [ n for n in model_WT.network.names if n not in excluded ]\n",
    "nodes.sort()\n",
    "\n",
    "\n",
    "# TNF sensitivity for each mutant of the model\n",
    "for mutant_node in nodes:\n",
    "    print(mutant_node, end=' ')\n",
    "    mut_model = maboss.copy_and_mutate(model_WT, [mutant_node], 'OFF')\n",
    "    tnf_effect(workdir, mut_model, '%s' % mutant_node, ratios)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final results are diplayed in terms of population ratios (white = 0 to black = 1) at 96h for each mutant in the form of a matrix using the following comand lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the TNF sensitivity results for the WT condition and for all mutants \n",
    "figure = plt.figure(figsize = (10,10))\n",
    "sns.set(font_scale=1.5)\n",
    "sns.heatmap(ratios, annot=False, cmap=colormap_scale, linewidths=1)\n",
    "save_figure(figure, 'heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this matrix the protocols involving \"No initial TNF treatment\" (No pulse TNF) and \"Initial TNF treatment\" (Pulse TNF) are displayed separately. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further process these simulation results to display the relative changes in population ratio for the two conditions, i.e. when the cells received an initial pulse of TNF treatment or not. The resulting ratios are displayed in a matrix after hierarchical clustering.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# More abstract summary: cluster of the ratios of ratios\n",
    "ratios_ratios = pd.DataFrame()\n",
    "ratios_ratios['Pulse TNF'] = ratios['Pulse TNF, TNF'] / ratios['Pulse TNF, no TNF']\n",
    "ratios_ratios['No pulse TNF'] = ratios['No pulse TNF, TNF'] / ratios['No pulse TNF, no TNF']\n",
    "\n",
    "sns.set(font_scale=1)\n",
    "figure = sns.clustermap(ratios_ratios, annot=True, cmap=colormap_ratios, center=1, col_cluster=False)\n",
    "\n",
    "# Add a rectangle around the WT row\n",
    "wt_index = figure.dendrogram_row.reordered_ind.index(0)\n",
    "figure.ax_heatmap.add_patch(Rectangle((0, wt_index), 2, 1, fill=False, edgecolor='red', lw=3))\n",
    "\n",
    "save_figure(figure, 'cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relative changes in population ratio shown in this matrix correspond to the ratios between the population receiving the constitutive TNF treatment at t = 48 hours divided by the control population with no TNF at t = 48 hours. \n",
    "We consider that TNF treatment has an effect when the relative change in population ratio is reduced (ratio value below 1, in blue).\n",
    "\n",
    "A first group of mutants, including IKK and other co-clustered mutants, but also  RIP1 knockdown, show no resistance. Indeed, TNF treatment reduces the population size, whether the cells were pre-treated by TNF or not.\n",
    "\n",
    "For a second group of mutants, including RIP1K (in the bottom part) knockdowns, together with the wild type model, TNF constitutive treatment leads to a reduction in population size, but when the cells were pre-treated by a pulse of TNF, this effect disappears, hence indicating a resistance.\n",
    "\n",
    "Finally, TNFR knockdown leads to insensitive cells, as expected.\n",
    "\n",
    "The time dependant population ratios, for IKK and BCL2 mutants, are produced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_IKK = maboss.copy_and_mutate(model_WT, ['IKK'], 'OFF')\n",
    "uppModel_IKK = maboss.UpdatePopulation(model_IKK, upp_file)\n",
    "run_IKK = uppModel_IKK.run('IKK')\n",
    "#\n",
    "model_IKK_noTNF = model_IKK.copy()\n",
    "model_IKK_noTNF.network.set_istate('TNF', [1,0])\n",
    "# Run UPMaBoSS for the model with no TNF\n",
    "uppModel_IKK_noTNF = maboss.UpdatePopulation(model_IKK_noTNF, upp_file)\n",
    "run_IKK_noTNF = uppModel_IKK_noTNF.run('IKK_noTNF')\n",
    "\n",
    "\n",
    "# Create a model for a second treatment of TNF\n",
    "model_IKK_reTNF = maboss.copy_and_update_parameters(model_IKK, {'$TNF_induc': 20})\n",
    "\n",
    "# Run UPMaBoSS for the second treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_reTNF\" is created with the 48 runs\n",
    "uppModel_IKK_TNF_reTNF = maboss.UpdatePopulation(model_IKK_reTNF, upp_file, previous_run=run_IKK)\n",
    "run_IKK_TNF_reTNF = uppModel_IKK_TNF_reTNF.run('IKK_TNF_reTNF')\n",
    "pop_ratios_IKK_TNF_reTNF = run_IKK_TNF_reTNF.get_population_ratios('TNF_reTNF')\n",
    "\n",
    "# Run UPMaBoSS for a treatment of TNF after a run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_reTNF\" \"is created with the 48 runs\n",
    "uppModel_IKK_noTNF_reTNF = maboss.UpdatePopulation(model_IKK_reTNF, upp_file, previous_run=run_IKK_noTNF)\n",
    "run_IKK_noTNF_reTNF = uppModel_IKK_noTNF_reTNF.run('IKK_noTNF_reTNF')\n",
    "pop_ratios_IKK_noTNF_reTNF = run_IKK_noTNF_reTNF.get_population_ratios('noTNF_reTNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_IKK_TNF_noTNF = maboss.UpdatePopulation(model_IKK, upp_file, previous_run=run_IKK)\n",
    "# note that model_WT has TNF initially ON, but this initial conditition is replaced by previous_run=run_WT\n",
    "run_IKK_TNF_noTNF = uppModel_IKK_TNF_noTNF.run('IKK_TNF_noTNF')\n",
    "pop_ratios_IKK_TNF_noTNF = run_IKK_TNF_noTNF.get_population_ratios('TNF_noTNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_IKK_noTNF_noTNF = maboss.UpdatePopulation(model_IKK, upp_file, previous_run=run_IKK_noTNF)\n",
    "run_IKK_noTNF_noTNF = uppModel_IKK_noTNF_noTNF.run('IKK_noTNF_noTNF')\n",
    "pop_ratios_IKK_noTNF_noTNF = run_IKK_noTNF_noTNF.get_population_ratios('noTNF_noTNF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "colormap_scale = sns.light_palette(\"black\", as_cmap=True)\n",
    "colormap_ratios = sns.diverging_palette(240, 0, center='light', as_cmap=True)\n",
    "# Plot population ratios for a second treatment of TNF with or without the initial TNF stimulation, starting at time=48\n",
    "# Plot the control in dashed lines\n",
    "# A figure \"figure_second_steps_ratios.pdf\" is saved\n",
    "figure = plt.figure(figsize = (12,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "plt.axis((48,96,0,1.7))\n",
    "norm_pop_ratios_IKK_TNF_reTNF = pd.core.series.Series(index=pop_ratios_IKK_TNF_reTNF.index,data=pop_ratios_IKK_TNF_reTNF.values/pop_ratios_IKK_TNF_reTNF.values[0])\n",
    "norm_pop_ratios_IKK_TNF_reTNF.name = \"TNFPulse_TNF\"\n",
    "outPlot = norm_pop_ratios_IKK_TNF_reTNF.plot(style='.-r', legend=False,fontsize=20)\n",
    "\n",
    "norm_pop_ratios_IKK_TNF_noTNF = pd.core.series.Series(index=pop_ratios_IKK_TNF_noTNF.index,data=pop_ratios_IKK_TNF_noTNF.values/pop_ratios_IKK_TNF_noTNF.values[0])\n",
    "norm_pop_ratios_IKK_TNF_noTNF.name = \"TNFPulse_noTNF\"\n",
    "norm_pop_ratios_IKK_TNF_noTNF.plot(style='--r', legend=False)\n",
    "\n",
    "norm_pop_ratios_IKK_noTNF_reTNF = pd.core.series.Series(index=pop_ratios_IKK_noTNF_reTNF.index,data=pop_ratios_IKK_noTNF_reTNF.values/pop_ratios_IKK_noTNF_reTNF.values[0])\n",
    "norm_pop_ratios_IKK_noTNF_reTNF.name = \"noTNF_TNF\"\n",
    "norm_pop_ratios_IKK_noTNF_reTNF.plot(style='.-g', legend=False)\n",
    "## add 0.005 to pop ratio because is is the same than TNF_noTNF\n",
    "norm_pop_ratios_IKK_noTNF_noTNF = pd.core.series.Series(index=pop_ratios_IKK_noTNF_noTNF.index,data=.01+pop_ratios_IKK_noTNF_noTNF.values/pop_ratios_IKK_noTNF_noTNF.values[0])\n",
    "norm_pop_ratios_IKK_noTNF_noTNF.name = \"noTNF_noTNF\"\n",
    "outPlot = norm_pop_ratios_IKK_noTNF_noTNF.plot(style='--g', legend=False)\n",
    "outPlot.set_xlabel(\"hours\",fontsize=15)\n",
    "\n",
    "outPlot.set_ylabel(\"population ratio\",fontsize=15)\n",
    "save_figure(figure, 'second_steps_norm_ratios_IKK')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_RIP1K = maboss.copy_and_mutate(model_WT, ['RIP1K'], 'OFF')\n",
    "uppModel_RIP1K = maboss.UpdatePopulation(model_RIP1K, upp_file)\n",
    "run_RIP1K = uppModel_RIP1K.run('RIP1K')\n",
    "#\n",
    "model_RIP1K_noTNF = model_RIP1K.copy()\n",
    "model_RIP1K_noTNF.network.set_istate('TNF', [1,0])\n",
    "# Run UPMaBoSS for the model with no TNF\n",
    "uppModel_RIP1K_noTNF = maboss.UpdatePopulation(model_RIP1K_noTNF, upp_file)\n",
    "run_RIP1K_noTNF = uppModel_RIP1K_noTNF.run('RIP1K_noTNF')\n",
    "\n",
    "\n",
    "# Create a model for a second treatment of TNF\n",
    "model_RIP1K_reTNF = maboss.copy_and_update_parameters(model_RIP1K, {'$TNF_induc': 20})\n",
    "\n",
    "# Run UPMaBoSS for the second treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_reTNF\" is created with the 48 runs\n",
    "uppModel_RIP1K_TNF_reTNF = maboss.UpdatePopulation(model_RIP1K_reTNF, upp_file, previous_run=run_RIP1K)\n",
    "run_RIP1K_TNF_reTNF = uppModel_RIP1K_TNF_reTNF.run('RIP1K_TNF_reTNF')\n",
    "pop_ratios_RIP1K_TNF_reTNF = run_RIP1K_TNF_reTNF.get_population_ratios('TNF_reTNF')\n",
    "\n",
    "# Run UPMaBoSS for a treatment of TNF after a run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_reTNF\" \"is created with the 48 runs\n",
    "uppModel_RIP1K_noTNF_reTNF = maboss.UpdatePopulation(model_RIP1K_reTNF, upp_file, previous_run=run_RIP1K_noTNF)\n",
    "run_RIP1K_noTNF_reTNF = uppModel_RIP1K_noTNF_reTNF.run('RIP1K_noTNF_reTNF')\n",
    "pop_ratios_RIP1K_noTNF_reTNF = run_RIP1K_noTNF_reTNF.get_population_ratios('noTNF_reTNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with TNF ON\n",
    "# A folder named \"TNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_RIP1K_TNF_noTNF = maboss.UpdatePopulation(model_RIP1K, upp_file, previous_run=run_RIP1K)\n",
    "# note that model_WT has TNF initially ON, but this initial conditition is replaced by previous_run=run_WT\n",
    "run_RIP1K_TNF_noTNF = uppModel_RIP1K_TNF_noTNF.run('RIP1K_TNF_noTNF')\n",
    "pop_ratios_RIP1K_TNF_noTNF = run_RIP1K_TNF_noTNF.get_population_ratios('TNF_noTNF')\n",
    "\n",
    "# Run UPMaBoSS for non treatment of TNF after a WT run with no TNF (where TNF was initially OFF)\n",
    "# A folder named \"noTNF_noTNF\" \"is created with the 48 runs\n",
    "uppModel_RIP1K_noTNF_noTNF = maboss.UpdatePopulation(model_RIP1K, upp_file, previous_run=run_RIP1K_noTNF)\n",
    "run_RIP1K_noTNF_noTNF = uppModel_RIP1K_noTNF_noTNF.run('RIP1K_noTNF_noTNF')\n",
    "pop_ratios_RIP1K_noTNF_noTNF = run_RIP1K_noTNF_noTNF.get_population_ratios('noTNF_noTNF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot population ratios for a second treatment of TNF with or without the initial TNF stimulation, starting at time=48\n",
    "# Plot the control in dashed lines\n",
    "# A figure \"figure_second_steps_ratios.pdf\" is saved\n",
    "figure = plt.figure(figsize = (12,5))\n",
    "plt.rcParams.update({'legend.fontsize': 15})\n",
    "plt.axis((48,96,0,1.7))\n",
    "norm_pop_ratios_RIP1K_TNF_reTNF = pd.core.series.Series(index=pop_ratios_RIP1K_TNF_reTNF.index,data=pop_ratios_RIP1K_TNF_reTNF.values/pop_ratios_RIP1K_TNF_reTNF.values[0])\n",
    "norm_pop_ratios_RIP1K_TNF_reTNF.name = \"TNF_reTNF\"\n",
    "outPlot = norm_pop_ratios_RIP1K_TNF_reTNF.plot(style='.-r', legend=False,fontsize=20)\n",
    "\n",
    "norm_pop_ratios_RIP1K_TNF_noTNF = pd.core.series.Series(index=pop_ratios_RIP1K_TNF_noTNF.index,data=pop_ratios_RIP1K_TNF_noTNF.values/pop_ratios_RIP1K_TNF_noTNF.values[0])\n",
    "norm_pop_ratios_RIP1K_TNF_noTNF.name = \"TNF_noTNF\"\n",
    "norm_pop_ratios_RIP1K_TNF_noTNF.plot(style='--r', legend=False)\n",
    "\n",
    "norm_pop_ratios_RIP1K_noTNF_reTNF = pd.core.series.Series(index=pop_ratios_RIP1K_noTNF_reTNF.index,data=pop_ratios_RIP1K_noTNF_reTNF.values/pop_ratios_RIP1K_noTNF_reTNF.values[0])\n",
    "norm_pop_ratios_RIP1K_noTNF_reTNF.name = \"noTNF_reTNF\"\n",
    "norm_pop_ratios_RIP1K_noTNF_reTNF.plot(style='.-g', legend=False)\n",
    "norm_pop_ratios_RIP1K_noTNF_noTNF = pd.core.series.Series(index=pop_ratios_RIP1K_noTNF_noTNF.index,data=pop_ratios_RIP1K_noTNF_noTNF.values/pop_ratios_RIP1K_noTNF_noTNF.values[0])\n",
    "norm_pop_ratios_RIP1K_noTNF_noTNF.name = \"noTNF_noTNF\"\n",
    "outPlot = norm_pop_ratios_RIP1K_noTNF_noTNF.plot(style='--g', legend=False)\n",
    "outPlot.set_xlabel(\"hours\",fontsize=15)\n",
    "\n",
    "outPlot.set_ylabel(\"population ratio\",fontsize=15)\n",
    "save_figure(figure, 'second_steps_norm_ratios_RIP1K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}