reproduction_fig_8_15_16.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproduction of fig 8 and 14 and 15 (heatmaps) \n",
    "\n",
    "In this notebook we describe how to generate the heatmaps from the paper.\n",
    "\n",
    "The number of simulations as well as the size of the granules are, by default, set really low in order to reduce the computational time of this notebook. The expected time here is around 5min. It differ from the article where each cell of each heatmaps contains 20 runs for the \"unzoom\" area with N = 5000 glucose units.\n",
    "\n",
    "### NB: Fig 15\n",
    "Fig 15 is all the heatmaps obtained with the strict location model concatenated. To generate a heatmaps using the strict location model, one have to change:\n",
    "\n",
    "`elif reaction == 'Act_gbe':\n",
    "    my_granule.Act_gbe_flexible_model()`\n",
    "    \n",
    "into:\n",
    "\n",
    "`elif reaction == 'Act_gbe':\n",
    "    my_granule.Act_gbe_strict_location()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the parameter grid\n",
    "\n",
    "\n",
    "Here we generate a single heatmaps as an example. We chose to vary:<br>\n",
    "$L_{\\textrm{min}}^{\\textrm{spacing}} \\in \\{ 1,2,3,4,5,6 \\}$ <br>\n",
    "$L_{\\textrm{min}}^{\\textrm{transferred}} \\in \\{ 3 \\}$ <br>\n",
    "$\\Gamma \\in  \\{ 0.1, 0.2, 0.3,0.4,0.6,0.8,1.0,2.0,5.0 \\}$<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from pylab import *\n",
    "import os, os.path\n",
    "import json\n",
    "import glycogen_module as gm\n",
    "import random\n",
    "import pandas as pd\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of simulations for a given set of parameter:\n",
    "\n",
    "\n",
    "values_for_spacing = range(1,7)\n",
    "values_for_leftover = range(3,4)\n",
    "\n",
    "#range for Gamma is obtained by providing given couples (GS, GBE):\n",
    "values_for_gamma = []\n",
    "values_for_gys = [0.1,0.2,0.3,0.4,0.6,0.8,1,1,1] \n",
    "values_for_gbe = [1,1,1,1,1,1,1,0.5,0.2]\n",
    "\n",
    "#values_for_gys = [0.4,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.8] \n",
    "#values_for_gbe = [1,1,1,1,1,1,1,1,1]\n",
    "\n",
    "\n",
    "for i in range(len(values_for_gbe)):\n",
    "    values_for_gamma.append(values_for_gys[i]/values_for_gbe[i])\n",
    "\n",
    "\n",
    "def Gillespie_step(structure, C):\n",
    "    ''' This functions takes concentrations of the enzymes and the structure info of a glycogen granules and\n",
    "    return what is the next reaction to occurs and which time has been spent. (Following a gillespie algorithm)\n",
    "    '''\n",
    "    #propensity assuming mass action kinetics\n",
    "    h_gs = C[\"GS\"]*len(structure.Find_chain_for_gs())\n",
    "    h_gp = C[\"GP\"]*len(structure.Find_chain_for_gp())\n",
    "    h_gbe = C[\"GBE\"]*len(structure.Find_chain_for_gbe())\n",
    "    h_gde = C[\"GDE\"]*len(structure.Find_chain_for_gde())\n",
    "    \n",
    "    a = h_gs + h_gp + h_gbe + h_gde\n",
    "    \n",
    "    if a == 0:\n",
    "        return \"no reaction can be proceed, all propensities are zero\",0\n",
    "    r2=random.uniform(0,a)    \n",
    "    r1=random.uniform(0,1)\t\n",
    "    \n",
    "    d_t = (1/a)*math.log(1/r1)\n",
    "    if r2 < h_gs :\n",
    "        return \"Act_gs\",d_t\n",
    "    elif r2 >= h_gs and r2 < h_gs + h_gp :\n",
    "        return \"Act_gp\",d_t\n",
    "    elif r2 >=  h_gs + h_gp  and r2 < h_gs + h_gp + h_gbe :\n",
    "        return \"Act_gbe\",d_t\n",
    "    elif r2 >=  h_gs + h_gp + h_gbe  and r2 < h_gs + h_gp + h_gbe + +h_gde:\n",
    "        return \"Act_gde\",d_t   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"parameters.json\", \"r\") as f:\n",
    "    parameters = json.load(f) \n",
    "\n",
    "parameters['b']=5.4\n",
    "parameters['number_of_glucose_to_be_fixed']=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[Errno 17] File exists: '/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/data_for_heatmaps'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb Cell 6\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb#W5sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m path_of_local_folder \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mgetcwd()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb#W5sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m folder_name \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/data_for_heatmaps\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb#W5sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m os\u001b[39m.\u001b[39;49mmkdir(path_of_local_folder\u001b[39m+\u001b[39;49mfolder_name)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb#W5sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m data_path \u001b[39m=\u001b[39m path_of_local_folder\u001b[39m+\u001b[39mfolder_name\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/reproduction_fig_8_14_15.ipynb#W5sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mfor\u001b[39;00m leftover \u001b[39min\u001b[39;00m values_for_leftover:\n",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: '/home/yvan/Desktop/Stochastic-modeling-of-a-three-dimensional-glycogen-granule/data_for_heatmaps'"
     ]
    }
   ],
   "source": [
    "Number_of_simulations = 5\n",
    "\n",
    "\n",
    "path_of_local_folder = os.getcwd()\n",
    "folder_name = '/data_for_heatmaps'\n",
    "os.mkdir(path_of_local_folder+folder_name)\n",
    "data_path = path_of_local_folder+folder_name\n",
    "\n",
    "for leftover in values_for_leftover:\n",
    "    parameters['size_spec_gbe_leftover'] = leftover\n",
    "    parameters['size_spec_gbe_transferred'] = leftover\n",
    "    for spacing in values_for_spacing:\n",
    "        parameters['size_spec_gbe_spacing'] = spacing\n",
    "        for gs,gbe in zip(values_for_gys,values_for_gbe): \n",
    "            \n",
    "            identifier = str(spacing)+str(parameters['size_spec_gbe_leftover'])+str(parameters['size_spec_gbe_transferred'])+'_'+str(gs/gbe)\n",
    "\n",
    "            os.mkdir(data_path+'/'+identifier)\n",
    "            folder_path =data_path+'/'+identifier\n",
    "\n",
    "            for i in range(Number_of_simulations):\n",
    "                my_granule = gm.glycogen_structure(parameters, 2,1)\n",
    "\n",
    "                N = my_granule.number_of_glucose_fixed()  \n",
    "\n",
    "                C0 = {\"GS\":gs,\"GBE\":gbe,\"GP\":0.00,\"GDE\":0.00}          # here set GS = 1.0 and GBE = 0.1 if you want to produce the figure for gamma = 10.0\n",
    "                \n",
    "                while N < parameters['number_of_glucose_to_be_fixed']:\n",
    "                    if N%5000 == 0:\n",
    "                        print('avancement = ', 100*N/parameters['number_of_glucose_to_be_fixed'], '%','   part 1/2')\n",
    "                    reaction, time = Gillespie_step(my_granule, C0)\n",
    "\n",
    "                    if reaction == 'Act_gs':\n",
    "                        my_granule.Act_gs()\n",
    "                    elif reaction == 'Act_gp' and N>10:\n",
    "                        my_granule.Act_gp()\n",
    "                    elif reaction == 'Act_gbe':\n",
    "                        my_granule.Act_gbe_flexible_model()\n",
    "                    elif reaction == 'Act_gde':\n",
    "                        my_granule.Act_gde()\n",
    "\n",
    "\n",
    "                    N = my_granule.number_of_glucose_fixed()\n",
    "                    #print(reaction,time)\n",
    "                name = 'glycogen_structure'+str(i+1)+'.json'\n",
    "                my_granule.write_structure( folder_path+'/'+name)   \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now generated the glycogen granules for our parameters grid.\n",
    "\n",
    "## Best-fit part.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('wt-sullivan.csv')\n",
    "Y_wt = df['dp'].tolist()\n",
    "X = np.linspace(0,40,41)\n",
    "path_of_local_folder = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cld(dict):\n",
    "    histo=[]\n",
    "    for chain in dict.values(): \n",
    "        histo.append(len(chain['glucose_location']))\n",
    "    return histo \n",
    "\n",
    "def hist_into_curve(distrib:list):\n",
    "    'this function takes an histogram list and return X and Y arrays to plot the associated curve'\n",
    "    \n",
    "    total=len(distrib)\n",
    "    maximum=max(distrib)\n",
    "    #print(maximum)\n",
    "    X=np.linspace(0,maximum,maximum+1)\n",
    "    Y=[]\n",
    "    for k in X:\n",
    "        counter=0\n",
    "        for i in range(len(distrib)):\n",
    "            if k==distrib[i]:\n",
    "                counter+=1\n",
    "        Y.append(counter)   \n",
    "    \n",
    "    Y=np.asarray(Y)\n",
    "    return X, Y*1.0/total  \n",
    "\n",
    "def cumulative_cld(path_of_folder:str) -> array:\n",
    "    cld_cumulated=[];\n",
    "\n",
    "    for i in range(len(os.listdir(path_of_folder))):\n",
    "        file_name='glycogen_structure'+str(i+1)+'.json'\n",
    "        path=path_of_folder+'/'+file_name\n",
    "\n",
    "        with open(path, \"r\") as f:\n",
    "            my_granule_data = json.load(f)\n",
    "        \n",
    "        distrib = cld(my_granule_data)\n",
    "        cld_cumulated= cld_cumulated + distrib\n",
    "\n",
    "    return(cld_cumulated)\n",
    "\n",
    "def plot_cld_from_distrib(liste):\n",
    "\n",
    "    bins1 = np.linspace(0,max(liste), max(liste)+1)\n",
    "    plt.figure(figsize=(14,7))\n",
    "    plt.hist(liste, bins= bins1, color = 'grey', alpha =0.5, edgecolor = 'black', label = 'packed', density = 1)\n",
    "    plt.xlabel('DP', fontsize = 20)\n",
    "    plt.ylabel ('relative abundances', fontsize = 20)\n",
    "    plt.legend()\n",
    "    plt.tick_params(axis='both', which='major', labelsize=17)\n",
    "    plt.xlim([0,50])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_name = '/data_for_heatmaps'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "curves = []\n",
    "curves_index = []\n",
    "likelyhood = []\n",
    "i = 0\n",
    "for leftover in values_for_leftover:\n",
    "    i+=1\n",
    "    j=0\n",
    "    heatmap = np.zeros((len(values_for_spacing),len(values_for_gamma)))\n",
    "\n",
    "    for spacing in values_for_spacing:\n",
    "        j+=1\n",
    "        k=0\n",
    "\n",
    "        for gs,gbe in zip(values_for_gys,values_for_gbe): \n",
    "            k+=1\n",
    "\n",
    "            identifier = str(spacing)+str(leftover)+str(leftover)+'_'+str(gs/gbe)  \n",
    "            folder_path = path_of_local_folder+folder_name+'/'+identifier\n",
    "            \n",
    "            distrib = cumulative_cld(folder_path)\n",
    "          \n",
    "\n",
    "            DP, Abundance = hist_into_curve(distrib)\n",
    "            Abundance = list(Abundance)\n",
    "            while len(Abundance)<41:\n",
    "                Abundance.append(0)\n",
    "            Abundance = Abundance[:len(Y_wt)]\n",
    "            Abundance = 100*np.asarray(Abundance)\n",
    "            curves.append(Abundance)\n",
    "            curves_index.append([leftover,spacing,gs/gbe])\n",
    "\n",
    "            heatmap[j-1][k-1] = np.sum((Abundance-np.asarray(Y_wt))**2)\n",
    "            \n",
    "    likelyhood.append(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ticks = [k for k in range(0,len(values_for_gamma))]\n",
    "y_ticks = [k for k in range(0,len(values_for_spacing))]\n",
    "\n",
    "fig, ax1 = plt.subplots(1,1,figsize=(12,25))\n",
    "im1 = ax1.imshow(likelyhood[0])\n",
    "ax1.set_title(r'$L_{leftover / transferred} = $'+str(leftover),fontsize='25')\n",
    "ax1.set_xticks(x_ticks)\n",
    "ax1.set_xticklabels(values_for_gamma,fontsize='25')\n",
    "ax1.set_yticks(y_ticks)\n",
    "ax1.set_yticklabels(values_for_spacing,fontsize='25') \n",
    "divider1 = make_axes_locatable(ax1)\n",
    "cax1 = divider1.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "cbar1 = plt.colorbar(im1, cax=cax1)\n",
    "im1.set_clim(0,250)\n",
    "ax1.set_xlabel('$\\Gamma$',fontsize='25')\n",
    "ax1.set_ylabel(r'$L_{spacing}$',fontsize='25')\n",
    "cbar1.set_label('fitting score', rotation=270, labelpad=40,fontsize='25')\n",
    "cbar1.ax.tick_params(labelsize=20)\n",
    "\n",
    "for (j,i),label in np.ndenumerate(likelyhood[0]):\n",
    "    ax1.text(i,j,round(label,1),ha='center',va='center',color = 'white')\n",
    "\n",
    "plt.savefig('heatmaps_example.png', dpi = 350 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "2afb6e1a12f5a73040c3781616f0d213fad29ab43894d4d2d871d39c0f275adb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}