reproduction_fig_8_14_15.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": 8,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 = k[\"k_GS\"]*C[\"GS\"]*len(structure.Find_chain_for_gs())\n",
    "    h_gp = k[\"k_GP\"]*C[\"GP\"]*len(structure.Find_chain_for_gp())\n",
    "    h_gbe = k[\"k_GBE\"]*C[\"GBE\"]*len(structure.Find_chain_for_gbe())\n",
    "    h_gde = k[\"k_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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [],
   "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",
    "\n",
    "k = {\"k_GS\":1,\"k_GBE\":1,\"k_GP\":1,\"k_GDE\":1}\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",
    "\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": 32,
   "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": 38,
   "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_name = '/data_for_heatmaps'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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 )"
   ]
  }
 ],
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