Fig3ab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9dc0a0e4-cec0-453a-84ca-4b477fc54918",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from itertools import chain\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import time\n",
    "\n",
    "import glob\n",
    "\n",
    "\n",
    "params = {\n",
    "   'axes.labelsize': 14,\n",
    "   'font.size': 14,\n",
    "   'legend.fontsize': 14,\n",
    "   'xtick.labelsize': 14,\n",
    "   'ytick.labelsize': 14,\n",
    "   'text.usetex': False,\n",
    "   'figure.figsize': [4.5, 4.5],\n",
    "    'savefig.facecolor':'white'\n",
    "   }\n",
    "\n",
    "mpl.rcParams.update(params)\n",
    "\n",
    "\n",
    "timestr = time.strftime(\"%Y%m%d%H%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bb67f27-e03e-4a0e-add9-33db97a7388f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "sol_saveat_dt = [1,1e-4][0]\n",
    "\n",
    "print(sol_saveat_dt)\n",
    "\n",
    "inpath = \"output/\"\n",
    "outpath = \"figures/supp2a/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3eb24459-423a-4ca3-94be-c3e8e5ed9a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "# set to True to save fig\n",
    "\n",
    "savefigures = True #False #True \n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67b563e3-a900-4778-b4fb-9452fb16f9f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "! ls -lh output | grep \"_u\\.\" | grep \"sim_\" | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "753bd907-9c3b-497a-a507-044e38706426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "print(len(glob.glob(inpath+\"*_u.csv\"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d4cf2ec-0b5f-421a-b261-45389520da41",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = [i.rstrip(\"_u.csv\") for i in glob.glob(inpath+\"*_u.csv\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f06e8f-dfe0-4700-a03f-b1628af8d9bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74af76c0-5de1-4f80-b7df-22c48d7750c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = sorted(input_file_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "521ab90d-037a-482c-bdd3-acc96acb7e0d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_dfs = []\n",
    "\n",
    "for fid in input_file_ids:\n",
    "\n",
    "    dfn = fid + \"_u.csv\"\n",
    "    tfn = fid + \"_t.csv\"\n",
    "    nfn = fid + \"_unames.csv\"\n",
    "    \n",
    "    df = pd.read_csv(dfn,header=None)\n",
    "    tf = pd.read_csv(tfn,header=None)\n",
    "    nf = pd.read_csv(nfn,header=None)\n",
    "    \n",
    "    df.columns = [i[:-1] for i in nf[0].values.tolist() ]\n",
    "    \n",
    "    df['time_s'] = tf[0].copy()\n",
    "    df['condition'] = fid.replace(\"output/sim_\",\"\").split(\"_extraTag\",1)[0] \n",
    "    \n",
    "    input_dfs.append(df)\n",
    "    \n",
    "input_df = pd.concat(input_dfs, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0f12bf9-edc7-4720-964b-300649638715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58900, 185)\n"
     ]
    }
   ],
   "source": [
    "print(input_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9759053a-0cb3-4ec5-9a8a-30da7cd35cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = input_df.reindex(sorted(input_df.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd2edbb9-15b5-4f3d-9d31-8da6f0634f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADP_a</th>\n",
       "      <th>ADP_i_a</th>\n",
       "      <th>ADP_i_n</th>\n",
       "      <th>ADP_mi_a</th>\n",
       "      <th>ADP_mi_n</th>\n",
       "      <th>ADP_mx_a</th>\n",
       "      <th>ADP_mx_n</th>\n",
       "      <th>ADP_n</th>\n",
       "      <th>ADPmito_a</th>\n",
       "      <th>ADPmito_n</th>\n",
       "      <th>...</th>\n",
       "      <th>f26bp_a</th>\n",
       "      <th>h</th>\n",
       "      <th>hIP3Ca_a</th>\n",
       "      <th>mGluRboundRatio_a</th>\n",
       "      <th>n</th>\n",
       "      <th>nBK_a</th>\n",
       "      <th>pgate</th>\n",
       "      <th>sTRP_a</th>\n",
       "      <th>time_s</th>\n",
       "      <th>vV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044347</td>\n",
       "      <td>0.057207</td>\n",
       "      <td>0.043629</td>\n",
       "      <td>0.056055</td>\n",
       "      <td>1.144319</td>\n",
       "      <td>1.129008</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.744560</td>\n",
       "      <td>1.851257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012170</td>\n",
       "      <td>0.981124</td>\n",
       "      <td>0.653157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034674</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.035539</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044306</td>\n",
       "      <td>0.057167</td>\n",
       "      <td>0.043589</td>\n",
       "      <td>0.056016</td>\n",
       "      <td>1.144333</td>\n",
       "      <td>1.128973</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.744587</td>\n",
       "      <td>1.851184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0.990555</td>\n",
       "      <td>0.653153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023402</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.025259</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044290</td>\n",
       "      <td>0.057161</td>\n",
       "      <td>0.043573</td>\n",
       "      <td>0.056010</td>\n",
       "      <td>1.144311</td>\n",
       "      <td>1.128928</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.744545</td>\n",
       "      <td>1.851093</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012172</td>\n",
       "      <td>0.990651</td>\n",
       "      <td>0.653133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023267</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.025131</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 185 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ADP_a   ADP_i_a   ADP_i_n  ADP_mi_a  ADP_mi_n  ADP_mx_a  ADP_mx_n  \\\n",
       "0  0.045  0.044347  0.057207  0.043629  0.056055  1.144319  1.129008   \n",
       "1  0.045  0.044306  0.057167  0.043589  0.056016  1.144333  1.128973   \n",
       "2  0.045  0.044290  0.057161  0.043573  0.056010  1.144311  1.128928   \n",
       "\n",
       "      ADP_n  ADPmito_a  ADPmito_n  ...   f26bp_a         h  hIP3Ca_a  \\\n",
       "0  0.047784   1.744560   1.851257  ...  0.012170  0.981124  0.653157   \n",
       "1  0.047784   1.744587   1.851184  ...  0.012171  0.990555  0.653153   \n",
       "2  0.047784   1.744545   1.851093  ...  0.012172  0.990651  0.653133   \n",
       "\n",
       "   mGluRboundRatio_a         n     nBK_a     pgate    sTRP_a  time_s      vV  \n",
       "0                0.0  0.034674  0.000007  0.035539  0.001247     1.0  0.0237  \n",
       "1                0.0  0.023402  0.000007  0.025259  0.001247     2.0  0.0237  \n",
       "2                0.0  0.023267  0.000007  0.025131  0.001247     3.0  0.0237  \n",
       "\n",
       "[3 rows x 185 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb83a159-5e35-4934-bf4f-dea4065625d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "all_cnds_l = input_df['condition'].unique()\n",
    "\n",
    "group1_control = [i for i in all_cnds_l if i.startswith('121_def') ]\n",
    "\n",
    "print(len(group1_control))\n",
    "\n",
    "\n",
    "group2_GLUT1 = [i for i in all_cnds_l if i.startswith('g1_61') and (\"_dose\" not in i) ]\n",
    "\n",
    "print(len(group2_GLUT1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbcbe8f3-7712-4005-96c3-2c2f5c1fe010",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_cnds_all = group1_control + group2_GLUT1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84306270-f6d4-4302-8fcb-1a8f7979d5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "NADtot_c = 0.212 # mM # cyto\n",
    "NADPtot = 0.0303 # mM\n",
    "NADtot_m = 0.726 # mM\n",
    "\n",
    "ATDPtot_n = 1.4449961078157665\n",
    "ATDPtot_a = 1.3434724532826217\n",
    "qAK = 0.92\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c6972bb-febc-4680-9194-7da1d0e285a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['121_default', 'g1_61_def']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_cnds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "777f1256-13fc-4874-a293-b35d407a537c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lut_ratios = {\n",
    "    \n",
    " '121_default': \"#069e1d\",\n",
    " 'g1_61_def': \"#fa6f0c\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f074636-61be-4717-af72-a50071869ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratios2plot_names = [ \"NADc/NADHc n\", \"NADc/NADHc a\", \"NADm/NADHm n\",\"NADm/NADHm a\", \"NADPc/NADPHc n\",\"NADPc/NADPHc a\", \n",
    "                     \"ATPc/ADPc n\",\"ATPc/ADPc a\", \"ATPm/ADPm n\",\"ATPm/ADPm a\", \"ATPi/ADPi n\",\"ATPi/ADPi a\"]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ab30f5-7beb-42f8-bed3-d1369792b8a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7e5fbca-da13-4f48-ab58-4a83b9fcbf21",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=6, ncols=2, figsize=(5,12)) \n",
    "\n",
    "\n",
    "for i,selected_cnds in enumerate(selected_cnds_all):\n",
    "    \n",
    "    NADcNADHc_n = (NADtot_c - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_n\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_n\"].values\n",
    "    NADcNADHc_a = (NADtot_c - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_a\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_a\"].values\n",
    "\n",
    "    NADmNADHm_n = (NADtot_m - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADHmito_n\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADHmito_n\"].values\n",
    "    NADmNADHm_a = (NADtot_m - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADHmito_a\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADHmito_a\"].values\n",
    "\n",
    "    NADPcNADPHc_n = (NADtot_c - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_n\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_n\"].values\n",
    "    NADPcNADPHc_a = (NADtot_c - input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_a\"].values)/input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"NADH_a\"].values\n",
    "\n",
    "    ADP_n = (input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_n\"].values)/2*(-qAK+np.sqrt(qAK*qAK+4*qAK*(ATDPtot_n/(input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_n\"].values)-1)))\n",
    "    ADP_a = (input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_a\"].values)/2*(-qAK+np.sqrt(qAK*qAK+4*qAK*(ATDPtot_a/(input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_a\"].values)-1)))\n",
    "\n",
    "    ATPADPc_n = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_n\"].values / ADP_n\n",
    "    ATPADPc_a = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_a\"].values / ADP_a\n",
    "\n",
    "    ATPADPm_n = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATPmito_n\"].values / input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ADPmito_n\"].values \n",
    "    ATPADPm_a = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATPmito_a\"].values / input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ADPmito_a\"].values \n",
    "    \n",
    "    ATPADPi_n = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_i_n\"].values / input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ADP_i_n\"].values \n",
    "    ATPADPi_a = input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ATP_i_a\"].values / input_df.loc[(input_df['time_s']>=180) & (input_df['condition']==selected_cnds) ,\"ADP_i_a\"].values \n",
    "    \n",
    "\n",
    "    ratios2plot = [ NADcNADHc_n, NADcNADHc_a, NADmNADHm_n,NADmNADHm_a, NADPcNADPHc_n,NADPcNADPHc_a, ATPADPc_n,ATPADPc_a, ATPADPm_n,ATPADPm_a, ATPADPi_n,ATPADPi_a ]\n",
    "    \n",
    "    j = 0\n",
    "    \n",
    "    for met, ax in zip(ratios2plot, axs.ravel()):\n",
    "\n",
    "        \n",
    "        ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "        g = sns.lineplot(x=np.arange(180,int(1+input_df['time_s'].max())), y=met, lw=2,alpha=0.75, ax=ax,\n",
    "                        color=lut_ratios[selected_cnds],label=selected_cnds_all[i] ) \n",
    "        \n",
    "        # if you don't like lines having different style, remove from this line the following: ,style=\"condition\"\n",
    "\n",
    "        ax.legend([],[], frameon=False)\n",
    "\n",
    "        \n",
    "        \n",
    "        ax.set_xlabel(\"t, s\")\n",
    "        ax.set_ylabel(\"Ratio, a.u.\")\n",
    "\n",
    "        ax.set_title(ratios2plot_names[j] ) # + \" \" + selected_cnds\n",
    "        \n",
    "        j = j + 1\n",
    "        \n",
    "        ax.spines.top.set_visible(False)\n",
    "        ax.spines.right.set_visible(False)\n",
    "\n",
    "        \n",
    "        \n",
    "plt.subplots_adjust(hspace=1)        \n",
    "plt.subplots_adjust(wspace=0.35)        \n",
    "\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "labels2names = {'121_default':'control', 'g1_61_def':'GLUT1-def.'}\n",
    "\n",
    "fig.legend(handles, [labels2names[i] for i in labels],bbox_to_anchor=(1.5, 1.0),frameon=False)  \n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fe752d9a-a85c-47b0-8f5b-9df2f5cea9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"ratios_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "149c9f7a-e1bd-4104-acbc-d8b7ed3c9079",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e240682-1cde-486e-b6a6-26655b5c6186",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50ff007-399c-44ef-9e79-e8651d94c4b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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