Biomass data - maize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cobra import io\n",
    "from cobra.core import Metabolite\n",
    "from cobra import flux_analysis\n",
    "import pandas as pd\n",
    "from Functions import *\n",
    "from IPython import display\n",
    "import logging\n",
    "logging.basicConfig()\n",
    "logger = logging.getLogger('logger')\n",
    "\n",
    "#import sbml file\n",
    "fname = \"./../plantcoremetabolism-model/PlantCoreMetabolism_v2_1_0.xml\"\n",
    "model = io.sbml.read_sbml_model(fname)\n",
    "display.clear_output()\n",
    "\n",
    "#change Biomass_tx to Protein_biomass\n",
    "rxn = model.reactions.get_by_id(\"Biomass_tx\")\n",
    "rxn.id = \"Protein_biomass_tx\"\n",
    "for met in [\"K_b\",\"Ca_b\",\"Mg_b\"]:\n",
    "    met  = model.metabolites.get_by_id(met)\n",
    "    coeff = rxn.metabolites.get(met)\n",
    "    rxn.add_metabolites({met:-1*coeff})\n",
    "met = Metabolite(\"Protein_b\",name=\"Protein_biomass\")\n",
    "formula_dict = rxn.check_mass_balance()\n",
    "met.formula = \"\".join([atom+str(formula_dict[atom]*-1) for atom in formula_dict.keys() if atom != \"charge\"])\n",
    "met.charge = formula_dict[\"charge\"]*-1\n",
    "rxn.add_metabolites({met:1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Name</strong></td>\n",
       "                <td>PlantCoreMetabolism_v2_0_0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x01f0a9b77460</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of metabolites</strong></td>\n",
       "                <td>886</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of reactions</strong></td>\n",
       "                <td>934</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of groups</strong></td>\n",
       "                <td>208</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Objective expression</strong></td>\n",
       "                <td>1.0*AraCore_Biomass_tx - 1.0*AraCore_Biomass_tx_reverse_514c3</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartments</strong></td>\n",
       "                <td>Mitochondrion, Cytoplasm, Biomass, Plastid, Vacuole, Peroxisome, Endoplasmic reticulum, Mitochondrion innermembrane interacting with cristal space, Mitochondrion innermembrane interacting with inter membrane space, Extracellular, Thylakoid, Mitochondrial intermembrane space</td>\n",
       "            </tr>\n",
       "          </table>"
      ],
      "text/plain": [
       "<Model PlantCoreMetabolism_v2_0_0 at 0x1f0a9b77460>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove charged state of PGPs since they are minor (0.03)\n",
    "removeSpecificMetChargedState(model,[\"aL_1_PHOSPHATIDYL_GLYCEROL_P_p\",\"aL_1_PHOSPHATIDYL_GLYCEROL_P_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>type</th>\n",
       "      <th>leaf</th>\n",
       "      <th>stem</th>\n",
       "      <th>root</th>\n",
       "      <th>seed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sSUCROSE_b</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLC_c</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRU_c</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starch_b</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cellulose_b</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pLEU_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pPRO_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCYS_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pTRP_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUMARATE_c</th>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                type  leaf  stem  root  seed\n",
       "                                            \n",
       "sSUCROSE_b             0.0   0.0   0.0   0.0\n",
       "GLC_c                  0.0   0.0   0.0   0.0\n",
       "FRU_c                  0.0   0.0   0.0   0.0\n",
       "Starch_b               0.0   0.0   0.0   0.0\n",
       "Cellulose_b            0.0   0.0   0.0   0.0\n",
       "...              ...   ...   ...   ...   ...\n",
       "pLEU_b       protein   0.0   0.0   0.0   0.0\n",
       "pPRO_b       protein   0.0   0.0   0.0   0.0\n",
       "pCYS_b       protein   0.0   0.0   0.0   0.0\n",
       "pTRP_b       protein   0.0   0.0   0.0   0.0\n",
       "COUMARATE_c            0.0   0.0   0.0   0.0\n",
       "\n",
       "[81 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "biomass = createEmptyBiomassDataFrame()\n",
    "biomass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 17.2020450391 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.8863668319 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 4.9536245179 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 9.2182716051 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 0.1420323181 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "proteinMW = model.metabolites.get_by_id(\"Protein_b\").formula_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mature leaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract sucrose, starch and glucose diel content from Jeannette et al 2000, Figure 3\n",
    "\n",
    "<img src=\"references/Screenshot_20190719_151346.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starch:sucrose ratio=\n",
      "0.6127788177450991\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sucrose dawn (g/m2)</th>\n",
       "      <th>sucrose dusk (g/m2)</th>\n",
       "      <th>starch dawn (g/m2)</th>\n",
       "      <th>starch dusk (g/m2)</th>\n",
       "      <th>glucose dawn (g/m2)</th>\n",
       "      <th>glucose dusk (g/m2)</th>\n",
       "      <th>Δ sucrose(g/m2)</th>\n",
       "      <th>Δ starch(g/m2)</th>\n",
       "      <th>Δ glucose(g/m2)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.184615</td>\n",
       "      <td>3.030769</td>\n",
       "      <td>0.900990</td>\n",
       "      <td>1.811881</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.554839</td>\n",
       "      <td>1.846154</td>\n",
       "      <td>0.910891</td>\n",
       "      <td>0.254839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.184615</td>\n",
       "      <td>2.292308</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>1.594059</td>\n",
       "      <td>0.380645</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>1.107692</td>\n",
       "      <td>0.910891</td>\n",
       "      <td>-0.009677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.492308</td>\n",
       "      <td>3.323077</td>\n",
       "      <td>0.504950</td>\n",
       "      <td>1.396040</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.661290</td>\n",
       "      <td>1.830769</td>\n",
       "      <td>0.891089</td>\n",
       "      <td>0.361290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.584615</td>\n",
       "      <td>3.553846</td>\n",
       "      <td>0.900990</td>\n",
       "      <td>2.326733</td>\n",
       "      <td>0.203226</td>\n",
       "      <td>0.301613</td>\n",
       "      <td>1.969231</td>\n",
       "      <td>1.425743</td>\n",
       "      <td>0.098387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sucrose dawn (g/m2)  sucrose dusk (g/m2)  starch dawn (g/m2)  \\\n",
       "day                                                                 \n",
       "1               1.184615             3.030769            0.900990   \n",
       "2               1.184615             2.292308            0.683168   \n",
       "3               1.492308             3.323077            0.504950   \n",
       "4               1.584615             3.553846            0.900990   \n",
       "\n",
       "     starch dusk (g/m2)  glucose dawn (g/m2)  glucose dusk (g/m2)  \\\n",
       "day                                                                 \n",
       "1              1.811881             0.300000             0.554839   \n",
       "2              1.594059             0.380645             0.370968   \n",
       "3              1.396040             0.300000             0.661290   \n",
       "4              2.326733             0.203226             0.301613   \n",
       "\n",
       "     Δ sucrose(g/m2)  Δ starch(g/m2)  Δ glucose(g/m2)  \n",
       "day                                                    \n",
       "1           1.846154        0.910891         0.254839  \n",
       "2           1.107692        0.910891        -0.009677  \n",
       "3           1.830769        0.891089         0.361290  \n",
       "4           1.969231        1.425743         0.098387  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matureleaf_data = pd.DataFrame(data={\"day\":[1,2,3,4],\n",
    "                                        \"sucrose dawn (g/m2)\":[1.184615384615384,1.1846153846153795,\n",
    "                                                               1.4923076923076835,1.5846153846153763],\n",
    "                                        \"sucrose dusk (g/m2)\":[3.030769230769228,2.2923076923076877,\n",
    "                                                               3.323076923076915,3.5538461538461448],\n",
    "                                        \"starch dawn (g/m2)\":[0.9009900990099027,0.6831683168316811,\n",
    "                                                              0.504950495049501,0.9009900990098938],\n",
    "                                        \"starch dusk (g/m2)\":[1.8118811881188108,1.594059405940591,\n",
    "                                                              1.3960396039603875,2.326732673267319],\n",
    "                                        \"glucose dawn (g/m2)\":[0.3000000000000027,0.380645161290325,\n",
    "                                                              0.30000000000000293,0.20322580645161725],\n",
    "                                        \"glucose dusk (g/m2)\":[0.5548387096774199,0.3709677419354862,\n",
    "                                                              0.661290322580645,0.3016129032258099]})\n",
    "df_matureleaf_data.set_index(\"day\",inplace=True,drop=True)\n",
    "df_matureleaf_data[\"Δ sucrose(g/m2)\"] = df_matureleaf_data[\"sucrose dusk (g/m2)\"]- \\\n",
    "                                                df_matureleaf_data[\"sucrose dawn (g/m2)\"]\n",
    "df_matureleaf_data[\"Δ starch(g/m2)\"] = df_matureleaf_data[\"starch dusk (g/m2)\"]- \\\n",
    "                                                df_matureleaf_data[\"starch dawn (g/m2)\"]\n",
    "df_matureleaf_data[\"Δ glucose(g/m2)\"] = df_matureleaf_data[\"glucose dusk (g/m2)\"]- \\\n",
    "                                                df_matureleaf_data[\"glucose dawn (g/m2)\"]\n",
    "print(\"Starch:sucrose ratio=\")\n",
    "print(find_average(df_matureleaf_data[\"Δ starch(g/m2)\"])/find_average(df_matureleaf_data[\"Δ sucrose(g/m2)\"]))\n",
    "df_matureleaf_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Extract day-time starch:sucrose accumulation rate ratio from Lunn and Hatch 1995, Figure 1\n",
    "\n",
    "<img src=\"references/Screenshot_20190714_104104.png\" height=25% width=25%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hexose (umol hexose/mgChl)</th>\n",
       "      <th>Sucrose (umol hexose/mgChl)</th>\n",
       "      <th>Starch (umol hexose/mgChl)</th>\n",
       "      <th>Starch (dusk-dawn)</th>\n",
       "      <th>Sucrose (dusk-dawn)</th>\n",
       "      <th>Hexose (dusk-dawn)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dawn</th>\n",
       "      <td>3.046875</td>\n",
       "      <td>5.156250</td>\n",
       "      <td>12.109375</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dusk</th>\n",
       "      <td>6.328125</td>\n",
       "      <td>29.296875</td>\n",
       "      <td>82.890625</td>\n",
       "      <td>70.7812</td>\n",
       "      <td>24.1406</td>\n",
       "      <td>3.28125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hexose (umol hexose/mgChl)  Sucrose (umol hexose/mgChl)  \\\n",
       "Time                                                            \n",
       "Dawn                    3.046875                     5.156250   \n",
       "Dusk                    6.328125                    29.296875   \n",
       "\n",
       "      Starch (umol hexose/mgChl) Starch (dusk-dawn) Sucrose (dusk-dawn)  \\\n",
       "Time                                                                      \n",
       "Dawn                   12.109375                  -                   -   \n",
       "Dusk                   82.890625            70.7812             24.1406   \n",
       "\n",
       "     Hexose (dusk-dawn)  \n",
       "Time                     \n",
       "Dawn                  -  \n",
       "Dusk            3.28125  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matureleaf_data_2 = pd.DataFrame(data={\"Time\":[\"Dawn\",\"Dusk\"],\n",
    "                                          \"Hexose (umol hexose/mgChl)\":[20.312499999999986-17.265624999999943,\n",
    "                                                                        118.51562499999999-112.18749999999999],\n",
    "                                          \"Sucrose (umol hexose/mgChl)\":[17.265624999999943-12.109375000000004,\n",
    "                                                                         112.18749999999999-82.89062499999999],\n",
    "                                          \"Starch (umol hexose/mgChl)\":[12.109375000000004,82.89062499999999]})\n",
    "\n",
    "Delta_sucrose = float(df_matureleaf_data_2[\"Sucrose (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dusk\"]) - \\\n",
    "float(df_matureleaf_data_2[\"Sucrose (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dawn\"])\n",
    "\n",
    "Delta_hexose =float(df_matureleaf_data_2[\"Hexose (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dusk\"])- \\\n",
    "float(df_matureleaf_data_2[\"Hexose (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dawn\"])\n",
    "\n",
    "Delta_starch = float(df_matureleaf_data_2[\"Starch (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dusk\"])-\\\n",
    "float(df_matureleaf_data_2[\"Starch (umol hexose/mgChl)\"][df_matureleaf_data_2[\"Time\"]==\"Dawn\"])\n",
    "\n",
    "df_matureleaf_data_2[\"Starch (dusk-dawn)\"]=[\"-\",Delta_starch]\n",
    "df_matureleaf_data_2[\"Sucrose (dusk-dawn)\"]=[\"-\",Delta_sucrose]\n",
    "df_matureleaf_data_2[\"Hexose (dusk-dawn)\"]=[\"-\",Delta_hexose]\n",
    "df_matureleaf_data_2.set_index(\"Time\",drop=True,inplace=True)\n",
    "df_matureleaf_data_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract lipid  from Gu et al 2017, Figure 3\n",
    "\n",
    "<img src=\"references/Screenshot_20190719_162426.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>DGDG (mol%lipids)</th>\n",
       "      <th>MGDG (mol%lipids)</th>\n",
       "      <th>SQDG (mol%lipids)</th>\n",
       "      <th>PG (mol%lipids)</th>\n",
       "      <th>PC (mol%lipids)</th>\n",
       "      <th>PE (mol%lipids)</th>\n",
       "      <th>PI (mol%lipids)</th>\n",
       "      <th>PA (mol%lipids)</th>\n",
       "      <th>LPG (mol%lipids)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.432177</td>\n",
       "      <td>45.788644</td>\n",
       "      <td>1.498423</td>\n",
       "      <td>4.479495</td>\n",
       "      <td>5.047319</td>\n",
       "      <td>0.315457</td>\n",
       "      <td>2.160883</td>\n",
       "      <td>0.315457</td>\n",
       "      <td>0.362776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DGDG (mol%lipids)  MGDG (mol%lipids)  SQDG (mol%lipids)  PG (mol%lipids)  \\\n",
       "0          19.432177          45.788644           1.498423         4.479495   \n",
       "\n",
       "   PC (mol%lipids)  PE (mol%lipids)  PI (mol%lipids)  PA (mol%lipids)  \\\n",
       "0         5.047319         0.315457         2.160883         0.315457   \n",
       "\n",
       "   LPG (mol%lipids)  \n",
       "0          0.362776  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_1 = pd.DataFrame(data={\"DGDG (mol%lipids)\":[19.43217665615142],\n",
    "                                       \"MGDG (mol%lipids)\":[45.78864353312303],\n",
    "                                       \"SQDG (mol%lipids)\":[1.4984227129337468],\n",
    "                                       \"PG (mol%lipids)\":[4.479495268138808],\n",
    "                                       \"PC (mol%lipids)\":[5.047318611987379],\n",
    "                                       \"PE (mol%lipids)\":[0.31545741324921295],\n",
    "                                       \"PI (mol%lipids)\":[2.1608832807570963],\n",
    "                                       \"PA (mol%lipids)\":[0.31545741324921295],\n",
    "                                       \"LPG (mol%lipids)\":[0.3627760252365908]})\n",
    "df_leaf_content_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>DGDG (g%lipids)</th>\n",
       "      <th>MGDG (g%lipids)</th>\n",
       "      <th>SQDG (g%lipids)</th>\n",
       "      <th>PG (g%lipids)</th>\n",
       "      <th>PC (g%lipids)</th>\n",
       "      <th>PE (g%lipids)</th>\n",
       "      <th>PI (g%lipids)</th>\n",
       "      <th>PA (g%lipids)</th>\n",
       "      <th>LPG (g%lipids)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.298471</td>\n",
       "      <td>54.576095</td>\n",
       "      <td>1.937576</td>\n",
       "      <td>5.272781</td>\n",
       "      <td>6.040546</td>\n",
       "      <td>0.355891</td>\n",
       "      <td>2.853813</td>\n",
       "      <td>0.332704</td>\n",
       "      <td>0.332122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DGDG (g%lipids)  MGDG (g%lipids)  SQDG (g%lipids)  PG (g%lipids)  \\\n",
       "0        28.298471        54.576095         1.937576       5.272781   \n",
       "\n",
       "   PC (g%lipids)  PE (g%lipids)  PI (g%lipids)  PA (g%lipids)  LPG (g%lipids)  \n",
       "0       6.040546       0.355891       2.853813       0.332704        0.332122  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_1[\"DGDG (g of mol%lipids)\"] = df_leaf_content_1[\"DGDG (mol%lipids)\"]*model.metabolites.Galactosyl_galactosyl_diacyl_glycerols_p.formula_weight\n",
    "df_leaf_content_1[\"MGDG (g of mol%lipids)\"] = df_leaf_content_1[\"MGDG (mol%lipids)\"]*model.metabolites.D_Galactosyl_12_diacyl_glycerols_p.formula_weight\n",
    "df_leaf_content_1[\"SQDG (g of mol%lipids)\"] = df_leaf_content_1[\"SQDG (mol%lipids)\"]*model.metabolites.SULFOQUINOVOSYLDIACYLGLYCEROL_p.formula_weight\n",
    "df_leaf_content_1[\"PG (g of mol%lipids)\"] = df_leaf_content_1[\"PG (mol%lipids)\"]*model.metabolites.L_1_PHOSPHATIDYL_GLYCEROL_p.formula_weight\n",
    "df_leaf_content_1[\"PC (g of mol%lipids)\"] = df_leaf_content_1[\"PC (mol%lipids)\"]*model.metabolites.PHOSPHATIDYL_CHOLINE_r.formula_weight\n",
    "df_leaf_content_1[\"PE (g of mol%lipids)\"] = df_leaf_content_1[\"PE (mol%lipids)\"]*model.metabolites.L_1_PHOSPHATIDYL_ETHANOLAMINE_r.formula_weight\n",
    "df_leaf_content_1[\"PI (g of mol%lipids)\"] = df_leaf_content_1[\"PI (mol%lipids)\"]*model.metabolites.L_1_phosphatidyl_inositols_r.formula_weight\n",
    "df_leaf_content_1[\"PA (g of mol%lipids)\"] = df_leaf_content_1[\"PA (mol%lipids)\"]*model.metabolites.L_PHOSPHATIDATE_p.formula_weight\n",
    "df_leaf_content_1[\"LPG (g of mol%lipids)\"] = df_leaf_content_1[\"LPG (mol%lipids)\"]*model.metabolites.Lysophosphatidylglycerols_r.formula_weight\n",
    "\n",
    "cols2proc = [col for col in df_leaf_content_1.columns if \"(g of mol%lipids)\" in col]\n",
    "tot=0\n",
    "for col in cols2proc:\n",
    "    tot = tot + df_leaf_content_1[col][0]\n",
    "df_leaf_content_1_proc = pd.DataFrame(data={})\n",
    "for col in cols2proc:\n",
    "    df_leaf_content_1_proc[col.replace(\"(g of mol%lipids)\",\"(g%lipids)\")] = 100*df_leaf_content_1[col]/tot\n",
    "df_leaf_content_1_proc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract biomass composition from Seaver 2015 maize leaf model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sbml file (had to remove 4-5 metabolite duplicates)\n",
    "Sam_model = io.sbml.read_sbml_model(\"Data/Seaver2015_Evidenced-Maize-Leaf-Model_corrected.sbml\")\n",
    "display.clear_output()\n",
    "rxn = Sam_model.reactions.get_by_id(\"biomass0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>stoichiometry from Seaver et al 2015 leaf model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mets</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>0.2469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fructose</th>\n",
       "      <td>0.0363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xylose</th>\n",
       "      <td>0.1654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>galactose</th>\n",
       "      <td>0.0346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malate</th>\n",
       "      <td>0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citrate</th>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>0.0112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>0.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>0.0119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>0.0135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>0.0160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>0.0182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>0.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>0.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>0.0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>0.0241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           stoichiometry from Seaver et al 2015 leaf model\n",
       "mets                                                      \n",
       "glucose                                             0.2469\n",
       "fructose                                            0.0363\n",
       "xylose                                              0.1654\n",
       "galactose                                           0.0346\n",
       "malate                                              0.0060\n",
       "citrate                                             0.0020\n",
       "Arg                                                 0.0112\n",
       "His                                                 0.0044\n",
       "Lys                                                 0.0119\n",
       "Asp                                                 0.0135\n",
       "Glu                                                 0.0160\n",
       "Ser                                                 0.0182\n",
       "Thr                                                 0.0151\n",
       "Asn                                                 0.0078\n",
       "Gln                                                 0.0087\n",
       "Cys                                                 0.0045\n",
       "Gly                                                 0.0418\n",
       "Pro                                                 0.0157\n",
       "Ala                                                 0.0360\n",
       "Val                                                 0.0203\n",
       "Ile                                                 0.0117\n",
       "Leu                                                 0.0241\n",
       "Met                                                 0.0059\n",
       "Phe                                                 0.0080\n",
       "Tyr                                                 0.0048\n",
       "Trp                                                 0.0020"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_2 = pd.DataFrame(data={\"mets\":[\"glucose\",\"fructose\",\"xylose\",\"galactose\",\"malate\",\"citrate\",\n",
    "                                               \"Arg\",\"His\",\"Lys\",\"Asp\",\"Glu\",\"Ser\",\"Thr\",\"Asn\",\"Gln\",\"Cys\",\n",
    "                                               \"Gly\",\"Pro\",\"Ala\",\"Val\",\"Ile\",\"Leu\",\"Met\",\"Phe\",\"Tyr\",\"Trp\"],\n",
    "                                       \"stoichiometry from Seaver et al 2015 leaf model\":\n",
    "                                       [abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd19001_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd30321_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd01487_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00724_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00130_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00137_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00051_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00119_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00039_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00041_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00023_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00054_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00161_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00132_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00053_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00084_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00033_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00129_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00035_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00156_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00322_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00107_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00060_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00066_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00069_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_model.metabolites.get_by_id(\"cpd00065_c0\")))]})\n",
    "df_leaf_content_2.set_index(\"mets\",drop=True,inplace=True)\n",
    "df_leaf_content_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather lignin monomer content from Abiven et al 2011, Table 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>vanillin (mg C-lignin /kg C-plant)</th>\n",
       "      <th>acetovanillone (mg C-lignin /kg C-plant)</th>\n",
       "      <th>vanillic acid (mg C-lignin /kg C-plant)</th>\n",
       "      <th>syringaldehyde (mg C-lignin /kg C-plant)</th>\n",
       "      <th>acetosyringone (mg C-lignin /kg C-plant)</th>\n",
       "      <th>syringic acid (mg C-lignin /kg C-plant)</th>\n",
       "      <th>p-coumaric acid (mg C-lignin /kg C-plant)</th>\n",
       "      <th>ferulic acid (mg C-lignin /kg C-plant)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tissue</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>leaf</th>\n",
       "      <td>12.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stem</th>\n",
       "      <td>23.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10</td>\n",
       "      <td>35.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>64.9</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>root</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>58.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vanillin (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                       \n",
       "leaf                                  12.6   \n",
       "stem                                  23.7   \n",
       "root                                  28.0   \n",
       "\n",
       "        acetovanillone (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                             \n",
       "leaf                                         1.3   \n",
       "stem                                         1.3   \n",
       "root                                         0.6   \n",
       "\n",
       "        vanillic acid (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                            \n",
       "leaf                                          1   \n",
       "stem                                         10   \n",
       "root                                          1   \n",
       "\n",
       "        syringaldehyde (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                             \n",
       "leaf                                         3.5   \n",
       "stem                                        35.8   \n",
       "root                                        58.2   \n",
       "\n",
       "        acetosyringone (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                             \n",
       "leaf                                         5.2   \n",
       "stem                                         6.0   \n",
       "root                                         6.5   \n",
       "\n",
       "        syringic acid (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                            \n",
       "leaf                                        2.5   \n",
       "stem                                        3.5   \n",
       "root                                        2.3   \n",
       "\n",
       "        p-coumaric acid (mg C-lignin /kg C-plant)  \\\n",
       "tissue                                              \n",
       "leaf                                         15.1   \n",
       "stem                                         64.9   \n",
       "root                                         93.0   \n",
       "\n",
       "        ferulic acid (mg C-lignin /kg C-plant)  \n",
       "tissue                                          \n",
       "leaf                                      13.7  \n",
       "stem                                      15.9  \n",
       "root                                      22.3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_3 = pd.DataFrame(data={\"tissue\":[\"leaf\",\"stem\",\"root\"],\n",
    "                                       \"vanillin (mg C-lignin /kg C-plant)\":[12.6,23.7,28.0],\n",
    "                                       \"acetovanillone (mg C-lignin /kg C-plant)\":[1.3,1.3,0.6],\n",
    "                                       \"vanillic acid (mg C-lignin /kg C-plant)\":[1,10,1],\n",
    "                                       \"syringaldehyde (mg C-lignin /kg C-plant)\":[3.5,35.8,58.2],\n",
    "                                       \"acetosyringone (mg C-lignin /kg C-plant)\":[5.2,6,6.5],\n",
    "                                       \"syringic acid (mg C-lignin /kg C-plant)\":[2.5,3.5,2.3],\n",
    "                                       \"p-coumaric acid (mg C-lignin /kg C-plant)\":[15.1,64.9,93],\n",
    "                                       \"ferulic acid (mg C-lignin /kg C-plant)\":[13.7,15.9,22.3]})\n",
    "df_leaf_content_3.set_index(\"tissue\",drop=True,inplace=True)\n",
    "df_leaf_content_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather cell wall composition from Hatfield and Marita 2017, Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Neutral sugars (% cellwall)</th>\n",
       "      <th>Lignin (% cellwall)</th>\n",
       "      <th>Uronosyls (% cellwall)</th>\n",
       "      <th>Phenolics (% cellwall)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corn internode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>80.679328</td>\n",
       "      <td>13.440467</td>\n",
       "      <td>5.003652</td>\n",
       "      <td>0.876552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>82.389274</td>\n",
       "      <td>13.151552</td>\n",
       "      <td>3.630612</td>\n",
       "      <td>0.843628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80.785809</td>\n",
       "      <td>14.688329</td>\n",
       "      <td>3.580902</td>\n",
       "      <td>0.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>81.004807</td>\n",
       "      <td>14.188246</td>\n",
       "      <td>3.674988</td>\n",
       "      <td>1.116452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>82.012579</td>\n",
       "      <td>13.003145</td>\n",
       "      <td>3.757862</td>\n",
       "      <td>1.242138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82.115946</td>\n",
       "      <td>13.455328</td>\n",
       "      <td>3.198524</td>\n",
       "      <td>1.230201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neutral sugars (% cellwall)  Lignin (% cellwall)  \\\n",
       "corn internode                                                     \n",
       "9                                 80.679328            13.440467   \n",
       "10                                82.389274            13.151552   \n",
       "11                                80.785809            14.688329   \n",
       "12                                81.004807            14.188246   \n",
       "13                                82.012579            13.003145   \n",
       "14                                82.115946            13.455328   \n",
       "\n",
       "                Uronosyls (% cellwall)  Phenolics (% cellwall)  \n",
       "corn internode                                                  \n",
       "9                             5.003652                0.876552  \n",
       "10                            3.630612                0.843628  \n",
       "11                            3.580902                0.961538  \n",
       "12                            3.674988                1.116452  \n",
       "13                            3.757862                1.242138  \n",
       "14                            3.198524                1.230201  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_4 = pd.DataFrame(data={\"corn internode\":[9,10,11,12,13,14],\n",
    "                                       \"Neutral sugars\":[441.8,546.9,487.3,522.4,521.6,534],\n",
    "                                       \"Lignin g/kg\":[73.6,87.3,88.6,91.5,82.7,87.5],\n",
    "                                       \"Uronosyls\":[27.4,24.1,21.6,23.7,23.9,20.8],\n",
    "                                       \"Phenolics\":[4.8,5.6,5.8,7.2,7.9,8.0],\n",
    "                                       \"Total cell wall\":[547.6,663.8,603.2,644.9,636.0,650.3]})\n",
    "df_leaf_content_4_std = pd.DataFrame(data={\"corn internode\":[9,10,11,12,13,14],\n",
    "                                       \"Neutral sugars (% cellwall)\":100 * \\\n",
    "                                           df_leaf_content_4[\"Neutral sugars\"]/ \\\n",
    "                                           df_leaf_content_4[\"Total cell wall\"],\n",
    "                                       \"Lignin (% cellwall)\":100 * \\\n",
    "                                           df_leaf_content_4[\"Lignin g/kg\"]/ \\\n",
    "                                           df_leaf_content_4[\"Total cell wall\"],\n",
    "                                       \"Uronosyls (% cellwall)\":100 * \\\n",
    "                                           df_leaf_content_4[\"Uronosyls\"]/ \\\n",
    "                                           df_leaf_content_4[\"Total cell wall\"],\n",
    "                                       \"Phenolics (% cellwall)\":100 * \\\n",
    "                                           df_leaf_content_4[\"Phenolics\"]/ \\\n",
    "                                           df_leaf_content_4[\"Total cell wall\"]})\n",
    "df_leaf_content_4_std.set_index(\"corn internode\",drop=True,inplace=True)\n",
    "df_leaf_content_4_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data generated by Schnable lab, email recieved from Lee on 30/09/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Moisture (%FW)  Ash (%FW)  Protein (%FW)  Lipid (%FW)  Carbohydrate (%FW)\n",
      "0           64.44       5.77           7.98         1.32               20.49\n",
      "1           64.87       3.66           7.25         1.15               23.08\n"
     ]
    },
    {
     "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>Ash (mg/gDW)</th>\n",
       "      <th>Protein (mg/gDW)</th>\n",
       "      <th>Lipid (mg/gDW)</th>\n",
       "      <th>Carbohydrate (mg/gDW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162.260967</td>\n",
       "      <td>224.409449</td>\n",
       "      <td>37.120360</td>\n",
       "      <td>576.209224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104.184458</td>\n",
       "      <td>206.376317</td>\n",
       "      <td>32.735554</td>\n",
       "      <td>656.988329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ash (mg/gDW)  Protein (mg/gDW)  Lipid (mg/gDW)  Carbohydrate (mg/gDW)\n",
       "0    162.260967        224.409449       37.120360             576.209224\n",
       "1    104.184458        206.376317       32.735554             656.988329"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_5 = pd.DataFrame(data={\"Moisture (%FW)\":[64.44,64.87],\n",
    "                                       \"Ash (%FW)\":[5.77,3.66],\n",
    "                                       \"Protein (%FW)\":[7.98,7.25],\n",
    "                                       \"Lipid (%FW)\":[1.32,1.15],\n",
    "                                       \"Carbohydrate (%FW)\":[20.49,23.08]})\n",
    "print(df_leaf_content_5)\n",
    "\n",
    "df_leaf_content_5_std = pd.DataFrame(data={\"Ash (mg/gDW)\":1000*df_leaf_content_5[\"Ash (%FW)\"]/(100 - df_leaf_content_5[\"Moisture (%FW)\"]),\n",
    "                                           \"Protein (mg/gDW)\":1000*df_leaf_content_5[\"Protein (%FW)\"]/(100 - df_leaf_content_5[\"Moisture (%FW)\"]),\n",
    "                                           \"Lipid (mg/gDW)\":1000*df_leaf_content_5[\"Lipid (%FW)\"]/(100 - df_leaf_content_5[\"Moisture (%FW)\"]),\n",
    "                                           \"Carbohydrate (mg/gDW)\":1000*df_leaf_content_5[\"Carbohydrate (%FW)\"]/(100 - df_leaf_content_5[\"Moisture (%FW)\"])})\n",
    "df_leaf_content_5_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Data generated by Schnable lab was used to gather basic composition\n",
    "- Lipid composition from Gu et al 2017 was used\n",
    "- Carbohydrate composition from Seaver et al 2015 was used\n",
    "- biomass composition avialable in mg/gDW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = \"stoichiometry from Seaver et al 2015 leaf model\"\n",
    "totalCarb = df_leaf_content_2.at[\"glucose\",temp]+df_leaf_content_2.at[\"fructose\",temp]+\\\n",
    "            df_leaf_content_2.at[\"xylose\",temp]+df_leaf_content_2.at[\"galactose\",temp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 17.2020450391 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.8863668319 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 4.9536245179 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 9.2182716051 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 0.1420323181 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "biomass.at[\"GLC_c\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Carbohydrate (mg/gDW)\"])* \\\n",
    "                           (df_leaf_content_2.at[\"glucose\",temp]/totalCarb)/ \\\n",
    "                           model.metabolites.GLC_c.formula_weight\n",
    "biomass.at[\"FRU_c\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Carbohydrate (mg/gDW)\"])* \\\n",
    "                           (df_leaf_content_2.at[\"fructose\",temp]/totalCarb)/ \\\n",
    "                           model.metabolites.FRU_c.formula_weight\n",
    "biomass.at[\"Cellulose_b\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Carbohydrate (mg/gDW)\"])* \\\n",
    "                                 (df_leaf_content_2.at[\"galactose\",temp]/totalCarb)/ \\\n",
    "                                 model.metabolites.CELLULOSE_c.formula_weight\n",
    "biomass.at[\"Xylan_b\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Carbohydrate (mg/gDW)\"])* \\\n",
    "                             (df_leaf_content_2.at[\"xylose\",temp]/totalCarb)/ \\\n",
    "                             model.metabolites.Xylan_b.formula_weight\n",
    "biomass.at[\"Protein_b\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Protein (mg/gDW)\"])/ \\\n",
    "                               model.metabolites.Protein_b.formula_weight\n",
    "biomass.at[\"Galactosyl_galactosyl_diacyl_glycerols_p\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                              (df_leaf_content_1_proc[\"DGDG (g%lipids)\"]/100)/ \\\n",
    "                                                              model.metabolites.Galactosyl_galactosyl_diacyl_glycerols_p.formula_weight\n",
    "biomass.at[\"D_Galactosyl_12_diacyl_glycerols_p\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                        (df_leaf_content_1_proc[\"MGDG (g%lipids)\"]/100)/ \\\n",
    "                                                        model.metabolites.D_Galactosyl_12_diacyl_glycerols_p.formula_weight\n",
    "biomass.at[\"SULFOQUINOVOSYLDIACYLGLYCEROL_p\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                     (df_leaf_content_1_proc[\"SQDG (g%lipids)\"]/100)/ \\\n",
    "                                                     model.metabolites.SULFOQUINOVOSYLDIACYLGLYCEROL_p.formula_weight\n",
    "biomass.at[\"L_1_PHOSPHATIDYL_GLYCEROL_p\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                 (df_leaf_content_1_proc[\"PG (g%lipids)\"]/100)/ \\\n",
    "                                                 model.metabolites.L_1_PHOSPHATIDYL_GLYCEROL_p.formula_weight\n",
    "biomass.at[\"PHOSPHATIDYL_CHOLINE_r\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                            (df_leaf_content_1_proc[\"PC (g%lipids)\"]/100)/ \\\n",
    "                                            model.metabolites.PHOSPHATIDYL_CHOLINE_r.formula_weight\n",
    "biomass.at[\"L_1_PHOSPHATIDYL_ETHANOLAMINE_r\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                     (df_leaf_content_1_proc[\"PE (g%lipids)\"]/100)/ \\\n",
    "                                                     model.metabolites.L_1_PHOSPHATIDYL_ETHANOLAMINE_r.formula_weight\n",
    "biomass.at[\"L_1_phosphatidyl_inositols_r\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                  (df_leaf_content_1_proc[\"PI (g%lipids)\"]/100)/ \\\n",
    "                                                  model.metabolites.L_1_phosphatidyl_inositols_r.formula_weight\n",
    "biomass.at[\"L_PHOSPHATIDATE_p\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                       (df_leaf_content_1_proc[\"PA (g%lipids)\"]/100)/ \\\n",
    "                                       model.metabolites.L_PHOSPHATIDATE_p.formula_weight\n",
    "biomass.at[\"Lysophosphatidylglycerols_r\",\"leaf\"]=find_average(df_leaf_content_5_std[\"Lipid (mg/gDW)\"])* \\\n",
    "                                                 (df_leaf_content_1_proc[\"LPG (g%lipids)\"]/100)/ \\\n",
    "                                                 model.metabolites.Lysophosphatidylglycerols_r.formula_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stem\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather sucrose content from Sekhon et al 2016, Figure 2\n",
    "\n",
    "<img src=\"references/Screenshot_20190723_163940.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Sucrose (g/100gDW)</th>\n",
       "      <th>Sucrose (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.070064</td>\n",
       "      <td>70.700637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.261146</td>\n",
       "      <td>72.611465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.796178</td>\n",
       "      <td>107.961783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.885350</td>\n",
       "      <td>88.853503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.305732</td>\n",
       "      <td>63.057325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.445860</td>\n",
       "      <td>54.458599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.025478</td>\n",
       "      <td>80.254777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.745223</td>\n",
       "      <td>97.452229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.019108</td>\n",
       "      <td>60.191083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.394904</td>\n",
       "      <td>43.949045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.171975</td>\n",
       "      <td>91.719745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.070064</td>\n",
       "      <td>70.700637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.974522</td>\n",
       "      <td>69.745223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.783439</td>\n",
       "      <td>67.834395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.783439</td>\n",
       "      <td>67.834395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.974522</td>\n",
       "      <td>69.745223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.783439</td>\n",
       "      <td>67.834395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.936306</td>\n",
       "      <td>99.363057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sucrose (g/100gDW)  Sucrose (mg/gDW)\n",
       "Type                                      \n",
       "1               7.070064         70.700637\n",
       "2               7.261146         72.611465\n",
       "3              10.796178        107.961783\n",
       "4               8.885350         88.853503\n",
       "5               6.305732         63.057325\n",
       "6               5.445860         54.458599\n",
       "7               8.025478         80.254777\n",
       "8               9.745223         97.452229\n",
       "9               6.019108         60.191083\n",
       "10              4.394904         43.949045\n",
       "11              9.171975         91.719745\n",
       "12              7.070064         70.700637\n",
       "13              6.974522         69.745223\n",
       "14              6.783439         67.834395\n",
       "15              6.783439         67.834395\n",
       "16              6.974522         69.745223\n",
       "17              6.783439         67.834395\n",
       "18              9.936306         99.363057"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_1 = pd.DataFrame(data={\"Type\":range(1,19),\n",
    "                                       \"Sucrose (g/100gDW)\":[7.07006369426752,7.26114649681529,10.796178343949,\n",
    "                                                             8.88535031847134,6.30573248407643,5.44585987261146,\n",
    "                                                             8.02547770700637,9.7452229299363,6.01910828025478,\n",
    "                                                             4.39490445859873,9.17197452229299,7.07006369426751,\n",
    "                                                             6.97452229299363,6.78343949044586,6.78343949044586,\n",
    "                                                             6.97452229299363,6.78343949044586,9.93630573248408]})\n",
    "df_stem_content_1.set_index(\"Type\",drop=True,inplace=True)\n",
    "df_stem_content_1[\"Sucrose (mg/gDW)\"]=df_stem_content_1[\"Sucrose (g/100gDW)\"]*10\n",
    "df_stem_content_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average sucrose content (mg/gDW) =74.68152866242036\n"
     ]
    }
   ],
   "source": [
    "print(\"Average sucrose content (mg/gDW) =\"+str(find_average(df_stem_content_1[\"Sucrose (mg/gDW)\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather glucose content from Sekhon et al 2016, Figure 2\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_092838.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Glucose (g/100gDW)</th>\n",
       "      <th>Glucose (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.913043</td>\n",
       "      <td>89.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.565217</td>\n",
       "      <td>95.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.826087</td>\n",
       "      <td>128.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.937888</td>\n",
       "      <td>99.378882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.515528</td>\n",
       "      <td>75.155280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.677019</td>\n",
       "      <td>66.770186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.658385</td>\n",
       "      <td>96.583851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.708075</td>\n",
       "      <td>117.080745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.142857</td>\n",
       "      <td>71.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.118012</td>\n",
       "      <td>61.180124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.428571</td>\n",
       "      <td>114.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.006211</td>\n",
       "      <td>90.062112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.819876</td>\n",
       "      <td>88.198758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.354037</td>\n",
       "      <td>83.540373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.447205</td>\n",
       "      <td>84.472050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.378882</td>\n",
       "      <td>93.788820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.795031</td>\n",
       "      <td>77.950311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.521739</td>\n",
       "      <td>115.217391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Glucose (g/100gDW)  Glucose (mg/gDW)\n",
       "Type                                      \n",
       "1               8.913043         89.130435\n",
       "2               9.565217         95.652174\n",
       "3              12.826087        128.260870\n",
       "4               9.937888         99.378882\n",
       "5               7.515528         75.155280\n",
       "6               6.677019         66.770186\n",
       "7               9.658385         96.583851\n",
       "8              11.708075        117.080745\n",
       "9               7.142857         71.428571\n",
       "10              6.118012         61.180124\n",
       "11             11.428571        114.285714\n",
       "12              9.006211         90.062112\n",
       "13              8.819876         88.198758\n",
       "14              8.354037         83.540373\n",
       "15              8.447205         84.472050\n",
       "16              9.378882         93.788820\n",
       "17              7.795031         77.950311\n",
       "18             11.521739        115.217391"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_2 = pd.DataFrame(data={\"Type\":range(1,19),\n",
    "                                       \"Glucose (g/100gDW)\":[8.91304347826087,9.56521739130435,\n",
    "                                                             12.8260869565217,9.93788819875777,\n",
    "                                                             7.51552795031056,6.67701863354037,\n",
    "                                                             9.6583850931677,11.7080745341615,\n",
    "                                                             7.14285714285714,6.11801242236025,\n",
    "                                                             11.4285714285714,9.00621118012422,\n",
    "                                                             8.81987577639751,8.35403726708075,\n",
    "                                                             8.4472049689441,9.37888198757764,\n",
    "                                                             7.79503105590062,11.5217391304348]})\n",
    "df_stem_content_2.set_index(\"Type\",drop=True,inplace=True)\n",
    "df_stem_content_2[\"Glucose (mg/gDW)\"]=df_stem_content_2[\"Glucose (g/100gDW)\"]*10\n",
    "df_stem_content_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average glucose content (mg/gDW) =91.5631469979296\n"
     ]
    }
   ],
   "source": [
    "print(\"Average glucose content (mg/gDW) =\"+str(find_average(df_stem_content_2[\"Glucose (mg/gDW)\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather starch content from Sekhon et al 2016, Figure S3\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_085846.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Starch (g/100gDW)</th>\n",
       "      <th>Starch (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.402597</td>\n",
       "      <td>4.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.558442</td>\n",
       "      <td>5.584416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.064935</td>\n",
       "      <td>10.649351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>6.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.558442</td>\n",
       "      <td>5.584416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.441558</td>\n",
       "      <td>4.415584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.324675</td>\n",
       "      <td>3.246753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.558442</td>\n",
       "      <td>5.584416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.207792</td>\n",
       "      <td>2.077922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.207792</td>\n",
       "      <td>2.077922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>6.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.168831</td>\n",
       "      <td>1.688312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.207792</td>\n",
       "      <td>2.077922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.324675</td>\n",
       "      <td>3.246753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.402597</td>\n",
       "      <td>4.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.402597</td>\n",
       "      <td>4.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.441558</td>\n",
       "      <td>4.415584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.987013</td>\n",
       "      <td>9.870130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Starch (g/100gDW)  Starch (mg/gDW)\n",
       "Type                                    \n",
       "1              0.402597         4.025974\n",
       "2              0.558442         5.584416\n",
       "3              1.064935        10.649351\n",
       "4              0.636364         6.363636\n",
       "5              0.558442         5.584416\n",
       "6              0.441558         4.415584\n",
       "7              0.324675         3.246753\n",
       "8              0.558442         5.584416\n",
       "9              0.207792         2.077922\n",
       "10             0.207792         2.077922\n",
       "11             0.636364         6.363636\n",
       "12             0.168831         1.688312\n",
       "13             0.207792         2.077922\n",
       "14             0.324675         3.246753\n",
       "15             0.402597         4.025974\n",
       "16             0.402597         4.025974\n",
       "17             0.441558         4.415584\n",
       "18             0.987013         9.870130"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_3 = pd.DataFrame(data={\"Type\":range(1,19),\n",
    "                                       \"Starch (g/100gDW)\":[0.402597402597402,0.558441558441559,\n",
    "                                                            1.06493506493507,0.636363636363637,\n",
    "                                                            0.558441558441559,0.441558441558441,\n",
    "                                                            0.324675324675324,0.558441558441559,\n",
    "                                                            0.207792207792208,0.207792207792208,\n",
    "                                                            0.636363636363637,0.168831168831169,\n",
    "                                                            0.207792207792208,0.324675324675326,\n",
    "                                                            0.402597402597402,0.402597402597402,\n",
    "                                                            0.441558441558443,0.987012987012987]})\n",
    "df_stem_content_3.set_index(\"Type\",drop=True,inplace=True)\n",
    "df_stem_content_3[\"Starch (mg/gDW)\"]=df_stem_content_3[\"Starch (g/100gDW)\"]*10\n",
    "df_stem_content_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average starch content (mg/gDW) =4.740259740259746\n"
     ]
    }
   ],
   "source": [
    "print(\"Average starch content (mg/gDW) =\"+str(find_average(df_stem_content_3[\"Starch (mg/gDW)\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather cell wall 6C content from Sekhon et al 2016, Figure 4\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_092056.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Cell wall glucose (g/100gDW)</th>\n",
       "      <th>Cell wall glucose (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.112150</td>\n",
       "      <td>191.121495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.121495</td>\n",
       "      <td>161.214953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.551402</td>\n",
       "      <td>85.514019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.149533</td>\n",
       "      <td>71.495327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.149533</td>\n",
       "      <td>71.495327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.962617</td>\n",
       "      <td>69.626168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.009346</td>\n",
       "      <td>120.093458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.261682</td>\n",
       "      <td>112.616822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.542056</td>\n",
       "      <td>115.420561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.663551</td>\n",
       "      <td>126.635514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.542056</td>\n",
       "      <td>115.420561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.102804</td>\n",
       "      <td>121.028037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.102804</td>\n",
       "      <td>121.028037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.233645</td>\n",
       "      <td>102.336449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.700935</td>\n",
       "      <td>107.009346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.261682</td>\n",
       "      <td>112.616822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.831776</td>\n",
       "      <td>88.317757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.841121</td>\n",
       "      <td>58.411215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cell wall glucose (g/100gDW)  Cell wall glucose (mg/gDW)\n",
       "Type                                                          \n",
       "1                        19.112150                  191.121495\n",
       "2                        16.121495                  161.214953\n",
       "3                         8.551402                   85.514019\n",
       "4                         7.149533                   71.495327\n",
       "5                         7.149533                   71.495327\n",
       "6                         6.962617                   69.626168\n",
       "7                        12.009346                  120.093458\n",
       "8                        11.261682                  112.616822\n",
       "9                        11.542056                  115.420561\n",
       "10                       12.663551                  126.635514\n",
       "11                       11.542056                  115.420561\n",
       "12                       12.102804                  121.028037\n",
       "13                       12.102804                  121.028037\n",
       "14                       10.233645                  102.336449\n",
       "15                       10.700935                  107.009346\n",
       "16                       11.261682                  112.616822\n",
       "17                        8.831776                   88.317757\n",
       "18                        5.841121                   58.411215"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_4 = pd.DataFrame(data={\"Type\":range(1,19),\n",
    "                                       \"Cell wall glucose (g/100gDW)\":[19.1121495327103,16.1214953271028,8.55140186915888,\n",
    "                                                            7.14953271028037,7.14953271028037,6.96261682242991,\n",
    "                                                            12.0093457943925,11.2616822429907,11.5420560747664,\n",
    "                                                            12.6635514018692,11.5420560747664,12.1028037383178,\n",
    "                                                            12.1028037383178,10.2336448598131,10.7009345794393,\n",
    "                                                            11.2616822429907,8.83177570093458,5.84112149532711]})\n",
    "df_stem_content_4.set_index(\"Type\",drop=True,inplace=True)\n",
    "df_stem_content_4[\"Cell wall glucose (mg/gDW)\"]=df_stem_content_4[\"Cell wall glucose (g/100gDW)\"]*10\n",
    "df_stem_content_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cell wall glucose (mg/gDW) =108.41121495327124\n"
     ]
    }
   ],
   "source": [
    "print(\"Average cell wall glucose (mg/gDW) =\"+str(find_average(df_stem_content_4[\"Cell wall glucose (mg/gDW)\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather cell wall 5C content from Sekhon et al 2016, Figure 5\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_092127.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Cell wall pentose (g/100gDW)</th>\n",
       "      <th>Cell wall pentose (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.703704</td>\n",
       "      <td>77.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.481481</td>\n",
       "      <td>64.814815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.629630</td>\n",
       "      <td>26.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.888889</td>\n",
       "      <td>28.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.740741</td>\n",
       "      <td>27.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.703704</td>\n",
       "      <td>27.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.629630</td>\n",
       "      <td>36.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.407407</td>\n",
       "      <td>34.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.037037</td>\n",
       "      <td>40.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.259259</td>\n",
       "      <td>42.592593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.370370</td>\n",
       "      <td>33.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.740741</td>\n",
       "      <td>37.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.518519</td>\n",
       "      <td>35.185185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.814815</td>\n",
       "      <td>38.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.296296</td>\n",
       "      <td>32.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.037037</td>\n",
       "      <td>40.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.740741</td>\n",
       "      <td>27.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.925926</td>\n",
       "      <td>19.259259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cell wall pentose (g/100gDW)  Cell wall pentose (mg/gDW)\n",
       "Type                                                          \n",
       "1                         7.703704                   77.037037\n",
       "2                         6.481481                   64.814815\n",
       "3                         2.629630                   26.296296\n",
       "4                         2.888889                   28.888889\n",
       "5                         2.740741                   27.407407\n",
       "6                         2.703704                   27.037037\n",
       "7                         3.629630                   36.296296\n",
       "8                         3.407407                   34.074074\n",
       "9                         4.037037                   40.370370\n",
       "10                        4.259259                   42.592593\n",
       "11                        3.370370                   33.703704\n",
       "12                        3.740741                   37.407407\n",
       "13                        3.518519                   35.185185\n",
       "14                        3.814815                   38.148148\n",
       "15                        3.296296                   32.962963\n",
       "16                        4.037037                   40.370370\n",
       "17                        2.740741                   27.407407\n",
       "18                        1.925926                   19.259259"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_5 = pd.DataFrame(data={\"Type\":range(1,19),\n",
    "                                       \"Cell wall pentose (g/100gDW)\":[7.7037037037037,6.48148148148148,\n",
    "                                                                       2.62962962962963,2.88888888888889,\n",
    "                                                                       2.74074074074074,2.7037037037037,\n",
    "                                                                       3.62962962962963,3.40740740740741,\n",
    "                                                                       4.03703703703703,4.25925925925926,\n",
    "                                                                       3.37037037037037,3.74074074074074,\n",
    "                                                                       3.51851851851852,3.81481481481481,\n",
    "                                                                       3.2962962962963,4.03703703703704,\n",
    "                                                                       2.74074074074074,1.92592592592593]})\n",
    "df_stem_content_5.set_index(\"Type\",drop=True,inplace=True)\n",
    "df_stem_content_5[\"Cell wall pentose (mg/gDW)\"]=df_stem_content_5[\"Cell wall pentose (g/100gDW)\"]*10\n",
    "df_stem_content_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cell wall pentose (mg/gDW) =37.18106995884775\n"
     ]
    }
   ],
   "source": [
    "print(\"Average cell wall pentose (mg/gDW) =\"+str(find_average(df_stem_content_5[\"Cell wall pentose (mg/gDW)\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data generated by Schnable lab, email recieved from Lee on 30/09/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Moisture (%FW)  Ash (%FW)  Protein (%FW)  Lipid (%FW)  Carbohydrate (%FW)\n",
      "0           78.46       0.20           0.89            0               20.46\n",
      "1           77.89       0.28           0.73            0               18.62\n"
     ]
    },
    {
     "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>Ash (mg/gDW)</th>\n",
       "      <th>Protein (mg/gDW)</th>\n",
       "      <th>Lipid (mg/gDW)</th>\n",
       "      <th>Carbohydrate (mg/gDW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.285051</td>\n",
       "      <td>41.318477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>949.860724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.663953</td>\n",
       "      <td>33.016735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>842.152872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ash (mg/gDW)  Protein (mg/gDW)  Lipid (mg/gDW)  Carbohydrate (mg/gDW)\n",
       "0      9.285051         41.318477             0.0             949.860724\n",
       "1     12.663953         33.016735             0.0             842.152872"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content_6 = pd.DataFrame(data={\"Moisture (%FW)\":[78.46,77.89],\n",
    "                                       \"Ash (%FW)\":[0.20,0.28],\n",
    "                                       \"Protein (%FW)\":[0.89,.73],\n",
    "                                       \"Lipid (%FW)\":[0,0],\n",
    "                                       \"Carbohydrate (%FW)\":[20.46,18.62]})\n",
    "print(df_stem_content_6)\n",
    "\n",
    "df_stem_content_6_std = pd.DataFrame(data={\"Ash (mg/gDW)\":1000*df_stem_content_6[\"Ash (%FW)\"]/(100 - df_stem_content_6[\"Moisture (%FW)\"]),\n",
    "                                           \"Protein (mg/gDW)\":1000*df_stem_content_6[\"Protein (%FW)\"]/(100 - df_stem_content_6[\"Moisture (%FW)\"]),\n",
    "                                           \"Lipid (mg/gDW)\":1000*df_stem_content_6[\"Lipid (%FW)\"]/(100 - df_stem_content_6[\"Moisture (%FW)\"]),\n",
    "                                           \"Carbohydrate (mg/gDW)\":1000*df_stem_content_6[\"Carbohydrate (%FW)\"]/(100 - df_stem_content_6[\"Moisture (%FW)\"])})\n",
    "df_stem_content_6_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Sugar, glucose, starch, C6 and C5 cellwall sugars data available in Sekhon et al 2016\n",
    "- unable to use lignin monomer content without knowing lignin content\n",
    "- Protein content available from data generated by Schnable lab\n",
    "\n",
    "### Issues\n",
    "\n",
    "- CHO content according to Schnable data is 900 mg/gDW (approx) while based on Sekhon et al 2016 data, this is 316 mg/gDW\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 17.2020450391 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.8863668319 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 4.9536245179 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 9.2182716051 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 0.1420323181 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "biomass.at[\"sSUCROSE_b\",\"stem\"]=find_average(df_stem_content_1[\"Sucrose (mg/gDW)\"])/ \\\n",
    "                                model.metabolites.SUCROSE_c.formula_weight\n",
    "biomass.at[\"GLC_c\",\"stem\"]=find_average(df_stem_content_2[\"Glucose (mg/gDW)\"])/ \\\n",
    "                           model.metabolites.GLC_c.formula_weight\n",
    "biomass.at[\"Starch_b\",\"stem\"]=find_average(df_stem_content_3[\"Starch (mg/gDW)\"])/ \\\n",
    "                              model.metabolites.Starch_b.formula_weight\n",
    "biomass.at[\"Cellulose_b\",\"stem\"]=find_average(df_stem_content_4[\"Cell wall glucose (mg/gDW)\"])/ \\\n",
    "                                 model.metabolites.Cellulose_b.formula_weight\n",
    "biomass.at[\"Xylan_b\",\"stem\"]=find_average(df_stem_content_5[\"Cell wall pentose (mg/gDW)\"])/ \\\n",
    "                             model.metabolites.Xylan_b.formula_weight\n",
    "biomass.at[\"Protein_b\",\"stem\"]=find_average(df_stem_content_6_std[\"Protein (mg/gDW)\"])/ \\\n",
    "                               model.metabolites.Protein_b.formula_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract amino acid and organic acid content from Carvalhais et al 2011 Figure 1\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_131713.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Exudation rate (pmol/gFWroot/hr)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolites</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>70.190114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>63.802281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>39.163498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>49.809886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>10.570342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>28.212928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>6.311787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>12.699620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>3.574144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GABA</th>\n",
       "      <td>11.178707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mal</th>\n",
       "      <td>231.901840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cit</th>\n",
       "      <td>27.914110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tAco</th>\n",
       "      <td>380.674847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Exudation rate (pmol/gFWroot/hr)\n",
       "metabolites                                  \n",
       "Asp                                 70.190114\n",
       "Glu                                 63.802281\n",
       "Ser                                 39.163498\n",
       "Gln                                 49.809886\n",
       "Thr                                 10.570342\n",
       "Ala                                 28.212928\n",
       "Tyr                                  6.311787\n",
       "Ile                                 12.699620\n",
       "Lys                                  3.574144\n",
       "GABA                                11.178707\n",
       "Mal                                231.901840\n",
       "Cit                                 27.914110\n",
       "tAco                               380.674847"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_exudate_content_1 = pd.DataFrame(data={\"metabolites\":[\"Asp\",\"Glu\",\"Ser\",\n",
    "                                                       \"Gln\",\"Thr\",\"Ala\",\n",
    "                                                       \"Tyr\",\"Ile\",\"Lys\",\n",
    "                                                       \"GABA\",\"Mal\",\"Cit\",\"tAco\"],\n",
    "                                        \"Exudation rate (pmol/gFWroot/hr)\":[70.1901140684411,63.8022813688213,\n",
    "                                                                            39.1634980988593,49.8098859315589,\n",
    "                                                                            10.5703422053232,28.212927756654,\n",
    "                                                                            6.31178707224334,12.6996197718631,\n",
    "                                                                            3.57414448669201,11.1787072243346,\n",
    "                                                                            231.90184049079647,\n",
    "                                                                            27.914110429447106,\n",
    "                                                                            380.67484662576635]})\n",
    "df_exudate_content_1.set_index(\"metabolites\",drop=True,inplace=True)\n",
    "df_exudate_content_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract sucrose, glucose and fructose content of seminal roots of maize seedlings from Ogawa et al 2009, Figure 1\n",
    "\n",
    "<img src=\"references/Screenshot_20190725_155512.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the root are from seedlings, data is declared irrelevant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data generated by Schnable lab, email recieved from Lee on 30/09/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Moisture (%FW)  Ash (%FW)  Protein (%FW)  Lipid (%FW)  Carbohydrate (%FW)\n",
      "0           83.85       2.14           1.06         0.00               12.96\n",
      "1           83.18       1.55           0.94         0.09               14.24\n"
     ]
    },
    {
     "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>Ash (mg/gDW)</th>\n",
       "      <th>Protein (mg/gDW)</th>\n",
       "      <th>Lipid (mg/gDW)</th>\n",
       "      <th>Carbohydrate (mg/gDW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132.50774</td>\n",
       "      <td>65.634675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>802.476780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92.15220</td>\n",
       "      <td>55.885850</td>\n",
       "      <td>5.350773</td>\n",
       "      <td>846.611177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ash (mg/gDW)  Protein (mg/gDW)  Lipid (mg/gDW)  Carbohydrate (mg/gDW)\n",
       "0     132.50774         65.634675        0.000000             802.476780\n",
       "1      92.15220         55.885850        5.350773             846.611177"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_root_content_1 = pd.DataFrame(data={\"Moisture (%FW)\":[83.85,83.18],\n",
    "                                       \"Ash (%FW)\":[2.14,1.55],\n",
    "                                       \"Protein (%FW)\":[1.06,0.94],\n",
    "                                       \"Lipid (%FW)\":[0,0.09],\n",
    "                                       \"Carbohydrate (%FW)\":[12.96,14.24]})\n",
    "print(df_root_content_1)\n",
    "\n",
    "df_root_content_1_std = pd.DataFrame(data={\"Ash (mg/gDW)\":1000*df_root_content_1[\"Ash (%FW)\"]/(100 - df_root_content_1[\"Moisture (%FW)\"]),\n",
    "                                           \"Protein (mg/gDW)\":1000*df_root_content_1[\"Protein (%FW)\"]/(100 - df_root_content_1[\"Moisture (%FW)\"]),\n",
    "                                           \"Lipid (mg/gDW)\":1000*df_root_content_1[\"Lipid (%FW)\"]/(100 - df_root_content_1[\"Moisture (%FW)\"]),\n",
    "                                           \"Carbohydrate (mg/gDW)\":1000*df_root_content_1[\"Carbohydrate (%FW)\"]/(100 - df_root_content_1[\"Moisture (%FW)\"])})\n",
    "df_root_content_1_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- amino acids and organic acids content from root exudates available\n",
    "- generic carbohydrate, lipid and protein content data generated by Schnable lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 17.2020450391 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.8863668319 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 4.9536245179 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 9.2182716051 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 0.1420323181 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "biomass.at[\"GLC_c\",\"root\"]=find_average(df_root_content_1_std[\"Carbohydrate (mg/gDW)\"])/ \\\n",
    "                           model.metabolites.GLC_c.formula_weight\n",
    "biomass.at[\"L_PHOSPHATIDATE_p\",\"root\"]=find_average(df_root_content_1_std[\"Lipid (mg/gDW)\"])/ \\\n",
    "                                       model.metabolites.L_PHOSPHATIDATE_p.formula_weight\n",
    "biomass.at[\"Protein_b\",\"root\"]=find_average(df_root_content_1_std[\"Protein (mg/gDW)\"])/ \\\n",
    "                               model.metabolites.Protein_b.formula_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering sugar, starch and protein content from Table 6 from an [OGTR report](http://www.ogtr.gov.au/internet/ogtr/publishing.nsf/Content/maize-3/$FILE/biologymaize08.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>pericarp (g/100gDW)</th>\n",
       "      <th>endosperm (g/100gDW)</th>\n",
       "      <th>embryo (g/100gDW)</th>\n",
       "      <th>pericarp (mg/gDW)</th>\n",
       "      <th>endosperm (mg/gDW)</th>\n",
       "      <th>embryo (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolite</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>3.70</td>\n",
       "      <td>8.00</td>\n",
       "      <td>18.4</td>\n",
       "      <td>37.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starch</th>\n",
       "      <td>7.30</td>\n",
       "      <td>87.60</td>\n",
       "      <td>8.3</td>\n",
       "      <td>73.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugar</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.62</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pericarp (g/100gDW)  endosperm (g/100gDW)  embryo (g/100gDW)  \\\n",
       "metabolite                                                                 \n",
       "protein                    3.70                  8.00               18.4   \n",
       "starch                     7.30                 87.60                8.3   \n",
       "sugar                      0.34                  0.62               10.8   \n",
       "\n",
       "            pericarp (mg/gDW)  endosperm (mg/gDW)  embryo (mg/gDW)  \n",
       "metabolite                                                          \n",
       "protein                  37.0                80.0            184.0  \n",
       "starch                   73.0               876.0             83.0  \n",
       "sugar                     3.4                 6.2            108.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_1 = pd.DataFrame(data={\"metabolite\":[\"protein\",\"starch\",\"sugar\"],\n",
    "                                       \"pericarp (g/100gDW)\":[3.7,7.3,0.34],\n",
    "                                       \"endosperm (g/100gDW)\":[8.0,87.6,0.62],\n",
    "                                       \"embryo (g/100gDW)\":[18.4,8.3,10.8]})\n",
    "df_seed_content_1.set_index(\"metabolite\",drop=True,inplace=True)\n",
    "df_seed_content_1[\"pericarp (mg/gDW)\"]=df_seed_content_1[\"pericarp (g/100gDW)\"]*10\n",
    "df_seed_content_1[\"endosperm (mg/gDW)\"]=df_seed_content_1[\"endosperm (g/100gDW)\"]*10\n",
    "df_seed_content_1[\"embryo (mg/gDW)\"]=df_seed_content_1[\"embryo (g/100gDW)\"]*10\n",
    "df_seed_content_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering glucose, fructose, sucrose, starch, protein, amino acids, organic acids  and fatty acids content on boiled sweet corn from Table 7 from an [OGTR report](http://www.ogtr.gov.au/internet/ogtr/publishing.nsf/Content/maize-3/$FILE/biologymaize08.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>g/100gFW</th>\n",
       "      <th>mg/gFW</th>\n",
       "      <th>mg/gDW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolites</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>0.400</td>\n",
       "      <td>4.00</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fructose</th>\n",
       "      <td>0.300</td>\n",
       "      <td>3.00</td>\n",
       "      <td>11.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sucrose</th>\n",
       "      <td>4.600</td>\n",
       "      <td>46.00</td>\n",
       "      <td>176.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starch</th>\n",
       "      <td>7.500</td>\n",
       "      <td>75.00</td>\n",
       "      <td>288.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>4.200</td>\n",
       "      <td>42.00</td>\n",
       "      <td>161.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moisture</th>\n",
       "      <td>74.000</td>\n",
       "      <td>740.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C16:0</th>\n",
       "      <td>0.250</td>\n",
       "      <td>2.50</td>\n",
       "      <td>9.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18:0</th>\n",
       "      <td>0.040</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C20:0</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18:1</th>\n",
       "      <td>0.640</td>\n",
       "      <td>6.40</td>\n",
       "      <td>24.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C20:1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18:2</th>\n",
       "      <td>1.270</td>\n",
       "      <td>12.70</td>\n",
       "      <td>48.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C18:3</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>0.335</td>\n",
       "      <td>3.35</td>\n",
       "      <td>12.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>0.159</td>\n",
       "      <td>1.59</td>\n",
       "      <td>6.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>0.257</td>\n",
       "      <td>2.57</td>\n",
       "      <td>9.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>0.190</td>\n",
       "      <td>1.90</td>\n",
       "      <td>7.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glt</th>\n",
       "      <td>0.713</td>\n",
       "      <td>7.13</td>\n",
       "      <td>27.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>0.145</td>\n",
       "      <td>1.45</td>\n",
       "      <td>5.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>0.124</td>\n",
       "      <td>1.24</td>\n",
       "      <td>4.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>0.155</td>\n",
       "      <td>1.55</td>\n",
       "      <td>5.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>0.467</td>\n",
       "      <td>4.67</td>\n",
       "      <td>17.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>0.135</td>\n",
       "      <td>1.35</td>\n",
       "      <td>5.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>0.105</td>\n",
       "      <td>1.05</td>\n",
       "      <td>4.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>0.192</td>\n",
       "      <td>1.92</td>\n",
       "      <td>7.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>0.325</td>\n",
       "      <td>3.25</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>0.198</td>\n",
       "      <td>1.98</td>\n",
       "      <td>7.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>0.199</td>\n",
       "      <td>1.99</td>\n",
       "      <td>7.653846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Try</th>\n",
       "      <td>0.032</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>0.155</td>\n",
       "      <td>1.55</td>\n",
       "      <td>5.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>0.224</td>\n",
       "      <td>2.24</td>\n",
       "      <td>8.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cit</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10.00</td>\n",
       "      <td>38.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mal</th>\n",
       "      <td>0.200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.692308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             g/100gFW  mg/gFW      mg/gDW\n",
       "metabolites                              \n",
       "glucose         0.400    4.00   15.384615\n",
       "fructose        0.300    3.00   11.538462\n",
       "sucrose         4.600   46.00  176.923077\n",
       "starch          7.500   75.00  288.461538\n",
       "protein         4.200   42.00  161.538462\n",
       "moisture       74.000  740.00    0.000000\n",
       "C16:0           0.250    2.50    9.615385\n",
       "C18:0           0.040    0.40    1.538462\n",
       "C20:0           0.010    0.10    0.384615\n",
       "C18:1           0.640    6.40   24.615385\n",
       "C20:1           0.010    0.10    0.384615\n",
       "C18:2           1.270   12.70   48.846154\n",
       "C18:3           0.020    0.20    0.769231\n",
       "Ala             0.335    3.35   12.884615\n",
       "Arg             0.159    1.59    6.115385\n",
       "Asp             0.257    2.57    9.884615\n",
       "Cys             0.190    1.90    7.307692\n",
       "Glt             0.713    7.13   27.423077\n",
       "Gly             0.145    1.45    5.576923\n",
       "His             0.124    1.24    4.769231\n",
       "Ile             0.155    1.55    5.961538\n",
       "Leu             0.467    4.67   17.961538\n",
       "Lys             0.135    1.35    5.192308\n",
       "Met             0.105    1.05    4.038462\n",
       "Phe             0.192    1.92    7.384615\n",
       "Pro             0.325    3.25   12.500000\n",
       "Ser             0.198    1.98    7.615385\n",
       "Thr             0.199    1.99    7.653846\n",
       "Try             0.032    0.32    1.230769\n",
       "Tyr             0.155    1.55    5.961538\n",
       "Val             0.224    2.24    8.615385\n",
       "Cit             1.000   10.00   38.461538\n",
       "Mal             0.200    2.00    7.692308"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_2 = pd.DataFrame(data={\"metabolites\":[\"glucose\",\"fructose\",\"sucrose\",\"starch\",\n",
    "                                                      \"protein\",\"moisture\",\"C16:0\",\"C18:0\",\n",
    "                                                      \"C20:0\",\"C18:1\",\"C20:1\",\"C18:2\",\"C18:3\",\n",
    "                                                      \"Ala\",\"Arg\",\"Asp\",\"Cys\",\"Glt\",\"Gly\",\"His\",\n",
    "                                                      \"Ile\",\"Leu\",\"Lys\",\"Met\",\"Phe\",\"Pro\",\"Ser\",\n",
    "                                                      \"Thr\",\"Try\",\"Tyr\",\"Val\",\"Cit\",\"Mal\"],\n",
    "                                       \"g/100gFW\":[0.4,0.3,4.6,7.5,4.2,74.0,0.25,0.04,0.01,0.64,\n",
    "                                                   0.01,1.27,0.02,0.335,0.159,0.257,0.095*2,0.713,\n",
    "                                                   0.145,0.124,0.155,0.467,0.135,0.105,0.192,0.325,\n",
    "                                                   0.198,0.199,0.032,0.155,0.224,1,0.2]})\n",
    "df_seed_content_2.set_index(\"metabolites\",inplace=True,drop=True)\n",
    "\n",
    "FW = 100\n",
    "DW = 100 - df_seed_content_2[\"g/100gFW\"][\"moisture\"]\n",
    "FW_DW_ratio = float(FW)/DW\n",
    "\n",
    "df_seed_content_2[\"mg/gFW\"]=df_seed_content_2[\"g/100gFW\"]*10\n",
    "df_seed_content_2[\"mg/gDW\"]=df_seed_content_2[\"mg/gFW\"]*FW_DW_ratio\n",
    "df_seed_content_2[\"mg/gDW\"][\"moisture\"]=0\n",
    "df_seed_content_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering biomass composition from Seaver et al 2005 endosperm model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sbml file (had to remove 6 metabolite and 1 reaction duplicates)\n",
    "Sam_endo_model = io.sbml.read_sbml_model(\"Data/Seaver2015_Evidenced-Maize-Endosperm-Model_corrected.sbml\")\n",
    "display.clear_output()\n",
    "rxn = Sam_endo_model.reactions.biomass0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>stoichiometry from Seaver et al 2015 leaf model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mets</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>5.204900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fructose</th>\n",
       "      <td>0.171000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xylose</th>\n",
       "      <td>0.216900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>galactose</th>\n",
       "      <td>0.063400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malate</th>\n",
       "      <td>0.004207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citrate</th>\n",
       "      <td>0.005624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arg</th>\n",
       "      <td>0.007300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>His</th>\n",
       "      <td>0.009500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lys</th>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asp</th>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glu</th>\n",
       "      <td>0.002700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ser</th>\n",
       "      <td>0.050100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thr</th>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asn</th>\n",
       "      <td>0.033700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gln</th>\n",
       "      <td>0.122600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cys</th>\n",
       "      <td>0.003400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gly</th>\n",
       "      <td>0.024500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>0.071000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ala</th>\n",
       "      <td>0.147400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Val</th>\n",
       "      <td>0.037900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ile</th>\n",
       "      <td>0.030100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leu</th>\n",
       "      <td>0.133400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Met</th>\n",
       "      <td>0.004700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phe</th>\n",
       "      <td>0.027900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyr</th>\n",
       "      <td>0.018200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trp</th>\n",
       "      <td>0.001300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           stoichiometry from Seaver et al 2015 leaf model\n",
       "mets                                                      \n",
       "glucose                                           5.204900\n",
       "fructose                                          0.171000\n",
       "xylose                                            0.216900\n",
       "galactose                                         0.063400\n",
       "malate                                            0.004207\n",
       "citrate                                           0.005624\n",
       "Arg                                               0.007300\n",
       "His                                               0.009500\n",
       "Lys                                               0.010000\n",
       "Asp                                               0.001500\n",
       "Glu                                               0.002700\n",
       "Ser                                               0.050100\n",
       "Thr                                               0.025000\n",
       "Asn                                               0.033700\n",
       "Gln                                               0.122600\n",
       "Cys                                               0.003400\n",
       "Gly                                               0.024500\n",
       "Pro                                               0.071000\n",
       "Ala                                               0.147400\n",
       "Val                                               0.037900\n",
       "Ile                                               0.030100\n",
       "Leu                                               0.133400\n",
       "Met                                               0.004700\n",
       "Phe                                               0.027900\n",
       "Tyr                                               0.018200\n",
       "Trp                                               0.001300"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_3 = pd.DataFrame(data={\"mets\":[\"glucose\",\"fructose\",\"xylose\",\"galactose\",\"malate\",\"citrate\",\n",
    "                                               \"Arg\",\"His\",\"Lys\",\"Asp\",\"Glu\",\"Ser\",\"Thr\",\"Asn\",\"Gln\",\"Cys\",\n",
    "                                               \"Gly\",\"Pro\",\"Ala\",\"Val\",\"Ile\",\"Leu\",\"Met\",\"Phe\",\"Tyr\",\"Trp\"],\n",
    "                                       \"stoichiometry from Seaver et al 2015 leaf model\":\n",
    "                                       [abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd19001_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd30321_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd01487_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00724_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00130_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00137_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00051_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00119_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00039_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00041_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00023_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00054_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00161_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00132_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00053_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00084_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00033_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00129_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00035_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00156_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00322_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00107_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00060_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00066_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00069_c0\"))),\n",
    "                                        abs(rxn.metabolites.get(Sam_endo_model.metabolites.get_by_id(\"cpd00065_c0\")))]})\n",
    "df_seed_content_3.set_index(\"mets\",drop=True,inplace=True)\n",
    "df_seed_content_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In paper, Seaver et al 2005 report starch is the major biomass component. Starch however was missing in the biomass reaction in the model. As a result, biomass composition gathered from biomass reaction is not currently not considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data generated by Schnable lab, email recieved from Lee on 30/09/2019:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Moisture (%FW)  Ash (%FW)  Protein (%FW)  Lipid (%FW)  Carbohydrate (%FW)\n",
      "0           54.56          0           4.19         0.87               40.38\n",
      "1           55.54          0           3.78         1.09               39.59\n"
     ]
    },
    {
     "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>Ash (mg/gDW)</th>\n",
       "      <th>Protein (mg/gDW)</th>\n",
       "      <th>Lipid (mg/gDW)</th>\n",
       "      <th>Carbohydrate (mg/gDW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>92.209507</td>\n",
       "      <td>19.146127</td>\n",
       "      <td>888.644366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>85.020243</td>\n",
       "      <td>24.516419</td>\n",
       "      <td>890.463338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ash (mg/gDW)  Protein (mg/gDW)  Lipid (mg/gDW)  Carbohydrate (mg/gDW)\n",
       "0           0.0         92.209507       19.146127             888.644366\n",
       "1           0.0         85.020243       24.516419             890.463338"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_4 = pd.DataFrame(data={\"Moisture (%FW)\":[54.56,55.54],\n",
    "                                       \"Ash (%FW)\":[0,0],\n",
    "                                       \"Protein (%FW)\":[4.19,3.78],\n",
    "                                       \"Lipid (%FW)\":[0.87,1.09],\n",
    "                                       \"Carbohydrate (%FW)\":[40.38,39.59]})\n",
    "print(df_seed_content_4)\n",
    "\n",
    "df_seed_content_4_std = pd.DataFrame(data={\"Ash (mg/gDW)\":1000*df_seed_content_4[\"Ash (%FW)\"]/(100 - df_seed_content_4[\"Moisture (%FW)\"]),\n",
    "                                           \"Protein (mg/gDW)\":1000*df_seed_content_4[\"Protein (%FW)\"]/(100 - df_seed_content_4[\"Moisture (%FW)\"]),\n",
    "                                           \"Lipid (mg/gDW)\":1000*df_seed_content_4[\"Lipid (%FW)\"]/(100 - df_seed_content_4[\"Moisture (%FW)\"]),\n",
    "                                           \"Carbohydrate (mg/gDW)\":1000*df_seed_content_4[\"Carbohydrate (%FW)\"]/(100 - df_seed_content_4[\"Moisture (%FW)\"])})\n",
    "df_seed_content_4_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering moisture content, protein, starch, fatty acid, amino acid and cellwall data from FAO website (http://www.fao.org/3/T0395E/T0395E03.htm) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "starch_content = 72.5 #72-73%\n",
    "lipid_content = 10.5 # 3-18%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Moisture (%FW)</th>\n",
       "      <th>Protein (%FW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Salpor</th>\n",
       "      <td>12.2</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crystalline</th>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Floury</th>\n",
       "      <td>9.6</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starchy</th>\n",
       "      <td>11.2</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweet</th>\n",
       "      <td>9.5</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pop</th>\n",
       "      <td>10.4</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>12.3</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Moisture (%FW)  Protein (%FW)\n",
       "variety                                   \n",
       "Salpor                 12.2            5.8\n",
       "Crystalline            10.5           10.3\n",
       "Floury                  9.6           10.7\n",
       "Starchy                11.2            9.1\n",
       "Sweet                   9.5           12.9\n",
       "Pop                    10.4           13.7\n",
       "Black                  12.3            5.2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_H2O_protein_temp = pd.DataFrame(data={\"variety\":[\"Salpor\",\"Crystalline\",\"Floury\",\n",
    "                                                    \"Starchy\",\"Sweet\",\"Pop\",\"Black\"],\n",
    "                                         \"Moisture (%FW)\":[12.2,10.5,9.6,11.2,9.5,10.4,12.3],\n",
    "                                         \"Protein (%FW)\":[5.8,10.3,10.7,9.1,12.9,13.7,5.2]})\n",
    "df_H2O_protein_temp.set_index(\"variety\",drop=True,inplace=True)\n",
    "df_H2O_protein_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C16:0 (%FA)</th>\n",
       "      <th>C18:0 (%FA)</th>\n",
       "      <th>C18:1 (%FA)</th>\n",
       "      <th>C18:2 (%FA)</th>\n",
       "      <th>C18:3 (%FA)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>QPM nutricta</th>\n",
       "      <td>15.71</td>\n",
       "      <td>3.12</td>\n",
       "      <td>36.45</td>\n",
       "      <td>43.38</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azotea</th>\n",
       "      <td>12.89</td>\n",
       "      <td>2.62</td>\n",
       "      <td>35.63</td>\n",
       "      <td>48.85</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xetzoc</th>\n",
       "      <td>11.75</td>\n",
       "      <td>3.54</td>\n",
       "      <td>40.07</td>\n",
       "      <td>44.65</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tropical White</th>\n",
       "      <td>15.49</td>\n",
       "      <td>2.40</td>\n",
       "      <td>34.64</td>\n",
       "      <td>47.47</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Apolonia</th>\n",
       "      <td>11.45</td>\n",
       "      <td>3.12</td>\n",
       "      <td>38.02</td>\n",
       "      <td>47.44</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                C16:0 (%FA)  C18:0 (%FA)  C18:1 (%FA)  C18:2 (%FA)  \\\n",
       "variety                                                              \n",
       "QPM nutricta          15.71         3.12        36.45        43.38   \n",
       "Azotea                12.89         2.62        35.63        48.85   \n",
       "Xetzoc                11.75         3.54        40.07        44.65   \n",
       "Tropical White        15.49         2.40        34.64        47.47   \n",
       "Santa Apolonia        11.45         3.12        38.02        47.44   \n",
       "\n",
       "                C18:3 (%FA)  \n",
       "variety                      \n",
       "QPM nutricta           0.42  \n",
       "Azotea                 0.00  \n",
       "Xetzoc                 0.00  \n",
       "Tropical White         0.00  \n",
       "Santa Apolonia         0.00  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df_lipid_content_temp = pd.DataFrame(data={\"variety\":[\"QPM nutricta\",\"Azotea\",\"Xetzoc\",\n",
    "                                                 \"Tropical White\",\"Santa Apolonia\"],\n",
    "                                      \"C16:0 (%FA)\":[15.71,12.89,11.75,15.49,11.45],\n",
    "                                      \"C18:0 (%FA)\":[3.12,2.62,3.54,2.40,3.12],\n",
    "                                      \"C18:1 (%FA)\":[36.45,35.63,40.07,34.64,38.02],\n",
    "                                      \"C18:2 (%FA)\":[43.38,48.85,44.65,47.47,47.44],\n",
    "                                      \"C18:3 (%FA)\":[0.42,0,0,0,0]})\n",
    "df_lipid_content_temp.set_index(\"variety\",drop=True,inplace=True)\n",
    "df_lipid_content_temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ala (mg/gDW)</th>\n",
       "      <th>Arg (mg/gDW)</th>\n",
       "      <th>Asp (mg/gDW)</th>\n",
       "      <th>Cystine (mg/gDW)</th>\n",
       "      <th>Glt (mg/gDW)</th>\n",
       "      <th>Gly (mg/gDW)</th>\n",
       "      <th>His (mg/gDW)</th>\n",
       "      <th>Ile (mg/gDW)</th>\n",
       "      <th>Leu (mg/gDW)</th>\n",
       "      <th>Lys (mg/gDW)</th>\n",
       "      <th>Met (mg/gDW)</th>\n",
       "      <th>Phe (mg/gDW)</th>\n",
       "      <th>Pro (mg/gDW)</th>\n",
       "      <th>Ser (mg/gDW)</th>\n",
       "      <th>Thr (mg/gDW)</th>\n",
       "      <th>Trp (mg/gDW)</th>\n",
       "      <th>Tyr (mg/gDW)</th>\n",
       "      <th>Val (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cuyuta (white)</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>12.320000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>8.40000</td>\n",
       "      <td>2.240000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>0.504000</td>\n",
       "      <td>2.320000</td>\n",
       "      <td>3.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSD (white)</th>\n",
       "      <td>10.703125</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>4.966250</td>\n",
       "      <td>1.198750</td>\n",
       "      <td>11.987500</td>\n",
       "      <td>2.397500</td>\n",
       "      <td>1.712500</td>\n",
       "      <td>2.311875</td>\n",
       "      <td>10.27500</td>\n",
       "      <td>1.798125</td>\n",
       "      <td>1.027500</td>\n",
       "      <td>3.938750</td>\n",
       "      <td>6.336250</td>\n",
       "      <td>3.596250</td>\n",
       "      <td>2.483125</td>\n",
       "      <td>0.402438</td>\n",
       "      <td>2.568750</td>\n",
       "      <td>3.510625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGY (yellow)</th>\n",
       "      <td>10.106875</td>\n",
       "      <td>4.023125</td>\n",
       "      <td>5.985625</td>\n",
       "      <td>1.373750</td>\n",
       "      <td>13.246875</td>\n",
       "      <td>2.845625</td>\n",
       "      <td>2.158750</td>\n",
       "      <td>3.336250</td>\n",
       "      <td>11.97125</td>\n",
       "      <td>2.551250</td>\n",
       "      <td>0.981250</td>\n",
       "      <td>4.317500</td>\n",
       "      <td>6.476250</td>\n",
       "      <td>4.513750</td>\n",
       "      <td>3.041875</td>\n",
       "      <td>0.500438</td>\n",
       "      <td>2.943750</td>\n",
       "      <td>4.219375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142-48 (yellow)</th>\n",
       "      <td>9.836250</td>\n",
       "      <td>3.316875</td>\n",
       "      <td>6.862500</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>17.156250</td>\n",
       "      <td>2.973750</td>\n",
       "      <td>2.401875</td>\n",
       "      <td>3.431250</td>\n",
       "      <td>15.32625</td>\n",
       "      <td>2.630625</td>\n",
       "      <td>1.143750</td>\n",
       "      <td>6.176250</td>\n",
       "      <td>10.980000</td>\n",
       "      <td>5.146875</td>\n",
       "      <td>3.431250</td>\n",
       "      <td>0.503250</td>\n",
       "      <td>3.774375</td>\n",
       "      <td>4.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4251 (hybrid)</th>\n",
       "      <td>7.286875</td>\n",
       "      <td>3.193125</td>\n",
       "      <td>5.076250</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>11.953750</td>\n",
       "      <td>2.701875</td>\n",
       "      <td>2.292500</td>\n",
       "      <td>2.701875</td>\n",
       "      <td>9.98875</td>\n",
       "      <td>2.374375</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>3.684375</td>\n",
       "      <td>8.433125</td>\n",
       "      <td>3.766250</td>\n",
       "      <td>2.701875</td>\n",
       "      <td>0.401188</td>\n",
       "      <td>2.783750</td>\n",
       "      <td>3.766250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO (high-oil white)</th>\n",
       "      <td>7.960000</td>\n",
       "      <td>5.721250</td>\n",
       "      <td>7.462500</td>\n",
       "      <td>1.865625</td>\n",
       "      <td>17.288125</td>\n",
       "      <td>4.228750</td>\n",
       "      <td>2.860625</td>\n",
       "      <td>4.353125</td>\n",
       "      <td>9.70125</td>\n",
       "      <td>3.980000</td>\n",
       "      <td>1.492500</td>\n",
       "      <td>3.606875</td>\n",
       "      <td>11.566875</td>\n",
       "      <td>5.970000</td>\n",
       "      <td>3.980000</td>\n",
       "      <td>0.696500</td>\n",
       "      <td>4.353125</td>\n",
       "      <td>2.611875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H5 (high-protein yellow)</th>\n",
       "      <td>15.120000</td>\n",
       "      <td>5.040000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>17.360000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>5.180000</td>\n",
       "      <td>19.04000</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>2.380000</td>\n",
       "      <td>7.420000</td>\n",
       "      <td>11.620000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.340000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>5.040000</td>\n",
       "      <td>6.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP (high-protein white)</th>\n",
       "      <td>18.005625</td>\n",
       "      <td>7.093125</td>\n",
       "      <td>11.094375</td>\n",
       "      <td>2.546250</td>\n",
       "      <td>23.280000</td>\n",
       "      <td>5.092500</td>\n",
       "      <td>4.001250</td>\n",
       "      <td>7.275000</td>\n",
       "      <td>27.64500</td>\n",
       "      <td>3.637500</td>\n",
       "      <td>2.000625</td>\n",
       "      <td>10.366875</td>\n",
       "      <td>12.367500</td>\n",
       "      <td>10.003125</td>\n",
       "      <td>6.001875</td>\n",
       "      <td>0.800250</td>\n",
       "      <td>7.456875</td>\n",
       "      <td>8.366250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teosinte</th>\n",
       "      <td>20.240625</td>\n",
       "      <td>6.905625</td>\n",
       "      <td>12.620625</td>\n",
       "      <td>2.619375</td>\n",
       "      <td>47.386875</td>\n",
       "      <td>5.238750</td>\n",
       "      <td>4.524375</td>\n",
       "      <td>11.191875</td>\n",
       "      <td>40.00500</td>\n",
       "      <td>3.095625</td>\n",
       "      <td>2.857500</td>\n",
       "      <td>13.573125</td>\n",
       "      <td>22.860000</td>\n",
       "      <td>12.382500</td>\n",
       "      <td>7.143750</td>\n",
       "      <td>0.904875</td>\n",
       "      <td>10.477500</td>\n",
       "      <td>11.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Ala (mg/gDW)  Arg (mg/gDW)  Asp (mg/gDW)  \\\n",
       "variety                                                              \n",
       "Cuyuta (white)                6.000000      2.800000      5.200000   \n",
       "SSD (white)                  10.703125      3.082500      4.966250   \n",
       "TGY (yellow)                 10.106875      4.023125      5.985625   \n",
       "142-48 (yellow)               9.836250      3.316875      6.862500   \n",
       "4251 (hybrid)                 7.286875      3.193125      5.076250   \n",
       "HO (high-oil white)           7.960000      5.721250      7.462500   \n",
       "H5 (high-protein yellow)     15.120000      5.040000      9.520000   \n",
       "HP (high-protein white)      18.005625      7.093125     11.094375   \n",
       "Teosinte                     20.240625      6.905625     12.620625   \n",
       "\n",
       "                          Cystine (mg/gDW)  Glt (mg/gDW)  Gly (mg/gDW)  \\\n",
       "variety                                                                  \n",
       "Cuyuta (white)                    1.120000     12.320000      2.480000   \n",
       "SSD (white)                       1.198750     11.987500      2.397500   \n",
       "TGY (yellow)                      1.373750     13.246875      2.845625   \n",
       "142-48 (yellow)                   1.830000     17.156250      2.973750   \n",
       "4251 (hybrid)                     1.310000     11.953750      2.701875   \n",
       "HO (high-oil white)               1.865625     17.288125      4.228750   \n",
       "H5 (high-protein yellow)          1.680000     17.360000      3.640000   \n",
       "HP (high-protein white)           2.546250     23.280000      5.092500   \n",
       "Teosinte                          2.619375     47.386875      5.238750   \n",
       "\n",
       "                          His (mg/gDW)  Ile (mg/gDW)  Leu (mg/gDW)  \\\n",
       "variety                                                              \n",
       "Cuyuta (white)                1.680000      2.080000       8.40000   \n",
       "SSD (white)                   1.712500      2.311875      10.27500   \n",
       "TGY (yellow)                  2.158750      3.336250      11.97125   \n",
       "142-48 (yellow)               2.401875      3.431250      15.32625   \n",
       "4251 (hybrid)                 2.292500      2.701875       9.98875   \n",
       "HO (high-oil white)           2.860625      4.353125       9.70125   \n",
       "H5 (high-protein yellow)      2.800000      5.180000      19.04000   \n",
       "HP (high-protein white)       4.001250      7.275000      27.64500   \n",
       "Teosinte                      4.524375     11.191875      40.00500   \n",
       "\n",
       "                          Lys (mg/gDW)  Met (mg/gDW)  Phe (mg/gDW)  \\\n",
       "variety                                                              \n",
       "Cuyuta (white)                2.240000      1.040000      3.520000   \n",
       "SSD (white)                   1.798125      1.027500      3.938750   \n",
       "TGY (yellow)                  2.551250      0.981250      4.317500   \n",
       "142-48 (yellow)               2.630625      1.143750      6.176250   \n",
       "4251 (hybrid)                 2.374375      1.310000      3.684375   \n",
       "HO (high-oil white)           3.980000      1.492500      3.606875   \n",
       "H5 (high-protein yellow)      2.940000      2.380000      7.420000   \n",
       "HP (high-protein white)       3.637500      2.000625     10.366875   \n",
       "Teosinte                      3.095625      2.857500     13.573125   \n",
       "\n",
       "                          Pro (mg/gDW)  Ser (mg/gDW)  Thr (mg/gDW)  \\\n",
       "variety                                                              \n",
       "Cuyuta (white)                6.480000      3.600000      2.480000   \n",
       "SSD (white)                   6.336250      3.596250      2.483125   \n",
       "TGY (yellow)                  6.476250      4.513750      3.041875   \n",
       "142-48 (yellow)              10.980000      5.146875      3.431250   \n",
       "4251 (hybrid)                 8.433125      3.766250      2.701875   \n",
       "HO (high-oil white)          11.566875      5.970000      3.980000   \n",
       "H5 (high-protein yellow)     11.620000      7.000000      4.340000   \n",
       "HP (high-protein white)      12.367500     10.003125      6.001875   \n",
       "Teosinte                     22.860000     12.382500      7.143750   \n",
       "\n",
       "                          Trp (mg/gDW)  Tyr (mg/gDW)  Val (mg/gDW)  \n",
       "variety                                                             \n",
       "Cuyuta (white)                0.504000      2.320000      3.280000  \n",
       "SSD (white)                   0.402438      2.568750      3.510625  \n",
       "TGY (yellow)                  0.500438      2.943750      4.219375  \n",
       "142-48 (yellow)               0.503250      3.774375      4.575000  \n",
       "4251 (hybrid)                 0.401188      2.783750      3.766250  \n",
       "HO (high-oil white)           0.696500      4.353125      2.611875  \n",
       "H5 (high-protein yellow)      0.602000      5.040000      6.020000  \n",
       "HP (high-protein white)       0.800250      7.456875      8.366250  \n",
       "Teosinte                      0.904875     10.477500     11.430000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aminoacid_content_temp = pd.DataFrame(data={\"variety\":[\"Cuyuta (white)\",\"SSD (white)\",\"TGY (yellow)\",\n",
    "                                                          \"142-48 (yellow)\",\"4251 (hybrid)\",\n",
    "                                                          \"HO (high-oil white)\",\"H5 (high-protein yellow)\",\n",
    "                                                          \"HP (high-protein white)\",\"Teosinte\"],\n",
    "                                               \"N content (assumed g/100gDW)\":[1.28,1.37,1.57,1.83,1.31,1.99,2.24,2.91,3.81],\n",
    "                                               \"Ala (g/16g N)\":[7.5,12.5,10.3,8.6,8.9,6.4,10.8,9.9,8.5],\n",
    "                                               \"Arg (g/16g N)\":[3.5,3.6,4.1,2.9,3.9,4.6,3.6,3.9,2.9],\n",
    "                                               \"Asp (g/16g N)\":[6.5,5.8,6.1,6.0,6.2,6.0,6.8,6.1,5.3],\n",
    "                                               \"Cystine (g/16g N)\":[1.4,1.4,1.4,1.6,1.6,1.5,1.2,1.4,1.1],\n",
    "                                               \"Glt (g/16g N)\":[15.4,14.0,13.5,15.0,14.6,13.9,12.4,12.8,19.9],\n",
    "                                               \"Gly (g/16g N)\":[3.1,2.8,2.9,2.6,3.3,3.4,2.6,2.8,2.2],\n",
    "                                               \"His (g/16g N)\":[2.1,2.0,2.2,2.1,2.8,2.3,2.0,2.2,1.9],\n",
    "                                               \"Ile (g/16g N)\":[2.6,2.7,3.4,3.0,3.3,3.5,3.7,4.0,4.7],\n",
    "                                               \"Leu (g/16g N)\":[10.5,12.0,12.2,13.4,12.2,7.8,13.6,15.2,16.8],\n",
    "                                               \"Lys (g/16g N)\":[2.8,2.1,2.6,2.3,2.9,3.2,2.1,2.0,1.3],\n",
    "                                               \"Met (g/16g N)\":[1.3,1.2,1.0,1.0,1.6,1.2,1.7,1.1,1.2],\n",
    "                                               \"Phe (g/16g N)\":[4.4,4.6,4.4,5.4,4.5,2.9,5.3,5.7,5.7],\n",
    "                                               \"Pro (g/16g N)\":[8.1,7.4,6.6,9.6,10.3,9.3,8.3,6.8,9.6],\n",
    "                                               \"Ser (g/16g N)\":[4.5,4.2,4.6,4.5,4.6,4.8,5.0,5.5,5.2],\n",
    "                                               \"Thr (g/16g N)\":[3.1,2.9,3.1,3.0,3.3,3.2,3.1,3.3,3.0],\n",
    "                                               \"Trp (g/16g N)\":[0.63,0.47,0.51,0.44,0.49,0.56,0.43,0.44,0.38],\n",
    "                                               \"Tyr (g/16g N)\":[2.9,3.0,3.0,3.3,3.4,3.5,3.6,4.1,4.4],\n",
    "                                               \"Val (g/16g N)\":[4.1,4.1,4.3,4.0,4.6,2.1,4.3,4.6,4.8]})\n",
    "df_aminoacid_content_temp.set_index(\"variety\",drop=True,inplace=True)\n",
    "\n",
    "df_aminoacid_content_temp_std = pd.DataFrame(data={\"variety\":[\"Cuyuta (white)\",\"SSD (white)\",\"TGY (yellow)\",\n",
    "                                                          \"142-48 (yellow)\",\"4251 (hybrid)\",\n",
    "                                                          \"HO (high-oil white)\",\"H5 (high-protein yellow)\",\n",
    "                                                          \"HP (high-protein white)\",\"Teosinte\"],\n",
    "                                               \"Ala (mg/gDW)\":(df_aminoacid_content_temp[\"Ala (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Arg (mg/gDW)\":(df_aminoacid_content_temp[\"Arg (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Asp (mg/gDW)\":(df_aminoacid_content_temp[\"Asp (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Cystine (mg/gDW)\":(df_aminoacid_content_temp[\"Cystine (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Glt (mg/gDW)\":(df_aminoacid_content_temp[\"Glt (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Gly (mg/gDW)\":(df_aminoacid_content_temp[\"Gly (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"His (mg/gDW)\":(df_aminoacid_content_temp[\"His (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Ile (mg/gDW)\":(df_aminoacid_content_temp[\"Ile (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Leu (mg/gDW)\":(df_aminoacid_content_temp[\"Leu (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Lys (mg/gDW)\":(df_aminoacid_content_temp[\"Lys (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Met (mg/gDW)\":(df_aminoacid_content_temp[\"Met (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Phe (mg/gDW)\":(df_aminoacid_content_temp[\"Phe (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Pro (mg/gDW)\":(df_aminoacid_content_temp[\"Pro (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Ser (mg/gDW)\":(df_aminoacid_content_temp[\"Ser (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Thr (mg/gDW)\":(df_aminoacid_content_temp[\"Thr (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Trp (mg/gDW)\":(df_aminoacid_content_temp[\"Trp (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Tyr (mg/gDW)\":(df_aminoacid_content_temp[\"Tyr (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10,\n",
    "                                               \"Val (mg/gDW)\":(df_aminoacid_content_temp[\"Val (g/16g N)\"]/16)* \\\n",
    "                                                            df_aminoacid_content_temp[\"N content (assumed g/100gDW)\"]*10})\n",
    "df_aminoacid_content_temp_std.set_index(\"variety\",drop=True,inplace=True)\n",
    "df_aminoacid_content_temp_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>hemicellulose</th>\n",
       "      <th>lignin</th>\n",
       "      <th>cellwall</th>\n",
       "      <th>cellulose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.98</td>\n",
       "      <td>0.14</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.05</td>\n",
       "      <td>0.12</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.25</td>\n",
       "      <td>0.13</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>13.1</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.44</td>\n",
       "      <td>0.14</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hemicellulose  lignin  cellwall  cellulose\n",
       "variety                                            \n",
       "1                 4.98    0.14       9.1       3.98\n",
       "2                 8.05    0.12      10.8       2.63\n",
       "3                 6.25    0.13      12.0       5.62\n",
       "4                 9.23    0.12      13.1       3.75\n",
       "5                11.44    0.14      14.2       2.62"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cellwall_content_temp = pd.DataFrame(data={\"variety\":[1,2,3,4,5],\n",
    "                                         \"hemicellulose\":[4.98,8.05,6.25,9.23,11.44],\n",
    "                                         \"lignin\":[0.14,0.12,0.13,0.12,0.14],\n",
    "                                         \"cellwall\":[9.1,10.8,12.0,13.1,14.2]})\n",
    "df_cellwall_content_temp.set_index(\"variety\",drop=True,inplace=True)\n",
    "df_cellwall_content_temp[\"cellulose\"]=df_cellwall_content_temp[\"cellwall\"]- \\\n",
    "                                        df_cellwall_content_temp[\"hemicellulose\"]- \\\n",
    "                                        df_cellwall_content_temp[\"lignin\"]\n",
    "df_cellwall_content_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- sugar, starch and protein content reported in table 6 of OGTR (Australian gov.)\n",
    "- glucose, fructose, sucrose, starch, protein, amino acids, organic acids and fatty acids content on boiled sweet corn. Starch content is considerably lower than Table 7 (because of boiling). Hence this data was not considered.\n",
    "- sugars, organic acid, amino acid composition gathered from Seaver et al 2005.In the paper, the authors report starch is the major biomass component. Starch however was missing in the biomass reaction in the model. As a result, biomass composition gathered from teh reaction is not currently not considered\n",
    "- moisture content, protein, starch, fatty acid, amino acid and cellwall data was gathered from UN's FAO website. \n",
    "\n",
    "Assumptions\n",
    "\n",
    "- processing FAO data, in the case of starch, and cell wall, tables were not clear of whether the units were in %FW or %DW, so %FW was considered as other data reported was in %FW. In the case of amino acids, data from a study was reported. The table legend also does not mention is units they are expressed in. Given how the data looks very similar to results from Mosse, Huet and Baudet 1988, the units were assumed to be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 17.2020450391 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.8863668319 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 4.9536245179 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 9.2182716051 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 0.1420323181 is not an integer (in formula C17.2020450391H32.8863668319N4.9536245179O9.2182716051S0.1420323181)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "FW_DW_ratio = 100/(100-find_average(df_H2O_protein_temp[\"Moisture (%FW)\"]))\n",
    "biomass.at[\"Starch_b\",\"seed\"]=starch_content*FW_DW_ratio*10/model.metabolites.GLC_c.formula_weight\n",
    "biomass.at[\"Protein_b\",\"seed\"]=find_average(df_H2O_protein_temp[\"Protein (%FW)\"])*FW_DW_ratio*10/ \\\n",
    "                               model.metabolites.Protein_b.formula_weight\n",
    "biomass.at[\"PALMITATE_p\",\"seed\"]=(find_average(df_lipid_content_temp[\"C16:0 (%FA)\"])* \\\n",
    "                                  find_average(df_seed_content_4_std[\"Lipid (mg/gDW)\"])/100)/ \\\n",
    "                                  model.metabolites.PALMITATE_p.formula_weight\n",
    "biomass.at[\"STEARIC_ACID_p\",\"seed\"]=(find_average(df_lipid_content_temp[\"C18:0 (%FA)\"])* \\\n",
    "                                     find_average(df_seed_content_4_std[\"Lipid (mg/gDW)\"])/100)/ \\\n",
    "                                     model.metabolites.STEARIC_ACID_p.formula_weight\n",
    "biomass.at[\"OLEATE_CPD_p\",\"seed\"]=(find_average(df_lipid_content_temp[\"C18:1 (%FA)\"])* \\\n",
    "                                   find_average(df_seed_content_4_std[\"Lipid (mg/gDW)\"])/100)/ \\\n",
    "                                   model.metabolites.OLEATE_CPD_p.formula_weight\n",
    "biomass.at[\"Octadecadienoate_p\",\"seed\"]=(find_average(df_lipid_content_temp[\"C18:2 (%FA)\"])* \\\n",
    "                                         find_average(df_seed_content_4_std[\"Lipid (mg/gDW)\"])/100)/ \\\n",
    "                                         model.metabolites.Octadecadienoate_p.formula_weight\n",
    "biomass.at[\"LINOLENIC_ACID_p\",\"seed\"]=(find_average(df_lipid_content_temp[\"C18:3 (%FA)\"])* \\\n",
    "                                       find_average(df_seed_content_4_std[\"Lipid (mg/gDW)\"])/100)/ \\\n",
    "                                       model.metabolites.LINOLENIC_ACID_p.formula_weight\n",
    "biomass.at[\"ARG_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Arg (mg/gDW)\"])/model.metabolites.ARG_c.formula_weight\n",
    "biomass.at[\"HIS_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"His (mg/gDW)\"])/model.metabolites.HIS_c.formula_weight\n",
    "biomass.at[\"LYS_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Lys (mg/gDW)\"])/model.metabolites.LYS_c.formula_weight\n",
    "biomass.at[\"sASP_b\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Asp (mg/gDW)\"])/model.metabolites.sASP_b.formula_weight\n",
    "biomass.at[\"sGLU_b\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Glt (mg/gDW)\"])/model.metabolites.sGLU_b.formula_weight\n",
    "biomass.at[\"sSER_b\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Ser (mg/gDW)\"])/model.metabolites.sSER_b.formula_weight\n",
    "biomass.at[\"THR_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Thr (mg/gDW)\"])/model.metabolites.THR_c.formula_weight\n",
    "biomass.at[\"CYS_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Cystine (mg/gDW)\"])*2/model.metabolites.CYS_c.formula_weight\n",
    "biomass.at[\"GLY_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Gly (mg/gDW)\"])/model.metabolites.GLY_c.formula_weight\n",
    "biomass.at[\"PRO_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Pro (mg/gDW)\"])/model.metabolites.PRO_c.formula_weight\n",
    "biomass.at[\"sALA_b\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Ala (mg/gDW)\"])/model.metabolites.sALA_b.formula_weight\n",
    "biomass.at[\"VAL_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Val (mg/gDW)\"])/model.metabolites.VAL_c.formula_weight\n",
    "biomass.at[\"ILE_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Ile (mg/gDW)\"])/model.metabolites.ILE_c.formula_weight\n",
    "biomass.at[\"LEU_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Leu (mg/gDW)\"])/model.metabolites.LEU_c.formula_weight\n",
    "biomass.at[\"MET_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Met (mg/gDW)\"])/model.metabolites.MET_c.formula_weight\n",
    "biomass.at[\"PHE_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Phe (mg/gDW)\"])/model.metabolites.PHE_c.formula_weight\n",
    "biomass.at[\"TYR_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Tyr (mg/gDW)\"])/model.metabolites.TYR_c.formula_weight\n",
    "biomass.at[\"TRP_c\",\"seed\"]=find_average(df_aminoacid_content_temp_std[\"Trp (mg/gDW)\"])/model.metabolites.TRP_c.formula_weight\n",
    "biomass.at[\"Cellulose_b\",\"seed\"]=(find_average(df_cellwall_content_temp[\"cellulose\"]+df_cellwall_content_temp[\"hemicellulose\"])* \\\n",
    "                                  FW_DW_ratio*10)/model.metabolites.GLC_c.formula_weight\n",
    "biomass.at[\"COUMARATE_c\",\"seed\"]=find_average(df_cellwall_content_temp[\"lignin\"])*FW_DW_ratio*10/ \\\n",
    "                                 model.metabolites.COUMARATE_c.formula_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass.to_csv(\"Data/biomass_maize.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>type</th>\n",
       "      <th>leaf</th>\n",
       "      <th>stem</th>\n",
       "      <th>root</th>\n",
       "      <th>seed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sSUCROSE_b</th>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.218178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLC_c</th>\n",
       "      <td></td>\n",
       "      <td>1.748833</td>\n",
       "      <td>0.508244</td>\n",
       "      <td>4.576836</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRU_c</th>\n",
       "      <td></td>\n",
       "      <td>0.257119</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starch_b</th>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.512262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cellulose_b</th>\n",
       "      <td></td>\n",
       "      <td>0.272308</td>\n",
       "      <td>0.668625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.728808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pLEU_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pPRO_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCYS_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pTRP_b</th>\n",
       "      <td>protein</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUMARATE_c</th>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                type      leaf      stem      root      seed\n",
       "                                                            \n",
       "sSUCROSE_b            0.000000  0.218178  0.000000  0.000000\n",
       "GLC_c                 1.748833  0.508244  4.576836  0.000000\n",
       "FRU_c                 0.257119  0.000000  0.000000  0.000000\n",
       "Starch_b              0.000000  0.029235  0.000000  4.512262\n",
       "Cellulose_b           0.272308  0.668625  0.000000  0.728808\n",
       "...              ...       ...       ...       ...       ...\n",
       "pLEU_b       protein  0.000000  0.000000  0.000000  0.000000\n",
       "pPRO_b       protein  0.000000  0.000000  0.000000  0.000000\n",
       "pCYS_b       protein  0.000000  0.000000  0.000000  0.000000\n",
       "pTRP_b       protein  0.000000  0.000000  0.000000  0.000000\n",
       "COUMARATE_c           0.000000  0.000000  0.000000  0.008934\n",
       "\n",
       "[81 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- Jeannette, E., Reyss, A., Grégory, N., Gantet, P. and Prioul, J. ‐L. (2000) Carbohydrate metabolism in a heat‐girdled maize source leaf. Plant. Cell Environ., 23, 61–69. https://doi.org/10.1046/j.1365-3040.2000.00519.x.\n",
    "- Gu, Y., He, L., Zhao, C., Wang, F., Yan, B., Gao, Y., Li, Z., Yang, K. and Xu, J. (2017) Biochemical and Transcriptional Regulation of Membrane Lipid Metabolism in Maize Leaves under Low Temperature. Front. Plant Sci., 8, 1–13. Available at: https://doi.org/10.3389/fpls.2017.02053.\n",
    "- Seaver, S.M.D., Bradbury, L.M.T., Frelin, O., Zarecki, R., Ruppin, E., Hanson, A.D. and Henry, C.S. (2015) Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. Front. Plant Sci., 6, 142. http://www.doi.org/10.3389/fpls.2015.00142.\n",
    "- Abiven, S., Heim, A. and Schmidt, M.W.I. (2011) Lignin content and chemical characteristics in maize and wheat vary between plant organs and growth stages: Consequences for assessing lignin dynamics in soil. Plant Soil, 343, 369–378. https://www.doi.org/10.1007/s11104-011-0725-y.\n",
    "- Hatfield, R.D. and Marita, J.M. (2017) Maize Development: Cell Wall Changes in Leaves and Sheaths. Am. J. Plant Sci., 08, 1248–1263. https://www.doi.org/10.4236/ajps.2017.86083.\n",
    "- Sekhon, R.S., Breitzman, M.W., Silva, R.R., Santoro, N., Rooney, W.L., Leon, N. de and Kaeppler, S.M. (2016) Stover Composition in Maize and Sorghum Reveals Remarkable Genetic Variation and Plasticity for Carbohydrate Accumulation. Front. Plant Sci., 7, 1–12. https://www.doi.org/10.3389/fpls.2016.00822.\n",
    "- Carvalhais, L.C., Dennis, P.G., Fedoseyenko, D., Hajirezaei, M.-R., Borriss, R. and Wirén, N. von (2011) Root exudation of sugars, amino acids, and organic acids by maize as affected by nitrogen, phosphorus, potassium, and iron deficiency. J. Plant Nutr. Soil Sci., 174, 3–11. https://www.doi.org/10.1002/jpln.201000085.\n",
    "- Ogawa, A., Ando, F., Toyofuku, K. and Kawashima, C. (2009) Sucrose Metabolism for the Development of Seminal Root in Maize Seedlings. Plant Prod. Sci., 12, 9–16. https://www.doi.org/10.1626/pps.12.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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