Generate SOY tissue specific models 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from cobra import io\n",
    "from cobra.core import Metabolite, Reaction\n",
    "from Functions import *\n",
    "\n",
    "import logging\n",
    "logging.basicConfig()\n",
    "logger = logging.getLogger('logger')\n",
    "\n",
    "\n",
    "fname = \"Data/Soy_core_model_GA.sbml\"\n",
    "model = io.sbml.read_sbml_model(fname,use_fbc_package=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate leaf model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "leaf_model = model.copy()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract diurnal starch, sucrose, hemicellulose and simple sugar content from Clements 1930, Figure 6, 9, 13 and 14. Based on Figure 1 and 2, dawn = 8am and dusk = 8pm\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_161155.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_160304.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_161259.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_160242.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starch_dawn (g/100gFW)</th>\n",
       "      <th>hemicellulose_dawn (g/100gFW)</th>\n",
       "      <th>simple sugars_dawn (g/100gFW)</th>\n",
       "      <th>sucrose_dawn (g/100gFW)</th>\n",
       "      <th>starch_dusk (g/100gFW)</th>\n",
       "      <th>hemicellulose_dusk (g/100gFW)</th>\n",
       "      <th>simple sugars_dusk (g/100gFW)</th>\n",
       "      <th>sucrose_dusk (g/100gFW)</th>\n",
       "      <th>Δstarch</th>\n",
       "      <th>Δhemicellulose</th>\n",
       "      <th>Δsimple sugars</th>\n",
       "      <th>Δsucrose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</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>1</th>\n",
       "      <td>0.424568</td>\n",
       "      <td>1.089463</td>\n",
       "      <td>0.271941</td>\n",
       "      <td>0.271941</td>\n",
       "      <td>1.328468</td>\n",
       "      <td>1.089463</td>\n",
       "      <td>0.032023</td>\n",
       "      <td>0.460078</td>\n",
       "      <td>0.903900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.239919</td>\n",
       "      <td>0.188136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.497429</td>\n",
       "      <td>1.892274</td>\n",
       "      <td>0.650125</td>\n",
       "      <td>0.454127</td>\n",
       "      <td>1.600012</td>\n",
       "      <td>0.663310</td>\n",
       "      <td>0.353374</td>\n",
       "      <td>0.187004</td>\n",
       "      <td>1.102582</td>\n",
       "      <td>-1.228963</td>\n",
       "      <td>-0.296751</td>\n",
       "      <td>-0.267123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.889538</td>\n",
       "      <td>0.861645</td>\n",
       "      <td>0.568619</td>\n",
       "      <td>0.097705</td>\n",
       "      <td>2.625428</td>\n",
       "      <td>0.951069</td>\n",
       "      <td>0.413878</td>\n",
       "      <td>0.396437</td>\n",
       "      <td>1.735890</td>\n",
       "      <td>0.089424</td>\n",
       "      <td>-0.154741</td>\n",
       "      <td>0.298732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.252666</td>\n",
       "      <td>1.122133</td>\n",
       "      <td>0.553381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.097803</td>\n",
       "      <td>1.072162</td>\n",
       "      <td>0.389194</td>\n",
       "      <td>0.142069</td>\n",
       "      <td>-0.154863</td>\n",
       "      <td>-0.049971</td>\n",
       "      <td>-0.164187</td>\n",
       "      <td>0.142069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        starch_dawn (g/100gFW)  hemicellulose_dawn (g/100gFW)  \\\n",
       "Series                                                          \n",
       "1                     0.424568                       1.089463   \n",
       "2                     0.497429                       1.892274   \n",
       "3                     0.889538                       0.861645   \n",
       "4                     1.252666                       1.122133   \n",
       "\n",
       "        simple sugars_dawn (g/100gFW)  sucrose_dawn (g/100gFW)  \\\n",
       "Series                                                           \n",
       "1                            0.271941                 0.271941   \n",
       "2                            0.650125                 0.454127   \n",
       "3                            0.568619                 0.097705   \n",
       "4                            0.553381                 0.000000   \n",
       "\n",
       "        starch_dusk (g/100gFW)  hemicellulose_dusk (g/100gFW)  \\\n",
       "Series                                                          \n",
       "1                     1.328468                       1.089463   \n",
       "2                     1.600012                       0.663310   \n",
       "3                     2.625428                       0.951069   \n",
       "4                     1.097803                       1.072162   \n",
       "\n",
       "        simple sugars_dusk (g/100gFW)  sucrose_dusk (g/100gFW)   Δstarch  \\\n",
       "Series                                                                     \n",
       "1                            0.032023                 0.460078  0.903900   \n",
       "2                            0.353374                 0.187004  1.102582   \n",
       "3                            0.413878                 0.396437  1.735890   \n",
       "4                            0.389194                 0.142069 -0.154863   \n",
       "\n",
       "        Δhemicellulose  Δsimple sugars  Δsucrose  \n",
       "Series                                            \n",
       "1             0.000000       -0.239919  0.188136  \n",
       "2            -1.228963       -0.296751 -0.267123  \n",
       "3             0.089424       -0.154741  0.298732  \n",
       "4            -0.049971       -0.164187  0.142069  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matureleaf_content_1 = pd.DataFrame(data={\"Series\":[1,2,3,4],\n",
    "                                           \"starch_dawn (g/100gFW)\":[0.4245676464308603,0.4974291547005305,\n",
    "                                                                     0.8895379007845832,1.2526663271935523],\n",
    "                                           \"hemicellulose_dawn (g/100gFW)\":[1.089463383389083,\n",
    "                                                                            1.8922736237500428,\n",
    "                                                                            0.8616451473404596,\n",
    "                                                                            1.1221330617548415],\n",
    "                                           \"simple sugars_dawn (g/100gFW)\":[0.2719414988257076,\n",
    "                                                                            0.6501251398452184,\n",
    "                                                                            0.5686189976083096,\n",
    "                                                                            0.5533809766290336],\n",
    "                                           \"sucrose_dawn (g/100gFW)\":[0.2719414988257076,0.454127308166961,\n",
    "                                                                      0.09770538968660913,0],\n",
    "                                           \"starch_dusk (g/100gFW)\":[1.3284677247171013,1.600011533626287,\n",
    "                                                                     2.6254282537133005,1.0978029358571406],\n",
    "                                           \"hemicellulose_dusk (g/100gFW)\":[1.089463383389083,\n",
    "                                                                            0.6633103814170216,\n",
    "                                                                            0.9510687588805857,\n",
    "                                                                            1.0721624730031074],\n",
    "                                           \"simple sugars_dusk (g/100gFW)\":[0.03202263184115184,\n",
    "                                                                            0.35337416237039254,\n",
    "                                                                            0.4138784209550903,\n",
    "                                                                            0.3891937806184267],\n",
    "                                           \"sucrose_dusk (g/100gFW)\":[0.46007757454985465,0.18700390989930638,\n",
    "                                                                      0.3964371762172485,0.14206905936682546]})\n",
    "df_matureleaf_content_1.set_index(\"Series\",drop=True,inplace=True)\n",
    "df_matureleaf_content_1[\"Δstarch\"] = df_matureleaf_content_1[\"starch_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_matureleaf_content_1[\"starch_dawn (g/100gFW)\"]\n",
    "df_matureleaf_content_1[\"Δhemicellulose\"] = df_matureleaf_content_1[\"hemicellulose_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_matureleaf_content_1[\"hemicellulose_dawn (g/100gFW)\"]\n",
    "df_matureleaf_content_1[\"Δsimple sugars\"] = df_matureleaf_content_1[\"simple sugars_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_matureleaf_content_1[\"simple sugars_dawn (g/100gFW)\"]\n",
    "df_matureleaf_content_1[\"Δsucrose\"] = df_matureleaf_content_1[\"sucrose_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_matureleaf_content_1[\"sucrose_dawn (g/100gFW)\"]\n",
    "df_matureleaf_content_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Clements 1930 data on starch accumulated in 12h to estimate starch, hexose and sucrose content when diel fluctuations are ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline starch (g/100gFW)</th>\n",
       "      <th>baseline hemicellulose (g/100gFW)</th>\n",
       "      <th>baseline sucrose (g/100gFW)</th>\n",
       "      <th>baseline simple sugars (g/100gFW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</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>0.424568</td>\n",
       "      <td>1.089463</td>\n",
       "      <td>0.271941</td>\n",
       "      <td>0.032023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.497429</td>\n",
       "      <td>0.663310</td>\n",
       "      <td>0.187004</td>\n",
       "      <td>0.353374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.889538</td>\n",
       "      <td>0.861645</td>\n",
       "      <td>0.097705</td>\n",
       "      <td>0.413878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        baseline starch (g/100gFW)  baseline hemicellulose (g/100gFW)  \\\n",
       "Series                                                                  \n",
       "1                         0.424568                           1.089463   \n",
       "2                         0.497429                           0.663310   \n",
       "3                         0.889538                           0.861645   \n",
       "\n",
       "        baseline sucrose (g/100gFW)  baseline simple sugars (g/100gFW)  \n",
       "Series                                                                  \n",
       "1                          0.271941                           0.032023  \n",
       "2                          0.187004                           0.353374  \n",
       "3                          0.097705                           0.413878  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlist = list()\n",
    "ylist = list()\n",
    "ylist2 = list()\n",
    "ylist3 = list()\n",
    "ylist4 = list()\n",
    "for i in df_matureleaf_content_1.index:\n",
    "    if i == 4:\n",
    "        continue\n",
    "    xlist.append(i)\n",
    "    ylist.append(min(df_matureleaf_content_1[\"simple sugars_dawn (g/100gFW)\"][i],\n",
    "                     df_matureleaf_content_1[\"simple sugars_dusk (g/100gFW)\"][i]))\n",
    "    ylist2.append(min(df_matureleaf_content_1[\"sucrose_dawn (g/100gFW)\"][i],\n",
    "                      df_matureleaf_content_1[\"sucrose_dusk (g/100gFW)\"][i]))\n",
    "    ylist3.append(min(df_matureleaf_content_1[\"hemicellulose_dawn (g/100gFW)\"][i],\n",
    "                      df_matureleaf_content_1[\"hemicellulose_dusk (g/100gFW)\"][i]))\n",
    "    ylist4.append(min(df_matureleaf_content_1[\"starch_dawn (g/100gFW)\"][i],\n",
    "                      df_matureleaf_content_1[\"starch_dusk (g/100gFW)\"][i]))\n",
    "df_leaf_content_4 = pd.DataFrame(data={\"Series\":xlist,\n",
    "                                       \"baseline starch (g/100gFW)\":ylist4,\n",
    "                                       \"baseline hemicellulose (g/100gFW)\":ylist3,\n",
    "                                       \"baseline sucrose (g/100gFW)\":ylist2,\n",
    "                                       \"baseline simple sugars (g/100gFW)\":ylist})\n",
    "df_leaf_content_4.set_index(\"Series\",drop=True,inplace=True)\n",
    "df_leaf_content_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract amino acid content from Chiozza et al 2010, Figure 2\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_143619.png\" height=50% width=50%>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>GABA (nmol/50mgFW)</th>\n",
       "      <th>Asn (nmol/50mgFW)</th>\n",
       "      <th>Gln (nmol/50mgFW)</th>\n",
       "      <th>Glt (nmol/50mgFW)</th>\n",
       "      <th>His (nmol/50mgFW)</th>\n",
       "      <th>Pro (nmol/50mgFW)</th>\n",
       "      <th>Ser (nmol/50mgFW)</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aphid resitant</th>\n",
       "      <td>0.828767</td>\n",
       "      <td>67.260274</td>\n",
       "      <td>82.876712</td>\n",
       "      <td>184.589041</td>\n",
       "      <td>11.778523</td>\n",
       "      <td>19.062500</td>\n",
       "      <td>87.282051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aphid sensitive</th>\n",
       "      <td>1.013699</td>\n",
       "      <td>86.164384</td>\n",
       "      <td>99.315068</td>\n",
       "      <td>234.931507</td>\n",
       "      <td>13.657718</td>\n",
       "      <td>20.681818</td>\n",
       "      <td>100.410256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GABA (nmol/50mgFW)  Asn (nmol/50mgFW)  Gln (nmol/50mgFW)  \\\n",
       "Variety                                                                     \n",
       "aphid resitant             0.828767          67.260274          82.876712   \n",
       "aphid sensitive            1.013699          86.164384          99.315068   \n",
       "\n",
       "                 Glt (nmol/50mgFW)  His (nmol/50mgFW)  Pro (nmol/50mgFW)  \\\n",
       "Variety                                                                    \n",
       "aphid resitant          184.589041          11.778523          19.062500   \n",
       "aphid sensitive         234.931507          13.657718          20.681818   \n",
       "\n",
       "                 Ser (nmol/50mgFW)  \n",
       "Variety                             \n",
       "aphid resitant           87.282051  \n",
       "aphid sensitive         100.410256  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaf_content_3 = pd.DataFrame(data={\"Variety\":[\"aphid resitant\",\"aphid sensitive\"],\n",
    "                                       \"GABA (nmol/50mgFW)\":[0.8287671232876719,1.0136986301369868],\n",
    "                                       \"Asn (nmol/50mgFW)\":[67.26027397260277,86.1643835616437],\n",
    "                                       \"Gln (nmol/50mgFW)\":[82.87671232876716,99.31506849315056],\n",
    "                                       \"Glt (nmol/50mgFW)\":[184.58904109589022,234.93150684931462],\n",
    "                                       \"His (nmol/50mgFW)\":[11.778523489932848,13.657718120805297],\n",
    "                                       \"Pro (nmol/50mgFW)\":[19.062499999999943,20.68181818181808],\n",
    "                                       \"Ser (nmol/50mgFW)\":[87.28205128205093,100.4102564102559]})\n",
    "df_leaf_content_3.set_index(\"Variety\",drop=True,inplace=True)\n",
    "df_leaf_content_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassMets = {\"sSUCROSE_b\":1000*find_average(df_leaf_content_4[\"baseline sucrose (g/100gFW)\"])/ \\\n",
    "                                                model.metabolites.get_by_id(\"SUCROSE_c\").formula_weight,\n",
    "               \"Starch_b\":1000*find_average(df_leaf_content_4[\"baseline starch (g/100gFW)\"])/ \\\n",
    "                                                model.metabolites.get_by_id(\"SUCROSE_c\").formula_weight,\n",
    "               \"GLC_c\":1000*find_average(df_leaf_content_4[\"baseline simple sugars (g/100gFW)\"])/ \\\n",
    "                                                model.metabolites.get_by_id(\"SUCROSE_c\").formula_weight,\n",
    "               \"ASN_c\":100*0.001*find_average(df_leaf_content_3[\"Asn (nmol/50mgFW)\"])/50,\n",
    "               \"sGABA_b\":100*0.001*find_average(df_leaf_content_3[\"GABA (nmol/50mgFW)\"])/50,\n",
    "               \"sGLN_b\":100*0.001*find_average(df_leaf_content_3[\"Gln (nmol/50mgFW)\"])/50,\n",
    "               \"sGLU_b\":100*0.001*find_average(df_leaf_content_3[\"Glt (nmol/50mgFW)\"])/50,\n",
    "               \"HIS_c\":100*0.001*find_average(df_leaf_content_3[\"His (nmol/50mgFW)\"])/50,\n",
    "               \"PRO_c\":100*0.001*find_average(df_leaf_content_3[\"Pro (nmol/50mgFW)\"])/50,\n",
    "               \"sSER_b\":100*0.001*find_average(df_leaf_content_3[\"Ser (nmol/50mgFW)\"])/50\n",
    "              }\n",
    "\n",
    "\n",
    "rxn = Reaction(\"Biomass_leaf_tx\")\n",
    "for met in biomassMets:\n",
    "    rxn.add_metabolites({leaf_model.metabolites.get_by_id(met):-1*biomassMets[met]})\n",
    "rxn.upper_bound = 1000\n",
    "rxn.lower_bound = 0\n",
    "\n",
    "leaf_model.add_reaction(rxn)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 CIT_v1 + 0.5 aCIT_v1 <=> 0.5 CIT_v2 + 0.5 aCIT_v2\n",
      "bHIS_v1 --> bHIS_v2\n",
      "0.7 MAL_v1 + 0.3 aMAL_v1 <=> 0.7 MAL_v2 + 0.3 aMAL_v2\n"
     ]
    }
   ],
   "source": [
    "from sweetlovegroup.transform import setupC3DielModel\n",
    "leaf_model = setupC3DielModel(leaf_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'leaf_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-439f195d810d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mleaf_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPhloem_output_tx1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_coefficient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mleaf_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPhloem_output_tx2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_coefficient\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mleaf_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPhoton_tx1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupper_bound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m500\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mleaf_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreactions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPhoton_tx1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower_bound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m500\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'leaf_model' is not defined"
     ]
    }
   ],
   "source": [
    "leaf_model.reactions.Phloem_output_tx1.objective_coefficient = 0\n",
    "leaf_model.reactions.Phloem_output_tx2.objective_coefficient = 0\n",
    "\n",
    "leaf_model.reactions.Photon_tx1.upper_bound = 500\n",
    "leaf_model.reactions.Photon_tx1.lower_bound = 500\n",
    "\n",
    "from cobra.flux_analysis import pfba\n",
    "sol = pfba(leaf_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backup = leaf_model.copy()\n",
    "xlist = list()\n",
    "ylist = list()\n",
    "for PPFD in range(200,800,100):\n",
    "    backup.reactions.Photon_tx1.upper_bound = PPFD\n",
    "    backup.reactions.Photon_tx1.lower_bound = PPFD\n",
    "    \n",
    "    ATPase = (0.0049*PPFD) + 2.7851\n",
    "    backup.reactions.ATPase_tx1.upper_bound = ATPase\n",
    "    backup.reactions.ATPase_tx1.lower_bound = ATPase\n",
    "    sol = pfba(backup)\n",
    "    ylist.append(backup.reactions.CO2_tx1.flux)\n",
    "    xlist.append(backup.reactions.STARCH_p_dielTransfer.flux)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(xlist,ylist)\n",
    "plt.xlabel(\"Starch accumulatione rate (umol Glc/m2/s)\")\n",
    "plt.ylabel(\"net CO2 uptake rate (umol CO2/m2/s)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import polyfit\n",
    "\n",
    "line = polyfit(xlist,ylist,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.57389598, -2.50390241])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.903364206540246"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(line[0]*xlist[0])+(line[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09080698666893934"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-line[1]/line[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.903364206458381"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ylist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.220103460829259"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(leaf_model.reactions.STARCH_p_dielTransfer.flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(leaf_model,\"Data/Soy_leaf_model_GA.sbml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Since amino acid content was only available in g/gFW and FW/DW data was not available, diel starch and sucrose levels (in g/gFW) were used to predict baseline starch and sucrose levels in leaf; instead of other available data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate stem model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stem_model = model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract diurnal starch, sucrose, hemicellulose and simple sugar content from Clements 1930, Figure 6, 9, 13 and 14. Based on Figure 1 and 2, dawn = 8am and dusk = 8pm\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_161155.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_160304.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_161259.png\" height=50% width=50%>\n",
    "\n",
    "<img src=\"references/Screenshot_20190717_160242.png\" height=50% width=50%>"
   ]
  },
  {
   "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>baseline sucrose (g/100gFW)</th>\n",
       "      <th>baseline simple sugars (g/100gFW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.086064</td>\n",
       "      <td>0.993034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.141176</td>\n",
       "      <td>0.229412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.255357</td>\n",
       "      <td>0.755800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        baseline sucrose (g/100gFW)  baseline simple sugars (g/100gFW)\n",
       "Series                                                                \n",
       "1                          0.000000                           0.089283\n",
       "2                          0.086064                           0.993034\n",
       "3                          0.141176                           0.229412\n",
       "4                          0.255357                           0.755800"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stem_content = pd.DataFrame(data={\"Series\":[1,2,3,4],\n",
    "                                     \"simple sugars_dawn (g/100gFW)\":[0.08928305478569998,1.0069230769230781,\n",
    "                                                                      0.22941176470588265,0.755800478743633],\n",
    "                                     \"sucrose_dawn (g/100gFW)\":[0.24999412110523256,0.10918376068376556,\n",
    "                                                                0.14117647058823524,0.36680114242250283],\n",
    "                                     \"simple sugars_dusk (g/100gFW)\":[0.27423868312757205,0.9930341880341897,\n",
    "                                                                      0.23368616214394122,0.8292719666520014],\n",
    "                                     \"sucrose_dusk (g/100gFW)\":[0,0.08606410256411046,\n",
    "                                                                0.1748626327321765,0.2553567833200341]})\n",
    "df_stem_content.set_index(\"Series\",drop=True,inplace=True)\n",
    "df_stem_content[\"avg. Δsimple sugars (g/100gFW)\"] = df_stem_content[\"simple sugars_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_stem_content[\"simple sugars_dawn (g/100gFW)\"]\n",
    "df_stem_content[\"avg. Δsucrose (g/100gFW)\"] = df_stem_content[\"sucrose_dusk (g/100gFW)\"]- \\\n",
    "                                                        df_stem_content[\"sucrose_dawn (g/100gFW)\"]\n",
    "\n",
    "xlist = list()\n",
    "ylist = list()\n",
    "ylist2 = list()\n",
    "for i in df_stem_content.index:\n",
    "    xlist.append(i)\n",
    "    ylist.append(min(df_stem_content[\"simple sugars_dawn (g/100gFW)\"][i],\n",
    "                     df_stem_content[\"simple sugars_dusk (g/100gFW)\"][i]))\n",
    "    ylist2.append(min(df_stem_content[\"sucrose_dawn (g/100gFW)\"][i],\n",
    "                      df_stem_content[\"sucrose_dusk (g/100gFW)\"][i]))\n",
    "df_stem_content_std = pd.DataFrame(data={\"Series\":xlist,\n",
    "                                       \"baseline sucrose (g/100gFW)\":ylist2,\n",
    "                                       \"baseline simple sugars (g/100gFW)\":ylist})\n",
    "df_stem_content_std.set_index(\"Series\",drop=True,inplace=True)\n",
    "df_stem_content_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Sucrose, hexose content data is per FW and conversion to DW is not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassMets = {\"sSUCROSE_b\":1000*find_average(df_stem_content_std[\"baseline sucrose (g/100gFW)\"])/ \\\n",
    "                                        stem_model.metabolites.get_by_id(\"SUCROSE_c\").formula_weight,\n",
    "               \"GLC_c\":1000*find_average(df_stem_content_std[\"baseline simple sugars (g/100gFW)\"])/ \\\n",
    "                                        stem_model.metabolites.get_by_id(\"GLC_c\").formula_weight\n",
    "              }\n",
    "\n",
    "rxn = Reaction(\"Biomass_stem_tx\")\n",
    "for met in biomassMets:\n",
    "    rxn.add_metabolites({stem_model.metabolites.get_by_id(met):-1*biomassMets[met]})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "\n",
    "stem_model.add_reaction(rxn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5415495157940694\n"
     ]
    }
   ],
   "source": [
    "stem_model.reactions.GLC_tx.upper_bound = 0\n",
    "stem_model.reactions.GLC_tx.lower_bound = 0\n",
    "stem_model.reactions.Sucrose_tx.upper_bound = 1\n",
    "stem_model.reactions.Sucrose_tx.lower_bound = 1\n",
    "stem_model.reactions.Photon_tx.upper_bound = 0\n",
    "stem_model.reactions.Photon_tx.lower_bound = 0\n",
    "stem_model.reactions.Phloem_output_tx.objective_coefficient=0\n",
    "stem_model.reactions.Biomass_stem_tx.objective_coefficient=1\n",
    "\n",
    "from cobra.flux_analysis import pfba\n",
    "sol = pfba(stem_model)\n",
    "print(stem_model.reactions.Biomass_stem_tx.flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(stem_model,\"Data/Soy_stem_model_GA.sbml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate root model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_model = model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gathering root fatty-acid content from Thomas et al 2007 Table I"
   ]
  },
  {
   "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>Fatty acid 16:1</th>\n",
       "      <th>Fatty acid 16:0</th>\n",
       "      <th>Fatty acid 18:1</th>\n",
       "      <th>Fatty acid 18:0</th>\n",
       "      <th>Fatty acid 20:0</th>\n",
       "      <th>Fatty acid 22:0</th>\n",
       "      <th>Total (ug/cm2)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tissue type</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>Conrad - Epidermis</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OX760-6 - Epidermis</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conrad - Endodermis</th>\n",
       "      <td>0.82</td>\n",
       "      <td>1.50</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.31</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OX760-6 - Endodermis</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.31</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Fatty acid 16:1  Fatty acid 16:0  Fatty acid 18:1  \\\n",
       "Tissue type                                                               \n",
       "Conrad - Epidermis               0.21             0.39             0.71   \n",
       "OX760-6 - Epidermis              0.05             0.25             0.18   \n",
       "Conrad - Endodermis              0.82             1.50             7.46   \n",
       "OX760-6 - Endodermis             0.39             0.72             1.46   \n",
       "\n",
       "                      Fatty acid 18:0  Fatty acid 20:0  Fatty acid 22:0  \\\n",
       "Tissue type                                                               \n",
       "Conrad - Epidermis               0.32             0.05             0.09   \n",
       "OX760-6 - Epidermis              0.04             0.03             0.04   \n",
       "Conrad - Endodermis              0.83             0.28             0.31   \n",
       "OX760-6 - Endodermis             0.30             0.49             0.31   \n",
       "\n",
       "                      Total (ug/cm2)  \n",
       "Tissue type                           \n",
       "Conrad - Epidermis              1.77  \n",
       "OX760-6 - Epidermis             0.59  \n",
       "Conrad - Endodermis            11.20  \n",
       "OX760-6 - Endodermis            3.67  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_root_content_1 = pd.DataFrame(data={\"Tissue type\":[\"Conrad - Epidermis\",\"OX760-6 - Epidermis\",\n",
    "                                                      \"Conrad - Endodermis\",\"OX760-6 - Endodermis\"],\n",
    "                                       \"Fatty acid 16:1\":[0.21,0.05,0.82,0.39],\n",
    "                                       \"Fatty acid 16:0\":[0.39,0.25,1.50,0.72],\n",
    "                                       \"Fatty acid 18:1\":[0.71,0.18,7.46,1.46],\n",
    "                                       \"Fatty acid 18:0\":[0.32,0.04,0.83,0.30],\n",
    "                                       \"Fatty acid 20:0\":[0.05,0.03,0.28,0.49],\n",
    "                                       \"Fatty acid 22:0\":[0.09,0.04,0.31,0.31],\n",
    "                                       \"Total (ug/cm2)\":[1.77,0.59,11.20,3.67]})\n",
    "df_root_content_1.set_index(\"Tissue type\",inplace=True,drop=True)\n",
    "df_root_content_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_root_content_1_std = pd.DataFrame(data={\"Fatty acids\":[\"16:0\",\"16:1\",\"18:0\",\"18:1\",\"20:0\",\"22:0\"],\n",
    "                                           \"fraction\":[find_average(df_root_content_1[\"Fatty acid 16:0\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"]),\n",
    "                                                     find_average(df_root_content_1[\"Fatty acid 16:1\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"]),\n",
    "                                                     find_average(df_root_content_1[\"Fatty acid 18:0\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"]),\n",
    "                                                     find_average(df_root_content_1[\"Fatty acid 18:1\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"]),\n",
    "                                                     find_average(df_root_content_1[\"Fatty acid 20:0\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"]),\n",
    "                                                     find_average(df_root_content_1[\"Fatty acid 22:0\"])/ \\\n",
    "                                                                find_average(df_root_content_1[\"Total (ug/cm2)\"])]})\n",
    "df_root_content_1_std.set_index(\"Fatty acids\",drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxn = Reaction(\"Root_fatty_acid_composition\")\n",
    "rxn.add_metabolites({root_model.metabolites.get_by_id(\"PALMITATE_c\"):-1*df_root_content_1_std[\"fraction\"][\"16:0\"],\n",
    "                     root_model.metabolites.get_by_id(\"CPD_9245_p\"):-1*df_root_content_1_std[\"fraction\"][\"16:1\"],\n",
    "                     root_model.metabolites.get_by_id(\"STEARIC_ACID_p\"):-1*df_root_content_1_std[\"fraction\"][\"18:0\"],\n",
    "                     root_model.metabolites.get_by_id(\"OLEATE_CPD_p\"):-1*df_root_content_1_std[\"fraction\"][\"18:1\"],\n",
    "                     root_model.metabolites.get_by_id(\"ARACHIDIC_ACID_p\"):-1*df_root_content_1_std[\"fraction\"][\"20:0\"],\n",
    "                     root_model.metabolites.get_by_id(\"DOCOSANOATE_p\"):-1*df_root_content_1_std[\"fraction\"][\"22:0\"],\n",
    "                     root_model.metabolites.get_by_id(\"Fatty_Acids_p\"):1})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "root_model.add_reaction(rxn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fatty_Acids_p\n",
      "C17.77016831108532H33.23099245502032O2.0000000000000004\n"
     ]
    }
   ],
   "source": [
    "generateMetaboliteFormula(rxn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#No biomass cannot optimmize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(root_model,\"Data/Soy_root_model_GA.sbml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate seed model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_model = model.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gathering seed fatty-acid content from Guleria et al 2008 Table I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>16:0 (mg/gDW fatty acds)</th>\n",
       "      <th>18:0 (mg/gDW fatty acds)</th>\n",
       "      <th>18:1 (mg/gDW fatty acds)</th>\n",
       "      <th>18:2 (mg/gDW fatty acds)</th>\n",
       "      <th>18:3 (mg/gDW fatty acds)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed type</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>large</th>\n",
       "      <td>132</td>\n",
       "      <td>29</td>\n",
       "      <td>269</td>\n",
       "      <td>513</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>122</td>\n",
       "      <td>38</td>\n",
       "      <td>245</td>\n",
       "      <td>518</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           16:0 (mg/gDW fatty acds)  18:0 (mg/gDW fatty acds)  \\\n",
       "seed type                                                       \n",
       "large                           132                        29   \n",
       "small                           122                        38   \n",
       "\n",
       "           18:1 (mg/gDW fatty acds)  18:2 (mg/gDW fatty acds)  \\\n",
       "seed type                                                       \n",
       "large                           269                       513   \n",
       "small                           245                       518   \n",
       "\n",
       "           18:3 (mg/gDW fatty acds)  \n",
       "seed type                            \n",
       "large                            56  \n",
       "small                            76  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content = pd.DataFrame(data={\"seed type\":[\"large\",\"small\"],\n",
    "                               \"16:0 (mg/gDW fatty acds)\":[132,122],\n",
    "                               \"18:0 (mg/gDW fatty acds)\":[29,38],\n",
    "                               \"18:1 (mg/gDW fatty acds)\":[269,245],\n",
    "                               \"18:2 (mg/gDW fatty acds)\":[513,518],\n",
    "                               \"18:3 (mg/gDW fatty acds)\":[56,76]})\n",
    "df_seed_content.set_index(\"seed type\",inplace=True,drop=True)\n",
    "df_seed_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatty acids</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16:0</th>\n",
       "      <td>0.127127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:0</th>\n",
       "      <td>0.033534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:1</th>\n",
       "      <td>0.257257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:2</th>\n",
       "      <td>0.516016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:3</th>\n",
       "      <td>0.066066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fraction\n",
       "Fatty acids          \n",
       "16:0         0.127127\n",
       "18:0         0.033534\n",
       "18:1         0.257257\n",
       "18:2         0.516016\n",
       "18:3         0.066066"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_std = pd.DataFrame(data={\"Fatty acids\":[\"16:0\",\"18:0\",\"18:1\",\"18:2\",\"18:3\"],\n",
    "                               \"fraction\":[float(find_average(df_seed_content[\"16:0 (mg/gDW fatty acds)\"]))/999,\n",
    "                                           float(find_average(df_seed_content[\"18:0 (mg/gDW fatty acds)\"]))/999,\n",
    "                                           float(find_average(df_seed_content[\"18:1 (mg/gDW fatty acds)\"]))/999,\n",
    "                                           float(find_average(df_seed_content[\"18:2 (mg/gDW fatty acds)\"]))/999,\n",
    "                                           float(find_average(df_seed_content[\"18:3 (mg/gDW fatty acds)\"]))/999]})\n",
    "df_seed_content_std.set_index(\"Fatty acids\",inplace=True,drop=True)\n",
    "df_seed_content_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxn = Reaction(\"Seed_fatty_acid_composition\")\n",
    "rxn.add_metabolites({seed_model.metabolites.get_by_id(\"PALMITATE_c\"):-1*df_seed_content_std[\"fraction\"][\"16:0\"],\n",
    "                     seed_model.metabolites.get_by_id(\"STEARIC_ACID_p\"):-1*df_seed_content_std[\"fraction\"][\"18:0\"],\n",
    "                     seed_model.metabolites.get_by_id(\"OLEATE_CPD_p\"):-1*df_seed_content_std[\"fraction\"][\"18:1\"],\n",
    "                     seed_model.metabolites.get_by_id(\"Octadecadienoate_p\"):-1*df_seed_content_std[\"fraction\"][\"18:2\"],\n",
    "                     seed_model.metabolites.get_by_id(\"LINOLENIC_ACID_p\"):-1*df_seed_content_std[\"fraction\"][\"18:3\"],\n",
    "                     seed_model.metabolites.get_by_id(\"Fatty_Acids_p\"):1})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.06606606606606606 LINOLENIC_ACID_p + 0.25725725725725723 OLEATE_CPD_p + 0.516016016016016 Octadecadienoate_p + 0.12712712712712712 PALMITATE_c + 0.033533533533533534 STEARIC_ACID_p --> Fatty_Acids_p'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rxn.reaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fatty_Acids_p\n",
      "C17.745745745745744H31.516516516516518O1.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "generateMetaboliteFormula(rxn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "rxn = Reaction(\"Seed_fatty_acid_ACP_composition\")\n",
    "rxn.add_metabolites({seed_model.metabolites.get_by_id(\"PALMITATE_c\"):-1*df_seed_content_std[\"fraction\"][\"16:0\"],\n",
    "                     seed_model.metabolites.get_by_id(\"STEARIC_ACID_p\"):-1*df_seed_content_std[\"fraction\"][\"18:0\"],\n",
    "                     seed_model.metabolites.get_by_id(\"OLEATE_CPD_p\"):-1*df_seed_content_std[\"fraction\"][\"18:1\"],\n",
    "                     seed_model.metabolites.get_by_id(\"Octadecadienoate_p\"):-1*df_seed_content_std[\"fraction\"][\"18:2\"],\n",
    "                     seed_model.metabolites.get_by_id(\"LINOLENIC_ACID_p\"):-1*df_seed_content_std[\"fraction\"][\"18:3\"],\n",
    "                     seed_model.metabolites.get_by_id(\"Fatty_Acids_p\"):1})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gather phospholipids, glycolipids, sterols and free fatty acid content from Guleria et al 2008, Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L_PHOSPHATIDATE_p --> Phospholipids_p\n",
      "Phospholipids_p\n",
      "C35.0H67.0O8.0P1.0\n"
     ]
    }
   ],
   "source": [
    "met = Metabolite(\"Phospholipids_p\",name=\"Phospholipids\",compartment=\"p\")\n",
    "\n",
    "rxn = Reaction(\"Phospholipid_composition\")\n",
    "rxn.add_metabolites({seed_model.metabolites.get_by_id(\"L_PHOSPHATIDATE_p\"):-1,met:1})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)\n",
    "\n",
    "print(rxn.reaction)\n",
    "\n",
    "generateMetaboliteFormula(rxn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>lipid content (mg/gDW)</th>\n",
       "      <th>phospholipids (mg/gDW lipid)</th>\n",
       "      <th>glycolipids (mg/gDW lipid)</th>\n",
       "      <th>sterols (mg/gDW lipid)</th>\n",
       "      <th>free fatty acids (mg/gDW lipid)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed type</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>large</th>\n",
       "      <td>241</td>\n",
       "      <td>57.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>26.8</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>224</td>\n",
       "      <td>58.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lipid content (mg/gDW)  phospholipids (mg/gDW lipid)  \\\n",
       "seed type                                                         \n",
       "large                         241                          57.1   \n",
       "small                         224                          58.7   \n",
       "\n",
       "           glycolipids (mg/gDW lipid)  sterols (mg/gDW lipid)  \\\n",
       "seed type                                                       \n",
       "large                            10.6                    26.8   \n",
       "small                            10.3                    26.4   \n",
       "\n",
       "           free fatty acids (mg/gDW lipid)  \n",
       "seed type                                   \n",
       "large                                  0.1  \n",
       "small                                  0.1  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_1 = pd.DataFrame(data={\"seed type\":[\"large\",\"small\"],\n",
    "                               \"lipid content (mg/gDW)\":[241,224],\n",
    "                               \"phospholipids (mg/gDW lipid)\":[57.1,58.7],\n",
    "                               \"glycolipids (mg/gDW lipid)\":[10.6,10.3],\n",
    "                               \"sterols (mg/gDW lipid)\":[26.8,26.4],\n",
    "                               \"free fatty acids (mg/gDW lipid)\":[0.1,0.1]})\n",
    "\n",
    "df_seed_content_1.set_index(\"seed type\",inplace=True,drop=True)\n",
    "df_seed_content_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>fraction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lipid entities</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free fatty acids</th>\n",
       "      <td>0.000430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glycolipids</th>\n",
       "      <td>0.044946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phospholipids</th>\n",
       "      <td>0.249032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sterols</th>\n",
       "      <td>0.114409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fraction\n",
       "lipid entities            \n",
       "free fatty acids  0.000430\n",
       "glycolipids       0.044946\n",
       "phospholipids     0.249032\n",
       "sterols           0.114409"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_1std = pd.DataFrame(data={\"lipid entities\":[\"free fatty acids\",\"glycolipids\",\"phospholipids\",\"sterols\"],\n",
    "                               \"fraction\":[float(find_average(df_seed_content_1[\"free fatty acids (mg/gDW lipid)\"]))/find_average(df_seed_content_1[\"lipid content (mg/gDW)\"]),\n",
    "                                           float(find_average(df_seed_content_1[\"glycolipids (mg/gDW lipid)\"]))/find_average(df_seed_content_1[\"lipid content (mg/gDW)\"]),\n",
    "                                           float(find_average(df_seed_content_1[\"phospholipids (mg/gDW lipid)\"]))/find_average(df_seed_content_1[\"lipid content (mg/gDW)\"]),\n",
    "                                           float(find_average(df_seed_content_1[\"sterols (mg/gDW lipid)\"]))/find_average(df_seed_content_1[\"lipid content (mg/gDW)\"])\n",
    "                                          ]})\n",
    "df_seed_content_1std.set_index(\"lipid entities\",inplace=True,drop=True)\n",
    "df_seed_content_1std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5532709068320986e-06 Fatty_Acids_p + 0.0003849771413172512 Phospholipids_p --> 0.00038653041222408327 Lipids_p\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 17.745745745745744 is not an integer (in formula C17.745745745745744H31.516516516516518O1.9999999999999998)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 31.516516516516518 is not an integer (in formula C17.745745745745744H31.516516516516518O1.9999999999999998)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 1.9999999999999998 is not an integer (in formula C17.745745745745744H31.516516516516518O1.9999999999999998)\n"
     ]
    }
   ],
   "source": [
    "met = Metabolite(\"Lipids_p\",name=\"Lipids\",compartment=\"p\")\n",
    "\n",
    "rxn = Reaction(\"Seed_lipid_composition\")\n",
    "rxn.add_metabolites({seed_model.metabolites.get_by_id(\"Fatty_Acids_p\"):-1*df_seed_content_1std[\"fraction\"][\"free fatty acids\"]/ \\\n",
    "                                                                seed_model.metabolites.get_by_id(\"Fatty_Acids_p\").formula_weight,\n",
    "                     seed_model.metabolites.get_by_id(\"Phospholipids_p\"):-1*df_seed_content_1std[\"fraction\"][\"phospholipids\"]/ \\\n",
    "                                                                seed_model.metabolites.get_by_id(\"Phospholipids_p\").formula_weight,\n",
    "                     met:(df_seed_content_1std[\"fraction\"][\"free fatty acids\"]/ \\\n",
    "                          seed_model.metabolites.get_by_id(\"Fatty_Acids_p\").formula_weight) + \\\n",
    "                         (df_seed_content_1std[\"fraction\"][\"phospholipids\"]/ \\\n",
    "                          seed_model.metabolites.get_by_id(\"Phospholipids_p\").formula_weight)\n",
    "                    })\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)\n",
    "\n",
    "\n",
    "print(rxn.reaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9959815040221683"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.0003849771413172512/0.00038653041222408327"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({<Gene Glyma.05G110500 at 0x7f5fbde1ac50>,\n",
       "           <Gene Glyma.17G156200 at 0x7f5fbde1aeb8>})"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaf_model.reactions.GLUCOKIN_RXN_c1.genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lipids_p\n",
      "C34.9306638486788H66.85740976434217O7.975889024133011P0.9959815040221683\n"
     ]
    }
   ],
   "source": [
    "generateMetaboliteFormula(rxn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generating Biomass equation for seed  \n",
    "Gather non-reducing sugars, reducing sugars, starch, protein and lipid content from Guleria et al 2008, Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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 (mg/gDW)</th>\n",
       "      <th>reducing sugars (mg/gDW)</th>\n",
       "      <th>total soluble sugars (mg/gDW)</th>\n",
       "      <th>proteins (mg/gDW)</th>\n",
       "      <th>lipid content (mg/gDW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed type</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>large</th>\n",
       "      <td>49.7</td>\n",
       "      <td>24.9</td>\n",
       "      <td>125</td>\n",
       "      <td>377</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small</th>\n",
       "      <td>42.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>101</td>\n",
       "      <td>264</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           starch (mg/gDW)  reducing sugars (mg/gDW)  \\\n",
       "seed type                                              \n",
       "large                 49.7                      24.9   \n",
       "small                 42.3                      27.7   \n",
       "\n",
       "           total soluble sugars (mg/gDW)  proteins (mg/gDW)  \\\n",
       "seed type                                                     \n",
       "large                                125                377   \n",
       "small                                101                264   \n",
       "\n",
       "           lipid content (mg/gDW)  \n",
       "seed type                          \n",
       "large                         241  \n",
       "small                         224  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seed_content_3 = pd.DataFrame(data={\"seed type\":[\"large\",\"small\"],\n",
    "                               \"starch (mg/gDW)\":[49.7,42.3],\n",
    "                               \"reducing sugars (mg/gDW)\":[24.9,27.7],\n",
    "                               \"total soluble sugars (mg/gDW)\":[125,101],\n",
    "                               \"proteins (mg/gDW)\":[377,264],\n",
    "                               \"lipid content (mg/gDW)\":[241,224]})\n",
    "\n",
    "df_seed_content_3.set_index(\"seed type\",inplace=True,drop=True)\n",
    "df_seed_content_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4812499042495865 GLC_c + 0.3602480102722086 Lipids_p + 0.8038744537410865 Protein_b + 0.2837043898937095 Starch_b + 0.07683397737540275 sSUCROSE_b --> \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 17.2020450391 is not an integer (in formula C17.2020450391H25.9492553613O5.7497158698N4.9536245179S0.1420323181)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 25.9492553613 is not an integer (in formula C17.2020450391H25.9492553613O5.7497158698N4.9536245179S0.1420323181)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 5.7497158698 is not an integer (in formula C17.2020450391H25.9492553613O5.7497158698N4.9536245179S0.1420323181)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 4.9536245179 is not an integer (in formula C17.2020450391H25.9492553613O5.7497158698N4.9536245179S0.1420323181)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 0.1420323181 is not an integer (in formula C17.2020450391H25.9492553613O5.7497158698N4.9536245179S0.1420323181)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 34.9306638486788 is not an integer (in formula C34.9306638486788H66.85740976434217O7.975889024133011P0.9959815040221683)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 66.85740976434217 is not an integer (in formula C34.9306638486788H66.85740976434217O7.975889024133011P0.9959815040221683)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 7.975889024133011 is not an integer (in formula C34.9306638486788H66.85740976434217O7.975889024133011P0.9959815040221683)\n",
      "cobra/core/metabolite.py:104 \u001b[1;31mUserWarning\u001b[0m: 0.9959815040221683 is not an integer (in formula C34.9306638486788H66.85740976434217O7.975889024133011P0.9959815040221683)\n"
     ]
    }
   ],
   "source": [
    "rxn = Reaction(\"Biomass_seed_tx\")\n",
    "rxn.add_metabolites({seed_model.metabolites.get_by_id(\"Starch_b\"):-1*find_average(df_seed_content_3[\"starch (mg/gDW)\"])/ \\\n",
    "                                                                 seed_model.metabolites.get_by_id(\"Starch_b\").formula_weight,\n",
    "                     seed_model.metabolites.get_by_id(\"sSUCROSE_b\"):-1*find_average(df_seed_content_3[\"reducing sugars (mg/gDW)\"])/ \\\n",
    "                                                                 seed_model.metabolites.get_by_id(\"sSUCROSE_b\").formula_weight,\n",
    "                     seed_model.metabolites.get_by_id(\"GLC_c\"):-1*(find_average(df_seed_content_3[\"total soluble sugars (mg/gDW)\"])-find_average(df_seed_content_3[\"reducing sugars (mg/gDW)\"]))/ \\\n",
    "                                                                 seed_model.metabolites.get_by_id(\"GLC_c\").formula_weight,\n",
    "                     seed_model.metabolites.get_by_id(\"Protein_b\"):-1*find_average(df_seed_content_3[\"proteins (mg/gDW)\"])/ \\\n",
    "                                                                 seed_model.metabolites.get_by_id(\"Protein_b\").formula_weight,\n",
    "                     seed_model.metabolites.get_by_id(\"Lipids_p\"):-1*find_average(df_seed_content_3[\"lipid content (mg/gDW)\"])/ \\\n",
    "                                                                 seed_model.metabolites.get_by_id(\"Lipids_p\").formula_weight\n",
    "                    })\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)\n",
    "\n",
    "print(rxn.reaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "backup = seed_model.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "seed_model = backup.copy()\n",
    "\n",
    "seed_model.metabolites.Fatty_acyl_ACP_p.remove_from_model()\n",
    "seed_model.metabolites.ACP_p.remove_from_model()\n",
    "\n",
    "seed_model.reactions.GLC_tx.upper_bound = 0\n",
    "seed_model.reactions.GLC_tx.lower_bound = 0\n",
    "seed_model.reactions.Sucrose_tx.upper_bound = 1\n",
    "seed_model.reactions.Sucrose_tx.lower_bound = 1\n",
    "seed_model.reactions.Photon_tx.upper_bound = 0\n",
    "seed_model.reactions.Photon_tx.lower_bound = 0\n",
    "seed_model.reactions.Phloem_output_tx.objective_coefficient=0\n",
    "seed_model.reactions.Biomass_seed_tx.objective_coefficient=1\n",
    "\n",
    "from cobra.flux_analysis import pfba\n",
    "sol = pfba(seed_model)\n",
    "print(seed_model.reactions.Biomass_seed_tx.flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RXN_9531_p\tDecanoyl_ACPs_p + MALONYL_ACP_p + PROTON_p --> 3_oxo_dodecanoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n",
      "RXN_9535_p\tDodecanoyl_ACPs_p + MALONYL_ACP_p + PROTON_p --> 3_oxo_myristoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n",
      "RXN_9527_p\tMALONYL_ACP_p + Octanoyl_ACPs_p + PROTON_p --> 3_oxo_decanoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n",
      "RXN_9523_p\tHexanoyl_ACPs_p + MALONYL_ACP_p + PROTON_p --> 3_Oxo_octanoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n",
      "2_PERIOD_3_PERIOD_1_PERIOD_180_RXN_p\tACETYL_COA_p + MALONYL_ACP_p + PROTON_p --> Acetoacetyl_ACPs_p + CARBON_DIOXIDE_p + CO_A_p\n",
      "RXN_9516_p\tButanoyl_ACPs_p + MALONYL_ACP_p + PROTON_p --> 3_oxo_hexanoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n",
      "MALONYL_COA_ACP_TRANSACYL_RXN_p\tACP_p + MALONYL_COA_p --> CO_A_p + MALONYL_ACP_p\n",
      "RXN_9539_p\tMALONYL_ACP_p + Myristoyl_ACPs_p + PROTON_p --> 3_oxo_palmitoyl_ACPs_p + ACP_p + CARBON_DIOXIDE_p\n"
     ]
    }
   ],
   "source": [
    "for rxn in seed_model.metabolites.get_by_id(\"MALONYL_ACP_p\").reactions:\n",
    "    print(rxn.id+\"\\t\"+str(rxn.reaction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(seed_model,\"Data/Soy_seed_model_GA.sbml\")"
   ]
  }
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