Generate WHEAT tissue specific models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling metabolism in WHEAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating WHEAT core model"
   ]
  },
  {
   "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 cobra import flux_analysis\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.read_sbml_model(fname)\n",
    "model.solver='glpk'\n",
    "display.clear_output()\n",
    "\n",
    "#from sweetlovegroup import transform\n",
    "#model = transform.fixModelCompatibilityIssueCobra015(model,fname)\n",
    "\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",
    "#met.compartment = \"b\"\n",
    "#rxn.add_metabolites({met:1})\n",
    "model.reactions.AraCore_Biomass_tx.remove_from_model()\n",
    "\n",
    "for rxn in model.reactions:\n",
    "    if rxn.lower_bound == -1000:\n",
    "        rxn.lower_bound = -3000\n",
    "    if rxn.upper_bound == 1000:\n",
    "        rxn.upper_bound = 3000\n",
    "\n",
    "#remove charged state of PGPs since they are minor (0.03)\n",
    "model = removeSpecificMetChargedState(model,[\"aL_1_PHOSPHATIDYL_GLYCEROL_P_p\",\"aL_1_PHOSPHATIDYL_GLYCEROL_P_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"Data/biomass_wheat.csv\")\n",
    "\n",
    "FA=[\"PALMITATE_p\",\"CPD_9245_p\",\"CPD_17412_p\",\"CPD_17291_p\",\"STEARIC_ACID_p\",\"OLEATE_CPD_p\",\n",
    "    \"Octadecadienoate_p\",\"LINOLENIC_ACID_p\",\"ARACHIDIC_ACID_p\",\"CPD_16709_p\",\"DOCOSANOATE_p\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass = df.set_index(\"Unnamed: 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use biomass composition to generate biomass equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L_ASPARTATE_ph + PROTON_e --> L_ASPARTATE_c + PROTON_c\n",
      "GLT_ph + PROTON_e --> GLT_c + PROTON_c\n",
      "PROTON_e + sSUCROSE_ph --> PROTON_c + sSUCROSE_b\n",
      "PROTON_e + TRP_ph --> PROTON_c + TRP_c\n",
      "L_ALPHA_ALANINE_ph + PROTON_e --> L_ALPHA_ALANINE_c + PROTON_c\n",
      "ILE_ph + PROTON_e --> ILE_c + PROTON_c\n",
      "PROTON_e + VAL_ph --> PROTON_c + VAL_c\n",
      "GLN_ph + PROTON_e --> GLN_c + PROTON_c\n",
      "GLC_ph + PROTON_e --> GLC_c + PROTON_c\n",
      "ASN_ph + PROTON_e --> ASN_c + PROTON_c\n",
      "CYS_ph + PROTON_e --> CYS_c + PROTON_c\n",
      "FRU_ph + PROTON_e --> FRU_c + PROTON_c\n",
      "HIS_ph + PROTON_e --> HIS_c + PROTON_c\n",
      "GLY_ph + PROTON_e --> GLY_c + PROTON_c\n",
      "LEU_ph + PROTON_e --> LEU_c + PROTON_c\n",
      "PHE_ph + PROTON_e --> PHE_c + PROTON_c\n",
      "PROTON_e + THR_ph --> PROTON_c + THR_c\n",
      "PROTON_e + SER_ph --> PROTON_c + SER_c\n",
      "4_AMINO_BUTYRATE_ph + PROTON_e --> 4_AMINO_BUTYRATE_c + PROTON_c\n",
      "PROTON_e + TYR_ph --> PROTON_c + TYR_c\n",
      "ARG_ph + PROTON_e --> ARG_c + PROTON_c\n",
      "MET_ph + PROTON_e --> MET_c + PROTON_c\n",
      "PROTON_e + PRO_ph --> PROTON_c + PRO_c\n",
      "LYS_ph + PROTON_e --> LYS_c + PROTON_c\n"
     ]
    }
   ],
   "source": [
    "temp  = model.copy()\n",
    "\n",
    "for met in temp.reactions.Phloem_output_tx.metabolites.keys():\n",
    "    met2 = met.copy()\n",
    "    if met.id==\"sSUCROSE_b\":\n",
    "        met2.id = met.id.replace(\"_b\",\"_ph\")\n",
    "    elif \"PROTON\" in met.id:\n",
    "        continue\n",
    "    else:\n",
    "        met2.id = met.id.replace(\"_c\",\"_ph\")\n",
    "    met2.compartment = \"ph\"\n",
    "    temp.add_metabolites(met2)\n",
    "    \n",
    "    rxn = Reaction(met2.id.replace(\"_ph\",\"_phloem_uptake\"),name=met2.id.replace(\"_ph\",\"_phloem_uptake\"))\n",
    "    rxn.add_metabolites({met2:-1,temp.metabolites.get_by_id(\"PROTON_e\"):-1,met:1,temp.metabolites.get_by_id(\"PROTON_c\"):1})\n",
    "    rxn.lower_bound = 0\n",
    "    rxn.upper_bound = 1000\n",
    "    temp.add_reaction(rxn)\n",
    "    \n",
    "    print(rxn.reaction)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def generateStemModel(model):\n",
    "#     for met in model.reactions.Phloem_output_tx.metabolites.keys():\n",
    "#         met2 = met.copy()\n",
    "#         if met.id==\"sSUCROSE_b\":\n",
    "#             met2.id = \"SUCROSE_ph\"\n",
    "#             met = model.metabolites.get_by_id(\"SUCROSE_c\")\n",
    "#         elif \"PROTON\" in met.id:\n",
    "#             continue\n",
    "#         else:\n",
    "#             met2.id = met.id.replace(\"_c\",\"_ph\")\n",
    "#         met2.compartment = \"ph\"\n",
    "#         model.add_metabolites(met2)\n",
    "        \n",
    "#         rxn = Reaction(met2.id+\"_exchange\")\n",
    "#         rxn.add_metabolites({met2:1})\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         rxn = Reaction(met2.id.replace(\"_ph\",\"_phloem_uptake\"),name=met2.id.replace(\"_ph\",\"_phloem_uptake\"))\n",
    "#         rxn.add_metabolites({met2:-1,model.metabolites.get_by_id(\"PROTON_e\"):-1,\n",
    "#                              met:1,model.metabolites.get_by_id(\"PROTON_c\"):1})\n",
    "#         rxn.lower_bound = 0\n",
    "#         rxn.upper_bound = 1000\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         #print(rxn.reaction)\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07899508449495768\n",
      "Biomass flux =0.029372491591109825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 40.42553191489362 is not an integer (in formula C40.42553191489362H77.72340425531915O13.829787234042554P2.127659574468085)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 77.72340425531915 is not an integer (in formula C40.42553191489362H77.72340425531915O13.829787234042554P2.127659574468085)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 13.829787234042554 is not an integer (in formula C40.42553191489362H77.72340425531915O13.829787234042554P2.127659574468085)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 2.127659574468085 is not an integer (in formula C40.42553191489362H77.72340425531915O13.829787234042554P2.127659574468085)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.757994947514675 is not an integer (in formula C16.757994947514675H32.15059882494136O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.15059882494136 is not an integer (in formula C16.757994947514675H32.15059882494136O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 37.757994947514675 is not an integer (in formula C37.757994947514675H67.15059882494137N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 67.15059882494137 is not an integer (in formula C37.757994947514675H67.15059882494137N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 2.9999999999999996 is not an integer (in formula C37.757994947514675H67.15059882494137N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.757994947514675 is not an integer (in formula C16.757994947514675H32.15059882494136O2.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.15059882494136 is not an integer (in formula C16.757994947514675H32.15059882494136O2.0)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from cobra import flux_analysis\n",
    "stem_model = model.copy()\n",
    "\n",
    "k = \"stem\"\n",
    "\n",
    "stem_model = updateFAcomposition(stem_model,k,biomass)\n",
    "\n",
    "rxn = Reaction(\"Biomass_stem_tx\")\n",
    "for met in df[\"Unnamed: 0\"]:\n",
    "    if met in FA or float(df[df[\"Unnamed: 0\"]==met][k])==0:\n",
    "        continue\n",
    "    else:\n",
    "        rxn.add_metabolites({stem_model.metabolites.get_by_id(met):-1*float(df[df[\"Unnamed: 0\"]==met][k])})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "stem_model.add_reaction(rxn)\n",
    "\n",
    "stem_model = generateStemModel(stem_model)\n",
    "\n",
    "for rxn in stem_model.reactions:\n",
    "    if rxn.id==\"SUCROSE_ph_exchange\":\n",
    "        rxn.lower_bound = 1\n",
    "        rxn.upper_bound = 1\n",
    "    elif \"ph_exchange\" in rxn.id:\n",
    "        rxn.lower_bound = 0\n",
    "        rxn.upper_bound = 0\n",
    "\n",
    "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 = 0\n",
    "stem_model.reactions.Sucrose_tx.lower_bound = 0\n",
    "stem_model.reactions.Photon_tx.upper_bound = 0\n",
    "stem_model.reactions.Photon_tx.lower_bound = 0\n",
    "stem_model.reactions.Biomass_stem_tx.objective_coefficient=1\n",
    "\n",
    "sol = flux_analysis.parsimonious.pfba(stem_model)\n",
    "print(\"Biomass flux =\"+str(stem_model.reactions.Biomass_stem_tx.flux))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Biomass_stem_tx\n"
     ]
    }
   ],
   "source": [
    "for rxn in stem_model.reactions:\n",
    "    if rxn.objective_coefficient!=0:\n",
    "        print(rxn.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem_model.reactions.SUCROSE_ph_exchange.flux*12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.585781088245101"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem_model.reactions.Biomass_stem_tx.flux*224.21594939662592"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.5277876782379476"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem_model.reactions.CO2_tx.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def generateRootModel(model):\n",
    "#     for met in model.reactions.Phloem_output_tx.metabolites.keys():\n",
    "#         met2 = met.copy()\n",
    "#         if met.id==\"sSUCROSE_b\":\n",
    "#             met2.id = \"SUCROSE_ph\"\n",
    "#             met = model.metabolites.get_by_id(\"SUCROSE_c\")\n",
    "#         elif \"PROTON\" in met.id:\n",
    "#             continue\n",
    "#         else:\n",
    "#             met2.id = met.id.replace(\"_c\",\"_ph\")\n",
    "#         met2.compartment = \"ph\"\n",
    "#         model.add_metabolites(met2)\n",
    "        \n",
    "#         rxn = Reaction(met2.id+\"_exchange\")\n",
    "#         rxn.add_metabolites({met2:1})\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         rxn = Reaction(met2.id.replace(\"_ph\",\"_phloem_uptake\"),name=met2.id.replace(\"_ph\",\"_phloem_uptake\"))\n",
    "#         rxn.add_metabolites({met2:-1,model.metabolites.get_by_id(\"PROTON_e\"):-1,\n",
    "#                              met:1,model.metabolites.get_by_id(\"PROTON_c\"):1})\n",
    "#         rxn.lower_bound = 0\n",
    "#         rxn.upper_bound = 1000\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#     #add xylem reactions\n",
    "#     for met in [\"CAII\",\"MGII\",\"KI\",\"NITRATE\",\"SULFATE\",\"AMMONIUM\",\"WATER\",\"GLT\",\"L_ASPARTATE\",\"ASN\",\"GLN\"]:\n",
    "#         met2 = model.metabolites.get_by_id(met+\"_c\").copy()\n",
    "#         met2.id = met+\"_xy\"\n",
    "#         met2.compartment = \"xy\"\n",
    "\n",
    "#         rxn = Reaction(met+\"_exchange\")\n",
    "#         rxn.add_metabolites({met2:-1})\n",
    "#         rxn.lower_bound = 0\n",
    "#         rxn.upper_bound = 1000\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         rxn = Reaction(met+\"_xylem_export\")\n",
    "#         rxn.add_metabolites({model.metabolites.get_by_id(met+\"_c\"):-1,met2:1})\n",
    "#         model.add_reaction(rxn)\n",
    "        \n",
    "#     #adding symbiont compartment\n",
    "#     from cobra import io\n",
    "#     rhizo = io.read_sbml_model(\"Data/iCC541.xml\")\n",
    "#     for met in rhizo.metabolites:\n",
    "#         met.compartment = met.compartment+\"_rhizo\"\n",
    "#     rhizo.compartments={\"c_rhizo\":\"rhizobe cytosol\",\"e_rhizo\":\"rhizobe extracellular\"}\n",
    "#     model = model+rhizo\n",
    "    \n",
    "# #     rxn = Reaction(\"Sucrose_exchange_symbiont\")\n",
    "# #     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "# #     rxn.add_metabolites({model.metabolites.get_by_id(\"SUCROSE_c\"):-1,model.metabolites.get_by_id(\"cpd00076[e0]\"):1})\n",
    "# #     rxn.lower_bound = -1000\n",
    "# #     rxn.upper_bound = 1000\n",
    "# #     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Alanine_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"L_ALPHA_ALANINE_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"ala__L[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Aspartate_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"L_ASPARTATE_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"asp__L[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Glutamate_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"GLT_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"glu__L[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Malate_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"MAL_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"mal__L[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Succinate_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"SUC_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"succ[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     rxn = Reaction(\"Ammonium_exchange_symbiont\")\n",
    "#     rxn.name = rxn.id.replace(\"_\",\" \")\n",
    "#     rxn.add_metabolites({model.metabolites.get_by_id(\"AMMONIUM_c\"):-1,\n",
    "#                          model.metabolites.get_by_id(\"fixedNH3[e]\"):1})\n",
    "#     rxn.lower_bound = -1000\n",
    "#     rxn.upper_bound = 1000\n",
    "#     model.add_reaction(rxn)\n",
    "    \n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07162516963517566\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.67231104150657 is not an integer (in formula C16.67231104150657H32.113075347436585O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.113075347436585 is not an integer (in formula C16.67231104150657H32.113075347436585O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 37.672311041506575 is not an integer (in formula C37.672311041506575H67.11307534743659N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 67.11307534743659 is not an integer (in formula C37.672311041506575H67.11307534743659N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 2.9999999999999996 is not an integer (in formula C37.672311041506575H67.11307534743659N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.67231104150657 is not an integer (in formula C16.67231104150657H32.113075347436585O2.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 32.113075347436585 is not an integer (in formula C16.67231104150657H32.113075347436585O2.0)\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Biomass flux = 0.0\n"
     ]
    }
   ],
   "source": [
    "from cobra import flux_analysis\n",
    "root_model = model.copy()\n",
    "\n",
    "k = \"root\"\n",
    "root_model = updateFAcomposition(root_model,k,biomass)\n",
    "\n",
    "rxn = Reaction(\"Biomass_root_tx\")\n",
    "for met in df[\"Unnamed: 0\"]:\n",
    "    if met in FA or float(df[df[\"Unnamed: 0\"]==met][k])==0:\n",
    "        continue\n",
    "    rxn.add_metabolites({root_model.metabolites.get_by_id(met):-1*float(df[df[\"Unnamed: 0\"]==met][k])})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "root_model.add_reaction(rxn)\n",
    "\n",
    "root_model = generateRootModel(root_model)\n",
    "\n",
    "root_model.reactions.GLC_tx.upper_bound = 0\n",
    "root_model.reactions.GLC_tx.lower_bound = 0\n",
    "root_model.reactions.Sucrose_tx.upper_bound = 0\n",
    "root_model.reactions.Sucrose_tx.lower_bound = 0\n",
    "root_model.reactions.Photon_tx.upper_bound = 0\n",
    "root_model.reactions.Photon_tx.lower_bound = 0\n",
    "\n",
    "\n",
    "root_model.reactions.Nitrate_tx.upper_bound = 0\n",
    "root_model.reactions.Nitrate_tx.lower_bound = 0\n",
    "root_model.reactions.NH4_tx.upper_bound = 0\n",
    "root_model.reactions.NH4_tx.lower_bound = 0\n",
    " \n",
    "    \n",
    "for rxn in root_model.reactions.query(\"_phloem_\"):\n",
    "    if \"SUCROSE\" not in rxn.id :\n",
    "        rxn.lower_bound = 0\n",
    "        rxn.upper_bound = 0\n",
    "\n",
    "for rxn in root_model.reactions:\n",
    "    if rxn.lower_bound == -1000:\n",
    "        rxn.lower_bound = -3000\n",
    "    if rxn.upper_bound == 1000:\n",
    "        rxn.upper_bound = 3000\n",
    "    rxn.objective_coefficient=0\n",
    "root_model.reactions.Biomass_root_tx.objective_coefficient=1\n",
    "flux_analysis.parsimonious.pfba(root_model)\n",
    "print(\"Biomass flux = \"+str(root_model.reactions.Biomass_root_tx.flux))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def generateSeedModel(model):\n",
    "#     for met in model.reactions.Phloem_output_tx.metabolites.keys():\n",
    "#         met2 = met.copy()\n",
    "#         if met.id==\"sSUCROSE_b\":\n",
    "#             met2.id = \"SUCROSE_ph\"\n",
    "#             met = model.metabolites.get_by_id(\"SUCROSE_c\")\n",
    "#         elif \"PROTON\" in met.id:\n",
    "#             continue\n",
    "#         else:\n",
    "#             met2.id = met.id.replace(\"_c\",\"_ph\")\n",
    "#         met2.compartment = \"ph\"\n",
    "#         model.add_metabolites(met2)\n",
    "        \n",
    "#         rxn = Reaction(met2.id+\"_exchange\")\n",
    "#         rxn.add_metabolites({met2:1})\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         rxn = Reaction(met2.id.replace(\"_ph\",\"_phloem_uptake\"),name=met2.id.replace(\"_ph\",\"_phloem_uptake\"))\n",
    "#         rxn.add_metabolites({met2:-1,model.metabolites.get_by_id(\"PROTON_e\"):-1,\n",
    "#                              met:1,model.metabolites.get_by_id(\"PROTON_c\"):1})\n",
    "#         rxn.lower_bound = 0\n",
    "#         rxn.upper_bound = 1000\n",
    "#         model.add_reaction(rxn)\n",
    "\n",
    "#         #print(rxn.reaction)\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "Biomass flux =3.5046362552021217\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 2.9999999999999996 is not an integer (in formula C37.0H66.0N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from cobra import flux_analysis\n",
    "seed_model = model.copy()\n",
    "\n",
    "seed_model = generateSeedModel(seed_model)\n",
    "\n",
    "k = \"seed\"\n",
    "seed_model = updateFAcomposition(seed_model,k,biomass)\n",
    "\n",
    "\n",
    "for rxn in seed_model.reactions:\n",
    "    if rxn.id==\"SUCROSE_ph_exchange\":\n",
    "        rxn.lower_bound = 1\n",
    "        rxn.upper_bound = 1\n",
    "    elif \"ph_exchange\" in rxn.id:\n",
    "        rxn.lower_bound = 0\n",
    "        rxn.upper_bound = 0\n",
    "\n",
    "\n",
    "\n",
    "rxn = Reaction(\"Biomass_seed_tx\")\n",
    "for met in df[\"Unnamed: 0\"]:\n",
    "    if met in FA or float(df[df[\"Unnamed: 0\"]==met][k])==0:\n",
    "        continue\n",
    "    rxn.add_metabolites({seed_model.metabolites.get_by_id(met):-1*float(df[df[\"Unnamed: 0\"]==met][k])})\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "seed_model.add_reaction(rxn)\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 = 0\n",
    "seed_model.reactions.Sucrose_tx.lower_bound = 0\n",
    "seed_model.reactions.Photon_tx.upper_bound = 0\n",
    "seed_model.reactions.Photon_tx.lower_bound = 0\n",
    "seed_model.reactions.Biomass_seed_tx.objective_coefficient=1\n",
    "sol=flux_analysis.parsimonious.pfba(seed_model)\n",
    "print(\"Biomass flux =\"+str(seed_model.reactions.Biomass_seed_tx.flux))\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2790.695488553395"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed_model.reactions.Biomass_seed_tx.flux*796.2867713906156"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed_model.reactions.SUCROSE_ph_exchange.flux*12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7042271016638176"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed_model.reactions.CO2_tx.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate diel leaf model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06642684859823242\n",
      "Biomass flux =0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.791156303205355 is not an integer (in formula C16.791156303205355H31.636917070762998O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 31.636917070762998 is not an integer (in formula C16.791156303205355H31.636917070762998O2)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 37.791156303205355 is not an integer (in formula C37.791156303205355H66.636917070763N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 66.636917070763 is not an integer (in formula C37.791156303205355H66.636917070763N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 2.9999999999999996 is not an integer (in formula C37.791156303205355H66.636917070763N7.0O17.0P2.9999999999999996S1.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 16.791156303205355 is not an integer (in formula C16.791156303205355H31.636917070762998O2.0)\n",
      "  warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cobra\\core\\metabolite.py:103: UserWarning: 31.636917070762998 is not an integer (in formula C16.791156303205355H31.636917070762998O2.0)\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from cobra import flux_analysis\n",
    "leaf_model = model.copy()\n",
    "\n",
    "k = \"leaf\"\n",
    "leaf_model = updateFAcomposition(leaf_model,k,biomass)\n",
    "\n",
    "rxn = Reaction(\"Biomass_leaf_tx\")\n",
    "for met in df[\"Unnamed: 0\"]:\n",
    "    if met in FA or float(df[df[\"Unnamed: 0\"]==met][k])==0:\n",
    "        continue\n",
    "    rxn.add_metabolites({leaf_model.metabolites.get_by_id(met):-1*float(df[df[\"Unnamed: 0\"]==met][k])})\n",
    "\n",
    "rxn.lower_bound = 0\n",
    "rxn.upper_bound = 1000\n",
    "leaf_model.add_reaction(rxn)\n",
    "\n",
    "leaf_model.reactions.GLC_tx.upper_bound = 0\n",
    "leaf_model.reactions.GLC_tx.lower_bound = 0\n",
    "leaf_model.reactions.Sucrose_tx.upper_bound = 0\n",
    "leaf_model.reactions.Sucrose_tx.lower_bound = 0\n",
    "leaf_model.reactions.Photon_tx.upper_bound = 0\n",
    "leaf_model.reactions.Photon_tx.lower_bound = 0\n",
    "leaf_model.reactions.Biomass_leaf_tx.objective_coefficient=1\n",
    "sol=flux_analysis.parsimonious.pfba(leaf_model)\n",
    "print(\"Biomass flux =\"+str(leaf_model.reactions.Biomass_leaf_tx.flux))\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phloem export rate =3.5504474309760568\n"
     ]
    }
   ],
   "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",
    "leaf_model.reactions.Biomass_leaf_tx1.objective_coefficient = 0\n",
    "leaf_model.reactions.Biomass_leaf_tx1.upper_bound=0\n",
    "leaf_model.reactions.Biomass_leaf_tx1.lower_bound=0\n",
    "leaf_model.reactions.Biomass_leaf_tx2.upper_bound=0\n",
    "leaf_model.reactions.Biomass_leaf_tx2.lower_bound=0\n",
    "\n",
    "\n",
    "from cobra.flux_analysis import pfba\n",
    "sol = pfba(leaf_model)\n",
    "print(\"Phloem export rate =\"+str(sol.fluxes[\"diel_biomass\"]*4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Biomass accumulation rate rate =0.18296995332247598\n"
     ]
    }
   ],
   "source": [
    "growing_leaf_model = leaf_model.copy()\n",
    "\n",
    "growing_leaf_model.reactions.Biomass_leaf_tx1.upper_bound=1000\n",
    "growing_leaf_model.reactions.Biomass_leaf_tx2.upper_bound=1000\n",
    "\n",
    "met1=Metabolite(\"Leaf_biomass1\",compartment=\"c1\")\n",
    "rxn = growing_leaf_model.reactions.Biomass_leaf_tx1\n",
    "rxn.add_metabolites({met1:1})\n",
    "\n",
    "met2=Metabolite(\"Leaf_biomass2\",compartment=\"c2\")\n",
    "rxn = growing_leaf_model.reactions.Biomass_leaf_tx2\n",
    "rxn.add_metabolites({met2:1})\n",
    "\n",
    "growing_leaf_model.reactions.diel_biomass.add_metabolites({growing_leaf_model.metabolites.X_Phloem_contribution_t1:3,\n",
    "                                                           growing_leaf_model.metabolites.X_Phloem_contribution_t2:1,\n",
    "                                                           met1:-0.75, met2:-0.25})\n",
    "\n",
    "\n",
    "from cobra.flux_analysis import pfba\n",
    "sol = pfba(growing_leaf_model)\n",
    "print(\"Biomass accumulation rate rate =\"+str(sol.fluxes[\"diel_biomass\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(root_model,\"C:\\\\Users\\\\sanus\\\\Scripts\\\\git\\\\tissue-specific-FBA-models\\\\Models\\\\wheat_root_model.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(stem_model,\"C:\\\\Users\\\\sanus\\\\Scripts\\\\git\\\\tissue-specific-FBA-models\\\\Models\\\\wheat_stem_model.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(seed_model,\"C:\\\\Users\\\\sanus\\\\Scripts\\\\git\\\\tissue-specific-FBA-models\\\\Models\\\\wheat_seed_model.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(leaf_model,\"C:\\\\Users\\\\sanus\\\\Scripts\\\\git\\\\tissue-specific-FBA-models\\\\Models\\\\wheat_sourceleaf_model.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.write_sbml_model(growing_leaf_model,\"C:\\\\Users\\\\sanus\\\\Scripts\\\\git\\\\tissue-specific-FBA-models\\\\Models\\\\wheat_sinkleaf_model.xml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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