LDR_constraints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constraints on light-dependent respiration\n",
    "\n",
    "Fisher NL, Halsey KH. Mechanisms that increase the growth efficiency of diatoms in low light. *Photosynth Res.* 2016;**129**: 183–197. [doi:10.1007/s11120-016-0282-6](http://doi.org/10.1007/s11120-016-0282-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cobra/__init__.py:33 \u001b[1;31mFutureWarning\u001b[0m: Python 2 is reaching end of life (see https://www.python.org/dev/peps/pep-0373/) and many cobra dependencies have already dropped support. At the moment it *should* still work but we will no longer actively maintain Python 2 support.\n"
     ]
    }
   ],
   "source": [
    "import sys, cobra, math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from cobra import Reaction\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cobra_config = cobra.Configuration()\n",
    "\n",
    "cobra_config.lower_bound = -10000.\n",
    "cobra_config.upper_bound = 10000.\n",
    "\n",
    "cobra_config.solver = 'glpk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "absorbLL = {'EX_photon410_e': 7732.432316,'EX_photon430_e': 7530.191719,'EX_photon450_e': 6777.205304,'EX_photon470_e': 5420.788645,'EX_photon490_e': 4462.950129,'EX_photon510_e': 3778.897393,'EX_photon530_e': 2988.068035,'EX_photon550_e': 2100.53568,'EX_photon570_e': 1300.702583,'EX_photon590_e': 1075.245665,'EX_photon610_e': 1123.387173,'EX_photon630_e': 1460.196353,'EX_photon650_e': 1580.653401,'EX_photon670_e': 4018.115597,'EX_photon690_e': 2155.737164}\n",
    "absorbML = {'EX_photon410_e': 4730.075289,'EX_photon430_e': 5817.128965,'EX_photon450_e': 5348.203973,'EX_photon470_e': 4050.000013,'EX_photon490_e': 3464.694801,'EX_photon510_e': 2649.794528,'EX_photon530_e': 1876.490736,'EX_photon550_e': 1334.544022,'EX_photon570_e': 873.4095179,'EX_photon590_e': 740.7816246,'EX_photon610_e': 888.7175101,'EX_photon630_e': 1082.718272,'EX_photon650_e': 1178.924274,'EX_photon670_e': 3322.974688,'EX_photon690_e': 1840.91646}\n",
    "absorbHL = {'EX_photon410_e': 4299.712311,'EX_photon430_e': 4827.773138,'EX_photon450_e': 4682.224642,'EX_photon470_e': 3843.094021,'EX_photon490_e': 3418.26565,'EX_photon510_e': 2978.620455,'EX_photon530_e': 2481.738844,'EX_photon550_e': 2039.337028,'EX_photon570_e': 1451.311983,'EX_photon590_e': 1255.298421,'EX_photon610_e': 1207.59605,'EX_photon630_e': 1475.056726,'EX_photon650_e': 1549.345022,'EX_photon670_e': 2969.670677,'EX_photon690_e': 1924.104339}\n",
    "cool_white = {'EX_photon410_e': 0.020538848,'EX_photon430_e': 0.044455157,'EX_photon450_e': 0.032857193,'EX_photon470_e': 0.036698467,'EX_photon490_e': 0.075174979,'EX_photon510_e': 0.040060786,'EX_photon530_e': 0.047049592,'EX_photon550_e': 0.233319199,'EX_photon570_e': 0.070267588,'EX_photon590_e': 0.103796078,'EX_photon610_e': 0.1568836,'EX_photon630_e': 0.0624902,'EX_photon650_e': 0.030806635,'EX_photon670_e': 0.024272336,'EX_photon690_e': 0.021329342}\n",
    "halogen = {'EX_photon410_e':0.001262457,'EX_photon430_e':0.004585348,'EX_photon450_e':0.008020995,'EX_photon470_e':0.015208428,'EX_photon490_e':0.032867028,'EX_photon510_e':0.049849784,'EX_photon530_e':0.065038153,'EX_photon550_e':0.079970685,'EX_photon570_e':0.093411994,'EX_photon590_e':0.111565142,'EX_photon610_e':0.115420353,'EX_photon630_e':0.117526779,'EX_photon650_e':0.1119338,'EX_photon670_e':0.103590713,'EX_photon690_e':0.089748342}\n",
    "\n",
    "def calc_photon_abs(I,chla,DW,absorb,relArea):\n",
    "    conv = (60*60)/1e7 #to mmol gDW-1 h-1\n",
    "    pfa = {f:I*relArea[f]*absorb[f]*conv*(chla/DW) for f in relArea.keys()}\n",
    "    return pfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = [5,60,200]\n",
    "absorb = [absorbLL,absorbML,absorbHL]\n",
    "\n",
    "DOC = [0.26*1e-3,2.43*1e-3,3.95*1e-3]\n",
    "\n",
    "# 95% CI\n",
    "lwrGross = [0.009502609,0.12697434,0.38567577]\n",
    "uprGross = [0.01592601,0.16241050,0.47702915]\n",
    "lwrNet = [-0.0101236265,0.044636908,0.234284802]\n",
    "uprNet = [0.0040989012,0.081973226,0.327647747]\n",
    "lwrDR = [-0.018875372,-0.061322432,-0.156790618]\n",
    "uprDR = [-0.000167587,-0.021101554,-0.05793793]\n",
    "lwrCup = [0.004934730,0.07092116,0.30134136]\n",
    "uprCup = [0.007436942,0.10177330,0.35092754]\n",
    "\n",
    "\n",
    "D1_m = [6.4751E-09,1.16783E-08,1.36247E-08]\n",
    "D1_b = [2.35114E-06,4.24045E-06,4.94719E-06]\n",
    "D1 = [D1_m[i]*I[i]+D1_b[i] for i in range(0,3)]\n",
    "\n",
    "pgchla = [0.627,0.192,0.164]\n",
    "mgchla = [chla*1e-9 for chla in pgchla]\n",
    "\n",
    "pgDW = [22.41006436,16.62883198,17.84261965]\n",
    "gDW = [DW*1e-12 for DW in pgDW]\n",
    "\n",
    "cells_mL = [1.43e6,2.21e6,1.56e6]\n",
    "cells_L = [cells_mL[i]*1e3 for i in range(0,3)]\n",
    "\n",
    "NGAM = [1.554270008,2.233386198,3.715204852]\n",
    "MMR = [1.1,5.3,7.9] # umol C (mg Chla h)-1\n",
    "MMR_se = [0.076,0.039,0.018]\n",
    "lwrMMR = [MMR[i]-1.96*MMR_se[i] for i in range(0,3)]\n",
    "uprMMR = [MMR[i]+1.96*MMR_se[i] for i in range(0,3)]\n",
    "LDR = [1.7,5.6,21.6]\n",
    "avail_C = [1.541189077,16.62490103,130.4128939]\n",
    "\n",
    "divide_h = [0.008368152,0.036051307,0.066270096]\n",
    "\n",
    "mu_d = [0.20,0.85,1.54]\n",
    "mu_d_se = [0.01/math.sqrt(3),0.03/math.sqrt(3),0.03/math.sqrt(3)]\n",
    "mu_d_lb = [mu_d[i]-(1.96*mu_d_se[i]) for i in range(0,3)]\n",
    "mu_d_ub = [mu_d[i]+(1.96*mu_d_se[i]) for i in range(0,3)]\n",
    "mu_h_lb = [mu/24. for mu in mu_d_lb]\n",
    "mu_h_ub = [mu/24. for mu in mu_d_ub]\n",
    "mu_h = [mu/24. for mu in mu_d]\n",
    "\n",
    "GAM_val = [2698,2217,3669]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pFBA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handlers could be found for logger \"cobra.io.sbml\"\n"
     ]
    }
   ],
   "source": [
    "ERRORTOL = 1e-8\n",
    "cobra_config.tolerance = ERRORTOL\n",
    "\n",
    "LL = cobra.io.read_sbml_model('Models/Thaps_LL_model_n.xml')\n",
    "ML = cobra.io.read_sbml_model('Models/Thaps_ML_model_n.xml')\n",
    "HL = cobra.io.read_sbml_model('Models/Thaps_HL_model_n.xml')\n",
    "\n",
    "models = [LL,ML,HL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cobra import Reaction\n",
    "gam_rxn = Reaction('GAM')\n",
    "sink_chryso_c = Reaction('sink_chryso_c')\n",
    "sink_g3p_h = Reaction('sink_g3p_h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown metabolite 'GAM_const_c' created\n",
      "Optimizing...\n",
      "optimal\n",
      "0.1992901479750177\n",
      "EX_co2_e -0.263164475949701\n",
      "EX_no3_e -0.06282132905539459\n",
      "EX_btn_e -2.59447703394122e-08\n",
      "EX_h_e -0.10391727543794062\n",
      "EX_h2o_e -0.11226457462228723\n",
      "EX_o2_e 0.3978406686285846\n",
      "EX_pi_e 0.011978587533159862\n",
      "EX_so4_e -0.0017137409926234481\n",
      "EX_mg2_e -0.00026720620328291023\n",
      "EX_thm_e -2.27717453079257e-06\n",
      "EX_urea_e 0.0010626216796486708\n",
      "CEF_h is limited\n",
      "EX_asp__L_e 0.0015529471614371447\n",
      "EX_photon410_e -7.998142733442801\n",
      "EX_photon410_e is limiting\n",
      "EX_photon430_e -16.858740430461005\n",
      "EX_photon430_e is limiting\n",
      "EX_photon450_e -11.214451652830846\n",
      "EX_photon450_e is limiting\n",
      "EX_photon470_e -10.018606971655732\n",
      "EX_photon470_e is limiting\n",
      "EX_photon490_e -16.89632643517483\n",
      "EX_photon490_e is limiting\n",
      "EX_photon510_e -7.623975779971577\n",
      "EX_photon510_e is limiting\n",
      "EX_photon530_e -7.080163478355117\n",
      "EX_photon530_e is limiting\n",
      "EX_photon550_e -24.681837112223626\n",
      "EX_photon550_e is limiting\n",
      "EX_photon570_e -4.60288358600427\n",
      "EX_photon570_e is limiting\n",
      "EX_photon590_e -5.620637623915249\n",
      "EX_photon590_e is limiting\n",
      "EX_photon610_e -8.875727279117879\n",
      "EX_photon610_e is limiting\n",
      "EX_photon630_e -4.595366100466583\n",
      "EX_photon630_e is limiting\n",
      "EX_photon650_e -2.452324038705711\n",
      "EX_photon650_e is limiting\n",
      "EX_photon670_e -4.911689951347541\n",
      "EX_photon670_e is limiting\n",
      "EX_photon690_e -2.315635552955565\n",
      "EX_photon690_e is limiting\n",
      "DM_pho_loss_e 79.79757108794738\n",
      "PSII_u is limited\n",
      "EX_sio4h4_e -0.023639913331654903\n",
      "DM_biomass_c 0.008303756165625738\n",
      "sink_chryso_c -6.013934159733984e-05\n",
      "sink_chryso_c is limiting\n",
      "sink_g3p_h -0.014253107841653302\n",
      "sink_g3p_h is limiting\n",
      "unknown metabolite 'GAM_const_c' created\n",
      "Optimizing...\n",
      "optimal\n",
      "0.8470114030366479\n",
      "EX_co2_e -1.2310846142281304\n",
      "EX_no3_e -0.18769861214589026\n",
      "EX_btn_e -1.4859281805941675e-07\n",
      "EX_h_e -0.274765078207395\n",
      "EX_h2o_e -0.776952743597036\n",
      "EX_o2_e 1.6961994821604518\n",
      "EX_pi_e 0.050377035175747986\n",
      "EX_so4_e -0.010013099447484608\n",
      "EX_mg2_e -0.0004926528793305351\n",
      "EX_thm_e -1.3043120996465861e-05\n",
      "CEF_h is limited\n",
      "EX_for_e 0.028057292334674584\n",
      "EX_photon410_e -24.229104835360616\n",
      "EX_photon410_e is limiting\n",
      "EX_photon430_e -64.49470716580896\n",
      "EX_photon430_e is limiting\n",
      "EX_photon450_e -43.8259819726762\n",
      "EX_photon450_e is limiting\n",
      "EX_photon470_e -37.06774632196785\n",
      "EX_photon470_e is limiting\n",
      "EX_photon490_e -64.95783392101998\n",
      "EX_photon490_e is limiting\n",
      "EX_photon510_e -26.474325219921937\n",
      "EX_photon510_e is limiting\n",
      "EX_photon530_e -22.01889503152874\n",
      "EX_photon530_e is limiting\n",
      "EX_photon550_e -77.65628593664606\n",
      "EX_photon550_e is limiting\n",
      "EX_photon570_e -15.306158321994138\n",
      "EX_photon570_e is limiting\n",
      "EX_photon590_e -19.176280750933373\n",
      "EX_photon590_e is limiting\n",
      "EX_photon610_e -34.772388100020784\n",
      "EX_photon610_e is limiting\n",
      "EX_photon630_e -16.874099876500598\n",
      "EX_photon630_e is limiting\n",
      "EX_photon650_e -9.057814194466996\n",
      "EX_photon650_e is limiting\n",
      "EX_photon670_e -20.115546835040472\n",
      "EX_photon670_e is limiting\n",
      "EX_photon690_e -9.792752388339434\n",
      "EX_photon690_e is limiting\n",
      "DM_pho_loss_e 287.53815697503154\n",
      "PSII_u is limited\n",
      "EX_pppi_e 4.724775731241345e-06\n",
      "EX_sio4h4_e -0.04191854192528303\n",
      "DM_biomass_c 0.03529214179319366\n",
      "sink_chryso_c -0.0011533691104183422\n",
      "sink_chryso_c is limiting\n",
      "sink_g3p_h -0.061678132586667864\n",
      "sink_g3p_h is limiting\n",
      "unknown metabolite 'GAM_const_c' created\n",
      "Optimizing...\n",
      "optimal\n",
      "1.5344086154479482\n",
      "EX_co2_e -1.374779822671378\n",
      "EX_no3_e -0.22175810733674453\n",
      "EX_btn_e -2.508839317405531e-07\n",
      "EX_h_e -0.3430829253605754\n",
      "EX_h2o_e -0.9934111269925836\n",
      "EX_o2_e 1.9595789596959974\n",
      "EX_pi_e 0.35045585970042115\n",
      "EX_so4_e -0.015257278985273124\n",
      "EX_mg2_e -0.0007060967404015628\n",
      "EX_thm_e -2.2020957176089978e-05\n",
      "CEF_h is limited\n",
      "EX_for_e 0.03630632791512486\n",
      "EX_photon410_e -58.44309486097331\n",
      "EX_photon410_e is limiting\n",
      "EX_photon430_e -142.032172190731\n",
      "EX_photon430_e is limiting\n",
      "EX_photon450_e -101.81234295952399\n",
      "EX_photon450_e is limiting\n",
      "EX_photon470_e -93.33545720356919\n",
      "EX_photon470_e is limiting\n",
      "EX_photon490_e -170.05791614005875\n",
      "EX_photon490_e is limiting\n",
      "EX_photon510_e -78.96822208862815\n",
      "EX_photon510_e is limiting\n",
      "EX_photon530_e -77.27333688455082\n",
      "EX_photon530_e is limiting\n",
      "EX_photon550_e -314.8887959313822\n",
      "EX_photon550_e is limiting\n",
      "EX_photon570_e -67.48908716521065\n",
      "EX_photon570_e is limiting\n",
      "EX_photon590_e -86.22747185508668\n",
      "EX_photon590_e is limiting\n",
      "EX_photon610_e -125.37673530628469\n",
      "EX_photon610_e is limiting\n",
      "EX_photon630_e -61.00119791455392\n",
      "EX_photon630_e is limiting\n",
      "EX_photon650_e -31.58712731502643\n",
      "EX_photon650_e is limiting\n",
      "EX_photon670_e -47.702110358971396\n",
      "EX_photon670_e is limiting\n",
      "EX_photon690_e -27.1596271470405\n",
      "EX_photon690_e is limiting\n",
      "DM_pho_loss_e 911.5571189010136\n",
      "PSII_u is limited\n",
      "EX_pppi_e 7.97693581257284e-06\n",
      "EX_sio4h4_e -0.03146196988799125\n",
      "DM_biomass_c 0.06393369231033118\n",
      "sink_chryso_c -0.01323951609067762\n",
      "sink_chryso_c is limiting\n",
      "sink_g3p_h -0.373083270872427\n",
      "sink_g3p_h is limiting\n"
     ]
    }
   ],
   "source": [
    "fluxes = []\n",
    "\n",
    "for i in range(0,3):\n",
    "    \n",
    "    with models[i]:\n",
    "        \n",
    "        models[i].add_reactions([sink_chryso_c,sink_g3p_h,gam_rxn])\n",
    "        models[i].reactions.sink_chryso_c.build_reaction_from_string('chryso_c <-- ')\n",
    "        models[i].reactions.sink_g3p_h.build_reaction_from_string('g3p_h <-- ')\n",
    "        models[i].reactions.GAM.build_reaction_from_string('GAM_const_c + {0} atp_c + {0} h2o_c --> {0} adp_c + {0} h_c + {0} pi_c'.format(str(GAM_val[i])))\n",
    "        gam_met = models[i].metabolites.GAM_const_c\n",
    "        bof = models[i].reactions.query('bof')[0]\n",
    "        bof.add_metabolites({gam_met:1})\n",
    "        \n",
    "        models[i].reactions.sink_chryso_c.lower_bound = -1*avail_C[i]*divide_h[i]*1e-3*(1./6.)*(mgchla[i]/gDW[i])\n",
    "        models[i].reactions.sink_g3p_h.lower_bound = -1*avail_C[i]*(1-divide_h[i])*1e-3*(1./3.)*(mgchla[i]/gDW[i])\n",
    "\n",
    "        # f/2\n",
    "        no3_mM = 0.250\n",
    "        pi_mM = 0.050\n",
    "        so4_mM = 28.8\n",
    "        sio4h4_mM = 0.106\n",
    "        cncbl3_mM = 3.69e-7\n",
    "        co2_mM = 0.0107215\n",
    "        hco3_mM = 1.72733\n",
    "        \n",
    "        models[i].reactions.EX_no3_e.lower_bound = -(no3_mM*mu_h[i])/(cells_L[i]*gDW[i])\n",
    "        models[i].reactions.EX_pi_e.lower_bound = -(pi_mM*mu_h[i])/(cells_L[i]*gDW[i])\n",
    "        models[i].reactions.EX_so4_e.lower_bound = -(so4_mM*mu_h[i])/(cells_L[i]*gDW[i])\n",
    "        models[i].reactions.EX_sio4h4_e.lower_bound = -(sio4h4_mM*mu_h[i])/(cells_L[i]*gDW[i])\n",
    "        models[i].reactions.EX_cncbl3_e.lower_bound = -(cncbl3_mM*mu_h[i])/(cells_L[i]*gDW[i])\n",
    "        models[i].reactions.EX_co2_e.lower_bound = -((0.21033224*co2_mM)/(0.000688433+co2_mM))*(mgchla[i]/gDW[i])\n",
    "        models[i].reactions.EX_hco3_e.lower_bound = -((0.226824637*hco3_mM)/(0.286767672+hco3_mM))*(mgchla[i]/gDW[i])\n",
    "\n",
    "\n",
    "        pfa = calc_photon_abs(I[i],mgchla[i],gDW[i],absorb[i],cool_white)\n",
    "\n",
    "        for k in pfa.keys():\n",
    "            models[i].reactions.get_by_id(k).lower_bound = pfa[k]*-1.\n",
    "            models[i].reactions.get_by_id(k).upper_bound = pfa[k]*-0.9999\n",
    "\n",
    "        models[i].reactions.EX_o2_e.lower_bound = -10000.\n",
    "        models[i].reactions.EX_o2_e.upper_bound = 10000.\n",
    "\n",
    "        models[i].reactions.PSII_u.reaction = '2.0 h2o_u + {1} h_h + 4.0 p680_exc_u + {2} pq_u + {0} ps2d1_u --> 4.0 h_u + o2_u + 4.0 p680_u + {2} pqh2_u + {0} ps2d1_exc_u'.format(str(D1[i]),str((2 - D1[i])*2),str(2 - D1[i]))\n",
    "        models[i].reactions.PSII_u.lower_bound = lwrGross[i]*(mgchla[i]/gDW[i])\n",
    "        models[i].reactions.PSII_u.upper_bound = uprGross[i]*(mgchla[i]/gDW[i])\n",
    "        \n",
    "        SEC = models[i].problem.Constraint(\n",
    "            models[i].metabolites.glyclt_e.elements['C']*models[i].reactions.EX_glyclt_e.flux_expression + models[i].metabolites.fol_e.elements['C']*models[i].reactions.EX_fol_e.flux_expression + models[i].metabolites.co_e.elements['C']*models[i].reactions.EX_co_e.flux_expression + models[i].metabolites.cyan_e.elements['C']*models[i].reactions.EX_cyan_e.flux_expression + models[i].metabolites.cynt_e.elements['C']*models[i].reactions.EX_cynt_e.flux_expression + models[i].metabolites.tcynt_e.elements['C']*models[i].reactions.EX_tcynt_e.flux_expression + models[i].metabolites.lac__D_e.elements['C']*models[i].reactions.EX_lac__D_e.flux_expression + models[i].metabolites.etoh_e.elements['C']*models[i].reactions.EX_etoh_e.flux_expression + models[i].metabolites.urea_e.elements['C']*models[i].reactions.EX_urea_e.flux_expression + models[i].metabolites.dmsp_e.elements['C']*models[i].reactions.EX_dmsp_e.flux_expression + models[i].metabolites.glyb_e.elements['C']*models[i].reactions.EX_glyb_e.flux_expression + models[i].metabolites.Nactaur_e.elements['C']*models[i].reactions.EX_Nactaur_e.flux_expression + models[i].metabolites.frmd_e.elements['C']*models[i].reactions.EX_frmd_e.flux_expression + models[i].metabolites.urate_e.elements['C']*models[i].reactions.EX_urate_e.flux_expression + models[i].metabolites.ac_e.elements['C']*models[i].reactions.EX_ac_e.flux_expression + models[i].metabolites.chol_e.elements['C']*models[i].reactions.EX_chol_e.flux_expression + models[i].metabolites.ura_e.elements['C']*models[i].reactions.EX_ura_e.flux_expression + models[i].metabolites.xan_e.elements['C']*models[i].reactions.EX_xan_e.flux_expression + models[i].metabolites.uacgam_e.elements['C']*models[i].reactions.EX_uacgam_e.flux_expression + models[i].metabolites.glu__L_e.elements['C']*models[i].reactions.EX_glu__L_e.flux_expression + models[i].metabolites.asp__L_e.elements['C']*models[i].reactions.EX_asp__L_e.flux_expression + models[i].metabolites.dhps_e.elements['C']*models[i].reactions.EX_dhps_e.flux_expression + models[i].metabolites.for_e.elements['C']*models[i].reactions.EX_for_e.flux_expression + models[i].metabolites.ile__L_e.elements['C']*models[i].reactions.EX_ile__L_e.flux_expression + models[i].metabolites.leu__L_e.elements['C']*models[i].reactions.EX_leu__L_e.flux_expression + models[i].metabolites.val__L_e.elements['C']*models[i].reactions.EX_val__L_e.flux_expression + models[i].metabolites.asn__L_e.elements['C']*models[i].reactions.EX_asn__L_e.flux_expression + models[i].metabolites.ala__L_e.elements['C']*models[i].reactions.EX_ala__L_e.flux_expression + models[i].metabolites.gln__L_e.elements['C']*models[i].reactions.EX_gln__L_e.flux_expression + models[i].metabolites.his__L_e.elements['C']*models[i].reactions.EX_his__L_e.flux_expression + models[i].metabolites.ser__L_e.elements['C']*models[i].reactions.EX_ser__L_e.flux_expression + models[i].metabolites.thr__L_e.elements['C']*models[i].reactions.EX_thr__L_e.flux_expression + models[i].metabolites.gly_e.elements['C']*models[i].reactions.EX_gly_e.flux_expression + models[i].metabolites.pro__L_e.elements['C']*models[i].reactions.EX_pro__L_e.flux_expression + models[i].metabolites.atp_e.elements['C']*models[i].reactions.EX_atp_e.flux_expression + models[i].metabolites.amp_e.elements['C']*models[i].reactions.EX_amp_e.flux_expression,\n",
    "            lb=DOC[i]*(mgchla[i]/gDW[i]),\n",
    "            ub=DOC[i]*(mgchla[i]/gDW[i]))\n",
    "        \n",
    "        ldr = models[i].problem.Constraint(\n",
    "            models[i].reactions.RUBISO_h.flux_expression + models[i].reactions.MEHLER_h.flux_expression + models[i].reactions.PTOX_h.flux_expression + models[i].reactions.GOX_x.flux_expression + models[i].reactions.GOX_m.flux_expression + models[i].reactions.AOX_m.flux_expression + models[i].reactions.CYOO_m.flux_expression,\n",
    "            lb=LDR[i]*1e-3*(mgchla[i]/gDW[i]),\n",
    "            ub=10000.)\n",
    "        \n",
    "        mmr = models[i].problem.Constraint(\n",
    "            models[i].reactions.PDH_E1a_m.flux_expression + models[i].reactions.IDH_m.flux_expression + models[i].reactions.ICDH_m.flux_expression + models[i].reactions.OGDH_E1_m.flux_expression,\n",
    "            lb = lwrMMR[i]*1e-3*(mgchla[i]/gDW[i]),\n",
    "            ub = uprMMR[i]*1e-3*(mgchla[i]/gDW[i]))\n",
    "                \n",
    "        PR = models[i].problem.Constraint(\n",
    "            models[i].reactions.RUBISO_h.flux_expression - 0.025*models[i].reactions.RUBISC_h.flux_expression,\n",
    "            lb=0.,\n",
    "            ub=0.)\n",
    "        \n",
    "        EC = models[i].problem.Constraint(\n",
    "            models[i].reactions.NADHOR_m.flux_expression - 0.0015*models[i].reactions.PSI_u.flux_expression,\n",
    "            lb=0.,\n",
    "            ub=0.)\n",
    "        \n",
    "        models[i].add_cons_vars([SEC,ldr,mmr]) # Fig 3a\n",
    "#         models[i].add_cons_vars([SEC,ldr,mmr,PR]) # Fig 3b\n",
    "#         models[i].add_cons_vars([SEC,ldr,mmr,EC]) # Fig 3c\n",
    "\n",
    "        models[i].reactions.CEF_h.upper_bound = 0.380324527\n",
    "        models[i].reactions.ATPM_c.upper_bound = NGAM[i]\n",
    "    \n",
    "        models[i].reactions.DM_biomass_c.upper_bound = 10000.\n",
    "        models[i].reactions.DM_eps_c.upper_bound = 0.\n",
    "        \n",
    "        models[i].objective = 'DM_biomass_c'\n",
    "    \n",
    "        \n",
    "        print 'Optimizing...'\n",
    "        soln = cobra.flux_analysis.parsimonious.pfba(models[i],fraction_of_optimum=1.)\n",
    "        print soln.status\n",
    "        print soln.fluxes['DM_biomass_c']*24\n",
    "        \n",
    "\n",
    "        for r in soln.fluxes.index:\n",
    "            if r.startswith('EX_') and abs(soln.fluxes.loc[r]) >= ERRORTOL:\n",
    "                print r, soln.fluxes.loc[r]\n",
    "                if soln.fluxes.loc[r] == models[i].reactions.get_by_id(r).lower_bound:\n",
    "                    print r, 'is limiting'\n",
    "            elif r.startswith('sink_') and abs(soln.fluxes.loc[r]) >= ERRORTOL:\n",
    "                print r, soln.fluxes.loc[r]\n",
    "                if soln.fluxes.loc[r] == models[i].reactions.get_by_id(r).lower_bound:\n",
    "                    print r, 'is limiting'\n",
    "            elif r.startswith('DM_') and abs(soln.fluxes.loc[r]) >= ERRORTOL:\n",
    "                print r, soln.fluxes.loc[r]\n",
    "            elif soln.fluxes.loc[r] == models[i].reactions.get_by_id(r).upper_bound and soln.fluxes.loc[r] != 0:\n",
    "                print r, 'is limited'\n",
    "            elif soln.fluxes.loc[r] == models[i].reactions.get_by_id(r).lower_bound and soln.fluxes.loc[r] < 0:\n",
    "                print r, 'is limiting'\n",
    "        \n",
    "        fluxes.append(soln.fluxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([fluxes[0],fluxes[1],fluxes[2]], axis=1, sort=True)\n",
    "df.columns = ['0.2','0.85','1.54']\n",
    "df.to_csv('../Thaps_draft/update_model/Halsey_SECresp.csv') # Fig 3a\n",
    "# df.to_csv('../Thaps_draft/update_model/Halsey_SECrespPR.csv') # Fig 3b\n",
    "# df.to_csv('../Thaps_draft/update_model/Halsey_SECrespEC.csv') # Fig 3c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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