{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Tutorial: Create iML1515 GECKO with auto-fitted turnover numbers\n",
    "\n",
    "We developed a series of GECKO models of iML1515, with model parameters being progressively refined from one model to the next. Our initial model was based on default values, requiring minimal bespoke configuration. For our second model, we adjusted transporter-related parameters, taking into account organism- and model-specific information. For our third model, we replaced default turnover numbers with AI-predicted turnover numbers for metabolic reactions. In the fourth model, flux distributions were corrected by adjusting predicted turnover numbers of a few reactions, considering organism-specific information and experimental data of quantitative proteomics. \n",
    "\n",
    "In this tutorial, we extend the concept introduced in the previous tutorial to automatically fit turnover numbers to proteomics data. This fitting process will not be perfect, but the model will improve significantly with respect to predicting protein levels. One limitation of the automatic fitting process is that it can only be applied to reactions and pathways carrying flux as per our predictions. This is also the reason why we corrected the flux balance between glycolysis and PPP. \n",
    "\n",
    "Quantitative proteomics is a valuable source of data for determining more realistic apparent turnover numbers, as required for enzyme constraint models, by adjusting default and predicted turnover numbers. This is due to the almost complete coverage and use of standardized experimental methods in quantitative proteomics. To circumvent the pitfalls of overfitting, we intend to impose constraints on the extent of adjustment.\n",
    "\n",
    "It should be noted, that alternative fitting procedures exist in GECKO 2.0 (Domenzain et al., 2022) for single conditions and using the PRESTO formalism (Wendering et al., 2023) across multiple growth conditions.\n",
    "\n",
    "Peter Schubert, Heinrich-Heine University Duesseldorf, Institute for Computational Cell Biology (Prof. Dr. M. Lercher), January, 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 minimal media conditions created for simulation\n",
      "2347 records of proteomics loaded from data/Ecoli_Schmidt_proteomics.xlsx\n",
      "2232 records with confidence level above 43.0\n"
     ]
    }
   ],
   "source": [
    "# Required imports and model names\n",
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import cobra\n",
    "\n",
    "from f2xba import XbaModel, EcModel\n",
    "from f2xba import EcmOptimization, EcmResults\n",
    "from f2xba import GeckoFitKcats\n",
    "from f2xba.utils.mapping_utils import load_parameter_file, write_parameter_file\n",
    "\n",
    "fba_model = 'iML1515'\n",
    "baseline_model = 'iML1515_manual_adjust_GECKO'\n",
    "target_model = 'iML1515_predicted_fit_GECKO'\n",
    "reference_cond = 'Glucose'\n",
    "        \n",
    "# Create media conditions\n",
    "media_grs = {'Acetate': ['ac', 0.29], 'Glycerol': ['glyc', 0.47], 'Fructose': ['fru', 0.54], \n",
    "             'L-Malate': ['mal__L', 0.55], 'Glucose': ['glc__D', 0.66], 'Glucose 6-Phosphate': ['g6p', 0.78]}\n",
    "base_medium = ['ca2', 'cbl1', 'cl', 'co2', 'cobalt2', 'cu2', 'fe2', 'fe3', 'h2o', 'h', 'k', 'mg2', \n",
    "               'mn2', 'mobd', 'na1', 'nh4', 'ni2', 'o2', 'pi', 'sel', 'slnt', 'so4', 'tungs', 'zn2']\n",
    "conditions = {}\n",
    "exp_grs = {}\n",
    "for cond, (carbon_sid, exp_gr )in media_grs.items():\n",
    "    conditions[cond] = {f'EX_{sidx}_e': 1000.0 for sidx in base_medium}\n",
    "    conditions[cond][f'EX_{carbon_sid}_e'] = 1000.0\n",
    "    exp_grs[cond] = exp_gr\n",
    "print(f'{len(conditions)} minimal media conditions created for simulation')\n",
    "\n",
    "# Load proteomics\n",
    "fname = os.path.join('data', 'Ecoli_Schmidt_proteomics.xlsx')\n",
    "with pd.ExcelFile(fname) as xlsx:\n",
    "    df_mpmf = pd.read_excel(xlsx, sheet_name='proteomics', index_col=0)\n",
    "    print(f'{len(df_mpmf)} records of proteomics loaded from {fname}')\n",
    "min_confidence_level = 43.0\n",
    "df_mpmf = df_mpmf[df_mpmf['confidence'] > min_confidence_level]\n",
    "print(f'{len(df_mpmf)} records with confidence level above {min_confidence_level}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Fit turnover values to proteomics (cobrapy)\n",
    "\n",
    "The workflow to automatically fit turnover numbers to proteomics begins with determining the flux distribution predicted for the baseline GECKO model on the reference condition, which is glucose minimal medium.\n",
    "\n",
    "Subsequently, the measured protein concentrations are extracted from the proteomics data. It is imperative to note that the four major outer membrane pores ('b0929', 'b2215', 'b1377', 'b0241') will be excluded from the automated fitting process to avoid interference with the turnover number adjustments performed in the 2nd tutorial. Additionally, the protein measurement for b2400\\gltX, which catalyzes glutamyl-tRNA charging, is excluded. In FBA and GECKO, this is used for the synthesis of biomass components protoheme (M_pheme_c) and siroheme (M_sheme_c), but not for tRNA charging.\n",
    "\n",
    "Finally, we perform the automated fit process using an instance of `GeckoFitKcats` to generate updated turnover numbers for the target model. The fitting process adjusts the coupling factors through updated turnover numbers to align predicted flux distribution to measured protein levels. In a simple example, if predicted flux through a given reaction predicts a protein concentration that is double of the measured level, the fitting process would double the respective turnover number. The fitting process adjusts turnover numbers for reactions that carry flux, and it can switch flux between iso-reactions and consider promiscuous enzymes that catalyze more than one reaction. To avoid overfitting, a maximum scaling factor can be provided. Finally, all turnover numbers will be rescaled for a given target enzyme saturation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 flux distribution in baseline model\n",
    "\n",
    "The predicted flux distribution is determined, and it is used as a reference for the fitting task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Set parameter LicenseID to value 2731209\n",
      "Academic license - for non-commercial use only - expires 2026-10-31\n",
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_manual_adjust_GECKO.xml (Thu Nov 20 18:58:37 2025)\n",
      "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\n",
      "Glucose: growth rate 0.661 h-1 (optimal)\n"
     ]
    }
   ],
   "source": [
    "# Load GECKO model and optimize for reference condition\n",
    "fname = os.path.join('SBML_models', f'{baseline_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "eo = EcmOptimization(fname, ecm)\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n",
    "\n",
    "# Calculate GECKO solution for given condition\n",
    "ecm.medium = conditions[reference_cond]\n",
    "solution = ecm.optimize()\n",
    "gr = solution.objective_value\n",
    "print(f'{reference_cond}: growth rate {gr:.3f} h-1 ({solution.status})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 measured protein concentrations\n",
    "\n",
    "The protein concentrations, which have been measured, must be collected so that the turnover numbers can be adjusted. It is important to note that certain proteins have been selected and are therefore excluded from this process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 996 proteins measured with total of 497.8 mg/gP\n",
      "   3 proteins excluded with total of  16.6 mg/gP\n"
     ]
    }
   ],
   "source": [
    "# Select proteins for the automatic fit\n",
    "excluded_loci = {'b0929', 'b2215', 'b1377', 'b0241', 'b2400'}\n",
    "measured_mpmfs = {} \n",
    "excluded_mpmfs = {} \n",
    "for locus, mpmf in df_mpmf[reference_cond].items():\n",
    "    if locus in eo.locus2uid:\n",
    "        if locus not in excluded_loci:\n",
    "            measured_mpmfs[locus] = mpmf        \n",
    "        else:\n",
    "            excluded_mpmfs[locus] = mpmf\n",
    "\n",
    "print(f'{len(measured_mpmfs):4d} proteins measured with total of {sum(measured_mpmfs.values()):5.1f} mg/gP')\n",
    "print(f'{len(excluded_mpmfs):4d} proteins excluded with total of {sum(excluded_mpmfs.values()):5.1f} mg/gP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 fit turnover numbers to proteomics\n",
    "\n",
    "The automated fitting task is performed by the class `GeckoGitKcats`. During instantiation, a reference to the instance of `EcmOptimization` and the turnover numbers in the baseline model are provided. Subsequently, `gfk.process_data()` is called with the flux distribution for the reference condition and the expected protein concentrations. Finally, the function `gfk.update_kcats()` is invoked to adjust the turnover numbers, taking into account a constraint on the maximum scaling (`max_scale_factor`)  and the anticipated target enzyme saturation (`target_sat`). A maximum scaling factor of 100.0 would allow turnover numbers to be adjusted to \\[1/100.0 … 100.0\\] * original value. Reactions for which the adjustment of turnover numbers was not possible due to the maximum scaling constraint are returned in the dictionary `exceeding_max_scale`. This information can be utilized to enhance the model further by adjusting pertinent model parameters.\n",
    "\n",
    "In accordance with the GECKO optimization problem, a distinction is made between active reactions, those carrying flux, and inactive reactions. Consequently, the total modeled proteome is partitioned between the active proteome, the set of proteins required to catalyze active reactions, and the inactive proteome. The inactive proteome has a total predicted concentration of zero, while the total available protein is divided between the proteins in the active proteome. Subsequently, the value of the total available protein can be adjusted to be equal to the sum of the measured proteins of the active proteome (`tot_fitted_mpmf`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5357 enzyme catalyzed iso-reactions (2221 reactions, 1494 proteins)\n",
      " 394 active catalyzed reactions with total protein of 537.9 mg/gP (based on GECKO simulation)\n",
      " 417 proteins potentially involved in active reactions\n",
      " 352 active catalyzed reactions using total protein of 356.7 mg/gP (based on proteomics)\n",
      "  42 active catalyzed reactions have no measured proteins provided\n",
      "5357 original kcat records loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx\n",
      " 584 kcat records fitted. Scale factor range [0.0101105, 110.0]\n",
      "  24 (net) reactions would exceed the maximum scaling factor of 100.0 and will not be scaled\n",
      "fitted kcats exported to data/iML1515_predicted_fit_GECKO_kcats.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Automatically fit kcat values to proteomics\n",
    "target_enz_sat = 0.5\n",
    "orig_kcats_fname = os.path.join('data', f'{baseline_model}_kcats.xlsx')\n",
    "fitted_kcats_fname = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "\n",
    "gfk = GeckoFitKcats(eo, orig_kcats_fname)\n",
    "\n",
    "tot_fitted_mpmf = gfk.process_data(solution.fluxes, measured_mpmfs)\n",
    "exceeding_max_scale = gfk.update_kcats(fitted_kcats_fname, target_sat=target_enz_sat, max_scale_factor=100.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Create GECKO model with fitted parameters\n",
    "\n",
    "The XBA and ECM configuration files are updated. Adjustments are made to the total available protein in the ECM parameter file. Thereafter, the target GECKO model is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Nov 20 18:58:14 2025)\n",
      "3 table(s) with parameters written to  data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:58:14 2025)\n",
      "1 table(s) with parameters written to  data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Create updated XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Create updated ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'),['general'])\n",
    "fitted_pmf = (tot_fitted_mpmf + sum(excluded_mpmfs.values()))/1000.0\n",
    "ecm_params['general'].at['pm2totpm_val_or_paxdb', 'value'] = fitted_pmf\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = target_enz_sat\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading: SBML_models/iML1515.xml (last modified: Thu Dec  5 10:03:46 2024)\n",
      "3 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx (Thu Nov 20 19:00:45 2025)\n",
      "  22 gene product(s) removed from reactions (1494 gene products remaining)\n",
      "   2 attributes on reaction instances updated\n",
      "extracting UniProt protein data from data/uniprot_organism_83333.tsv\n",
      "1494 proteins created\n",
      "1203 enzymes added with default stoichiometry\n",
      "1 table(s) with parameters loaded from data/iML1515_enzyme_composition_updated.xlsx (Thu Nov 20 18:52:47 2025)\n",
      "1203 enzyme compositions updated from data/iML1515_enzyme_composition_updated.xlsx\n",
      "2221 reactions catalyzed by 1203 enzymes\n",
      "default kcat values configured for ['metabolic', 'transporter'] reactions\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_kcats.xlsx (Thu Nov 20 19:00:42 2025)\n",
      "5357 kcat values updated from data/iML1515_predicted_fit_GECKO_kcats.xlsx\n",
      "   0 enzymes removed due to missing kcat values\n",
      "1877 constraints (+0); 2712 variables (+0); 1494 genes (-22); 5 parameters (+0)\n",
      ">>> BASELINE XBA model configured!\n",
      "\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx (Thu Nov 20 19:00:45 2025)\n",
      "modeled protein fraction of total protein mass 0.3733 g/g\n",
      "1494 protein constraints to add\n",
      "1494 constraint ids added to the model (3371 total constraints)\n",
      "protein constraint: 212.80 mg/gDW - modeled protein\n",
      "   1 constraint ids added to the model (3372 total constraints)\n",
      "1495 protein variables to add\n",
      "1495 variable ids added to the model (7343 total variables)\n",
      "3372 constraints (+1495); 7343 variables (+4631); 1494 genes (-22); 11 parameters (+6)\n",
      "model exported to SBML format: SBML_models/iML1515_predicted_fit_GECKO.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new GECKO model\n",
    "xba_model = XbaModel(os.path.join('SBML_models', f'{fba_model}.xml'))\n",
    "xba_model.configure(os.path.join('data', f'{target_model}_xba_parameters.xlsx'))\n",
    "ec_model = EcModel(xba_model)\n",
    "ec_model.configure(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'))\n",
    "ec_model.export(os.path.join('SBML_models', f'{target_model}.xml'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## Step 4: Load and optimize GECKO model (cobrapy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_fit_GECKO.xml (Thu Nov 20 19:01:10 2025)\n",
      "total modeled protein: 212.80 mg/gDW, average saturation level: 0.5\n",
      "1494 genes: (492) transporter, (1002) metabolic\n"
     ]
    }
   ],
   "source": [
    "# Load model using COBRApy\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "ecm = cobra.io.read_sbml_model(fname)\n",
    "total_protein = ecm.reactions.get_by_id('V_PC_total').upper_bound\n",
    "\n",
    "eo = EcmOptimization(fname, ecm)\n",
    "sigma = eo.avg_enz_saturation\n",
    "all_genes = set(eo.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = eo.get_tx_metab_genes()\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, average saturation level: {sigma}')\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.26e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.71e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.4562, p = 2.21e-42 ( 308 proteins log scale)\n",
      "Glycerol                 : r² = 0.4894, p = 5.06e-47 ( 311 proteins log scale)\n",
      "Fructose                 : r² = 0.5298, p = 4.40e-51 ( 302 proteins log scale)\n",
      "Glucose                  : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 373.3 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 331.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 280.5 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  51.3 mg/gP predicted\n",
      "      495 proteins not measured vs.  41.6 mg/gP predicted\n",
      "           495 actual  proteins      41.6 mg/gP predicted\n",
      "total           : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.9105, p = 0.00e+00 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.6008, p = 9.35e-49 ( 237 proteins lin scale)\n",
      "total           : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      " metabolic      : r² = 0.5762, p = 3.87e-51 ( 266 proteins log scale)\n",
      " transport      : r² = 0.5334, p = 5.65e-09 (  47 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using cobrapy and analyze results\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    with ecm as model:\n",
    "                \n",
    "        model.medium = medium \n",
    "        solution = model.optimize()\n",
    "        if solution.status == 'optimal':\n",
    "            gr = solution.objective_value\n",
    "            pred_results[cond] = solution\n",
    "            print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, '\n",
    "                  f'diff: {gr - exp_grs[cond]:6.3f}')\n",
    "        else:    \n",
    "            print(f'{cond} ended with status {solution.status}')\n",
    "            \n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight=reference_cond)\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels(reference_cond)\n",
    "er.plot_proteins(reference_cond)  \n",
    "er.save_to_escher(df_net_fluxes[reference_cond], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G6PDH2r        :    3.516 mmol/gDWh [b1852]\n",
      "EDA            :    0.000 mmol/gDWh [b1850]\n",
      "PGI            :    3.749 mmol/gDWh [b4025]\n",
      "PFK            :    5.560 mmol/gDWh [b1723 or b3916]\n",
      "FBA            :    5.560 mmol/gDWh [b2097 or b2925]\n",
      "TPI            :    5.473 mmol/gDWh [b3919]\n",
      "GAPD           :   11.884 mmol/gDWh [b1779]\n",
      "PGK            :  -11.884 mmol/gDWh [b2926]\n",
      "PGM            :  -10.831 mmol/gDWh [b0755 or b3612]\n",
      "ENO            :   10.831 mmol/gDWh [b2779]\n",
      "PYK            :    3.057 mmol/gDWh [b1676 or b1854]\n"
     ]
    }
   ],
   "source": [
    "# analyze reactions selected fluxes\n",
    "rids = ['G6PDH2r', 'EDA', 'PGI', 'PFK', 'FBA', 'TPI', 'GAPD', 'PGK', 'PGM', 'ENO', 'PYK']\n",
    "for rid in rids:\n",
    "    print(f'{rid:15s}: {df_net_fluxes.at[rid, reference_cond]:8.3f} mmol/gDWh [{df_net_fluxes.at[rid, \"gpr\"]}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 – GECKO model limitations\n",
    "\n",
    "The optimization results of the final GECKO model have been obtained, thus allowing for the discussion of some of the limitations of GECKO modeling. The cases in which GECKO does not predict protein usage for proteins that have been measured at high concentrations will be discussed, as well as the suggestion that proteomics measurements have a slight bias towards cytosolic proteins and that small integral proteins are not measured correctly, which impacts correlation of protein concentrations. While the AI-predicted turnover numbers should approximate the in vitro values, we will examine situations where there are substantial discrepancies. Additionally, we will analyze the impact of automated fitting to the AI-predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 inability to predict protein usage\n",
    "\n",
    "The GECKO model's inability to predict proteins that have been measured at high concentrations prompts the following inquiry:\n",
    "\n",
    "b0197/metQ and b3460/livJ are proteins associated with transporters, which are measured at relatively high concentrations. These proteins are utilized in methionine and branched amino acid uptake systems (ABC transporters) and function as periplasmic binding proteins. Given the nutrient conditions lacking amino acids, these transporters do not carry flux, and the related proteins are part of the inactive proteome. However, the cells utilized in the proteomics experiment expressed these proteins at high quantities, suggesting either constitutive expression due to cells evolving under rich conditions or in anticipation of such conditions.\n",
    "\n",
    "The b4015/aceA and b0605/ahpC genes encode metabolic proteins, which are measured at high concentrations; however, these proteins are not utilized by the model. b4015/aceA catalyzes the isocitrate lyase reaction (ICL) in the glyoxylate cycle. In the previous tutorial, we manually adjusted turnover values in the TCA cycle to generate flux through ICL. During automated fitting, this flux was switched off again, indicating the need for another manual adjustment. b0605/ahpC is a component of alkyl hydroperoxide reductase, which protects against oxidative stress. In the GECKO model, the protein is required to catalyze the NADH peroxidase reaction, which is inactive in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0197 pred   0.0000 vs.   5.9980 (rank 38); metQ: D-methionine-binding lipoprotein metQ\n",
      "b3460 pred   0.0000 vs.   6.1215 (rank 24); livJ: Leu/Ile/Val-binding protein\n"
     ]
    }
   ],
   "source": [
    "# transport related proteins measured but not predicted\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and gene in tx_genes:\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if exp > 5 and pred == 0.0:\n",
    "            description = df_proteins.at[gene, 'description']\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            rank = df_mpmf.at[gene, 'rank']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f} (rank {rank:2d}); {gname}: {description}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0605 pred   0.0000 vs.   8.2787 (rank 15); ahpC: Alkyl hydroperoxide reductase subunit C\n"
     ]
    }
   ],
   "source": [
    "# metabolic proteins measured but not predicted\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and gene in metab_genes:\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if (exp < 1 and pred > 5) or (exp > 5 and pred < 1):\n",
    "            description = df_proteins.at[gene, 'description']\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            rank = df_mpmf.at[gene, 'rank']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f} (rank {rank:2d}); {gname}: {description}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 biased proteomics\n",
    "\n",
    "A subsequent focus will be placed on protein concentrations related to transporters that have higher predicted than measured levels (red dots above the diagonal in the log10 correlation plot). Four proteins have predictions that are 20 times higher than measurements, and it is expected that automatic fitting would balance this out. The four proteins of interest, b3737/atpE, b2278/nuoL, b2276/nuoN, and b0430/cyoC, are components of larger enzymatic complexes that exhibit a fixed protein composition in the GECKO model. Consequently, they cannot be adjusted independently from the other proteins within the complex. It is hypothesized that the proteins may not have been accurately measured in the proteomics analysis due to their shared characteristic of being integral membrane proteins. \n",
    "\n",
    "The cytochrome bo3 ubiquinol:oxygen oxidoreductase complex, composed of four proteins expressed from the operon cyoABCD, serves as an example. The GECKO model predicts a protein requirement of 11.1 nmol/gP for the transporter and each of the four transporter components. However, the proteomics experiment yielded protein copy numbers of 0.1, 0.9, 6.5, and 17.9 nmol/gP. The low quantities of b0430/cycC and b0429/cyoD, which are small membrane proteins, may have led to their inaccurate quantification during the proteomics measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b3737 pred   2.5617 vs.   0.0161; atpE: ATP synthase subunit c, 159.35\n",
      "b2278 pred   0.3739 vs.   0.0008; nuoL: NADH-quinone oxidoreductase subunit L, 442.83\n",
      "b2276 pred   0.2929 vs.   0.0071; nuoN: NADH-quinone oxidoreductase subunit N, 41.17\n",
      "b0430 pred   0.1640 vs.   0.0028; cyoC: Cytochrome o ubiquinol oxidase subunit 3, 59.29\n"
     ]
    }
   ],
   "source": [
    "# transport proteins with overpredicted concentration\n",
    "for gene, pred in df_proteins[reference_cond].to_dict().items():\n",
    "    if gene in df_mpmf.index and (gene in tx_genes):\n",
    "        exp = df_mpmf.at[gene, reference_cond]\n",
    "        if exp > 0 and pred/exp > 20:\n",
    "            description = df_proteins.at[gene, 'description'][:50]\n",
    "            gname = df_proteins.at[gene, 'gene_name']\n",
    "            print(f'{gene} pred {pred:8.4f} vs. {exp:8.4f}; {gname}: {description}, {pred/exp:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0432/cyoA (34.9 kDa): pred 0.253 mg/gP (7.3 nmol/gP); exp 0.623 mg/gP ( 17.9 nmol/gP)\n",
      "b0431/cyoB (74.3 kDa): pred 0.539 mg/gP (7.3 nmol/gP); exp 0.485 mg/gP (  6.5 nmol/gP)\n",
      "b0430/cyoC (22.6 kDa): pred 0.164 mg/gP (7.3 nmol/gP); exp 0.003 mg/gP (  0.1 nmol/gP)\n",
      "b0429/cyoD (12.0 kDa): pred 0.087 mg/gP (7.3 nmol/gP); exp 0.011 mg/gP (  0.9 nmol/gP)\n"
     ]
    }
   ],
   "source": [
    "# check cytochrome bo3 ubiquinol:oxygen oxidoreductase complex\n",
    "for gene in ['b0432', 'b0431', 'b0430', 'b0429']:\n",
    "    pred = df_proteins.at[gene, reference_cond]\n",
    "    exp = df_mpmf.at[gene, reference_cond]\n",
    "    mw_da = df_mpmf.at[gene, 'mw_Da']\n",
    "    name = df_mpmf.at[gene, 'gene_name']\n",
    "    print(f'{gene}/{name:4s} ({mw_da/1000.0:.1f} kDa): '\n",
    "          f'pred {pred:.3f} mg/gP ({pred/mw_da*1e6:.1f} nmol/gP); '\n",
    "          f'exp {exp:.3f} mg/gP ({exp/mw_da*1e6:5.1f} nmol/gP)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 large difference predicted to fitted turnover numbers\n",
    "\n",
    "Assuming that the AI-predicted turnover numbers are close to the apparent in vitro turnover numbers, which are required for the GECKO model formulation, it can be expected that all of the turnover numbers should have been fitted, given our maximum scaling factor of 100. However, 24 turnover numbers exceeded that scaling factor and consequently were not adjusted. \n",
    "\n",
    "An analysis of the corresponding reactions indicates that, in most cases (21 out of 24), the fitted turnover numbers would be considerably lower than the AI-predicted values, resulting in proteins being predicted at a significantly lower level than measured. This discrepancy can be attributed to one or more of the following reasons: Firstly, the AI-predicted turnover number is, in fact, too high, which can be verified by comparing with experimental values derived from databases and/or literature. Secondly, the model may predict insufficient flux through the reaction, potentially due to the model's preference for an alternative pathway. Thirdly, the cell may require the protein for tasks not included in the model, such as repair or housekeeping. Fourthly, the gene may be constitutively expressed, thus not directly linked to reaction fluxes, as observed in the expression of certain periplasmic binding proteins. Finally, a portion of the cellular protein may be inactive due to regulatory mechanisms.\n",
    "\n",
    "Additionally, there are three cases where the AI-predicted turnover numbers are lower than the fitting to proteomics data suggests, and again, different causes can be identified. Firstly, proteomics may be biased to specific types of proteins, integral membrane proteins may be more difficult to isolate and measure correctly compared to soluble proteins. Secondly, the model predicts high flux through a reaction, while the cell uses another pathway. Thirdly, the AI-predicted turnover values are too high.\n",
    "\n",
    "\n",
    "An illustration of proteomics failing to adequately measure a specific protein is the gene product of b0175/cdsA, an integral membrane protein. The AI-predicted turnover number for b0175/cdsA was 1.65 $s^{-1}$, which is well within the measured range of turnover numbers for CdsA (see BRENDA). The protein mass fraction of CdsA is comparatively low (0.009 mg/gP), and its copy number is significantly lower compared to neighboring proteins in the pathway, indicating that not all copies of b0175/cdsA were measured.\n",
    "\n",
    "An illustration of the potential overestimation of the AI-predicted turnover numbers is evident in the synthesis of iron-sulfur clusters utilizing b2528/iscA or b2530/iscS. While the AI-predicted values are within the range of 15 $s^{-1}$, the proteomics data fitting suggests turnover numbers are three orders of magnitude lower. The turnover number for b2530/iscS has been measured at 0.202 $s^{-1}$  (Dunkle et al., 2019), which is more aligned with the proteomics-fitted value. The protein mass fraction for IscS, as measured, is relatively high (4.208 mg/gP). IscS has been shown to be required for Fe-S cluster repair and DNA housekeeping, which are not reflected in the GECKO model. \n",
    "\n",
    "Increasing the maximum scaling factor beyond 100.0 could improve the protein correlation. Setting the `max_scale_factor` to ‘None’ would result in unlimited scaling. However, it is important to note that limiting the maximum scaling provides a greater degree of control over the parametrization of the model and the potential for investigating and addressing inconsistencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 kcat values exceed max scaling, concerning 18 different gene products\n",
      "(3 kcat values would be much higher, 21 much lower)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .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>orig_kcat</th>\n",
       "      <th>fitted_kcat</th>\n",
       "      <th>factor</th>\n",
       "      <th>reaction_string</th>\n",
       "      <th>genes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_rid</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>ORPT_REV</th>\n",
       "      <td>8.66</td>\n",
       "      <td>1403.143135</td>\n",
       "      <td>162.025766</td>\n",
       "      <td>orot_c + prpp_c =&gt; orot5p_c + ppi_c</td>\n",
       "      <td>b3642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DASYN160</th>\n",
       "      <td>1.65</td>\n",
       "      <td>297.703107</td>\n",
       "      <td>180.426128</td>\n",
       "      <td>ctp_c + h_c + pa160_c =&gt; cdpdhdecg_c + ppi_c</td>\n",
       "      <td>b0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DASYN161</th>\n",
       "      <td>1.65</td>\n",
       "      <td>297.703107</td>\n",
       "      <td>180.426128</td>\n",
       "      <td>ctp_c + h_c + pa161_c =&gt; cdpdhdec9eg_c + ppi_c</td>\n",
       "      <td>b0175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          orig_kcat  fitted_kcat      factor  \\\n",
       "sub_rid                                        \n",
       "ORPT_REV       8.66  1403.143135  162.025766   \n",
       "DASYN160       1.65   297.703107  180.426128   \n",
       "DASYN161       1.65   297.703107  180.426128   \n",
       "\n",
       "                                         reaction_string  genes  \n",
       "sub_rid                                                          \n",
       "ORPT_REV             orot_c + prpp_c => orot5p_c + ppi_c  b3642  \n",
       "DASYN160    ctp_c + h_c + pa160_c => cdpdhdecg_c + ppi_c  b0175  \n",
       "DASYN161  ctp_c + h_c + pa161_c => cdpdhdec9eg_c + ppi_c  b0175  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kcats not fitted due to exceeding factor (max_scale_factor set to 100 during auto-fit)\n",
    "not_scaled = []\n",
    "for data in exceeding_max_scale.values():\n",
    "    sub_rid = data['iso_rid']\n",
    "    fitted_kcat = data['orig_kcat'] * data['factor']\n",
    "    gpr = eo.rdata[sub_rid]['gpr'] \n",
    "    react_str = eo.rdata[sub_rid]['reaction_str']\n",
    "    not_scaled.append([sub_rid, data['orig_kcat'], fitted_kcat, data['factor'], react_str, gpr])\n",
    "cols = ['sub_rid', 'orig_kcat', 'fitted_kcat', 'factor', 'reaction_string', 'genes']    \n",
    "df_not_scaled = pd.DataFrame(not_scaled, columns=cols).set_index('sub_rid')\n",
    "n_higher_kcat = len(df_not_scaled[df_not_scaled[\"factor\"] > 1.0])\n",
    "n_genes = len(df_not_scaled['genes'].unique())\n",
    "print(f'{len(df_not_scaled)} kcat values exceed max scaling, concerning {n_genes} different gene products')\n",
    "print(f'({n_higher_kcat} kcat values would be much higher, {len(df_not_scaled)- n_higher_kcat} much lower)')\n",
    "df_not_scaled[df_not_scaled[\"factor\"] > 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0175 cdsA (31.4 kDa): pred 1.583 mg/gP (50.40 nmol/gP); exp 0.009 mg/gP ( 0.30 nmol/gP)\n",
      "b2585 pssA (52.8 kDa): pred 0.099 mg/gP ( 1.87 nmol/gP); exp 0.107 mg/gP ( 2.02 nmol/gP)\n",
      "b4160 psd  (35.9 kDa): pred 0.368 mg/gP (10.26 nmol/gP); exp 0.397 mg/gP (11.06 nmol/gP)\n",
      "b0914 msbA (64.4 kDa): pred 0.062 mg/gP ( 0.96 nmol/gP); exp 0.067 mg/gP ( 1.03 nmol/gP)\n"
     ]
    }
   ],
   "source": [
    "# enzymes in the CDP-diacylglycerol biosynthesis pathway suggest issues with proteomics\n",
    "for gene in ['b0175',  'b2585', 'b4160', 'b0914']:\n",
    "    pred = df_proteins.at[gene, reference_cond]\n",
    "    row = df_mpmf.loc[gene]\n",
    "    exp = row[reference_cond]\n",
    "    mw_da = row[\"mw_Da\"]\n",
    "    print(f'{gene} {row[\"gene_name\"]:4s} ({mw_da/1000.0:.1f} kDa): '\n",
    "          f'pred {pred:.3f} mg/gP ({pred/mw_da*1e6:5.2f} nmol/gP); '\n",
    "          f'exp {exp:.3f} mg/gP ({exp/mw_da*1e6:5.2f} nmol/gP)')"
   ]
  },
  {
   "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>orig_kcat</th>\n",
       "      <th>fitted_kcat</th>\n",
       "      <th>factor</th>\n",
       "      <th>reaction_string</th>\n",
       "      <th>genes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_rid</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>ICYSDS</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>cys__L_c + iscs_c =&gt; ala__L_c + iscssh_c</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SS</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_c =&gt;...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2ST</th>\n",
       "      <td>11.34</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>4.0 h_c + iscu_2fe2s_c =&gt; 2fe2s_c + iscu_c</td>\n",
       "      <td>b2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SS2</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_2fe2...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I4FE4ST</th>\n",
       "      <td>11.34</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>4.0 h_c + iscu_4fe4s_c =&gt; 4fe4s_c + iscu_c</td>\n",
       "      <td>b2528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2FE2SR</th>\n",
       "      <td>15.24</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>2fe1s_c + iscssh_c + iscu_c =&gt; 4.0 h_c + iscs_...</td>\n",
       "      <td>b2530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          orig_kcat  fitted_kcat    factor  \\\n",
       "sub_rid                                      \n",
       "ICYSDS        15.24     0.014410  0.000946   \n",
       "I2FE2SS       15.24     0.014410  0.000946   \n",
       "I2FE2ST       11.34     0.018765  0.001655   \n",
       "I2FE2SS2      15.24     0.014410  0.000946   \n",
       "I4FE4ST       11.34     0.018765  0.001655   \n",
       "I2FE2SR       15.24     0.014410  0.000946   \n",
       "\n",
       "                                            reaction_string  genes  \n",
       "sub_rid                                                             \n",
       "ICYSDS             cys__L_c + iscs_c => ala__L_c + iscssh_c  b2530  \n",
       "I2FE2SS   fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_c =>...  b2530  \n",
       "I2FE2ST          4.0 h_c + iscu_2fe2s_c => 2fe2s_c + iscu_c  b2528  \n",
       "I2FE2SS2  fadh2_c + 2.0 fe2_c + 2.0 iscssh_c + iscu_2fe2...  b2530  \n",
       "I4FE4ST          4.0 h_c + iscu_4fe4s_c => 4fe4s_c + iscu_c  b2528  \n",
       "I2FE2SR   2fe1s_c + iscssh_c + iscu_c => 4.0 h_c + iscs_...  b2530  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# enzymes catalyzing Fe-S complexes suggest lowering the kcat value\n",
    "df_not_scaled[df_not_scaled[\"genes\"].str.contains('b25')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 predicted vs. fitted turnover numbers\n",
    "\n",
    "Less than 25% of all turnover numbers of metabolic reactions were fitted to proteomics data (542 out of 2349 kcat values). Although all turnover numbers undergo slight recalibration to attain target enzyme saturation, proteomics analysis is only applicable to reactions that facilitate traffic. The application of proteomics to these reactions has been observed to augment variance in turnover numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx (Thu Nov 20 18:58:14 2025)\n",
      "1 table(s) with parameters loaded from data/iML1515_predicted_fit_GECKO_kcats.xlsx (Thu Nov 20 19:00:42 2025)\n",
      "2349 metabolic sub-reactions catalyzed by enzymes\n",
      "     mean TurNuP kcats 32.1 s-1, fitted kcats: 34.5 s-1\n",
      " 550 kcat values fitted with mean factor 3.58\n",
      "     mean TurNuP kcats 52.4 s-1 (std: 456.8), fitted kcats: 62.5 s-1 (std: 245.0)\n"
     ]
    }
   ],
   "source": [
    "# collect original vs. auto-fitted\n",
    "import numpy as np\n",
    "\n",
    "df_orig_kcats = load_parameter_file(os.path.join('data', f'{baseline_model}_kcats.xlsx'))['kcats']\n",
    "df_fitted_kcats = load_parameter_file(os.path.join('data', f'{target_model}_kcats.xlsx'))['kcats']\n",
    "\n",
    "metab_kcats = []\n",
    "for key, row in df_orig_kcats.iterrows():\n",
    "    if row['info_type'] == 'metabolic':\n",
    "        metab_kcats.append([row['kcat_per_s'], df_fitted_kcats.at[key, 'kcat_per_s']])\n",
    "metab_kcats = np.array(metab_kcats)\n",
    "log_x = np.log10(metab_kcats[:,0])\n",
    "log_y = np.log10(metab_kcats[:,1])\n",
    "log_delta = np.log10(10.0)\n",
    "\n",
    "scale_factors = metab_kcats[:,1]/metab_kcats[:,0]\n",
    "fitted_factors = scale_factors[scale_factors != 1.0]\n",
    "fitted_kcats = metab_kcats[scale_factors != 1.0, :]\n",
    "print(f'{len(metab_kcats):4d} metabolic sub-reactions catalyzed by enzymes')\n",
    "print(f'     mean TurNuP kcats {np.mean(metab_kcats[:,0]):.1f} s-1, '\n",
    "      f'fitted kcats: { np.mean(metab_kcats[:,1]):.1f} s-1')\n",
    "print(f'{len(fitted_factors):4d} kcat values fitted with mean factor {np.mean(fitted_factors):.2f}')\n",
    "print(f'     mean TurNuP kcats {np.mean(fitted_kcats[:,0]):.1f} s-1 (std: {np.std(fitted_kcats[:,0]):.1f}), '\n",
    "      f'fitted kcats: { np.mean(fitted_kcats[:,1]):.1f} s-1 (std: {np.std(fitted_kcats[:,1]):.1f})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NGjQo8jqLFi3im2++ITo6mvT0dLKysgq0yGrRogUODg76r4OCgkhJSeHq1askJiaSnZ3Nww8/rN9vY2ND+/btOX36tEH3euzYsRJ/F4V5/fLLLzRq1IiQkBCL1ZlQFIWUlBR+//13Fi9enL/IzPHjkJYGQUHwxRdgZ5db9E0IIzVr1kz/DOX9bG1tmTFjhn61Umlt3rwZJycnsrOz0el0vPTSS6UuxCSEEGWpPI6R6enpBSYTGzZsmG/ZfadOnbh48SJz587lu+++A6Bv3776/c2bN6dDhw7Url2bdevWMXz4cKNitre3Jy0tzaj3CJGnQq7vPHz4MEuXLs2XlFqSp3PRKwpKc5yx7i+2oVKp8m3LS6B0Oh0pKSn079+fTz75pMB5fHyKLlq3du1a3nnnHf7zn/8QFBSEs7Mzc+bM4eDBgya6C8NIcTjL0Wq1zJs3j3Hjxlm0yMnNmzcZM2YMzz33XOH/wX/33dyE/Ndfwd297AMU5Z5arS7wGFZ2dnaB40pqhfPKK6/w2Wef8dFHHxWoQmzoNbp27crixYvRaDT4+vpWygJCRVUXltZPQpRPFXmMrFGjBnfv3i32GID27dvz119/Fbnfzc2NBg0acOHCBSB3hXBWVhYJCQn5Zs9v3ryJt7d3vvfGx8dTt27dEmMQojBG/xWwYMGCQrerVCrs7OyoV68ejz76qNn6CqakpPDyyy+zbNkyPvroI7Ncw1jtA9zxcbUjNjGj0OfOVYC3qx3tAyyfKLRu3ZqffvoJf3//Igc4jUaDVqvNt23fvn106tSJN954Q7/t4sWLBd57/Phx0tPT9Un0gQMHcHJyolatWtSoUQONRsO+ffuoXbs2kDsQHz582ODWbc2bN2fnzp0MGzbMoOOFaWzatInGjRszZswYiyYPOTk5LF++nClTpuT/cO7Agdxl602bwnffQbVqFotRlH8eHh7Exsbm6zJy7Ngxo8+jVquZNWsWAwYMKPBBkYeHBydPnsy37dixYwU+THV0dKRevXpGX1sIIcylIo+RrVq1IjIyssTjjh07VuykVEpKChcvXmTw4MEAtGnTBhsbG3bu3KmvBH/27Fmio6MJCgrK996TJ0/y7LPPGhyzEPcy+JnzPHPnzuX9999n/PjxhIaGEhoayvjx4wkJCWHKlCl0796dhg0bcvXqVXPES3BwMP369ctXkKEomZmZBfqxm4OVWsW0/oFAbiJ+r7yvp/UPxEpt+aW1wcHBxMfH8+KLL3L48GEuXrzItm3bGDZsmD4h9/f35+DBg1y+fJnbt2+j0+moX78+f//9N9u2bePcuXNMmTKl0CrrWVlZDB8+nMjISLZu3cq0adMYM2YMarUaR0dHRo8ezbvvvktYWBiRkZGMGDGCtLQ0g5cMTZs2je+//55p06Zx+vRpTpw4UegqAGEaOTk5fPXVV3Tr1o26detia2trkTiSk5MZOXIk69ev5/3338+fmOt0MGoUzJ2b+7WXFxSxzO5BmaPoZUm9VUvqqwoQHR1Nv379cHBwwNPTk3fffVd6ZRejS5cu3Lp1i08//ZSLFy+yaNGifB0tjNGvXz86dOjA0qVL823v1q0bf//9N99++y3nz59n2rRpBf4QFUKI8qgij5G9e/cuMCM+b948Nm7cyIULFzh58iTjx4/njz/+IDg4WH/MO++8w549e7h8+TL79+/nmWeewcrKihdffBEAV1dXhg8fzoQJE9i1axdHjhxh2LBhBAUF0bFjR/15Ll++zPXr1w3KU4QojNFTYB9//DFfffUVX3/9tX7JxoULF3j99dcZOXIkDz/8MC+88AJvvfUWP/74o0mDXbt2LREREcW23rrXrFmz8lUKN6c+TX1Y/ErrAn3OvctZn3NfX1/27dvHxIkT6dWrF5mZmdSuXZs+ffroW0q88847DBkyhMDAQNLT04mKiuL111/n6NGjPP/886hUKl588UXeeOONAoN19+7dqV+/Po8++iiZmZm8+OKL+Z4Nmj17NjqdjsGDB5OcnEzbtm3Ztm0b1Qyc6ezSpQvr16/nww8/ZPbs2bi4uPDoo4+a7Psj/vX7778TGBjIwIEDS1y6Zk6KojBz5kxeeuml/C1Wdu2CgADw94ctW3KTcjNbsWKFWc5bXG/VkvqqarVa+vXrh7e3N/v37ycmJoZXX30VGxsbPv74Y7PEm6eiLktu3LgxX375JR9//DEffvghAwcO5J133il1S9BPPvmETp065dvWu3dvpkyZwnvvvUdGRgavvfYar776KidOnDDFLQghKgAZI3OV5Rj58ssv895773H27Fn9c+ZZWVm8/fbbXL9+HQcHB5o3b86OHTvo2rWr/n3Xrl3jxRdf5M6dO3h4ePDII49w4MABPDw89MfMnTsXtVrNwIEDyczMpHfv3nz55Zf5rv/999/Tq1cv/QpRIYylUoycCqpbty4//fRTgUJgR48eZeDAgVy6dIn9+/czcOBAYmJM19v76tWrtG3blu3bt+tnzbp06ULLli2LLAiXmZlJZmam/uukpCRq1apFYmIiLvdVbc7IyCAqKoqAgIBiq9KXRKtTOBQVT1xyBp7OuUvZy8OMeVVlqp9rVZKdnc3GjRt59NFHqVatWoElZmUlKyuL6dOn07x5c1544YX7d0L9+vDiizB7ttljSU1N5auvvmL48OG4uroWOoaU1vTp09mwYUOhSwYTExPx8PBgzZo1+iVyZ86coXHjxoSHh9OxY0d+++03nnjiCW7cuKEv/rhkyRImTpzIrVu3iizWc7+kpKQi703+HZUPFfXn8KDPnBf1foDLdi8V/cbpiQadX5hGcWNIRSfjY8Xy7rvvkpSUVGC23tyysrKoX78+a9asyVf8+F7G/r4UN/7dq6J+EFSVGDpGGr2sPSYmptDlkjk5OfpWAr6+vvrWA6Zy5MgR4uLiaN26NdbW1lhbW7Nnzx4WLFiAtbV1gWekIbdSpIuLS76XuVmpVQTVrc5TLR8iqG51ScxFhfLXX3+RnJxMy5Yt8fT0tFhiDvD222/Tpk0bXnjhBbQ6hfCLdwj/4lsO/30erbUN7N0LZp4ZBvjyyy/JzMzkrbfeMts1iuqtakhf1fDwcJo1a6ZPzCF3RiIpKYlTp04Vec2yeuxHCCGEKEsffPABtWvXRqfTlel1o6Ojef/994tMzIUwhNHL2rt27crrr7/O119/TatWrYDcWfPRo0fr+w6eOHGCgIAAkwbavXv3Aktdhg0bRqNGjZg4caLZCtAJURVkZWVx8OBBfHx8sLW1tViBKp1OxxdffEHNmjVZsGABKpWKsJMxhG6KJCXuDn8uCWZRx+d4s9dLuY+LqI3+fNFgSUlJrFq1ijFjxpi1ZVxeb9WGDRsSExNDaGgonTt35uTJkwb1VY2Njc2XmOftz9tXlLJ87EcIIYQoK25ubrz//vtlft169epJgU/xwIz+y3b58uW4u7vTpk0bbG1tsbW1pW3btri7u7N8+XIAnJyc+M9//mPSQJ2dnWnatGm+l6OjI9WrV6dp06YmvZYQVcnx48fJzs7GxsaGevXqWfT58uDgYGxsbBgwYEBuYn7iBr9MXcjd2wkk2zrSf8g8lrV/htjEDEavjiDspOkenbnX4sWLURSF4OBgs/dy79u3L4MGDaJ58+b07t2brVu3kpCQwLp168x63ZCQEBITE/UvcxXxFEIIIYQQhjF65tzb25vt27dz5swZzp07B0DDhg31RReAfAUWhBDlk1ar5ezZs2RlZZGdnZ2v2mhZW7t2Lc7OzixYsEC/lF6rU/jyv3tZt+kzPugVzE/NunPVLbeXqEJuJ4TQTZH0DPQ22eMjCQkJ/PTTT/zf//2fxZb039tbtWfPniX2VfX29ubQoUP5zpFXzf3+3qv3yvtwVQghhBBClA+lXhPaqFEj+vfvT//+/fMl5mVp9+7dRRaDKy1ztEoSliM/z8JduXKFrKwsLl++TLt27Qosmy5LY8eO5cyZM/Tq1Ss3IVYU+OknDp2P4x+c6f3aQn5q1r3A+xQgJjGDQ1HxJoljxYoVWFlZ8dJLL1n0Wfu83qo+Pj75+qrmub+valBQECdOnCAuLk5/zPbt23FxcSEwMLDM4xdCiKqorJ9vFhWT/J6Ikhg9cw7w7bffMmfOHM6fPw9AgwYNePfddxk8eLBJgytLNjY2qFQqbt26hYeHh9mXsgrzUxSFW7duoVKpLJpslSeKohAbG8vp06dxcnLi8ccft1gsO3bswNramo8++ghXV9d/d5w6Bc89h2reCqA6V6r5FnueuOSMYveX5M6dO+zatYsBAwbg7Oz8QOcqjXfeeYf+/ftTu3Ztbty4wbRp0/S9Ve/tq+ru7o6Liwtjx47N11e1V69eBAYGMnjwYD799FNiY2OZPHkywcHBMjMuhKj0Fi9ezOLFi7l8+TKQ25py6tSp9O3bF8itjv3222+zdu3afO2v7q/VUVoajQa1Ws2NGzfw8PBAo9HI35CiAEVRyMrK4tatW6jVaoM7qYiqx+jk/PPPP2fKlCmMGTNGX43wr7/+YtSoUdy+fdusFY3NycrKipo1a3Lt2jX9AC8qPpVKRc2aNaVgIHD37l00Gg27d+/mxRdftGgskyZNIjk5mU8//TT3GXedDjZsgGeegaZN4fRpFKvqsOxAiefydC5965off/yRxx9/nEcffTT/BwRlqKTeqiX1VbWysmLz5s2MHj2aoKAgHB0dGTJkCDNmzLDI/QghRFmqWbMms2fPpn79+iiKwqpVq3jqqac4evQoTZo04a233mLLli2sX78eV1dXxowZw4ABA9i3b59Jrq9WqwkICCAmJoYbN26Y5Jyi8nJwcMDPzw+1GQvaiorN6D7nAQEBhIaG8uqrr+bbvmrVKqZPn05UVJRJAzQlQ/rLabVasrOzyzgyYS42NjaSmJPbp/vXX3+ld+/euLu7WyyOiIgIAKpXr07t2rX/3bF3L3TpAuHh0KEDkPvM+SOf/EFsYgaFDVIqwNvVjr8mdjP6mfP4+HiOHTtGvXr1qFWrlsGzHNLHV/r4WlJF/TlIn/OqoTyNj+7u7syZM4dnn30WDw8P1qxZw7PPPgvAmTNnaNy4MeHh4QbXWjHk3hRFIScnp9DWvkJA7ofp1tbWRq2skD7nlYehY6TRM+cxMTF06tSpwPZOnToRE2OeysllycrKSpI5UWlkZWWRkZHBunXr+L//+z+LxjJ79myOHTvGwoULqVGjBmRnQ1gY9O8Pjz4KkZHQqJH+eCu1imn9Axm9OgIV5EvQ8/6zNq1/oNGJ+fbt23nkkUfw8/PDz8/vge9LlG+KovD666/z448/cvfuXVxdXRk6dKjJ65UUZeXKlYwfP56EhIQyuZ4QVZlWq2X9+vWkpqYSFBTEkSNHyM7OpkePHvpjGjVqhJ+fX7HJeWZmJpmZmfqvk5KSSrx23iN08hidEOJBGJ2c16tXj3Xr1hXoH/jDDz9Qv359kwUmhHgwWq2WFStW8Pzzz1s0Mb906RKKotC9e3cmTpz47yfGGzfCCy/A6dNQv36+xDxPn6Y+LH6lNaGbIolJ/PfZcm9Xu9w+5019DI4jMTGRy5cv4+HhgbW1tfQiNYXpZfwoQClmQsPCwli5ciW7d++mTp06qNVq7O3t9fv9/f0ZP34848eP12+ThFqIiuXEiRMEBQWRkZGBk5MTv/zyC4GBgRw7dgyNRlOg6KmXlxexsbFFnm/WrFmEhoaaOWpRGRU20y2z2sIYRifnoaGhPP/88+zdu1f/zPm+ffvYuXOn2fvyClFVaXUKh6LiiUvOwNPZjvYB7kXOGCuKQmJiImvWrOGNN94o40jzx/HNN9/wyy+/sGjRItq1awcZGbBnD/TuDQMHwj//5CbmxejT1Ieegd4G339hjh49Sv369cnOzqZt27YPemuiAsmrfF/Yii8hROXQsGFDjh07RmJiIj/++CNDhgxhz549pT5fSEgIEyZM0H+dlJRErVq1TBGqEEIUy+hqBAMHDuTgwYPUqFGDDRs2sGHDBmrUqMGhQ4d45plnzBGjEFVa2MkYHvnkD15cdoBxa4/x4rIDPPLJH4SdLPwxki+++AIrKyuLJuZxcXHcunULT09PNm7c+O/z5d98k1v0LS4OVCowsNWXlVpFUN3qPNXyIYLqVjc4MU9PT+fKlSvcvn0bjUYjiXkVM3ToUMaOHUt0dDQqlQp/f3+6dOminyXv0qULV65c4a233kKlUqFSqdi9ezfDhg0jMTFRv2369OlA7lLXd955h4ceeghHR0c6dOjA7t27811z5cqV+Pn54eDgwDPPPMOdO3fK9qaFqII0Gg316tWjTZs2zJo1ixYtWjB//ny8vb3JysoqsArm5s2beHt7F3k+W1tbXFxc8r2EEKIslKqVWps2bVi9erWpYxFC3CfsZAyjV0cUKIgWm5jB6NURLH6ltX5p9507d1i3bh1vvvlm2Qd6j19//ZX58+fz5Zdf0r9/f0hNhcOHcwu+jRgB3bqBp6fZ47h69Sq2trZERUXRs2dPs19PlD/z58+nbt26fPXVVxw+fBgrKysGDRqk3//zzz/TokULRo4cyYgRI4DcQlLz5s1j6tSpnD17FgAnJycAxowZQ2RkJGvXrsXX15dffvmFPn36cOLECerXr8/BgwcZPnw4s2bN4umnnyYsLIxp06aV/Y1XFMU9FiHF3MQD0Ol0ZGZm0qZNG2xsbNi5cycDBw4E4OzZs0RHRxMUFGThKIUQoiCDknNDCmHkkU8XhTANrU4hdFNkoZXKFXKLooVuiqRnoDcL5s9j5MiRjB49uoyj/FdKSgqQWzRyy5Yt/1aT/uwz+OILuHIFHB0LfbbclLRaLYmJifz5558MGjSILl26mPV6ovxydXXF2dkZKyurQmfJ3N3dsbKywtnZOd9+V1dXVCpVvm3R0dGsWLGC6OhofH19gdwe9WFhYaxYsYKPP/6Y+fPn06dPH9577z0AGjRowP79+wkLCzPznQpRdYWEhNC3b1/8/PxITk5mzZo17N69m23btuHq6srw4cOZMGEC7u7uuLi4MHbsWIKCggyu1C6EEGXJoOTczc2txLL/iqKgUqmkhYQQJnIoKj5fEbT7KcC1G7FMnj2fj0PGG9Waw9T279/P5MmTmT9/Pq+//jokJMDRoxAUBO+8Ay+/nJuYm1lSUhJ3794lMjKSl14qpsWSEEY6ceIEWq2WBg0a5NuemZlJ9erVATh9+nSBx7uCgoIkORfCjOLi4nj11VeJiYnB1dWV5s2bs23bNv2Kqblz56JWqxk4cCCZmZn07t2bL7/80sJRCyFE4QxKznft2mXuOIQQ94lLLjoxB0g6vBGnln1o32egxRLzrKwsILc92c8///xvRdz3389tk3buXG5SbubK6IqikJmZyXfffcfrr7+ev4e6ECaQkpKClZUVR44cKdBuM2/ZuxCi7C1fvrzY/XZ2dixatIhFixaVUURCCFF6BiXnjz32mLnjEELcx9PZrtDtOcl3yLx6AufW/VBZWePt6lDGkeU6ffo048aN47PPPst9rvbWrdzZ8latIDQUQkLAulRlLYySk5PDpUuXOH36NMHBwWa/nqhcNBpNgRVfhW1r1aoVWq2WuLg4OnfuXOi5GjduzMGDB/NtO3DggGkDFkIIIUSlZXS1diFE2Wgf4I6Pqx33zoknH/sNK3sX7OsHobayxsc1t61YWdLpdGi1WhYtWsTKlStp3rx57o433sgt+KYo4OEBZdB2RqfT8fnnn1O3bl2eeuops19PVD7+/v7s3buX69evc/v2bf22lJQUdu7cye3bt0lLS6NBgwa8/PLLvPrqq/z8889ERUVx6NAhZs2axZYtuX1t33zzTcLCwvjss884f/48CxculCXtQgghhDCYJOdCmIBWpxB+8Q4bj10n/OIdtLrCyrgZx0qtYlr/3FZj2uTbpF04iGPjR1FZ22BlYwvAtP6BRvX7flDXrl3jmWee4dSpUyxcuBBfgDNncnd+/jls2ZLbIs3MFEUhMjKSjRs38t577xVYZiyEoWbMmMHly5epW7cuHh4eAHTq1IlRo0bx/PPP4+HhwaeffgrAihUrePXVV3n77bdp2LAhTz/9NIcPH8bPzw+Ajh07smzZMubPn0+LFi34/fffmTx5ssXuTQghhBAVi0pRlAfPIiqIpKQkXF1dSUxMlKrywmTCTsYQuikyX/E2H1c7pvUP1Lc5exCTPv2SXRkB3LiTgJW9i8nPbyitVsurr77KlClTaJRXcb1LF7C3h99+K7M4AGbOnElISAhqddl+vliZx5Di7i0jI4OoqCgCAgL+rcIvylxF/Tn4T9pS6PbLdsUUbbynlVpR7zfFOS7P7lf0+4VRqur4KMS9ChtrHmScKW78M9U1RNkwdBwx/wOhQlRixvQhN1ZsbCwXLlzgjRee4KOHanL48l3ikjPwdM5dyl5WM+Z3795l/PjxTJw4kf/+979w6VJuW7TatWHZMqhRo0ziADh+/DhXrlzhgw8+KLNrCiGEEKJ8MHXyK0R5I8vahSilkvqQQ24f8tIscd+2bRsuLi7UrFkTPz8/rK3UBNWtzlMtHyKobvUyS8xzcnJ49dVXGT16NIGBgaDTQf/+MGVK7gH160O1amUSy8cff0zz5s158skny+R6QgghhBBClCWjZs5Pnz7N2rVr+fPPP7ly5QppaWl4eHjQqlUrevfuzcCBA7G1tTVXrEKUK4b0IY9JzOBQVDxBdasbdM7bt28TFxeHp6cndnZ2+Pv7myZYI2VkZPDBBx/w+uuv8+uvv6I6cya3GruHB/zwQ+6seRmJiIggLi6OkJAQi/ZyF0IIIYQQwpwMmjmPiIigR48etGrVir/++osOHTowfvx4PvzwQ1555RUUReGDDz7A19eXTz75hMzMTHPHLUSZKqzgW0l9yPMYetyRI0dQq9VotVpatWpl9PPUpipKl52dzdNPP023bt1o0KABquxs6N4dZs/OPaBpU3B2LtW5jTVnzhxatmxJr169JDEXQgghhBCVmkEz5wMHDuTdd9/lxx9/xM3NrcjjwsPDmT9/Pv/5z394//33TRWjEBYVdjKG6b+eIjbp3w+dvF1sebG9n0Hvv3w7tcA2rU7hUFQ8cckZOKlzqOem5vbt27Rs2RJ3d+Nbo5miKJ1Wq2XOnDk899xzbNy4EdvTpyE1FRwdYdMmaNLE6LhKKyIigqSkJN58880yL/omhBBCCCGEJRiUnJ87dw4bG5sSjwsKCiIoKIjs7OwHDkyI0krJyOGtH44SfTcdv2r2zH2+FU52pat9GHYyhlGrIwpsj03KZO6O87g52JCYll3oc+d55u44T0NvZ32SvPWfG0zeeJL41GxykuIAcCOVz94YWKqWYMYUpbv3Q4F7C8tlZmYyYMAAnn32WQICAlAlJcGjj8L778OkSdCmjdFxldaXX37J8OHDUavVBo07ouzodDpLh1ClyfdfCCFMSwrMifLGoIzF2D+Q5Q9qYSlPLvyTf64l6b8+G5tM0+nbaF7ThV/HdDbqXFqdwqSfTxR7TFaOYX8sh/x8gp6B3nwadpqle6NQtNko2ZlkRJ/AsUlXUlSeparuXlJROhW5Rel6BnqzPTK2wOy6t4stD+tOMmHYIL777jvcL10CrRZcXeH338s0KT9+/DjZ2dm88MILUruinNFoNKjVam7cuIGHhwcajUYeMyhDiqKQlZXFrVu3UKvVaDQaS4ckhBBCCDMwWSu1mzdvsnTpUqZOnWqqUwphlPsT83v9cy2JJxf+aVSCfuDiHRLSil8FkpalpUOAOwej4os97m5aNvN3nGPp3ih0malok+PRpifi1LS7/hiFfxNpQ6uxG1qUbtiKQ+w9fzvfPl12Jie++4RzXnXp1v9Z+lVPg6AgWLoUXnsNOnY0KAZT+P777+nXrx9qtRonJ6cyu64wjFqtJiAggJiYGG7cuGHpcKosBwcH/Pz85FEPU5ruWsy+xKL3CSGEEGZgsuQ8NjaW0NBQSc6FRaRk5BSZmOf551oSKRk5Bi9xD790u+SDoMTEPM/SvRdQcrJIOfkHzq2fwEZVq8AxxlZ3N7TY3P2JefrFw2h8G+L26Ku0S0/ioy1n6DOpO1Z//AGdOhl0TlM4ffo0VlZWtGvXDhcXlzK7rjCeRqPBz8+PnJwctFqtpcOpcqysrLC2tpYVC0IIIUQlZnBy/s8//xS7/+zZsw8cjBCl9dYPRw0+btmQdoXuy8rR8V34Za7Ep1Hb3YGcUlY7L4yizSEp9jK6jBRc2vQv9lhDE24AT2c7o+LQZWdy949lqGzssKvdgsYp8Wz47m1eGziVQ1GtCOps3NL/0lIUhT/++IPAwECsrKzw9PQsk+uKB6NSqbCxsZFHl4QQQgghzMDg5Lxly5aoVCoUpWDCkrddPtEXlhJ9N/2Bjpu1NZJlf0Zxbz5uql9nRdGRdPgXXDo8a9C/kb8vx3Pkyl1quzswOMgfjXXRS1jbB7jj42pX7NL2PJk3zmJTww+npj1oY2XNSWsN5zz8efGFjwn3a8ZTRnwo8CAuX76Mra0tDg4O+PgY/nx9VTF79mxCQkIYN24c8+bNA3L7zr/99tusXbuWzMxMevfuzZdffomXl5f+fdHR0YwePZpdu3bh5OTEkCFDmDVrFtbWJlsgJYQQQgghzMjgB9fc3d1ZtmwZUVFRBV6XLl1i8+bN5oxTiGL5VbMv9XGztkbmPgt+3+dOhXwOZRRFUci8cZaMK//g2nGQwR9efXcgmm/Dr/DhltM0mvIbs7ZGFnmslVrFlH6Ni49Dm0PC3u9IProVFIXHtFlsXjWeVtfPABBeuzmoVEbPwhtLURROnDhBVlYW2dnZBAUFmfV6FdHhw4dZunQpzZs3z7f9rbfeYtOmTaxfv549e/Zw48YNBgwYoN+v1Wrp168fWVlZ7N+/n1WrVrFy5Up5zEgIIYQQogIxODlv06YNN27coHbt2oW+HnrooUJn1YUoC3Ofb1Wq47JydCz7M8ocIZEUvg6NTwPs/VuW+hw6BZbujSo2QXfUFD0zmn33Boo2G413fR5r+yRqWwcO1GrGK899yFHfhkBuRXcf19y2auZy69YtkpOTuXTpEg0aNMDPz7Ae8VVJSkoKL7/8MsuWLaNatWr67YmJiSxfvpzPP/+cbt260aZNG1asWMH+/fs5cOAAAL///juRkZGsXr2ali1b0rdvXz788EMWLVpEVlaWpW5JCCGEEEIYweDkfNSoUfj7+xe538/PjxUrVpgiJiGM5mRnTfOaxRcUa17TpUAxuO/CLxeYMS/0/Lb5+4/7uNrx5Uut8HG14/758Ixrp3Jnyzs9b7JHPZb9GVWgbZtWp/DkF38yZOXhAscrio6kI5u4u+sbFJ2WZ3U5/LrqLfzjr4NKxV8BrfKt25/WP9DgCvHGiomJISoqiri4OJ566imzXKMyCA4Opl+/fvTo0SPf9iNHjpCdnZ1ve6NGjfDz8yM8PByA8PBwmjVrlm+Ze+/evUlKSuLUqVOFXi8zM5OkpKR8LyGEEEIIYTkGP4z4zDPPFLu/WrVqDBky5IEDEqK0fh3Tuch2akX1Ob8Sn2bQuZ9pVZPHm/kQm5hOfGoW7k62VHO0ZUq/xgSvOYqK3LZlift/wLXT8w94JwXplNwPEoY+HMChqHh+johm/ZHCW1pp0xJRWVmjUlvT+eEXOWvnxLYGQbz63Awuuz+U71gfVzum9Q80qre6oVJTU7G2tua3337jtddeM/n5K5O1a9cSERHB4cMFP2iJjY1Fo9Hg5uaWb7uXlxexsbH6Y+5NzPP25+0rzKxZswgNDTVB9EIIIYQQwhQeqFLQvn37aNu2Lba2tqaKR4gH8uuYzqRk5PDWD0eJvpuOXzV75j7fqsj2abXdHQw8s8L2yFg2HLtBfOq/y4R9XO0Y+WgAqzds405aNi5Bz5ngLgq39/xtvv4rqtjib6mn/yTln9+p8dRERis6Plj9Ho+MWs4dRzf23be8vn9zb+a90NosM+apqamEh4dTq1YtScxLcPXqVcaNG8f27duxszPvc//3CgkJYcKECfqvk5KSqFWrYHs/IYQQoqrzn7SlwLbLs/tZIBJR2T1Qct63b1+OHTtGnTp1TBWPEA/Myc66yHZp9xsc5M/MradLXNr+3YHoQrfHJKQzZ84cVn3+IdWd7biVksnt5Ew+3HLa2LBLtOfcrSL3KTlZ6LIzyLkdTafHhnLRzokfm3XnYvVa3HF0y3esChj5aAAhjweWeE2tTuFQVDxxyRl4Ouc+l15cMp+dnY1arWbZsmWMHz/ewDur2o4cOUJcXBytW7fWb9Nqtezdu5eFCxeybds2srKySEhIyDd7fvPmTby9vQHw9vbm0KFD+c578+ZN/b7C2NraygerQgghhBDlyAMl51IATlR0Gms1wx8JKFVRuIxrp1BZ2eDc5ik+DjvLXxO7YaVWodUpfP1XFLGJGZTFv5CMK/+QGL4Wj2cmM1WlYsi6KTw86hvSNXa5ldj/x0FjxeNNvfl4QPNi27PlCTsZQ+imyHwz9cUtg9dqtWzdupVGjRpJYm6E7t27c+LEiXzbhg0bRqNGjZg4cSK1atXCxsaGnTt3MnDgQADOnj1LdHS0vuJ9UFAQM2fOJC4uTt8zfvv27bi4uBAYWPKHMEIIIYQQwvKkAa6o8Iyd3b1ft0ZeRiXniqKQcnQrjs16/O/ZbitiEjMY8OVfPNniIQYH+TOtfyCjV0fon0XPc//XD0JRdOjSkkg/8ydtuw7nqq0Dq9o8wZ/+rUjX5F8ePb57fcZ2r2/w9yXsZAyjV0cUiDU2MYPRqyNY/EprfYKuKAo6nY7PPvuMiRMnmuLWqhRnZ2eaNm2ab5ujoyPVq1fXbx8+fDgTJkzA3d0dFxcXxo4dS1BQEB07dgSgV69eBAYGMnjwYD799FNiY2OZPHkywcHBMjsuhBBCCFFBPFByvnTp0gJFiIQoS4XN7ro72vBUi4eoWc0ed0cN3q72xSbscclFP8N9v8zrZ1Bp7HFo/Chqm/xJz/FrSRy/lsTMracZ0TmAxa+0LhCbt6sdL7TzY+6Oc0beaX5ZNy9xd/cKPJ55nwVqNV02zKLbiKUk2LsQcV/V+kHtvBnfs4HB59bqFEI3RRb6IYJC7gcMoZsi6RnojZVaxbp162jdurUk5mY0d+5c1Go1AwcOJDMzk969e/Pll1/q91tZWbF582ZGjx5NUFAQjo6ODBkyhBkzZlgwaiGEEEIIYYwHSs5feuklkpKS2LBhAw0bNqRx48amikuIEhU1uxufms2K/ZfzbStuObanc8lFuBRFIe30HuwC2qC2sUVlrSny2Lze5K8/Cn9N7FZgVh9g7eHoUi97z0m+Q0r4DzR7dDBxGnu+aj+Qdc16olVbFTjWWg1zBrYx6vyHouKLLTqnADGJGew/H8dfP39DSEiIsbcgSrB79+58X9vZ2bFo0SIWLVpU5Htq167N1q1bzRyZEEIIIaDwInGGHifF5ERRDO5znue5555j4cKFAKSnp9O2bVuee+45mjdvzk8//WTyAIUoTHGzu4WJ+d9y7LCTMQX2tQ9wL7RfeZ6smxfRJt1C49MQK3vnYhPze321NwqtTiGobnWeavkQQXWrY6VWYaVWMa1/7nPAxtRJz06I5daGWajtHFmjUrPs98WgKFx39eSUd70Cx2usVFz42PjB35CVBCmndnH6wiVJzIUQQgghhDARo5PzvXv30rlzbr/oX375BUVRSEhIYMGCBXz00UcmDzDPrFmzaNeuHc7Oznh6evL0009z9uxZs11PlG8lze4WRiF3Obb2ntLsec+r923qrV+yrT9e0ZF+5ThqexfUtg7YVDOuF7gCvP/zP2h1CuEX77Dx2HXCL95Bq1Po09SHxa+0xtvVsNZZOUm3SNy2iLptn0ZtY8f8Ti8wod8EUBWe3u97rxvnZj5uVLx5iltJoGhzSDz4I46BXWjWyPCl8kIIIYQQQojiGb2sPTExEXf33KW5YWFhDBw4EAcHB/r168e7775r8gDz7Nmzh+DgYNq1a0dOTg7vv/8+vXr1IjIyEkdHR7NdV5RPxjwnfq+YxAwORcXTPsCdhX+c55t9USSm5+j3q1SgKJB95xpqB1fU1hqsXTxKHeePEdfZcTqOhPRs/TZvFzumP5m7xL5bIy86fryT+LSsQt+vTUskYe93uPcYSZg2h6zwH3ht0HTOe9Qu9PjD7/fAw+XBCoDlrSS4f9l9yqld2NVqimv7Afi42euX6AshhBBCCCEenNHJea1atQgPD8fd3Z2wsDDWrl0LwN27d7GzM2wWsDTCwsLyfb1y5Uo8PT05cuQIjz76qNmuK8onQ54TL8r2yFhG//cICWnZBfZpdQrZt6/874scbB968DoK9ybmALFJGYxaHcGYLnVYuPtSke/LSbxJ4pa5+D78IpnWGj7tOoy79i6FHmsFXDTR80t5y+7zqs3rcrJJPh6Gc8u+qK1yh4xp/QONqogvhBBCCCGEKJ7Ry9rHjx/Pyy+/TM2aNfH19aVLly5A7nL3Zs2amTq+IiUmJgLoZ/ELk5mZSVJSUr6XqBzyZndL45t9lwtNzHOS76DkZJKTeBONhz9WTtUeNMxiFZWY67LSubv3O6wcqvFnehIzT/4BwD8+Dbjq5l3oe1aP6GjS2PKW3dtEH0SbkYxT816orKzxdrXL10ZNCCGEEEIIYRpGz5y/8cYbdOjQgejoaHr27IlanZvf16lTx6zPnN9Lp9Mxfvx4Hn744QL9ge81a9YsQkNDyyQmUbbyZndHrY4w6n15y9bvl5N8h5yEGKxdvXGo18FEURovOyGW5M3/oVrHQahsNMzqOZrLJTzr7uNqZ/Il5unp6dw88jsHv3yb07ezuZWSWaoe8kIIIYQQ5iTV0EVlYvTMOUCbNm145plncHJy0m/r168fDz/8sMkCK05wcDAnT57UL6kvSkhICImJifrX1atXyyQ+UTb6NPVh+MP+Rr3n/sRcl5mKkpNNxuVj2NVqirVLDdMFaExc2hySjmzCzs6J8MQ4Qq6dAuCgXzNuOhcdkwrTLzHfvHkzaWlp9O3bl2purnSqVyNftXkhhBBCCCGE6RmUnM+ePZv09HSDTnjw4EG2bDGs719pjBkzhs2bN7Nr1y5q1qxZ7LG2tra4uLjke4nKpUdg4cu8DaHLSifj6im0aXdxatbdhFEZJycpjoQfJmNj54TWzolP+77JV+0HGPTe7o09TbbEPC0tjS1bttCqVSvc3Nzw9PQ0yXmFEEIIIYQQJTMoOY+MjMTPz4833niD3377jVu3bun35eTk8M8///Dll1/SqVMnnn/+eZydnU0eqKIojBkzhl9++YU//viDgIAAk19DVDztA9zxdjHu2XMlJxtFm0PKiR041GuPtYtlklBFUUg9uw8XVOyLi+LNlLsA7KnThngHV4POseN0HAMX78vXpq009uzZg6Io1KtXj4ceeggrK6tSnUcIIYQQQghROgY9c/7tt99y/PhxFi5cyEsvvURSUhJWVlbY2tqSlpYGQKtWrfi///s/hg4dapaq7cHBwaxZs4aNGzfi7OxMbGwsAK6urtjb25v8eqJisFKrmP6k4c+eKzotaef2Y1szEJc2/c0cXdG0KXdJ3fo51GlLaoMgvnh8HOF+zUt1riNXEjhy5RiQv02bIdLT0zlx4gQ1atRArVbTsGHDUsUghBBCCCGEeDAGF4Rr0aIFy5YtY+nSpfzzzz9cuXKF9PR0atSoQcuWLalRw7zP6i5evBhAXx0+z4oVKxg6dKhZry3Kt56B3rzVowFL914kLUtb6DGKooA2h+SITbgYuGTcXDKvn8bHWsPG6JN8F9iVX1RqtjXoZJJz57VpW2JARfXjx4/ToEEDcnJyaNKkiUmuL4QQQgghhCgdo6u1q9VqWrZsScuWLc0QTtGUwkpsiyov7GQM03+NJDYpo9jjUk9sxy6gjUUTc11GCulhX5DlXpM7nV9mxePj2WGmyvAhP5+gZ6B3oQXcsrOzuXLlCklJSWi1Wjp1Ms0HA0IIIYQQQojSK1W1diHKg7CTMYxaHVFsYq7kZJF48GecmvfC2rl6GUaXX3ZCLH7XT7P+3AGeqhmISqVmU+BjpGtM/wgIwN20bA5culNge3R0NJmZmZw/f57OnTvn67gghBBCCCGEsBxJzkWFpNUpTFh3vNhjko+FoctMxbWD5WbLlZwsMsMWknTwJ64GtGFT3zc5UMpny40VfvHf5FxRFG7dusXff/+Noij07du3TGIQQgghhBBCGEaSc1EhfbHzfJHPl+uyMkg6vBGnFr2xcqxWxpH9S5uRQsPI3Sz853e6NO+FSq3mp2bdybK2KaMIch8FSUxM5O7du+zevZsBAwaYpZuCEEIIIYQQ4sFIci4qHK1O4eu/LhW6L/lYGIo2C+e2/VGpCj5vXRYUnZacvd9xd/sSzjfpxsHeYzjtWfat/4Lq1CA9PZ0ff/wRW1tbBg0aVOYxCCGEEEIIIQxjdHL+2muvkZycXGB7amoqr732mkmCEqI4h6LiScnMP2uuy0wj+dhvODXviZW9CyqVZT53UrTZtDz4Mx8c/IlWD7+EYmXNuha9yLEyuvbiA3G1VVHHWcv69esZPnw4jo6OZXp9IYQQoizMmjWLdu3a4ezsjKenJ08//TRnz57Nd0xGRgbBwcFUr14dJycnBg4cyM2bNy0UsRBCFM3oDGbVqlWkp6cX2J6ens63335rkqCEKE5ccv4CcCknd4Kiw7FJN1RqK4vEpCgKqr9/5fbmzzndpj9Rvd7gWjVvC8WiIyjzCG6uLrz66qsWiUEIIYQoC3v27CE4OJgDBw6wfft2srOz6dWrF6mpqfpj3nrrLTZt2sT69evZs2cPN27cYMAAy7ZVFUKIwhg8nZeUlISiKCiKQnJyMnZ2/1aZ1mq1bN26FU9PT7MEKcS9PJ1zf/d0GSmkXzqCQ/0g1LYOFotHURQ67fqGBhGb+XXUN9zV2LGuRS+LxOGkTaK3600+nzKlyOO0OoVDUfHEJWfg6WxH+wD3QluuCSGEEOVdWFhYvq9XrlyJp6cnR44c4dFHHyUxMZHly5ezZs0aunXrBsCKFSto3LgxBw4coGPHjpYIWwghCmVwcu7m5oZKpUKlUtGgQYMC+1UqFaGhoSYNTojCtA9wR3P1EBmeTbHzb2nRxNz61G5io44Q2X0kPp7+3HV0s0gcbvY21L6+g+/nz8DBvuj2bLl94U8Rm5Sp3+btYsv0J5vQp6lPWYQqhBBCmE1iYiIA7u7uABw5coTs7Gx69OihP6ZRo0b4+fkRHh5eaHKemZlJZua//51MSkoyc9RCCJHL4OR8165dKIpCt27d+Omnn/SDHoBGo6F27dr4+vqaJUgh8iQkJHDo0CEmD32SKTtuoFZZLjHvvnUB6jN72TtqOYn2zvzctHuZXn9wRz/a+rujJN8h/vwRXpv2abHH5/WFv19sUiajVkew5JXWkqALIYSosHQ6HePHj+fhhx+madOmAMTGxqLRaHBzc8t3rJeXF7GxsYWeZ9asWTLhJISwCIOT88ceewyAqKgoatWqhVothd5F2chbhr19+3Ye7fwIfrX9cbaujkpV+H9UzUpRsI2K4Mb5A5x+5AVaB7Qk096lzMNQq2DKE02Y+585TJgwAZvOxfdO1+oUJv18othjJv18gp6B3rLEXQghRIUUHBzMyZMn+euvvx7oPCEhIUyYMEH/dVJSErVq1XrQ8IQQokRGZ9i1a9dGrVaTlpbGmTNn+Oeff/K9hDClsJMxBM3YzIAPV7Pk8B2GrDzCsJ+vsiPSAok58OSG2TTa+AnVO7/CDRdPNjd+FCzQsm1gfRt+XLeW9957DxubkvumH7h4h4S07GKPSUjL5sDFO6YKURho8eLFNG/eHBcXF1xcXAgKCuK3337T7zekynB0dDT9+vXDwcEBT09P3n33XXJycsr6VoQQwmLGjBnD5s2b2bVrFzVr1tRv9/b2Jisri4SEhHzH37x5E2/vwgu32tra6sfkvJcQQpQFo/s73bp1i2HDhuX74/FeWq220O1CGCvsZAyvfbYO6+o1QdGh8fAHIDYxg+X7LpdZHCpFhybmPLHnwznd6XlqNu8BDq5ldv371by6g1kfzdHXgDBE+KXbBh/3cP0aDxKeMFLNmjWZPXs29evXR1EUVq1axVNPPcXRo0dp0qQJb731Flu2bGH9+vW4uroyZswYBgwYwL59+4DcMbdfv354e3uzf/9+YmJiePXVV7GxseHjjz+28N0JIYR5KYrC2LFj+eWXX9i9ezcBAQH59rdp0wYbGxt27tzJwIEDATh79izR0dEEBQVZImQhhCiS0cn5+PHjSUhI4ODBg3Tp0oVffvmFmzdv8tFHH/Gf//zHHDGKKig1LZ2QVTvQZaahUqmx9fm3CKHyv/9V3fP/zemVdVPZE3uR1GELOO/iwXmvOmVw1YKy78bglHaD37+ehbW1sf90DZ3dlyXtZa1///75vp45cyaLFy/mwIED1KxZs8Qqw7///juRkZHs2LEDLy8vWrZsyYcffsjEiROZPn06Go3GErclhBBlIjg4mDVr1rBx40acnZ31z5G7urpib2+Pq6srw4cPZ8KECbi7u+Pi4sLYsWMJCgqSSu1CiHLH6OT8jz/+YOPGjbRt2xa1Wk3t2rXp2bMnLi4uzJo1i379+pkjTlGFXLlyhTO3s7lx7Sr2tVsUeZw5E3MrnRabhBhunj/EPx2exReIdvEw4xVz3Zsa33t/yUe34ty8F5+O6FZsNfaiBNWtzsJdFww6TliOVqtl/fr1pKamEhQUZFCV4fDwcJo1a4aXl5f+mN69ezN69GhOnTpFq1atCr2WVCMWwkSmF7OSanpi2cVRRS1evBiALl265Nu+YsUKhg4dCsDcuXNRq9UMHDiQzMxMevfuzZdfflnGkYqKwn/SFkuHUKEU9v26PFvywdIyOjlPTU3V9zOvVq0at27dokGDBjRr1oyIiIKVoIUwlFar5e7duxw4cACrgHbFJubm9sZ/3+O/CTexf/FjjtfwK7PrdmvkQfOabnx/KJrYpEyyE2LJSbxJnQ49+fC59kVWU8/K0fFd+GWuxKdR292BwUH+aKz/LSnRsU513Bxsin3uvJqDDR3rSHJuCSdOnCAoKIiMjAycnJz45ZdfCAwM5NixYyVWGY6Njc2XmOftz9tXFKlGLAxR3B+p8seXKA8UpeSP6u3s7Fi0aBGLFi0qg4iEEKL0jE7OGzZsyNmzZ/H396dFixYsXboUf39/lixZgo+PtGESpZOYmEhqaipHjhzh+eefJ9wChclstNmo0pKIv3yMQ22fpqaTO/FlmJgD7Dxzi51nbuHtYsej6nM8MbQ/rjbQo1XdIquoz9oaybI/o9Dd8/fJzK2nGdE5gJDHAwGwUquYPaBZoa3U9OcZ0EwqtVtIw4YNOXbsGImJifz4448MGTKEPXv2mPWaUo1YCCGEEKJ8Mbpa+7hx44iJiQFg2rRp/Pbbb/j5+bFgwQIpPiSMpigK6enpfP/999SoUUP//G37AHd8XO2KfAJaBdiYspufojB55Tisv52AdTVvDjbuzD+1mpjwAobLSYoj+txJ/oh3wdnRgd5t6hWbmC/dmz8xB9ApsHRvFLO2Ruq39Wnqw5JXWuPtkn9ZvI+rnfQ4tzCNRkO9evVo06YNs2bNokWLFsyfP9+gKsPe3t4FqrfnfV1UJWKQasRCCCGEEOWN0TPnr7zyiv7/t2nTJvf54DNn8PPzo0YNqfIsDJednc3169f5+++/GTHydQ5FxROXnIGnsx3tA9yZ0q8xb6w5WuB9eYXgsnUPHoNddgY5Oh3J10/ze6vHecirDmkPBT74iUsp/fIxbL3rASpsXDyYsfk0vZr4FJqcZ+XoWPZnVLHnW/ZnFG/3aqRf4t6nqQ89A70LfK9lxrx80el0ZGZmGlRlOCgoiJkzZxIXF6d/5Gj79u24uLgQGGi532UhhBBCCGEco5Pz+zk4ONC6dWtTxCKqEJ1Ox/z58xk/fjxnUmx55JM/iEnM0O93cyi6d7eXiy0ZOboS+3aXxEabzedfBzNepULp/n/sa/3EA53vQWhT76LLzkStsUdl64i1nRMKEJOYwaGo+EILtX0XfrnAjPn9dEruccM7/1th3kqtksJv5UhISAh9+/bFz8+P5ORk1qxZw+7du9m2bZtBVYZ79epFYGAggwcP5tNPPyU2NpbJkycTHByMra2the9OCCGEEOWFFG8r/4xOzgcOHEj79u2ZOHFivu2ffvophw8fZv369SYLTlRsWp1SYIZWrYKLFy9y5MgR3nnnHcJOxjB6dUSByuvFJd7Pta3Fgj9KrjxeFMfMNFKtbUi9Hc3a5t3xqN2KrJqNS32+B5UVF4Xa3hm02dj6NiywPy45o5B3wZX4NIPOb+hxwjLi4uJ49dVXiYmJwdXVlebNm7Nt2zZ69uwJlFxl2MrKis2bNzN69GiCgoJwdHRkyJAhzJgxw1K3JIQQQgghSsHo5Hzv3r1Mnz69wPa+fftKn3OhF3YyhtBNkflmw31c7Wh0cydf/+dD6tWrh1anELop0uiWaF88QGLunJnK18tG87KDMyltn2bvwy+V+lwPSpeZipKTTU5iHPYe/qhUhS8t93QuvHVabXcHg65j6HHCMpYvX17sfkOqDNeuXZutW7eaOjQhhBBCCFGGjE7OU1JS0Gg0Bbbb2NhIn1wBUOhsePbtq1w8c4XYRo+w/XQcfZr6cCgqPl/ybqjS9Dd3zEwjRWPP3cxUFjbshEvDh8nxa1aKM5lGTvJtlMx0FG02DvU7FHqMCvB2zV1xUJjBQf7M3Hq62KXtalXucUIIYXLS31sIIYQwKaPrXTdr1owffvihwPa1a9dK8SFR6Gx4Yvg6rKs/hEOjRwAI3RSJVqcUuVzb1LySb/PT0v/DZs1E0s6F82fPUcRbKDFXcrLRZaWTERWBdfWaaLzqFHpc3hz6tP6BRRZr01irGdE5oNjrjegckK/fuRBCCCGEEKJ8MnrmfMqUKQwYMICLFy/SrVs3AHbu3Mn3338vz5uLfLPhWbcuk5NwE5eOg/RLtu8tclbUcm1TcchKJ01jT4yNHR/WaYMmsAsuddqY9ZrF0WWlk33nKgBOzXsVe6y3qx3T+geW2N4sr4/5/X3O1Sry9TkXQgghhBBClG9GJ+f9+/dnw4YNfPzxx/z444/Y29vTvHlzduzYwWOPPWaOGEUFkjcbnnjwR1zaD8Cmhl+hz1LHJWfwRHNffFztiE3MKNVS9eLUux3NytXv8ZhfMxJ96rPvibdNfAXDKYoOdDpSjm/Dpd3TRR73Vo/6+NdwNLq9WcjjgbzdqxHfhV/mSnwatd0dGBzkLzPmQgghhBDlUGFV0y11jaKOkyrullGqVmr9+vWjX7+CPzBFUYosaiWqhuQbl0iPOopL26dQqYpODj2d7bBSq5jWP5DRqyP0vcsflH1WBukaOy66efOOX1No0gXXhg+b4Mylo+i0ZF4/jUptzaR33qbpQy7M2BxJbFKm/hhvF1umP9mkxFny4mis1fnapQkhhBBCCCEqFqOn1ubMmVPodq1Wy0svWa7ytbC8RYsW8Wz3Dvg3bYvaqvA+5Spyq7bnFTnr09SHxa+0xtv1wZe4t75+mt2Lh6H+fTF3D/zIgQGT0VooMVcUBUVRSDr4E3a1mmL7UCPW/h2NWq1i36TufD+iI/NfaMn3Izqyb1L3B0rMhRBCCCGEEBVfqZLz+1v/aLVaXnjhBY4dO2aquEQFcurUKcLDw3nppZews9UQ+kwL4N+iZnmKKnLWp6kPf03sRg3HwhP6kthl5y6lP+lZhzE+Dcnyqovrwy+W6lymknHlOFkx53ANek6/LT41m9GrI9geGUtQ3eo81fIhgupWN3j5uhBCCCGEEKLyMjo537JlC++88w4//vgjADk5OQwaNIhTp06xa9cukwcoyrf//ve/1K5dm6ZNm1KtWjWg6Nlwb1c7Fr3UCld7DRuPXSf84h20/6tiZqVWkaMz/vqPXTrC3iXDUYWvI+7Qzxx+bjrWLXpZ7PEKRVFI2L8We/+W2Po2LPSYvGr1QgghhBBCCJHH6GfO27Vrx08//cTTTz+NRqNh+fLlXLhwgV27duHl5WWOGEU5dO7cObKzswkKCsLJyanA/j5NfegZ6M2hqHjikjPwdLbjbmoWH26JzNfb3OeequSezhoS0rMNur5tThaZ1hqO+jbkdc86pKrVuHZ81mT3VxrpF/9G7eiGW6cXijzm3mr1QXWrl11wQgghhBBCiHKtVOWcu3XrxrfffsvAgQOJiopiz549kphXIdu3b8fNzY0aNWpQp07RRcis1Cr98u3E9CyC10TkS8wBYhMzGL06grCTMawd2cmg6z8ZuYedy0ahnPyDq8e3EfHcDBw6PFtsATpzUhSFxPB12NVpg613PYPe8+XuCyz/8xJZpVkuIIQQQgghhKh0DJo5HzBgQKHbPTw8cHNzY+TIkfptP//8s2kiE+VOdHQ0KpUKZ2dnPD09DX6fVqcQuimy0GrsCrnPooduiuSvid3wcNJwKyWr0PPkzZYfrhnIqOo1Sbp1GZfOgy3aISD94mGsnKvj0vFZo+L48/xt/jx/m5lbT0s/ciGEKOeKa0l0+cHrmQohhBCAgcm5q6trodt79+5t0mBE+XX8+HGcnZ1RqVR07NjRqPceioovMGN+r3uXei94sTUvLjtQ4JhXIrYw+OgWenQfwbm7MTg9Ow1XtZWxt2EyiqIj+fAGnNs+BSp1qT8g0CmwdG8UgCToQgghhBBCVGEGJecrVqwwdxyinLpz5w5qtZorV67w5JNPluoccclFJ+b3upGQztvrj+fbpsnJJsvahoO1mnIsKoLE03tx6zYClQUT8/SoCKydPXBu/YTJ4lj2ZxRv92qExtoyS/OFEEIIIYQQlmV0QThRdVy/fp24uDgcHBxKnZgDeDobtubv/sR8zP61PHYpgmd6vs6xO1exf+Jt3G0dSh3Hg1J0WlL+2Y5j4GOobGxN+oy7ToHvwi8zvHPRz/ALIYQQQlRGhT06cnl2v3JzPiHKikzTiQJSU1NJT09n27ZttGrVioYNC28JZqj2Ae74uNoV6HteFBttbsX2A37NmO7qyd39P2BXuwVqAxLzSX3qPkCkheveyANV7Cm0ybdxaBCEWmNvVGLe2MfZoOOuxKeVNkQhhBBCCCFEBVfhkvNFixbh7++PnZ0dHTp04NChQ5YOqVJJSUnh77//5uLFi7z22msmOaeVWsW0/rnPU5eUoE/ZuYxFGz8hJ/Emf2ZlcPyxIdR4OgQrx2rFvu+pZjW4+PHjrAq/bpKYIbfN26IXmtPP5RpTX+mFlaM7Vg6F118oyls9GvBs65oGHVvb3XKrAoQQQgghhBCWVaGS8x9++IEJEyYwbdo0IiIiaNGiBb179yYuLs7SoVV4OTk55OTksHz5ch577DGaNm1q0vP3aerD4lda4+1ayBJ3RcFamwPA/trNme/kzp1ti7B288baxaPEYmvnPurL/Jc7lFh4zhhv9ajPf7o40qGmPU2aNKFRQC1U1jZGncPdwYYx3eoxOMi/xA8l1CoYHORf6niFEEIIIYQQFVuFSs4///xzRowYwbBhwwgMDGTJkiU4ODjwzTffWDq0Ci0nJ4ft27dz+vRpxo0bZ7br9Gnqw5R+jQts/8+Wz5mxfQna9CS2OlbjaMu+eD47DRv3h4o938TeDbn48eMcuXKXjceus+/CLdMEqtPy1fqtVK9eHY1GQ8OGDWkf4I63i61Rp/no6WZYqVX8ceZmoW3k7jWic4AUgxNCCCGEEKIKM6gg3IIFCww+4ZtvvlnqYIqTlZXFkSNHCAkJ0W9Tq9X06NGD8PDwQt+TmZlJZmam/uukpCSzxFZRKYqCoih89tlnTJo0yazXysrR8f7P//BjxPW8i2Ol6NCqrdjn35Kz8Te4vXE2bl1eQ+MZUOL5Ln78ONsjY3nkkz9MNlsOkHU7GmsXD+6kZhFv5U5DFxcgd2n+i+39mLvjvEHnef3RAB5v7qPv8V4cR1sr3utT8EMLIYQQQgghRNVhUHI+d+7cfF/funWLtLQ03NzcAEhISMDBwQFPT0+zJee3b99Gq9Xi5eWVb7uXlxdnzpwp9D2zZs0iNDTULPFUdIqisGHDBho0aGD2xHzW1kiW/RmFLm/6WFFY/tMMTnrV5T8dn+WHhxqT41Qdj6BBqG2Kr+w+oLUHnz/XnrCTMYxeHVHijLShFEVHTvx1lOwMlJws7Go2KdACzr+Go0HnctBY0cov9xl5Q5bap2ZqORQVT1Dd6qULXgghhBBCCFHhGbSONioqSv+aOXMmLVu25PTp08THxxMfH8/p06dp3bo1H374obnjNUpISAiJiYn619WrVy0dUrmg0+mYNWsWzzzzDE2aNDHrtT7cHMnSvbmJuUrRoVJ0oFKxJ6A1v9u7EPfjDLTJt7H3b1liYv76owF8/lx7/Wy0qRLznKTbKDnZZMdfx9angb7o2/0t4AxtCZeWpWX06gjCTsYY3OPd0OOEEEIIIYQQlZPRD7lOmTKFL774Il97rYYNGzJ37lwmT55s0uDuVaNGDaysrLh582a+7Tdv3sTb27vQ99ja2uLi4pLvVdWtW7eO8+fP8/7775v9Wh9uPsnyv6KA3PZoP6yZxMtHf0PRaVnRqDOHHFzweHoSdn7Niz3P2O4BnPuoLyGP51Z8P3DpjkmWsiuKgjb1Ltm3r6BkpeFQv6N+n5uDDe0D3PMdfzc1C7Wh/eCA0E2R1HAy7Dl1QxN/UfnMmjWLdu3a4ezsjKenJ08//TRnz57Nd0xGRgbBwcFUr14dJycnBg4cWGAsjI6Opl+/fvpVTO+++y45OTlleStCCCGEEOIBGJ2cx8TEFPoHn1arLfDHoilpNBratGnDzp079dt0Oh07d+4kKCjIbNetLLRaLbNnz2bQoEEP3Le8xGvpFMauOcLyv66g1mkByLayYW9Aa/62dSTux1CyYi/gGNgFK/uiPzCp4WjD5dn9eLtnoL5YWtjJGIL/G/HAMeoyU1FyskiPisC+TpsCrdqGdQrA6p5MPOxkDMFrIv5dml8CBYhJzECnVYrt8a4it2Xb/R8EiKpjz549BAcHc+DAAbZv3052dja9evUiNTVVf8xbb73Fpk2bWL9+PXv27OHGjRsMGDBAv1+r1dKvXz+ysrLYv38/q1atYuXKlUydOtUStySEEEIIIUrBoGfO79W9e3def/11vv76a1q3bg3AkSNHGD16ND169DB5gPeaMGECQ4YMoW3btrRv35558+aRmprKsGHDzHrdim7dunW0bduW9957r8S2ZA9Cq1NY+Md5vtl3mcT0bJwzU/n++/dZ1HEQWxs+zIK2T5Pyz+9U7zMGaxfPIs+jAj4b1IKBbfL3BzfVc+a67AzSLx3Bzq85Tk27F9jv9r8WaHn3tP/8bd5Zf7xU1x279ijPt6vJV3ujUEG+c+T9JKb1D8z3QYCoWsLCwvJ9vXLlSjw9PTly5AiPPvooiYmJLF++nDVr1tCtWzcAVqxYQePGjTlw4AAdO3bk999/JzIykh07duDl5UXLli358MMPmThxItOnT0ej0Vji1oQQQghRQflP2lJg2+XZ/SwQSdVidHL+zTff6BNkG5vcvs85OTn07t2br7/+2uQB3uv555/n1q1bTJ06ldjYWFq2bElYWFiBInEiV3Z2NosWLWLMmDFYWxv9ozZK2MkYJv18goS07NzZcrUVyRoH/vRvxXlbB25vmIVj0264tH2y2PM42Kg5EdqnQLJqiufMFW0OLhq4cfQPnFs9XuRxswfktkC7955KKyE9m6/2RjHy0QB+PR6Tbzm+t6sd0/oH0qepT6nPLyqfxMREANzdc1dTHDlyhOzs7HwffjZq1Ag/Pz/Cw8Pp2LEj4eHhNGvWLN9Y2Lt3b0aPHs2pU6do1apVgetINwshhBBCiPLF6IzNw8ODrVu3cu7cOX2V9EaNGtGgQQOTB1eYMWPGMGbMmDK5VkX2888/07ZtW0aNGmX2xPyXiOu8te4YAJ7Jd1j9wxSm9BrNQb9mzH50MIn71+L6yEtoPPyLPU9NNw1/TepZ6D5Dqp4XR1F0pJzcybhRg+kwNJSdZ26y4dgN4lOz9Mf43JMsh52MYdTqB18+n+fX4zHsebcrR67cJS45A0/n3KXsMmMu7qXT6Rg/fjwPP/wwTZs2BSA2NhaNRqPvjpHHy8uL2NhY/TGFdbLI21cY6WYhhBBCCFG+lDpr8/f3R1EU6tata/bkTxguKyuL1atXM2jQIJycnMy6jD0rR8ejn+wkNjkLlaJDUam55VSN8NrNiLOy4c5vC7Cr0wa3R14u9jwaNXwysAXP3LeM/V6lrWauKApKVjopJ3bg0vZJFoTH4ROZxLT+gXzQL5BDUfEFkmWtTmH6r6dKdb1CYyD3+fMjV+5KuzRRrODgYE6ePMlff/1l9muFhIQwYcIE/ddJSUnUqlXL7NcVQgghhBCFM7ogXFpaGsOHD8fBwYEmTZoQHR0NwNixY5k9e7bJAxSG++2337h79y5PPvkkzs7OZk3MP9oUSYPJvxGbnEVA/HV2fP0GDW5dRlGpmdZzNEfO/Ilj0244Nny4yHN0aeDB9yM6cvqjx4tNzKH01cxTjm5BUXT5ltPHJGYwenUE207GFPqeQ1HxxCZlFrrvQUi7NFGcMWPGsHnzZnbt2kXNmv/+e/D29iYrK4uEhIR8x9/bqcLb27vQThZ5+woj3SyEEEIIIcoXo6e8Q0JCOH78OLt376ZPnz767T169GD69OlMmjTJpAGKkmVkZLBt2zbat29P9erVzb6S4cmFf/LPtST9bPl1F08O1QwkVW3F3d0rsfVtgHv3EcWeY/gjtZnyRFODrqfVKeh0Cm72NiSkG/b8ty4zlZSTf+DSpn+h+xVgzPdH81Vfz1vWnpmjM+gaxpJ2aaIwiqIwduxYfvnlF3bv3k1AQEC+/W3atMHGxoadO3cycOBAAM6ePUt0dLS+U0VQUBAzZ84kLi4OT8/cYovbt2/HxcWFwMDAsr0hIYQQQghRKkZncRs2bOCHH36gY8eO+WZmmzRpwsWLF00anCjZn3/+SYsWLWjcuDE+PuYtLJbbIi2Cf64l0ST2AvM3fcbg5z8kxsWDkL5vcidsIfZ1WuPQoFOR51ABIx8N0Pcsv/fchS0xDzsZQ+imSKOeN0/6+1ecmvUoMjHPc39btNj/zaiP72H6+glOtlbSLk0UKjg4mDVr1rBx40acnZ31z4i7urpib2+Pq6srw4cPZ8KECbi7u+Pi4sLYsWMJCgqiY8eOAPTq1YvAwEAGDx7Mp59+SmxsLJMnTyY4OBhbW1tL3p4QQghRKoVVCxeFk+9V5WF0cn7r1i39zMy9UlNTzbqMWuSXmZlJREQEHh4eWFtbm70gX9jJGKZtPElcUgao1ERX8+GYb0O0KCQd3oB1NV/cewcX+ztgZ6Pm6JRe2GusCpz7/gTcx9WOJ1v48NXeKIMrtGvTk0k78yfObfqX6ndRIffDg1Xhl3GwUZOWbboZ9M71PaT4myjU4sWLAejSpUu+7StWrGDo0KEAzJ07F7VazcCBA8nMzKR37958+eWX+mOtrKzYvHkzo0ePJigoCEdHR4YMGcKMGTPK6jaEEEIIIcQDMjo5b9u2LVu2bGHs2LEA+iTo66+/1i+xFOZ1/PhxGjRogKIoNGrUyKzXyutdPnfHeTpEn2DljqU8/9JskuyceKffW9zeOg9bnwbY121XYkKcka3j2NUE2ge462fJL99OZe6O8wWOjU3MYOneKIPjTI7YjGPT7ji16P1AHxIpkK+Ce3Hu71tenFc61i5tSKKSU5SSf4vs7OxYtGgRixYtKvKY2rVrs3XrVlOGJoQQQgghypDRyfnHH39M3759iYyMJCcnh/nz5xMZGcn+/fvZs2ePOWIU/5OTk8PFixdJTU0lOzubTp2KXj5uCmEnY5j+aySxiemgUhFVzZeTXvVQa3NIObULK8dqVO8djMrKxuBzbo+MZcK6YyUuUzd4tjwtkfRLR3KTciPiMAVDY3SytaZjHanSLoQQQgghhCia0dXaH3nkEY4dO0ZOTg7NmjXj999/x9PTk/DwcNq0aWOOGAVw5coVsrKyuHjxIp06dTJ7ZeWwkzGMXh1Bk4g9bPjubWyzM4lzrs67/cZz8c/V5CTEYufXzOiE+Jt9lx+oX/m9ko//jsrGDocGnco0MfdxteO1h/0NPv7Tgc1lSbsQQgghhBCiWKUq6123bl2WLVtm6lhEIRRFIS4ujn/++YfOnTvz+OOPm/2aWp1C6K+nUIDLbr6c9vDHRqclISoClY09bl2GYmXnZPR51aqCRdhKFV/qXTJvnMOhYSfUNsUXu+rTxItDl+OJTzWsyntxxnStx8P1auiX5X+z73KJ73miuQ+PNzdvoT4hhBBCVE2FFQK7PLufBSIRQpiC0TPnVlZWxMXFFdh+584drKysCnmHKK3ExESSk5PZvXs3/fv3x83NrUyue2neUj776m3UOi0Xa9QipO+bRO9bQ/qFQ2i8AoxOzPPmjE2RmKee+QuVxgFb34YlxuHmYMOil9uwb2J3TFGrsL6XE0F1q2OlVtE+wB0fVzuKO62bvTXzX2j14BcWQgghRKH27t1L//798fX1RaVSsWHDhnz7FUVh6tSp+Pj4YG9vT48ePTh/vmCtGyGEKA+MTs6LKl6UmZmJRqN54IBErrS0NP1/YJ5//vmyuej/frYx7j6cr+6HjU5LZsx5sm5dxrn1E7j3HIXapuhe3cMf8WfJK63xcc1/jLerHcONWAZeGG1aIhnXTqHxrofKWoOVo1uJ7xnUpiZWahWrD1zBgJpbJbq3T7mVWsW0/rnt4ApL0FXAbFnOLoQQQphVamoqLVq0KLJg5qeffsqCBQtYsmQJBw8exNHRkd69e5ORYZpH7IQQwpQMXta+YMECILc6+9dff42T07+zllqtlr1795q9cnhVkJ2dTXJyMhs3bmTYsGFmvda9vcWbbVxDwD8HUf24HpvOjzDttBWJ4T+Qffsq7j1fx8q+6Gfcn239EB8PaI7GOveznp6B3gV6lh+Kime5AcvAC5N++Ri2DzXCyrEaNm7eBr/v54jrtKpVjbnbz5Xquvdyd7Qp0Ke8T1MfFr/SutA2cNP6B9KnqSxnF0IIIcypb9++9O3bt9B9iqIwb948Jk+ezFNPPQXAt99+i5eXFxs2bOCFF14oy1CFEKJEBifnc+fOBXIHuiVLluRbwq7RaPD392fJkiWmj7AK0el0fPXVVwwZMsTsifnWf2KYvPEk8SmZoFLR43wyj962xuv4NfwdMqhOMlm1W+Ia9HyRrcm8XWyZ/mSTAkmolVpFUN381cnzloHHJmYYXok9IwVt8h3Uto6orGywqeZr1D3eSc3ijTURRr2nKB891bTQWfA+TX0K/TBCZsyFEEIIy4qKiiI2NpYePXrot7m6utKhQwfCw8OLTM4zMzPJzMzUf52UlGT2WIUQAoxIzqOicntOd+3alZ9//plq1aqZLaiqRlEUbt++zS+//EJwcLDZrzdrayRL90bxRvg6HkqK44PeY9hRvwPb67UnddKn1E0/w1tTZjF1R+5seWHJ9Fs9GjCmWz2Dk9C8ZeCjV0cY1B88K+4S1i6eKNpsbH3qG3V/pvb6owE83rzoDwYK+zBCCCGEEJYVGxsLgJeXV77tXl5e+n2FmTVrFqGhoWaNTQghCmP0M+ddu3bF1rZghez09HRmzJhhkqCqmrlz5+Lo6MjIkSPNfq2t/9xg6Z5LANxydOOGswcoCtrUu+gyUlA7uKDq9R4vdWvD4lda433f8+M+rnYseaU143rUN3p2OG8Z+P3nvJeSnUlO0m20yfGoNPbYetcz/iZNaP4LLQl5PNCiMQghhBCi7ISEhJCYmKh/Xb161dIhCSGqCKNbqYWGhjJq1CgcHBzybU9LSyM0NJSpU6eaLLjKLjY2lk2bNjFhwoQyuZ5Wp3DrjfFM0Kn5/NHBrG/eC4C0c+EkR2zGvXcwDg06EZucxaGoeLMs2b73nDsiY/nl2HV9m7Oc5Nt4OdtR2zGHCJe2JrnnB1XDsfhWbUIIIYQon7y9c+vU3Lx5Ex+ffx/Bu3nzJi1btizyfba2toVORAkhhLkZnZwrilLoM8jHjx/H3d29kHeIwsyZM4exY8cyYsSIMrvmoah4rts4kWVlA4AuMw3UanKSb+P57HRU1jb6Y+OScwucmWPJdt45g+pW5/1+gew/H0d03B3OHLjMlPEvY2VlRZuPtpOQ9uC9yR9U+KXbPFy/hqXDEEIIIYSRAgIC8Pb2ZufOnfpkPCkpiYMHDzJ69GjLBieEEIUwODmvVq0aKpUKlUpFgwYN8iXoWq2WlJQURo0aZZYgK5Pr16+zY8cO3nnnnSILrZmCvhJ7UjodZ4fg0aktcY89y1cdBgKQce0UCX/+l+q9RuPSpn+B99/bNsyc0lJT8FYnkZFxnZff/fd5+9kDmjF6dYTBxePu5e5ow0dPNeXDLaeNKkBXOCnsJoQQQpRXKSkpXLhwQf91VFQUx44dw93dHT8/P8aPH89HH31E/fr1CQgIYMqUKfj6+vL0009bLmghhCiCwcn5vHnzUBSF1157jdDQUFxdXfX78qq1BwUFmSXIymLu3LkEBwfzyiuvmDUxDzsZk6+913vRGdzRXselSRqKNhtQkXEpAs8BH6C2dSzw/uqOmgJtw0xNURSysrJYsWIFY8aMoX79/EXfimpTZojJjwdSzdGWvk29+WbfZYMK0BVFCr0JIYQQ5dfff/9N165d9V/nPSo4ZMgQVq5cyXvvvUdqaiojR44kISGBRx55hLCwMOzsymYSQhTkP2lLlbquEMYwODkfMmQIkLtEqFOnTtjY2JTwDpHnypUrHDp0iODgYDQajVmvFXYyhjdXHeQ/mz9nW4MgNjd+lE8fG4oKyPp+O0m7v8al60jcHh1c5Dk+LKJtmKnk5ORw5swZrl69yptvvlnkcfc/8347OZMPt5wu8fxTNp4kNUv7wHFWc7ChYx1JzoUQQojyqkuXLihK0R/Bq1QqZsyYIUWLhRAVgkHV2u/t79iqVSvS09NJSkoq9CXyW7JkCb6+vvTr18/siblWpxC6KZIsKxuS7BzJVud+9qIoOnQ6LckRm6k94F1satQq8hy5bcN8itz/oBRF4bPPPqNJkyb07du3xOPznk9/quVDDH04AJ9iKr3nuT8xL+2s+awBzaRfuRBCCCGEEKJMGDRzXq1aNWJiYvD09MTNza3QJdl5heK02gefsawMoqOjOXPmDC+88AI2NjbmX2mQlETiM8/h7/koMbVb8EHvMQDkJN0ifvtiqnUZhnvvYFKAt3rUZ+3hq/mWi+c9p11cP+8HoSgKx44dIyYmhkmTJpX6PM+1rcX8nedNGFlBPq52TOsfSJ+m5vuQQgghhBBCCCHuZVBy/scff+grse/atcusAVUG33//Pc888wyOjo64ubmVzUWdnEizscNa9++HI4o2h7u7vqFa1+HYuD+k3+5fw5G/JnYzaYu0ksycOZMPPviAVq1aler99z9Hbw5jutbl4XoeZv9eCCGEEEIIIcT9DErO58+fT6tWrXBxceHKlSs8//zz0v+xENeuXePGjRt07NgRW1tb8xcbiYuDwYPhk0+gZUuuLlrOn8sOoE1P5u7Or3B9+EU8nppY4G2eznZmaZFWmEOHDnH37l0mT55c6nOEnYwpdeV2Q6gAb1c73urZUJJyIYQQQgghCiFF9czPoGfON2/eTGpqKgDDhg0jMTHRrEFVRL///juurq54eXkREBBg1mrsem5uoNHA/3427QPc8XK04s7mz3Bu/QQ21fIvUVeRu2Tb3JXY88ycOZN27drRu3fvfNu1OoXwi3fYeOw64RfvoNUVnXbnPUdvzsQcYFr/QEnMhRBCCCGEEBZj0Mx5o0aNCAkJoWvXriiKwrp163BxcSn02FdffdWkAZZ3cXFxJCQkUK1aNRwdHXF2djbvBa9cgf/7P/j6a6hdGzZtAiA9PZ3333+fN/q8yLSUaahU6nwJbVkmoQcOHCAtLY2QkJACH1IUtjy9uGe8D0XFm2wpu5uDDXbWVsQm/Xs+b3m+XAghhBBCCFEOGJScL1myhAkTJrBlyxZUKhWTJ08udGZYpVJVqeT82LFj+Pj4kJ2dTbt27crmotWqgUoFCQm5yTmQkZHBU089xVtvvUXf3u3xeqhgAlxWSeicOXOYMGECarW60MS8sOXpsYkZjF4dweJXWheILy7ZdM+Yzx7QLF9rtrJ41l4IIYQQQgghDGFQct6pUycOHDgAgFqt5ty5c3h6epo1sPIsOTmZlJQUrl27RosWLfDy8jLvBc+cgTffhO+/h+rV4fffgdx+4XPmzGHw4MH8+uuv+mfc7+8PXhZJ6KFDh9BqtYwdOxYrK6sC+4tbnq6QO7MfuimSnoHe+eL0dH7w5/bdHGyYPaCZPvEvi2fthRBCCCGEEMIYBj1zfq+oqCg8PDzMEUuFcO3aNW7evMn169d54oknyubZcldXyMmB+Hj9prS0NJ588kkeeughHnrooQLF5+7tDx5Ut7pZE/MlS5bQrFkz2rZtW2QRvJKWpytATGIGh6Li821vH+COj6sdpYnezcGGt3o04MjknrJsXZRbe/fupX///vj6+qJSqdiwYUO+/YqiMHXqVHx8fLC3t6dHjx6cP5+/nWB8fDwvv/wyLi4uuLm5MXz4cFJSUsrwLoQQQpiS/6QtBV5CiMrPoJnze9X+31JqgGbNmrF161Zq1apl0qDKo6ysLDIyMtixYwdDhgwxf1IeEQFTp8K6deDjA3/8AeT+ob5ixQqeeOIJvv32W2rUqGHeOIpx5MgRbGxsePbZZ7G3ty/2WEOXp99/nJVaxbT+gYxeHYEKCjxHr5Dbt92/hiM1nGxBgdupmbJkXVQYqamptGjRgtdee40BAwYU2P/pp5+yYMECVq1aRUBAAFOmTKF3795ERkbqPwx7+eWXiYmJYfv27WRnZzNs2DBGjhzJmjVryvp2hBBCCCFEKRmdnN/r8uXLZGdnmyqWcis5OZmLFy+SlpbG0KFDy+aiTk6QlpY7W+7gAOT+ET9s2DDat29P9erVC10+XhYURWH9+vX06NEDW1tbHB0dS3yPocvTCzuuT1MfFr/S2mLP0QthTn379qVv376F7lMUhXnz5jF58mSeeuopAL799lu8vLzYsGEDL7zwAqdPnyYsLIzDhw/Ttm1bAL744gsef/xxPvvsM3x9fQs9txBCCCGEKF8eKDmv7HQ6HTk5OXzzzTe8+eab5p8t37sXFiyAtWuhQQP9bDnAli1beOSRR5g5cyb169c3bxzFOHnyJI6OjrRs2RJ3d8NbsuUtT49NzCj0ufO8XuNFtXmzxHP0QlhaVFQUsbGx9OjRQ7/N1dWVDh06EB4ezgsvvEB4eDhubm76xBygR48eqNVqDh48yDPPPFPouTMzM8nMzNR/nZSUZL4bEUIIIYQQJXqg5Lxz584lLmeuqHJycvj777/Jzs5m3LhxZXNRjQaSkyExMbfwG7nPlo8fPx5XV1e6d+9uscRcURT27NlD3bp1sbe3N3o5fUnL06HkNm95z9ELUVXExsYCFCg66eXlpd8XGxtboECntbU17u7u+mMKM2vWLEJDQ00csRDCaNNdi9mXWHZxCCGEsDijC8Lda+vWrfj4VL4lxYqiMGfOHDp27Ejnzp3Ne7HffoPXXgNFgY4dYds2fWJ++PBhFEVh2LBhzJkzp8hia+Z26dIl7ty5g7W1NbVq1Sr1c+55y9O9XfPfh7erXaFt1IQQ5hMSEkJiYqL+dfXqVUuHJIQQQghRpZVq5lyr1bJhwwZOnz4NQJMmTXjyySct9gy0qSiKwp9//olKpSIkJKRsLqrVwu3buc+X/+/Z7aysLEJDQ4mJiWHBggUEBQWVTSz3URSF06dPoygKGo2GRx555IHPKcvThTCct7c3ADdv3sz3QejNmzdp2bKl/pi4uLh878vJySE+Pl7//sLY2tpia2tr+qBFuVJchefLs/uVYSSiqJ/FZct87i6EEKIcMjo5v3DhAv369ePatWs0bNgQyF0eWatWLbZs2ULdunVNHmRZmTlzJpMnTzb/hdavh/37Ye5ceOKJ3Nf/XLx4EQ8PDzp06MCTTz5p/liKEBcXh6OjI2fPni3ymdXSkuXpQhgmICAAb29vdu7cqU/Gk5KSOHjwIKNHjwYgKCiIhIQEjhw5Qps2bQD4448/0Ol0dOjQwVKhi4qgqOXUspRaCCGEsAijk/M333yTOnXqEB4eri8IdufOHV555RXefPNNtmypeH0Yd+7cib29fdkk5pD7XPnNm7m9y61zfwQ6nY4FCxawZ88eVq1aZdHE/ObNm1y4cAFfX1+DE3OtTikwGw7IDLkQJUhJSeHChQv6r6Oiojh27Bju7u74+fkxfvx4PvroI+rXr69vpebr68vTTz8NQOPGjenTpw8jRoxgyZIlZGdnM2bMGF544QWp1C6EEEIIUYEYnZzv2bOHAwcO5KvUXb16dWbPns3DDz9s0uDyXL58mQ8//JA//viD2NhYfH19eeWVV/jggw/QaDSlPq+iKHz88ce8//775q/Evnw53LgBU6bAsGG5z5n/z507d1AUBQcHB37++Wfzx1KElJQUNBoNmzdvZvjw4Qa/L+xkTIE2Z24ONgAkpP3bas9HWp8JUcDff/9N165d9V9PmDABgCFDhrBy5Uree+89UlNTGTlyJAkJCTzyyCOEhYXlq0Hx3//+lzFjxtC9e3fUajUDBw5kwYIFZX4vQgghhBCi9IxOzm1tbUlOTi6wPS+xM4czZ86g0+lYunQp9erV4+TJk4wYMYLU1FQ+++yzUp1zx44duLq6EhISUjbJcFwcxMTkFn6753pr167lm2++4aeffmLkyJHmj6MIaWlp7N27l4YNGxqdmI9eHVGgPdq9SXme2MQMRq+OkOJvQtyjS5cuKEphDQZzqVQqZsyYwYwZM4o8xt3dnTVr1pgjPCGEEEIIUUaMTs6feOIJRo4cyfLly2nfvj0ABw8eZNSoUWZbit2nTx/69Omj/7pOnTqcPXuWxYsXlyo5nz9/PiEhIVhZWZk3MZ87N7c9WnAwTJqULynPzMzk7t27nD17li1btmBjY2O+OIqRnZ2NSqXiq6++Yvz48Ua9V6tTCN0UWWjf8sIo5LZNC90USc9Ab1niLoQQQohKq7AigOW9EOODxFwR71eUHfn9MIzRrdQWLFhA3bp1CQoKws7ODjs7Ox5++GHq1avHvHnzzBBi4RITE/MtrS9MZmYmSUlJ+V4Ar7/+OtbW1uafMb96Fa5fz/3/91xrx44d9OvXD2dnZ6ZNm2axxDyv6n50dLTRiTnkPk9+71J2QyhATGIGh6Lijb6eEEIIIYQQQlRWRs+cu7m5sXHjRi5cuKBvpda4cWPq1atn8uCKcuHCBb744osSZ81nzZpFaGhoge1m6xeuKPDhh9CgAbzwAvznP/mSckVRuHHjBhs2bGDjxo04/q91WllTFAWtVsvnn3/Oe++9V+rzxCUbl5ib6r1CCCGEEEIIUdkYPXM+Y8YM0tLSqFevHv3796d///7Uq1eP9PT0Yp+JLMykSZNQqVTFvs6cOZPvPdevX6dPnz4MGjSIESNGFHv+kJAQEhMT9a+rV68ae7vGUang/HmIjv736/+JiIjg8ccfp3r16ixcuNBiiTnA6tWruX79+gMl5gCezqX/kONB3iuEEEIIIYQQlY3RM+ehoaGMGjUKBweHfNvT0tIIDQ1l6tSpBp/r7bffZujQocUeU6dOHf3/v3HjBl27dqVTp0589dVXJZ7f1tYWW1tbg+Mplexs+OAD6NULevSAb7/Nl5QDXLlyhTlz5vDdd9+Zb9beAFlZWcydO5eJEyea5HztA9zxcbUjNjHD4OfOVYC367+t1oQQQgghhBBClCI5VxSl0Ge1jx8/XuIz4Pfz8PDAw8PDoGOvX79O165dadOmDStWrECtNnrS3zysreHUKahfP/fre743ly5d4v3332fVqlV8//33Fgow14oVK+jVq5fJEnMAK7WKaf0DGb06AhWUmKDnfWem9Q+UYnBCCCGEEBReKKu8MzTminhvovwx9PeoLArMmbuwncHJebVq1fRLzRs0aJAvQddqtaSkpDBq1CiTBXav69ev06VLF2rXrs1nn33GrVu39Pu8vb3Ncs1ipaXBe+/B8OHQqhVs3lxgtvzixYuMGzeOhQsXmn/2vhgZGRl8+eWXvPXWW2YpgNenqQ+LX2ltUJ9zb+lzLoQQQgghhBCFMjg5nzdvHoqi8NprrxEaGoqrq6t+n0ajwd/fn6CgILMEuX37di5cuMCFCxeoWbNmvn3F9Qc2GxsbOHo09/nyVq3yJeZxcXFMnz6d+fPn8+uvv1p0hn/lypX06dOHcePGmbUyfZ+mPvQM9OZQVDxxyRl4Ov+7bP3+bTJjLoQQQgghhBAFGZycDxkyBICAgAA6depUpu2/hg4dWuKz6WaXmAjvvpvbr7xOHfjrrwKz5efPn2fUqFF8/vnnFmuPBrnP/69atYrhw4ej0WjK5JpWahVBdasX2F7YNiGEEEIIIYQQ+Rn9zHlAQAAxMTFF7vfz83uggMottRoOHYJz53KT83sS85SUFD799FMmT57Mli1bLFr0be3atfTo0YMhQ4aUWWIuhBBCCAua7lrE9sSyjUMIIcQDMTo59/f3L3aJtFarfaCAypW4OAgJgU8+gRo1ICIiN0m/x4ULFxg5ciSTJ0+2aDKckpLCpk2b6Nu3L66urmZdxi6EEEIIIYz3IAXSHqQI24MUrJKibsJY8jtTekYn50ePHs33dXZ2NkePHuXzzz9n5syZJgusXFAU2L8/d7a8Ro18iXl2djZff/01Q4YM4eeff8bNzc1iYf7666889thjdO3a1aJxCCGEEMI8ivtj97LlFuwJIYQwIaOT8xYtWhTY1rZtW3x9fZkzZw4DBgwwSWAWc+0ahIbCvHng5ZXbJu2+2fLLly8zcuRIRowYgYODQ4Ge72UlOTmZ8PBwWrZsiZOTU74ifUIIIYQQQgghKg6TlRJv2LAhhw8fNtXpLCc9HfbsgUuXcr++JzHX6XRs2LABV1dXVqxYwaBBgywUJOzevRtra2tq1aqFn58fVlZWFotFCCGEEEIIIcSDMTo5T0pKyvdKTEzkzJkzTJ48mfr165sjRvO7cAFGj4acHKhfH06fhmbN8h1y48YNBgwYwPXr13Fzc+Ohhx6ySKhpaWlERETo+843btzYInEIIYQQQgghhDAdo5e1u7m5FSg2pigKtWrVYu3atSYLrEzdvQt//AFXr0JAANw3C33o0CF8fX2ZPXs2jRo1slCQ8M8//1CvXj0yMjLo1KmTxeIQQgghhBAVmxTtEqL8MTo537VrV76v1Wo1Hh4e1KtXD2tro09nOSdPwooV8Nln0K4dREYWSMrv3r3L+PHj8ff3Z/r06RargJ6dnc21a9e4ffs29erVk8RcCCGquqJaZ4G0zxJCCCEqKKOz6ccee8wccZS9q1chLAwmTQIPjwKJ+ZUrV0hNTWX06NF07NjRQkHCtWvXcHJy4ty5c/Tu3dticQghhBBCCCGEMJ9ST3VHRkYSHR1NVlZWvu1PPvnkAwdVJvr2hZ494b7Z/oyMDN5//32ysrL44osvLDZbrtPpuHv3Lvv27aN///6SmAshRHlRRrPW0jpLCCGEqFqMTs4vXbrEM888w4kTJ1CpVCiKAqBPYrVarWkjNIfUVHBxKZCYJyYmcvbsWbp3706/fv0sFFxui7S0tDT27dvH888/b7E4hBBCmElRCb4sSRdCCCGqLKOT83HjxhEQEMDOnTsJCAjg0KFD3Llzh7fffpvPPvvMHDGanqNjvi+1Wi1z5szh7NmzfPPNNxabLYfcmfv//ve/DB06tOL3jBdCCCFE+Sc1DIQQolwwOjkPDw/njz/+oEaNGqjVatRqNY888gizZs3izTff5OjRo+aI02yys7PZt28fXl5eTJw40WKJeU5ODjdv3mTPnj2MGjXKIjEIIYQQQojKRyqzi/LI1L+XhZ3v8mzLrYYuDaP7nGu1WpydnQGoUaMGN27cAKB27dqcPXvWtNGZkaIorFixguHDh9OlSxeGDRtm0efL58+fj4eHBy+99JJFYhBCVHyLFi3C398fOzs7OnTowKFDhywdUpXmP2lLkS8hRNmTMVIIUd4ZPXPetGlTjh8/TkBAAB06dODTTz9Fo9Hw1VdfUadOHXPEaHKKovD7779z8+ZNVqxYYdE4oqOj2bt3L2+//bbF4hBCVHw//PADEyZMYMmSJXTo0IF58+bRu3dvzp49i6enp6XDMx1TLL+V572FqHKqzBgphKjQjE7OJ0+eTGpqKgAzZszgiSeeoHPnzlSvXp0ffvjB5AGaw5gxY/juu+8sXgH9k08+4Z133mHw4MEWjUMIUfF9/vnnjBgxgmHDhgGwZMkStmzZwjfffMOkSZMsHF3FI5XSRWVU1O91VfidljFSCFERGJ2c35vQ1qtXjzNnzhAfH0+1atUsWkjNEHmV5adPn05SUpLF4rh06RJ///03b7zxBmlpaRaLQwhhnLxxI28sKS+ysrI4cuQIISEh+m1qtZoePXoQHh5e6HsyMzPJzMzUf52YmDtrXOzYOKtm0ftCrhkWbFHnuO/9TadtK/Swk3bFfO/vib2o9xd7jnver8ssemxOUhkWQ6nOUYViMOs9SAwF3m+KGIr7d7Vf9RpQ/sZHMH6MLNX4WIjifmZCiLJh6pyvsH/XhlzD4L8hlSrk6tWrCiAveclLXg/0unr1qqWHs3yuX7+uAMr+/fvzbX/33XeV9u3bF/qeadOmWfz7KC95yavyvS5evFgWw55RjB0jZXyUl7zkZa5XSX9DGj1zXpH5+vpy9epVnJ2dLTrLn5SURK1atbh69SouLi4Wi6O8kO9HQfI9ya+8fD8URSE5ORlfX1+LxWAqISEhTJgwQf91QkICtWvXJjo6GldXVwtGZnrl5ffH1CrrfYHcW0WUmJiIn58f7u7ulg7lgd0/Pup0OuLj46levbrBfz9W1p9zSeS+5b6rgtLct6F/Q1ap5FytVlOzZjHLMsuYi4tLlfpFLol8PwqS70l+5eH7UR4T1xo1amBlZcXNmzfzbb958ybe3t6FvsfW1hZbW9sC211dXS3+PTaX8vD7Yw6V9b5A7q0iUquNbgRkdsaOkYWNj25ubqW6dmX9OZdE7rtqkfs2jCF/Q5a/EVQIIYRRNBoNbdq0YefOnfptOp2OnTt3EhQUZMHIhBDC8mSMFEJUFFVq5lwIISqrCRMmMGTIENq2bUv79u2ZN28eqamp+srEQghRlckYKYSoCCQ5twBbW1umTZtW6JLSqki+HwXJ9yQ/+X6U7Pnnn+fWrVtMnTqV2NhYWrZsSVhYGF5eXga9vzJ/jyvrvVXW+wK5t4qovN/Xg46Rxirv3w9zkfuW+64KzHnfKkUphz0vhBBCCCGEEEKIKkSeORdCCCGEEEIIISxMknMhhBBCCCGEEMLCJDkXQgghhBBCCCEsTJJzIYQQQgghhBDCwiQ5t6DLly8zfPhwAgICsLe3p27dukybNo2srCxLh1amFi1ahL+/P3Z2dnTo0IFDhw5ZOiSLmDVrFu3atcPZ2RlPT0+efvppzp49a+mwyo3Zs2ejUqkYP368pUOp1Cr7uDRz5kw6deqEg4MDbm5ulg7ngVTGsXPv3r30798fX19fVCoVGzZssHRIJlGZx/fFixfTvHlzXFxccHFxISgoiN9++83SYZUblX1MLU5lGm+LUxnH4pJU1rG6JGUxlktybkFnzpxBp9OxdOlSTp06xdy5c1myZAnvv/++pUMrMz/88AMTJkxg2rRpRERE0KJFC3r37k1cXJylQytze/bsITg4mAMHDrB9+3ays7Pp1asXqamplg7N4g4fPszSpUtp3ry5pUOp9Cr7uJSVlcWgQYMYPXq0pUN5IJV17ExNTaVFixYsWrTI0qGYVGUe32vWrMns2bM5cuQIf//9N926deOpp57i1KlTlg6tXKjsY2pxKst4W5zKOhaXpLKO1SUpk7FcEeXKp59+qgQEBFg6jDLTvn17JTg4WP+1VqtVfH19lVmzZlkwqvIhLi5OAZQ9e/ZYOhSLSk5OVurXr69s375deeyxx5Rx48ZZOqQqpzKOSytWrFBcXV0tHUapVYWxE1B++eUXS4dhFpV9fK9WrZry9ddfWzqMcqsyjqnFqejjbXGqwlhckso8VpfEHGO5zJyXM4mJibi7u1s6jDKRlZXFkSNH6NGjh36bWq2mR48ehIeHWzCy8iExMRGgyvw+FCU4OJh+/frl+z0RZasqjUsVgYydFV9lHd+1Wi1r164lNTWVoKAgS4dTbsmYWjnIWCzMMZZbm+xM4oFduHCBL774gs8++8zSoZSJ27dvo9Vq8fLyyrfdy8uLM2fOWCiq8kGn0zF+/HgefvhhmjZtaulwLGbt2rVERERw+PBhS4dSZVW1cakikLGzYquM4/uJEycICgoiIyMDJycnfvnlFwIDAy0dVrkkY2rlIWNx1WausVxmzs1g0qRJqFSqYl/3/6O9fv06ffr0YdCgQYwYMcJCkYvyIjg4mJMnT7J27VpLh2IxV69eZdy4cfz3v//Fzs7O0uFUeJV5XCrNvQlhKZVxfG/YsCHHjh3j4MGDjB49miFDhhAZGWnpsMyqMo+pxZHxVohc5hrLZebcDN5++22GDh1a7DF16tTR//8bN27QtWtXOnXqxFdffWXm6MqPGjVqYGVlxc2bN/Ntv3nzJt7e3haKyvLGjBnD5s2b2bt3LzVr1rR0OBZz5MgR4uLiaN26tX6bVqtl7969LFy4kMzMTKysrCwYYcVSmcclY++topOxs+KqrOO7RqOhXr16ALRp04bDhw8zf/58li5dauHIzKcyj6nFqWrjbXFkLK66zDmWS3JuBh4eHnh4eBh07PXr1+natStt2rRhxYoVqNVVZzGDRqOhTZs27Ny5k6effhrIXSKyc+dOxowZY9ngLEBRFMaOHcsvv/zC7t27CQgIsHRIFtW9e3dOnDiRb9uwYcNo1KgREydOlMTcSJV5XDLm3ioDGTsrnqo2vut0OjIzMy0dhllV5jG1OFVtvC2OjMVVT1mM5ZKcW9D169fp0qULtWvX5rPPPuPWrVv6fVXlE7cJEyYwZMgQ2rZtS/v27Zk3bx6pqakMGzbM0qGVueDgYNasWcPGjRtxdnYmNjYWAFdXV+zt7S0cXdlzdnYu8AyPo6Mj1atXrzTPaZZHlX1cio6OJj4+nujoaLRaLceOHQOgXr16ODk5WTY4I1TWsTMlJYULFy7ov46KiuLYsWO4u7vj5+dnwcgeTGUe30NCQujbty9+fn4kJyezZs0adu/ezbZt2ywdWrlQ2cfU4lSW8bY4lXUsLkllHatLUiZjucnqvgujrVixQgEKfVUlX3zxheLn56doNBqlffv2yoEDBywdkkUU9buwYsUKS4dWbkgrNfOr7OPSkCFDCr23Xbt2WTo0o1XGsXPXrl2F/nyGDBli6dAeSGUe31977TWldu3aikajUTw8PJTu3bsrv//+u6XDKjcq+5hanMo03hanMo7FJamsY3VJymIsV/3vQkIIIYQQQgghhLCQivvQixBCCCGEEEIIUUlIci6EEEIIIYQQQliYJOdCCCGEEEIIIYSFSXIuhBBCCCGEEEJYmCTnQgghhBBCCCGEhUlyLoQQQgghhBBCWJgk50IIIYQQQgghhIVJci7E/7d371FRVXscwL+QzDAwPJKHCCIEIwqKiq8M7hKur9FLhFammMgjFRUR8IGaJlyFyow0ugJiJpKSjxT14oNQoRDNUO8oLxEQxBJURPMBKMLv/uHiLA/MjAxKEOzPWqzV7HPO3r9zjN/Ze/ZhH4ZhGIZhGIZhmHbGBudMh6empoYDBw60dxgAgDt37sDY2BilpaVt1saxY8cwePBgNDQ0KN3P29sbkyZNarM4GIZpPRcXFwQFBbV3GJxRo0YhMTGxzeqvrKyEsbExfv/9d6X7xcfHQ19fv83iYBhGuY6Wm9pCaWkp1NTUIJPJXqqeAwcOQCKR4LXXXus016ygoAAmJiZ48OABgI6dk2NjY+Hm5tbeYfzl2OC8E1FTU1P6ExYW1qp6XVxcoKamhl27dvHKN27cCEtLS5Xq8vb25uIRCASQSCRYs2YNnj592qrYXgVVBrkRERFwd3dX+byft3DhQgwdOhRCoRCDBw9utn3ChAnQ0NDAzp07W91Ga9XW1sLb2xv29vbo1q2bwuuSnp6OIUOGQCgUQiKRID4+/i+Nk2G6OlU6VIcOHcLNmzcxbdq0VrcXFxcHFxcX6OrqQk1NDffu3eNtNzQ0xMyZMxEaGtrqNl5GREQEHB0doaWlpfC6lJWVwdXVFVpaWjA2NsbSpUvb9d7DMJ1RRx7sqcrPzw/vv/8+rl+/jrVr1750fa/qS4OXsWLFCgQEBEBHR6dN23kVOdnX1xcXLlxARkZGm8ba0bDBeSdSXl7O/WzcuBG6urq8siVLlqhU35MnT7j/1tTUxKpVq1BXV/fScU6YMAHl5eUoLCzE4sWLERYWhvXr1790vW2turoaW7duxUcfffTSdfn6+mLq1KkKt3t7eyMqKuql21FVfX09RCIRFi5ciLFjx8rdp6SkBK6urvjnP/8JmUyGoKAgzJo1CykpKX9xtAzDtERUVBR8fHygrt76W351dTUmTJiAjz/+WOE+Pj4+2LlzJ6qqqlrdTms9efIEU6ZMwbx58+Rur6+vh6urK548eYLTp09j+/btiI+Px+rVq//iSBmG+Tt4+PAhbt26BalUClNT0zYfzKqqNf3xsrIyJCcnw9vb+9UH1MSryMkCgQDTp09vl/5wuyKmU9q2bRvp6elxn0NDQ2nQoEG8fTZs2EAWFhbcZy8vL3J3d6fw8HDq2bMnWVpaEhGRs7Mz+fj4kIGBAW3atOmFxz8vMDCQnJ2dle4zbtw4GjlypMJzAUBJSUnc59WrV5OJiQldvHiRiIhqa2spJCSEevXqRQKBgKytrenbb78lIqKnT5+Sr68vWVpakqamJtnY2NDGjRt51wUA7yctLU1uHHv37iUjIyNeWVVVFU2fPp0MDQ1JU1OTJBIJfffddwrP5Xny/k0aXbt2jQBQUVGRwuObXsva2loKCAggIyMjEgqF5OTkRL/99hvvmIMHD5JEIiGhUEguLi4UHx9PAOju3bsvrL9RSEgI9e/fn1c2depUkkqlCmNlmK7G2dmZAgMDuc/Jycmkq6tLO3bs4Mq2bt1KdnZ2JBAIyMTEhPz9/bltkZGRNGDAANLS0qJevXrRvHnz6MGDB0RElJaW1ixvhYaGyo3j1q1bpKamRjk5OVxZQ0MDhYaGkrm5OQkEAurZsycFBAS06Lwa25aXM4iI3njjDS7/ytP03kREFB0dTVZWVqShoUE2NjaUkJDA256fn09OTk4kFArJ1taWUlNTm90XlNVPRHTkyBFSV1eniooKriwmJoZ0dXXp8ePHCuNlmM6mo+QmmUxGLi4uJBaLSUdHh4YMGUJZWVnc9lOnTpGzszOJRCLS19en8ePHU1VVFRERHT16lJycnEhPT4+6d+9Orq6uvP5SSUkJAaD//e9/XFl2djZNmDCBtLW1ydjYmGbMmEG3b9+WG5u880hLS6PKykqaNm0amZqakkgkogEDBlBiYiLv2Pr6elq3bh1ZW1uTQCAgc3NzCg8PJyJqVmdj/7i+vp7+/e9/k5mZGQkEAho0aBAdPXq02fns2rWLRo0aRUKhkLZt20alpaX09ttvk76+PmlpaZGdnR0dPnxY7jkREa1fv56GDRvGK+voOfnnn38mgUBA1dXVCs+rs2Ez5wzPiRMnUFBQgNTUVCQnJ3Plurq6WLlyJdasWYNHjx690jZFIhFvll4RIkJAQAASEhKQkZGBgQMHAgBmzpyJH374AVFRUcjPz8fmzZshFosBAA0NDejVqxf27t2LvLw8rF69Gh9//DH27NkDAFiyZAk++OADbja/vLwcjo6OctvPyMjA0KFDeWWffPIJ8vLycPToUeTn5yMmJgaGhoYvczkAAL1790aPHj1UepQnJCQE+/btw/bt23HhwgVIJBJIpVJuFqukpATvv/8+Jk2ahIsXL8LPzw8rV65UObYzZ840m1WXSqU4c+aMynUxTFeQmJgIDw8P7Ny5Ex9++CEAICYmBv7+/pgzZw6ys7Nx6NAhSCQS7hh1dXVERUUhNzcX27dvx8mTJxESEgIAcHR0bPZ0lKIno06dOgUtLS3Y2tpyZfv27cOGDRuwefNmFBYW4sCBA7C3t38l5zpixAiV8lZSUhICAwOxePFi5OTkwM/PDz4+PkhLSwPwbHZl0qRJ0NLSwtmzZxEXF9fqvGVvb48ePXpwZVKpFPfv30dubq7K9TFMZ9CeuenDDz9Er169kJWVhfPnz2P58uXQ0NAAAMhkMowZMwZ2dnY4c+YMTp06BTc3N9TX1wMAHj16hEWLFuHcuXM4ceIE1NXVMXnyZIVr9dy7dw+jR4+Gg4MDzp07h2PHjuHmzZv44IMP5O7v6OiIgoICAM/yZWPfsLa2FkOHDsXhw4eRk5ODOXPmwNPTE7/99ht37IoVK/D5559z/cPExEQu7zTud/z4cZSXl2P//v0AgK+//hqRkZH48ssvcenSJUilUrzzzjsoLCzkxbV8+XIEBgYiPz8fUqkU/v7+ePz4MX755RdkZ2dj3bp1XP9XnoyMDAwbNkzhdqDj5eRhw4bh6dOnOHv2rMpt/G2197cDTNto7cx5jx49ms0iNH7LWltbSxYWFrRmzRqFx6syc97Q0ECpqakkFAppyZIlCs8FAO3du5emT59Otra29Pvvv3PbCgoKCAClpqYqvhhN+Pv703vvvac0bnnc3d3J19eXV+bm5kY+Pj4tbvt5ymbOiYgcHBwoLCxM4fbn43748CFpaGjQzp07ue1PnjwhU1NT+uKLL4iIaNmyZTRgwABeHStXrlR55rxPnz706aef8soOHz5MALrUN5sMo0xj3vzPf/5Denp6lJ6ezttuampKK1eubHF9e/fuJQMDA+6zotmIpjZs2EBWVla8ssjISLKxsaEnT560uP1GL5o5Dw4OJhcXF4XHN43b0dGRZs+ezdtnypQp9K9//YuIns2QdevWjcrLy7ntrZmlmT17No0fP55X9ujRIwJAR44cURgvw3Q2HSU36ejoUHx8vNxtHh4e5OTk1OIYbt++TQAoOzubiJrPnK9du7bZ7//169cJABUUFMit8+7du0qfpmzk6upKixcvJiKi+/fvk1AopC1btsjdV96MPtGzax4REcErGz58OM2fP5933PNPfhIR2dvbK+0nNjVo0CCuD9/o75CTX3/9dYX/r3RGbOac4bG3t4dAIJC7TSgUYs2aNfjyyy9RWVnZ6jaSk5MhFouhqamJiRMnYurUqS9crC44OBhnz57FL7/8AjMzM65cJpPhtddeg7Ozs8JjN23ahKFDh8LIyAhisRhxcXEoKytTOe6amhpoamryyubNm4ddu3Zh8ODBCAkJwenTp7ltEydOhFgshlgsRv/+/VVuTyQSobq6ukX7FhcXo66uDk5OTlyZhoYGRowYgfz8fADPVugcPnw477gRI0aoHBfDMC3z448/Ijg4GKmpqbwcdevWLdy4cQNjxoxReOzx48cxZswYmJmZQUdHB56enrhz506Lc0IjeXlrypQpqKmpgZWVFWbPno2kpCRuEZ5PP/2Uy1tisVjlXKlK3gKA/Px8Xt4CACcnJ17eMjc3h4mJCbed5S2GeTkdITctWrQIs2bNwtixY/H555+juLiY29Y4c65IYWEhPDw8YGVlBV1dXW6RXkX56uLFi0hLS+Pltn79+gEAr90Xqa+vx9q1a2Fvb4/u3btDLBYjJSWFazc/Px+PHz9WGntT9+/fx40bN5TmwUZNZ70XLlyI8PBwODk5ITQ0FJcuXVLalrz7QVMdMSerel/5u2OD8y5CXV0dRMQrk7eYhLa2ttJ6ZsyYAQsLC4SHh7e6jcaFxAoLC1FTU4Pt27e/sN1x48bhjz/+aLbomEgkUnrcrl27sGTJEnz00Uf46aefIJPJ4OPj06LH6JsyNDTE3bt3eWUTJ07EtWvXEBwczN3QGh/h+vbbbyGTySCTyXDkyBGV26uqqoKRkZHKx7U1ExMT3Lx5k1d28+ZN6OrqvvDfg2G6EgcHBxgZGeG7777j5cYX/Z6Ulpbi7bffxsCBA7Fv3z6cP38emzZtAgCVc5e8vGVubo6CggJER0dDJBJh/vz5GDVqFOrq6jB37lwub8lkMpiamqrU3t8tbzVuY5iupCPkprCwMOTm5sLV1RUnT56EnZ0dkpKSWhSHm5sbqqqqsGXLFpw9e5Z75FlRDA8fPoSbmxsvtzX2Q0eNGtXimNevX4+vv/4ay5YtQ1paGmQyGaRSKdduW/eBmvaVZ82ahatXr8LT0xPZ2dkYNmwYvvnmG4XHy7sftBdVcnJHva+0FTY47yKMjIxQUVHBS8KteZWDuro6PvvsM8TExDR717eRkRHKy8t5ZfLa0NbWhkQiQe/evdGtW7cWtfvOO+8gMTERs2bN4r3Szd7eHg0NDfj555/lHpeZmQlHR0fMnz8fDg4OkEgkzb4lFQgE3N8xKePg4IC8vLxm5UZGRvDy8sKOHTuwceNGxMXFAQDMzMwgkUggkUhgYWHRovNsVFtbi+LiYjg4OLRof2trawgEAmRmZnJldXV1yMrKgp2dHQCgb9++OHfuHO+4rKwsleICgLfeegsnTpzglaWmpuKtt95SuS6G6cysra2RlpaGgwcPIiAggCvX0dGBpaVls9+jRufPn0dDQwMiIyMxcuRI2NjY4MaNG7x9VMlbFRUVzTpkIpEIbm5uiIqKQnp6Os6cOYPs7Gx0796dy1sSiaTFObpRTk5Oi/MWANja2vLyFvAsbz+ft65fv87rxLU2b2VnZ+PWrVtcWWpqKnR1dbm2GKar6Ai5CQBsbGwQHByMn376Ce+++y62bdsGABg4cKDCGO7cuYOCggKsWrUKY8aMga2t7QsHnEOGDEFubi4sLS15+U0ikbxwcuh5mZmZcHd3x4wZMzBo0CBYWVnhypUr3PY+ffpAJBIpjL3xydTnr4+uri5MTU2V5kFlzM3NMXfuXOzfvx+LFy/Gli1bFO6rqB/7vI6Wk4uLi1FbW6vSfeXvjg3OuwgXFxfcvn0bX3zxBYqLi7Fp0yYcPXq0VXW5urrizTffxObNm3nlo0ePxrlz55CQkIDCwkKEhoYiJyfnVYQPAJg8eTK+//57+Pj44McffwQAWFpawsvLC76+vjhw4ABKSkqQnp7OLfjWp08fnDt3DikpKbhy5Qo++eSTZknE0tISly5dQkFBASorKxW+nkIqlSI3N5d3E1i9ejUOHjyIoqIi5ObmIjk5mbfwkjxFRUWQyWSoqKhATU0N9w3u89/4/vrrrxAKhS0e8Gpra2PevHlYunQpjh07hry8PMyePRvV1dXcq9/8/Pxw+fJlLFu2DFeuXMGePXu495OrqalxdeXl5UEmk6Gqqgp//vknF1+juXPn4urVqwgJCcHly5cRHR2NPXv2IDg4uEWxMkxXYmNjg7S0NOzbtw9BQUFceVhYGCIjIxEVFYXCwkJcuHCBm/GQSCSoq6vDN998g6tXr+L7779HbGwsr15LS0s8fPgQJ06cQGVlpcJH/hwcHGBoaMjrbMXHx2Pr1q3IycnB1atXsWPHDohEIqVfIlZUVEAmk6GoqAgAkJ2dzeWJRtXV1Th//jzGjx/f4uuzdOlSxMfHIyYmBoWFhfjqq6+wf/9+7gmkcePGwdraGl5eXrh06RIyMzOxatUqAPy8VVZWBplMhrKyMtTX13N56+HDhwCA8ePHw87ODp6enrh48SJSUlKwatUq+Pv7QygUtjhehuks2jM31dTUYMGCBUhPT8e1a9eQmZmJrKwsrv+0YsUKZGVlYf78+bh06RIuX76MmJgYVFZW4vXXX4eBgQHi4uJQVFSEkydPYtGiRUrP1d/fH1VVVfDw8EBWVhaKi4uRkpICHx+fFn+RADzrU6ampuL06dPIz8+Hn58fb5CqqamJZcuWISQkBAkJCSguLsavv/6KrVu3AgCMjY0hEom4Ben+/PNPAM/y4Lp167B7924UFBRg+fLlkMlkCAwMVBpPUFAQUlJSUFJSggsXLiAtLU1pH7Rx8V5l59zRcnJGRgasrKxgbW2t9Fp0Ku36F+9Mm5G3AENMTAyZm5uTtrY2zZw5kyIiIl64oBtR89duEBGdPn2aAPCOJ3r2mrMePXqQnp4eBQcH04IFC174KrUXQZNFJnbv3k2ampq0b98+IiKqqamh4OBg6tmzJwkEAt7rzGpra8nb25v09PRIX1+f5s2bR8uXL+ctxHbr1i0aN24cicXiFy7+MWLECIqNjeU+r127lmxtbUkkElH37t3J3d2drl69qvR8nJ2dm71OAwCVlJRw+8yZM4f8/PyU1tP0WtbU1FBAQAAZGhq2+FVqMTExBIBqamq4fSwsLOTG97y0tDQaPHgwCQQCsrKyom3btimNlWG6mqZ5My8vj4yNjWnRokVcWWxsLPXt25c0NDSavc7sq6++op49e5JIJCKpVEoJCQnNFmKbO3cuGRgYKH1dEdGz1x9OmzaN+5yUlERvvvkm6erqkra2No0cOZKOHz+u9HzkvXYSAO93PzExkfr27au0npd5bY9AIKB+/frRf//7XwJAx44d4/bx8vKSG9/z+by0tJQmTpxIIpGIDA0NafHixVRXV6c0XobpbDpCbnr8+DFNmzaNe52jqakpLViwgNcXSU9PJ0dHRxIKhaSvr09SqZRrIzU1lWxtbUkoFNLAgQMpPT2d11eUt/DalStXaPLkyaSvr08ikYj69etHQUFB1NDQIPc6yVsQ7s6dO+Tu7k5isZiMjY1p1apVNHPmTF5frL6+nsLDw8nCwoI0NDSod+/evEV0t2zZQubm5qSurs57lVpYWBiZmZmRhoaGwlepNV1IbsGCBWRtbU1CoZCMjIzI09OTKisr5Z4PEVFdXR2ZmprycmdHz8njx4+nzz77TOE5dUZqRE3+SJhhGIUOHz6MpUuXIicnB+rqbfPgSWVlJfcI+htvvNEmbTSKiIhAbGwsrl+/3qbtMAzTfioqKtC/f39cuHBB5T+xUcXIkSOxcOFCTJ8+vc3aAJ49YvmPf/wDRUVFXWs2hWEY5iVt2rQJhw4daraG08toq5ycm5uL0aNH48qVK9DT03tl9XZ0qv0xGcN0ca6urigsLMQff/wBc3PzNmmjtLQU0dHRbTIwj46OxvDhw2FgYIDMzEysX78eCxYseOXtMAzTcZiYmGDr1q0oKytrs8F5ZWUl3n33XXh4eLzyupOSkiAWi9GnTx8UFRUhMDAQTk5ObGDOMAyjIj8/P9y7dw8PHjyAjo5Oq+r4q3JyeXk5EhISutTAHADYzDnDdCHBwcHYvXs3qqqq0Lt3b3h6emLFihUqL/rEMAzzV0lISEB4eDjKyspgaGiIsWPHIjIyEgYGBu0dGsMwTJfDcnLbYoNzhmEYhmEYhmEYhmlnbLV2hmEYhmEYhmEYhmlnbHDOMAzDMAzDMAzDMO2MDc4ZhmEYhmEYhmEYpp2xwTnDMAzDMAzDMAzDtDM2OGcYhmEYhmEYhmGYdsYG5wzDMAzDMAzDMAzTztjgnGEYhmEYhmEYhmHaGRucMwzDMAzDMAzDMEw7+z+bx/SvdfpiqgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x247.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "marker2 = mpl.markers.MarkerStyle('o', fillstyle='full')\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12.0, 4.0 * .618), squeeze=False)\n",
    "for gcol in [0, 1, 2]:\n",
    "    ax = axs[0, gcol]\n",
    "    if gcol == 0: \n",
    "        xy_range = (-3.0, 5.0)\n",
    "        #log_x, log_y = er.get_log10_xy(all_kcats)\n",
    "        ax.scatter(log_x, log_y, marker=marker2, label='metabolic')\n",
    "        slope, intercept, rvalue, pvalue, stderr = scipy.stats.linregress(log_x, log_y)\n",
    "        ax.plot((xy_range[0], xy_range[1]),\n",
    "                (slope * xy_range[0] + intercept, slope * xy_range[1] + intercept), 'r:', lw=1)\n",
    "        ax.plot((xy_range[0] + log_delta, xy_range[1]), (xy_range[0], xy_range[1] - log_delta), 'k:', lw=0.5)\n",
    "        ax.plot((xy_range[0], xy_range[1] - log_delta), (xy_range[0] + log_delta, xy_range[1]), 'k:', lw=0.5)\n",
    "\n",
    "        ax.set_xlabel(r'TurNuP kcat (s-1) log10')\n",
    "        ax.set_ylabel(r'auto-fitted kcat (s-1) log10')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=xy_range, ylim=xy_range)\n",
    "        ax.plot(xy_range, xy_range, 'k--', lw=0.5)\n",
    "    elif gcol == 1:\n",
    "        ax.hist((log_x, log_y), bins=20, linewidth=0.5, label=['TurNuP', 'fitted'])\n",
    "        ax.set_xlabel(r'kcat (s-1) log10')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=(-2, 3))\n",
    "    elif gcol == 2:\n",
    "        ax.hist(np.log10(fitted_factors), bins=50, linewidth=0.5, label=f'TurNuP ({len(fitted_factors)})')\n",
    "        ax.set_xlabel(r'kcat scale factors (log10)')\n",
    "        ax.legend(loc='upper left')\n",
    "        ax.set(xlim=(-2.2, 2.2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Track progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) with parameters loaded from protein_predictions.xlsx (Thu Nov 20 18:59:09 2025)\n",
      "1 table(s) with parameters written to  protein_predictions.xlsx\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>lin r2</th>\n",
       "      <th>log r2</th>\n",
       "      <th>lin proteins</th>\n",
       "      <th>log proteins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</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>iML1515_default_GECKO</td>\n",
       "      <td>0.033244</td>\n",
       "      <td>0.190225</td>\n",
       "      <td>1018</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iML1515_modified_GECKO</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.201613</td>\n",
       "      <td>999</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iML1515_predicted_GECKO</td>\n",
       "      <td>0.078803</td>\n",
       "      <td>0.268769</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iML1515_manual_adjust_GECKO</td>\n",
       "      <td>0.205413</td>\n",
       "      <td>0.333470</td>\n",
       "      <td>999</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iML1515_predicted_fit_GECKO</td>\n",
       "      <td>0.869850</td>\n",
       "      <td>0.549757</td>\n",
       "      <td>999</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          model    lin r2    log r2  lin proteins  \\\n",
       "No                                                                  \n",
       "1         iML1515_default_GECKO  0.033244  0.190225          1018   \n",
       "2        iML1515_modified_GECKO  0.135981  0.201613           999   \n",
       "3       iML1515_predicted_GECKO  0.078803  0.268769           999   \n",
       "4   iML1515_manual_adjust_GECKO  0.205413  0.333470           999   \n",
       "5   iML1515_predicted_fit_GECKO  0.869850  0.549757           999   \n",
       "\n",
       "    log proteins  \n",
       "No                \n",
       "1            299  \n",
       "2            322  \n",
       "3            308  \n",
       "4            308  \n",
       "5            313  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "\n",
    "number = 5\n",
    "xy = np.array([[df_mpmf.at[gene, reference_cond], df_proteins.at[gene, reference_cond]] \n",
    "                for gene in df_proteins.index if gene in df_mpmf.index])\n",
    "log10_x, log10_y = er.get_log10_xy(xy)\n",
    "lin_pearson_r, _ = scipy.stats.pearsonr(xy[:, 0], xy[:, 1])\n",
    "log_pearson_r, _ = scipy.stats.pearsonr(log10_x, log10_y)\n",
    "\n",
    "predictions = load_parameter_file('protein_predictions.xlsx')\n",
    "data = [[number, target_model, lin_pearson_r**2,log_pearson_r**2, len(xy), len(log10_x)]]\n",
    "cols = ['No', 'model', 'lin r2', 'log r2', 'lin proteins', 'log proteins']\n",
    "df = pd.DataFrame(data, columns=cols).set_index('No')\n",
    "if number in predictions[reference_cond].index:\n",
    "    predictions[reference_cond].drop(index=number, inplace=True)\n",
    "predictions[reference_cond] = pd.concat((predictions[reference_cond], df)).sort_index()\n",
    "write_parameter_file('protein_predictions.xlsx', predictions)\n",
    "predictions[reference_cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closing remarks\n",
    "\n",
    "While artificial intelligence (AI)-predicted turnover numbers might serve as a valuable starting point, manual adjustments and automatic fitting of turnover numbers to proteomics data hold significant potential for enhancing the prediction of protein concentrations under both reference conditions and other nutrient conditions. This enhancement can be attributed to the fact that the AI-prediction model is based on experimentally derived turnover numbers that are scarce, noisy, and acquired by non-standardized methods. In contrast, quantitative proteomics is a standardized, high-throughput method with almost complete coverage of the proteome.\n",
    "\n",
    "Nevertheless, there appears to be a potential for further improvements, particularly with regard to different reactions and pathways. These improvements could involve additional cycles of manual parameter adjustments followed by automatic fitting to proteomics. With varying nutrient conditions, different areas of the metabolic network could be explored and subjected to manual adjustment and automatic fitting workflows.\n",
    "\n",
    "However, achieving perfect correlation between predicted and measured protein concentrations will not be feasible for a complex metabolic network. This discrepancy arises from the modeling of enzymes with integer-based stoichiometry of participating proteins, while the measured proteins exhibit variable copy number ratios. Additionally, proteins may be components of different enzyme complexes, precluding their independent adjustment.  \n",
    "\n",
    "The final GECKO model of iML1515 was created through a gradual improvement of model parameters, commencing with an initial GECKO model that employed default parametrization. The configuration data of this final GECKO model will serve as a baseline for the construction of a resource balance model (RBA) of iML1515, as well as for the creation of thermodynamics constraint TGECKO and TRBA models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## (Alternative) gurobipy – automatic fitting to proteomics data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_manual_adjust_GECKO.xml (Thu Nov 20 18:58:37 2025)\n",
      "LP Model of iML1515_GECKO\n",
      "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n",
      "total modeled protein: 306.59 mg/gDW, enzyme saturation level: 0.56\n",
      "Glucose: growth rate 0.661 h-1 (optimal)\n"
     ]
    }
   ],
   "source": [
    "# Load GECKO model and optimize for reference condition using gurobipy\n",
    "fname = os.path.join('SBML_models', f'{baseline_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "total_protein = eo.get_variable_bounds('V_PC_total')['V_PC_total'][1]\n",
    "sigma = eo.avg_enz_saturation\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, enzyme saturation level: {sigma}')\n",
    "\n",
    "# Calculate GECKO solution for given condition\n",
    "eo.medium = conditions[reference_cond]\n",
    "solution = eo.optimize()\n",
    "gr = solution.objective_value\n",
    "print(f'{reference_cond}: growth rate {gr:.3f} h-1 ({solution.status})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 996 proteins measured with total of 497.8 mg/gP\n",
      "   3 proteins excluded with total of  16.6 mg/gP\n",
      "5357 enzyme catalyzed iso-reactions (2221 reactions, 1494 proteins)\n",
      " 394 active catalyzed reactions with total protein of 537.9 mg/gP (based on GECKO simulation)\n",
      " 417 proteins potentially involved in active reactions\n",
      " 352 active catalyzed reactions using total protein of 356.7 mg/gP (based on proteomics)\n",
      "  42 active catalyzed reactions have no measured proteins provided\n",
      "5357 original kcat records loaded from data/iML1515_manual_adjust_GECKO_kcats.xlsx\n",
      " 584 kcat records fitted. Scale factor range [0.0101105, 110.0]\n",
      "  24 (net) reactions would exceed the maximum scaling factor of 100.0 and will not be scaled\n",
      "fitted kcats exported to data/iML1515_predicted_fit_GECKO_kcats.xlsx\n",
      "3 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_xba_parameters.xlsx (Thu Nov 20 18:58:14 2025)\n",
      "3 table(s) with parameters written to  data/iML1515_predicted_fit_GECKO_xba_parameters.xlsx\n",
      "1 table(s) with parameters loaded from data/iML1515_manual_adjust_GECKO_ecm_parameters.xlsx (Thu Nov 20 18:58:14 2025)\n",
      "1 table(s) with parameters written to  data/iML1515_predicted_fit_GECKO_ecm_parameters.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Select proteins for the automatic fit\n",
    "excluded_loci = {'b0929', 'b2215', 'b1377', 'b0241', 'b2400'}\n",
    "measured_mpmfs = {} \n",
    "excluded_mpmfs = {} \n",
    "for locus, mpmf in df_mpmf[reference_cond].items():\n",
    "    if locus in eo.locus2uid:\n",
    "        if locus not in excluded_loci:\n",
    "            measured_mpmfs[locus] = mpmf        \n",
    "        else:\n",
    "            excluded_mpmfs[locus] = mpmf\n",
    "print(f'{len(measured_mpmfs):4d} proteins measured with total of {sum(measured_mpmfs.values()):5.1f} mg/gP')\n",
    "print(f'{len(excluded_mpmfs):4d} proteins excluded with total of {sum(excluded_mpmfs.values()):5.1f} mg/gP')\n",
    "\n",
    "# Automatically fit kcat values to proteomics\n",
    "target_enz_sat = 0.5\n",
    "orig_kcats_fname = os.path.join('data', f'{baseline_model}_kcats.xlsx')\n",
    "fitted_kcats_fname = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "\n",
    "gfk = GeckoFitKcats(eo, orig_kcats_fname)\n",
    "tot_fitted_mpmf = gfk.process_data(solution.fluxes, measured_mpmfs)\n",
    "exceeding_max_scale = gfk.update_kcats(fitted_kcats_fname, target_sat=target_enz_sat, max_scale_factor=100.0)\n",
    "\n",
    "# Create updated XBA parameter file\n",
    "xba_params = load_parameter_file(os.path.join('data', f'{baseline_model}_xba_parameters.xlsx'))\n",
    "xba_params['general'].at['kcats_fname', 'value'] = os.path.join('data', f'{target_model}_kcats.xlsx')\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_xba_parameters.xlsx'), xba_params)\n",
    "\n",
    "# Create updated ECM parameter file\n",
    "ecm_params = load_parameter_file(os.path.join('data', f'{baseline_model}_ecm_parameters.xlsx'),['general'])\n",
    "fitted_pmf = (tot_fitted_mpmf + sum(excluded_mpmfs.values()))/1000.0\n",
    "ecm_params['general'].at['pm2totpm_val_or_paxdb', 'value'] = fitted_pmf\n",
    "ecm_params['general'].at['avg_enz_sat', 'value'] = target_enz_sat\n",
    "write_parameter_file(os.path.join('data', f'{target_model}_ecm_parameters.xlsx'), ecm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBML model loaded by sbmlxdf: SBML_models/iML1515_predicted_fit_GECKO.xml (Thu Nov 20 19:01:10 2025)\n",
      "LP Model of iML1515_GECKO\n",
      "7343 variables, 3372 constraints, 28438 non-zero matrix coefficients\n",
      "total modeled protein: 212.80 mg/gDW, average saturation level: 0.5\n",
      "1494 genes: (492) transporter, (1002) metabolic\n"
     ]
    }
   ],
   "source": [
    "# Load model using gurobipy\n",
    "fname = os.path.join('SBML_models', f'{target_model}.xml')\n",
    "eo = EcmOptimization(fname)\n",
    "total_protein = eo.get_variable_bounds('V_PC_total')['V_PC_total'][1]\n",
    "sigma = eo.avg_enz_saturation\n",
    "all_genes = set(eo.m_dict['fbcGeneProducts']['label'].values)\n",
    "tx_genes, metab_genes = eo.get_tx_metab_genes()\n",
    "print(f'total modeled protein: {total_protein:.2f} mg/gDW, average saturation level: {sigma}')\n",
    "print(f'{len(all_genes)} genes: ({len(tx_genes)}) transporter, ({len(metab_genes)}) metabolic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acetate                  : pred gr: 0.341 h-1 vs. exp 0.290, diff:  0.051\n",
      "Glycerol                 : pred gr: 0.540 h-1 vs. exp 0.470, diff:  0.070\n",
      "Fructose                 : pred gr: 0.544 h-1 vs. exp 0.540, diff:  0.004\n",
      "L-Malate                 : pred gr: 0.526 h-1 vs. exp 0.550, diff: -0.024\n",
      "Glucose                  : pred gr: 0.613 h-1 vs. exp 0.660, diff: -0.047\n",
      "Glucose 6-Phosphate      : pred gr: 0.608 h-1 vs. exp 0.780, diff: -0.172\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x247.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein mass fractions:\n",
      "Acetate                  : r² = 0.2511, p = 1.25e-64 ( 999 proteins lin scale)\n",
      "Glycerol                 : r² = 0.2292, p = 2.26e-58 ( 999 proteins lin scale)\n",
      "Fructose                 : r² = 0.6175, p = 2.71e-210 ( 999 proteins lin scale)\n",
      "Glucose                  : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      "Acetate                  : r² = 0.4562, p = 2.21e-42 ( 308 proteins log scale)\n",
      "Glycerol                 : r² = 0.4894, p = 5.06e-47 ( 311 proteins log scale)\n",
      "Fructose                 : r² = 0.5298, p = 4.40e-51 ( 302 proteins log scale)\n",
      "Glucose                  : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      "\n",
      "condition: Glucose\n",
      "1494 proteins in model with total predicted mass fraction of 373.3 mg/gP\n",
      "      999 have been measured with mpmf of  514.4 mg/gP vs. 331.8 mg/gP predicted\n",
      "           762 metabolic proteins measured 414.0 mg/gP vs. 280.5 mg/gP predicted\n",
      "           237 transport proteins measured 100.4 mg/gP vs.  51.3 mg/gP predicted\n",
      "      495 proteins not measured vs.  41.6 mg/gP predicted\n",
      "           495 actual  proteins      41.6 mg/gP predicted\n",
      "total           : r² = 0.8699, p = 0.00e+00 ( 999 proteins lin scale)\n",
      " metabolic      : r² = 0.9105, p = 0.00e+00 ( 762 proteins lin scale)\n",
      " transport      : r² = 0.6008, p = 9.35e-49 ( 237 proteins lin scale)\n",
      "total           : r² = 0.5498, p = 7.85e-56 ( 313 proteins log scale)\n",
      " metabolic      : r² = 0.5762, p = 3.87e-51 ( 266 proteins log scale)\n",
      " transport      : r² = 0.5334, p = 5.65e-09 (  47 proteins log scale)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x247.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 file(s) exported for \"Load reaction data\" into Escher maps\n"
     ]
    }
   ],
   "source": [
    "# Optimize model using gurobipy and analyze results\n",
    "pred_results = {}\n",
    "for cond, medium in conditions.items():\n",
    "    eo.medium = medium\n",
    "    solution = eo.optimize()\n",
    "\n",
    "    if solution.status == 'optimal':\n",
    "        pred_results[cond] = solution\n",
    "        gr = solution.objective_value\n",
    "        print(f'{cond:25s}: pred gr: {gr:.3f} h-1 vs. exp {exp_grs[cond]:.3f}, diff: {gr - exp_grs[cond]:6.3f}')\n",
    "    else:    \n",
    "        print(f'{cond} ended with status {solution.status}')\n",
    "        \n",
    "# Analyze results\n",
    "er = EcmResults(eo, pred_results, df_mpmf)\n",
    "df_fluxes = er.collect_fluxes()\n",
    "df_net_fluxes = er.collect_fluxes(net=True)\n",
    "df_proteins = er.collect_protein_results()\n",
    "er.plot_grs(exp_grs, highlight='Glucose')\n",
    "\n",
    "print(f'Protein mass fractions:')\n",
    "er.report_proteomics_correlation(scale='lin')\n",
    "er.report_proteomics_correlation(scale='log')\n",
    "print()\n",
    "er.report_protein_levels('Glucose')\n",
    "er.plot_proteins('Glucose', plot_fname=os.path.join('plots', f'{target_model}_proteins_Glucose.pdf'))  \n",
    "er.save_to_escher(df_net_fluxes['Glucose'], os.path.join('escher', target_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "## References\n",
    "\n",
    "- Domenzain, I., Sánchez, B., Anton, M., Kerkhoven, E. J., Millán-Oropeza, A., Henry, C., Siewers, V., Morrissey, J. P., Sonnenschein, N., & Nielsen, J. (2022). Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun, 13(1), 3766. https://doi.org/10.1038/s41467-022-31421-1 \n",
    "- Dunkle, J. A., Bruno, M. R., Outten, F. W., & Frantom, P. A. (2019). Structural Evidence for Dimer-Interface-Driven Regulation of the Type II Cysteine Desulfurase, SufS. Biochemistry, 58(6), 687-696. https://doi.org/10.1021/acs.biochem.8b01122 \n",
    "- Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023). Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications, 14(1), 1485. https://doi.org/10.1038/s41467-023-37151-2 \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.12",
   "language": "python",
   "name": "python310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}