diff --git a/dev/CB_under_construction.ipynb b/dev/CB_under_construction.ipynb
index 65770650..8e180c1f 100644
--- a/dev/CB_under_construction.ipynb
+++ b/dev/CB_under_construction.ipynb
@@ -2,19 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/pydantic/_internal/_config.py:322: UserWarning: Valid config keys have changed in V2:\n",
-      "* 'underscore_attrs_are_private' has been removed\n",
-      "  warnings.warn(message, UserWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# ideas for a source test\n",
     "import cobra\n",
@@ -25,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,12770 +43,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from refinegems.utility.io import load_model\n",
-    "from refinegems.classes.reports import ModelInfoReport\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "test_model_path = '/Users/brune/Documents/11_Test_Data/test_SPECIMEN/thesis/Kp_std/03_refinement/step4-smoothing/Kp_std_smooth.xml'\n",
-    "test_model = load_model(test_model_path,'cobra')\n",
-    "\n",
-    "rep = ModelInfoReport(test_model)\n",
-    "fig = rep.visualise()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "------\n",
-    "# EGC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cobra\n",
-    "import argparse\n",
-    "from tqdm import tqdm\n",
-    "from multiprocess import Pool\n",
-    "from functools import partial\n",
-    "from itertools import product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def main(input_path, output_path, change_model):\n",
-    "    model = cobra.io.read_sbml_model(input_path)\n",
-    "\n",
-    "    # check for egcs before hand -> is solution necessary? -> increases performance -> less egcs to check\n",
-    "    egc_reactions, obj_vals = find_egcs(model)\n",
-    "\n",
-    "    # resolve egcs\n",
-    "    results = find_mods_resolve_egcs(model, present_egcs=egc_reactions)\n",
-    "\n",
-    "    # bring results to condensed format\n",
-    "    condensed_results = condense_results(results)\n",
-    "\n",
-    "    # get all possible solutions\n",
-    "    all_solutions = get_all_solutions(condensed_results)\n",
-    "\n",
-    "    # find minimal changes\n",
-    "    score, best_score_idx = find_minimal_changes(all_solutions)\n",
-    "\n",
-    "    # print best result\n",
-    "    best_solution = all_solutions[best_score_idx]\n",
-    "    print(f\"One optimal solution is: {best_solution}\")\n",
-    "    print(f\"With a score of: {score[best_score_idx]}\")\n",
-    "\n",
-    "    # print other results?\n",
-    "    # this is heuristic...\n",
-    "\n",
-    "    # TODO save changes\n",
-    "    if output_path and change_model:\n",
-    "        out_model = model\n",
-    "        out_model = apply_modifications(out_model, best_solution)\n",
-    "\n",
-    "        # TODO check whole model again in the end to verfy we havent established anyother egcs\n",
-    "        check_egc_reactions, obj_vals = find_egcs(out_model)\n",
-    "        if not check_egc_reactions:\n",
-    "            cobra.io.write_sbml_model(out_model, output_path)\n",
-    "            print(f\"Removed EGCs from model.\\nModel is saved to {output_path}.\")\n",
-    "\n",
-    "    elif change_model and not output_path:\n",
-    "        print(\"No output path provided. Model will not be changed.\")\n",
-    "\n",
-    "    elif output_path and not change_model:\n",
-    "        print(f\"'--change' flag set to {change_model}. Please set to 'TRUE' to change and save the model.\")\n",
-    "\n",
-    "    else:\n",
-    "        print(\"Calculated one optimal solution.\")\n",
-    "\n",
-    "    return 1\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "--------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from refinegems.classes.medium import Medium\n",
-    "from refinegems.curation.biomass import test_biomass_presence\n",
-    "from refinegems.analysis.growth import set_bounds_to_default\n",
-    "\n",
-    "from typing import Literal\n",
-    "import warnings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# needed information\n",
-    "\n",
-    "# namespace to use\n",
-    "namespace = 'BiGG'\n",
-    "\n",
-    "# compartment to search for metabolites\n",
-    "compartment = 'c'\n",
-    "compartment_2 = 'p'\n",
-    "\n",
-    "# @TODO\n",
-    "# NOTE: Ammonia in DB is NH3 - this should be NH4 - change it?\n",
-    "# reactions equations to add / factors etc.\n",
-    "DISSIPATION_RXNS = {\n",
-    "    \"ATP\": {\"ATP [Adenosine triphosphate]\": -1, \"Water [H2O]\": -1, \"ADP [Adenosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"CTP\": {\"CTP [Cytidine triphosphate]\": -1, \"Water [H2O]\": -1, \"CDP [Cytidine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"GTP\": {\"GTP [Guanosine triphosphate]\": -1, \"Water [H2O]\": -1, \"GDP [Guanosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"UTP\": {\"UTP [Uridine triphosphate]\": -1, \"Water [H2O]\": -1, \"UDP [Uridine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"ITP\": {\"ITP [Inosine triphosphate]\": -1, \"Water [H2O]\": -1, \"IDP [Inosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"NADH\": {\"NADH [reduced Nicotinamide adenine dinucleotide]\": -1, \"Hydrogen [H(+)]\": 1, \"NAD [oxidized Nicotinamide adenine dinucleotide]\": 1},\n",
-    "    \"NADPH\": {\"NADPH [reduced Nicotinamide adenine dinucleotide phosphate]\": -1, \"Hydrogen [H(+)]\": 1, \"NADP [oxidized Nicotinamide adenine dinucleotide phosphate]\": 1},\n",
-    "    \"FADH2\": {\"FADH2 [reduced Flavin adenine dinucleotide]\": -1, \"Hydrogen [H(+)]\": 2, \"FAD [oxidized Flavin adenine dinucleotide]\": 1},\n",
-    "    \"FMNH2\": {\"FMNH2 [reduced Flavin mononucleotide]\": -1,  \"Hydrogen [H(+)]\": 2, \"FMN [oxidized Flavin mononucleotide]\": 1},\n",
-    "    \"Q8H2\": {\"Ubiquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"Ubiquinol-8\": 1},\n",
-    "    \"MQL8\": {\"Menaquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"Menaquinol-8\": 1},\n",
-    "    \"DMMQL8\": {\"2-Demethylmenaquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"2-Demethylmenaquinol-8\": 1},\n",
-    "    \"ACCOA\": {\"Acetyl-CoA\": -1, \"Water [H2O]\": -1, \"Hydrogen [H(+)]\": 1, \"Acetate [Acetic acid]\": 1, \"Coenzyme A\": 1},\n",
-    "    \"GLU\": {\"D-Glucose\": -1, \"Water [H2O]\": -1, \"2-Oxoglutarate [Oxoglutaric acid]\": 1, \"Ammonia\": 1, \"Hydrogen [H(+)]\": 2},\n",
-    "    \"PROTON\": {\"Hydrogen [H(+)]\": 1, \"Hydrogen [H(+)] transported\": -1}\n",
-    "}\n",
-    "\n",
-    "ECG_SCORING_MATRIX = {'MR':1, 'RB':3, 'RF':3,'RM':6}\n",
-    "\n",
-    "# 1. if reaction is not reversible -> make reaction reversible (MR)\n",
-    "# 2. limit backward reaction (RB)\n",
-    "# 3. limit forward reaction (RF)\n",
-    "# 4. \"delete\" reaction by setting fluxes to 0 (RM)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# find EGC\n",
-    "# --------\n",
-    "\n",
-    "def add_DISSIPATIONRXNS(model: cobra.Model,\n",
-    "                        namespace:Literal['BiGG']='BiGG',\n",
-    "                        compartment:list=['c','p']) -> cobra.Model:\n",
-    "    \n",
-    "    def check_metab_integration(metabolites: dict[str: int], model: cobra.Model,\n",
-    "                            metab_info:Medium, namespace:Literal['BiGG']='BiGG',compartment:list=['c','p']) -> None|dict:\n",
-    "\n",
-    "        found_ids = {}\n",
-    "\n",
-    "        c1_metab = [_.id for _ in model.metabolites if _.compartment == compartment[0]]\n",
-    "        c2_metab = [_.id for _ in model.metabolites if _.compartment == compartment[1]]\n",
-    "\n",
-    "        for meta in list(metabolites.keys()):\n",
-    "            # get metabolite database annotations\n",
-    "            pos_ids = metab_info.substance_table[metab_info.substance_table['name']==meta][['db_type','db_id']]\n",
-    "\n",
-    "            # check namespace availability \n",
-    "            if not any(namespace in _ for _ in list(pos_ids['db_type'])):\n",
-    "                return None\n",
-    "            # check metabolite availability in model\n",
-    "            id = pos_ids[pos_ids['db_type'].str.contains(namespace)]['db_id']\n",
-    "            for i in id:\n",
-    "                match namespace:\n",
-    "                    # BiGG ID needs the compartment with a '_' as suffix\n",
-    "                    case 'BiGG':\n",
-    "                        if len(metabolites) == 2: # special case proton\n",
-    "                            i_p = i + '_' + compartment[1]\n",
-    "                            i_c = i + '_' + compartment[0]\n",
-    "                            if i_p not in c2_metab or i_c not in c1_metab:\n",
-    "                                return None\n",
-    "                            else:\n",
-    "                                found_ids[i_p] = metabolites['Hydrogen [H(+)] transported']\n",
-    "                                found_ids[i_c] = metabolites['Hydrogen [H(+)]']\n",
-    "                                return found_ids\n",
-    "                        else:\n",
-    "                            i += '_' + compartment[0]\n",
-    "                    # No namespacce given\n",
-    "                    case _:\n",
-    "                        mes = f'Unknown namespace or no namespace given: {namespace}'\n",
-    "                        warnings.warn(mes)\n",
-    "                        return None\n",
-    "\n",
-    "                if i not in c1_metab:\n",
-    "                    return None\n",
-    "                else:\n",
-    "                    found_ids[i] = metabolites[meta]\n",
-    "                    break \n",
-    "\n",
-    "        return found_ids\n",
-    "\n",
-    "    # retrieve information about dissipation reaction metabolites\n",
-    "    metab_info = Medium('dissipation reaction metabolites')\n",
-    "    metab_info.add_subset('DiReM')\n",
-    "    \n",
-    "    # add dissipations reactions to model\n",
-    "    for name, metabolites in DISSIPATION_RXNS.items():\n",
-    "        if \"DISSI_\"+name not in model.reactions:\n",
-    "            ids_in_namespace = check_metab_integration(metabolites, model,\n",
-    "                                       metab_info, namespace,compartment)\n",
-    "            if ids_in_namespace:\n",
-    "                rea = cobra.Reaction(name=name, id=\"DISSI_\"+name)\n",
-    "                model.add_reactions([rea])\n",
-    "                rea.add_metabolites(ids_in_namespace)\n",
-    "\n",
-    "    return model\n",
-    "\n",
-    "\n",
-    "def limit_bounds(model: cobra.Model) -> cobra.Model:\n",
-    "    \"\"\"Limits upper and lower bounds of\n",
-    "    exchange reactions to (0, 0)\n",
-    "    reversible reactions to (-1, 1)\n",
-    "    irreversible reactions to (0, 1)\n",
-    "\n",
-    "    excludes dissipation reactions\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): cobrapy model\n",
-    "\n",
-    "    Returns:\n",
-    "        cobra.Model: cobrapy model\n",
-    "    \"\"\"\n",
-    "    external_comp = cobra.medium.find_external_compartment(model)\n",
-    "    # set fluxes for each reaction within model\n",
-    "    for rea in model.reactions:\n",
-    "        # except dissipation reactions\n",
-    "        if \"DISSI_\" in rea.id:  \n",
-    "            continue\n",
-    "        # turn off exchange reactions\n",
-    "        elif cobra.medium.is_boundary_type(rea,'exchange',external_comp):\n",
-    "            rea.bounds = (0.0, 0.0)\n",
-    "        # limit reversible reactions to [-1, 1] -> flux 0.1\n",
-    "        elif rea.reversibility: \n",
-    "            rea.bounds = (-1.0, 1.0)\n",
-    "        # limit irreversible reactions to [0, 1] -> flux 0.1\n",
-    "        else: \n",
-    "            rea.bounds = (0.0, 1.0)\n",
-    "\n",
-    "    return model\n",
-    "\n",
-    "\n",
-    "\n",
-    "def find_egcs(model: cobra.Model) -> tuple[dict, dict]:\n",
-    "    \"\"\"Checks a cobra.Model for 15 Energy Generating Cycles (EGCs) specified in \n",
-    "    DISSIPATION_RXN by adding the dissipation reactions turning off exchange reactions\n",
-    "    and limit other reactions to 0.1% flux.\n",
-    "\n",
-    "    Based on Fritzemaier et al. (2017, https://doi.org/10.1371/journal.pcbi.1005494)    \n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): GEM read in with cobra\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple[dict, dict]: (egc_reactions, objective_values)\n",
-    "    \"\"\"\n",
-    "    # add 15 energy dissipation reactions\n",
-    "    with model as mod_model:  \n",
-    "\n",
-    "        # ensure no model modifications\n",
-    "        mod_model = add_DISSIPATIONRXNS(mod_model)\n",
-    "\n",
-    "        # set fluxes for each reaction within model\n",
-    "        mod_model = limit_bounds(mod_model)\n",
-    "\n",
-    "        # set each dissipation reaction as objective & optimize\n",
-    "        egc_reactions = {}\n",
-    "        obj_vals = {}\n",
-    "        for name in DISSIPATION_RXNS.keys():\n",
-    "            rea_id = \"DISSI_\"+name\n",
-    "            if rea_id in mod_model.reactions:\n",
-    "                mod_model.objective = \"DISSI_\"+name\n",
-    "                solution = mod_model.optimize()\n",
-    "                fluxes = solution.fluxes\n",
-    "                objval = solution.objective_value\n",
-    "\n",
-    "                if objval > 0.0:  # optimization > 0 --> EGC detected\n",
-    "                    obj_vals[name] = objval\n",
-    "                    egc_reactions[name] = {}\n",
-    "\n",
-    "                    for rea, flux in fluxes.items():\n",
-    "                        # cutoff for flux 1.0e-10 (=no growth) \n",
-    "                        if  (flux > 1.0e-10 or flux < -1.0e-10) and not rea.startswith(\"DISSI\"):\n",
-    "                            egc_reactions[name][rea] = flux\n",
-    "\n",
-    "    return (egc_reactions, obj_vals)\n",
-    "\n",
-    "\n",
-    "# find solutions for EGCs\n",
-    "# -----------------------\n",
-    "\n",
-    "# ...........................................................\n",
-    "# condense, shorten etc. - way to bulky currently \n",
-    "# OPTIMIZE\n",
-    "# + very dangerous naming\n",
-    "# ...........................................................\n",
-    "# naming dangerous, as it returns True when EGC is NOT present\n",
-    "def check_single_egc(model: cobra.Model, egc: str) -> bool:\n",
-    "    \"\"\"Checks if an EGC is NOT present. E.g. objective value of model optimized\n",
-    "    for DISSI_ATP > 0.\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): cobra Model\n",
-    "        egc (str): Name of the dissipation reaction, e.g. ATP\n",
-    "\n",
-    "    Returns:\n",
-    "        bool: True if objective value == 0.0. False if objective value > 0.0.\n",
-    "    \"\"\"\n",
-    "    with model:  # need to add dissi reas & limit bounds each time\n",
-    "        rea_id = \"DISSI_\"+egc\n",
-    "        # add dissipation rxns -> needed to added each time\n",
-    "        model = add_DISSIPATIONRXNS(model)\n",
-    "        # set fluxes for each reaction within model\n",
-    "        mod_model = limit_bounds(model)\n",
-    "\n",
-    "        if rea_id in mod_model.reactions:\n",
-    "            mod_model.objective = rea_id\n",
-    "            solution = mod_model.optimize()\n",
-    "            if solution.objective_value > 0.0:\n",
-    "                return False\n",
-    "            else:\n",
-    "                return True\n",
-    "\n",
-    "# why rich? \n",
-    "# ......            \n",
-    "def sim_growth_richMedium(model: cobra.Model, bof:str, threshold: float = 0.0) -> bool:\n",
-    "    \"\"\"Simulates growth of a model in rich medium. Sets the model objective to\n",
-    "    'Growth' before simulation. Returns True if growth exceeds threshold.\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): cobra Model\n",
-    "        threshold (float, optional): Threshold which objective value has to exceed.\n",
-    "        Defaults to 0.0.\n",
-    "\n",
-    "    Returns:\n",
-    "        bool: True if obj_val >= threshold. False otherwise.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    with model as mod_model:\n",
-    "        # cleanup bounds\n",
-    "        set_bounds_to_default(mod_model)\n",
-    "        # set model objective to growth reaction\n",
-    "        mod_model.objective = bof\n",
-    "        solution = mod_model.optimize()\n",
-    "\n",
-    "        if solution.objective_value > threshold:\n",
-    "            return True\n",
-    "\n",
-    "    return False\n",
-    "\n",
-    "\n",
-    "def check_egc_growth(model: cobra.Model, egc: str, bof:str) -> bool:\n",
-    "    \"\"\"Checks a model if a specified EGC is NOT present & the model can grow in rich medium.\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): GEM loaded with cobrapy\n",
-    "        egc (str): Name of the EGC\n",
-    "\n",
-    "    Returns:\n",
-    "        bool: True == EGC missing & model can grow; False otherwise\n",
-    "    \"\"\"\n",
-    "    if check_single_egc(model, egc):\n",
-    "        if sim_growth_richMedium(model, bof):\n",
-    "            return True\n",
-    "        else:\n",
-    "            return False\n",
-    "    else:\n",
-    "        return False\n",
-    "\n",
-    "# ...........................................................\n",
-    "    \n",
-    "\n",
-    "def test_modifications(reaction: cobra.Reaction, model: cobra.Model, present_egc: dict, results: dict, bof: str) -> dict:\n",
-    "    \"\"\"Tries four cases for a Reaction:\n",
-    "    1. if reaction is not reversible -> make reaction reversible (MR)\n",
-    "    2. limit backward reaction (RB)\n",
-    "    3. limit forward reaction (RF)\n",
-    "    4. \"delete\" reaction by setting fluxes to 0 (RM)\n",
-    "    -> for each case the EGCs which are present in the model are checked if they are removed\n",
-    "    -> if EGCs are removed we check if the model still grows on optimal medium\n",
-    "    => When both limitations are True reaction is saved to corresponding dictionary\n",
-    "\n",
-    "    Args:\n",
-    "        reaction (cobra.Reaction): Reaction from a cobra.Model\n",
-    "        model (cobra.Model): The corresponding GEM loaded with cobrapy\n",
-    "        present_egc (dict): Dictionary of present EGCs {\"egc\": {}} -> EGCs are keys\n",
-    "        results (dict): \"Empty\" dictionary of dictionary formated of results {\"egc\": {\"MR\":[], \"RB\":[], \"RF\":[], \"RM\":[]}}\n",
-    "\n",
-    "    Returns:\n",
-    "        dict: {\"egc\": {\"MR\":[potential_solutions],\n",
-    "                       \"RB\":[potential_solutions],\n",
-    "                       \"RF\":[potential_solutions],\n",
-    "                       \"RM\":[potential_solutions]}}\n",
-    "    \"\"\"\n",
-    "    for egc in present_egc:  # skip egc which are not present -> increase performance\n",
-    "        with model:\n",
-    "            if not reaction.reversibility:  # skip if reaction is already reversible\n",
-    "                reaction.bounds = (-1000.0, 1000.0)  # irreversible -> reversible\n",
-    "                if check_egc_growth(model, egc, bof):\n",
-    "                    results[egc][\"MR\"].append(reaction.id)  # MR = make reversible\n",
-    "\n",
-    "            reaction.bounds = (0, 1000.0)  # limit backward reaction\n",
-    "            if check_egc_growth(model, egc, bof):\n",
-    "                results[egc][\"RB\"].append(reaction.id)   # Remove Backward (RB) reaction\n",
-    "\n",
-    "            reaction.bounds = (-1000.0, 0)  # limit forward reaction\n",
-    "            if check_egc_growth(model, egc, bof):\n",
-    "                results[egc][\"RF\"].append(reaction.id)   # Remove Forward (RF) reaction\n",
-    "\n",
-    "            reaction.bounds = (0, 0)  # \"delete\" reaction\n",
-    "            if check_egc_growth(model, egc, bof):\n",
-    "                results[egc][\"RM\"].append(reaction.id)   # ReMove reaction\n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "# @TEST - NOTE: changed global param to local, make sure it works correctly\n",
-    "# @TEST good default for limit and chunksize\n",
-    "# params not optimal and runtime options need rechecking\n",
-    "def find_mods_resolve_egcs(model: cobra.Model, present_egcs: dict,\n",
-    "                           limit:int=8, chunksize:int=5) -> dict:\n",
-    "    \"\"\"Find the modifications to reactions in a cobra.Model and returns these in a dictionary.\n",
-    "    Splits the modification check in multiple processes.\n",
-    "    Run-time around 20 min for 1300 reactions (roughly 0.92s/reaction).\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): input GEM\n",
-    "\n",
-    "    Returns:\n",
-    "        dict: Dictionary of potential modifications to resolve EGCs\n",
-    "              {\"egc\": {\"MR\":[potential_solutions],\n",
-    "                       \"RB\":[potential_solutions],\n",
-    "                       \"RF\":[potential_solutions],\n",
-    "                       \"RM\":[potential_solutions]}}\n",
-    "    \"\"\"\n",
-    "    results = {\n",
-    "            \"ATP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"CTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"GTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"UTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"ITP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"NADH\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"NADPH\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"FADH2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"FMNH2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"Q8H2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"MQL8\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"DMMQL8\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"ACCOA\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"GLU\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"PROTON\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []}\n",
-    "            }\n",
-    "    output_list = []\n",
-    "\n",
-    "    print(\"_____________________________________\")\n",
-    "    print(f\"Try to resolve the following EGCs: {[egc for egc in present_egcs]} \\nThis might take a while...\")\n",
-    "\n",
-    "    # try to find BOF\n",
-    "    pos_bofs = test_biomass_presence(model)\n",
-    "    if pos_bofs:\n",
-    "        bof = pos_bofs[0]\n",
-    "    else:\n",
-    "        mes = 'No growth or biomass objectuve function in model. Cannot solve EGCs.'\n",
-    "        raise KeyError(mes)\n",
-    "\n",
-    "    # might limit processes with Pool(process=limit) -> otherwise it consumes all cores\n",
-    "    # limit to half of machine cores -> at least for me no speed increment with more cores\n",
-    "    with Pool(processes=limit) as pool:\n",
-    "                  \n",
-    "        # partial -> creates function with fixed variables to call with each iteration\n",
-    "        # needed since pool.imap cannot do that\n",
-    "        part_test_mods = partial(test_modifications,\n",
-    "                                model=model,\n",
-    "                                present_egc=present_egcs,\n",
-    "                                bof=bof,\n",
-    "                                results=results)\n",
-    "        # increment in chunksize will reduce computation time -> but progressbar update also...\n",
-    "        for res in list(tqdm(pool.imap(func=part_test_mods, iterable=model.reactions, chunksize=chunksize),\n",
-    "                        total=len(model.reactions),\n",
-    "                        desc=\"Resolve EGCs\")):\n",
-    "            \n",
-    "            output_list.append(res)\n",
-    "        \n",
-    "\n",
-    "    # merge output_list to the final results\n",
-    "    for output_dict in output_list:\n",
-    "        for egc, modifications in output_dict.items():\n",
-    "            for mod, reactions in modifications.items():\n",
-    "                results[egc][mod] = list(set(results[egc][mod] + reactions))\n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "# find (one of the) best solution(s)\n",
-    "# ----------------------------------\n",
-    "\n",
-    "# ...?\n",
-    "def condense_results(results: dict) -> dict:\n",
-    "    \"\"\"Condenses the output from find_mods_resolve_egcs().\n",
-    "    For easier handling of the dictionary.\n",
-    "\n",
-    "    Args:\n",
-    "        results (dict): result from find_mods_resolve_egcs()\n",
-    "\n",
-    "    Returns:\n",
-    "        dict: condensed dictionary without empty entries.\n",
-    "    \"\"\"\n",
-    "    return {key: {inner_key: inner_value for inner_key, inner_value in value.items() if inner_value} for key, value in results.items() if any(value.values())}\n",
-    "\n",
-    "\n",
-    "def get_all_solutions(result: dict) -> list:\n",
-    "    \"\"\"Builds all possible solutions to resolve all EGCs.\n",
-    "    Output is in form of:\n",
-    "    {modification: {reaction: [EGCs]}}\n",
-    "\n",
-    "    Args:\n",
-    "        result (dict): output from condense_results\n",
-    "\n",
-    "    Returns:\n",
-    "        list: list of dictionaries, each representing a possible solution to resolve all EGCs.\n",
-    "    \"\"\"\n",
-    "    def unflatten(pairs: tuple) -> dict:\n",
-    "        \"\"\"Intern function: unflattens the solution produced in here.\n",
-    "\n",
-    "        Args:\n",
-    "            pairs (tuple): possible solutions in flatted form\n",
-    "\n",
-    "        Returns:\n",
-    "            dict: Output dictionary of get_all_solutions()\n",
-    "        \"\"\"\n",
-    "        m = {}\n",
-    "        for egc, (mod, react) in pairs:\n",
-    "            try:\n",
-    "                m[mod][react].append(egc)\n",
-    "            except KeyError:\n",
-    "                try:\n",
-    "                    m[mod].update({react: [egc]})\n",
-    "                except KeyError:\n",
-    "                    m[mod] = {react: [egc]}\n",
-    "        return m\n",
-    "\n",
-    "    def paths(flat_solution, needle):\n",
-    "        \"\"\"Intern function to get the paths of a possible solution for an EGC (needle).\n",
-    "\n",
-    "        Args:\n",
-    "            flat_solution (_type_): _description_\n",
-    "            needle (_type_): _description_\n",
-    "\n",
-    "        Returns:\n",
-    "            _type_: _description_\n",
-    "        \"\"\"\n",
-    "        return [(mod, react) for (egc, mod, react) in flat_solution if egc == needle]\n",
-    "\n",
-    "    def flatten(egcs):\n",
-    "        \"\"\"Intern function to flatten the input dictionary.\n",
-    "\n",
-    "        Args:\n",
-    "            egcs (_type_): _description_\n",
-    "\n",
-    "        Returns:\n",
-    "            _type_: _description_\n",
-    "        \"\"\"\n",
-    "        return [(egc, mod, react) for egc, mods in egcs.items() for mod, reacts in mods.items() for react in reacts]\n",
-    "\n",
-    "    flat_result = flatten(result)\n",
-    "    egcs = list(set([egc for egc, mod, react in flat_result]))\n",
-    "    flat_solutions = []\n",
-    "\n",
-    "    for prod in product(*[[(egc, modreact) for egc in egcs for modreact in paths(flat_result, egc)]]):\n",
-    "        flat_solutions.append(list(prod))\n",
-    "\n",
-    "    return [unflatten(solution) for solution in flat_solutions]\n",
-    "\n",
-    "\n",
-    "def find_minimal_changes(all_solutions: list) -> tuple[list, int]:\n",
-    "    \"\"\"Calculates the score and returns a list of all scores and the index of the minimal score.\n",
-    "\n",
-    "    Args:\n",
-    "        all_solutions (list): All possible solutions in form of a list of dictionaries.\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple: tuple of (list, int) list == all scores, int == index of lowest score\n",
-    "    \"\"\"\n",
-    "    sums = []\n",
-    "    for sol in all_solutions:\n",
-    "        counter = 0\n",
-    "        for mod, reas in sol.items(): \n",
-    "                counter += ECG_SCORING_MATRIX[mod] * len(reas)\n",
-    "        sums.append(counter)\n",
-    "        \n",
-    "    return sums, sums.index(min(sums))\n",
-    "\n",
-    "\n",
-    "# change the model\n",
-    "# ----------------\n",
-    "\n",
-    "def apply_modifications(model: cobra.Model, solution: dict) -> cobra.Model:\n",
-    "    \"\"\"Apply the modifications to reactions in solution to the model.\n",
-    "    4 modifications are possible:\n",
-    "    \"RM\" -> removes the reaction\n",
-    "    \"RB\" -> removes the backwards reaction\n",
-    "    \"RF\" -> removes the forward reaction\n",
-    "    \"MR\" -> makes reaction reversible\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): Input model\n",
-    "        solution (dict): Best solution from calculation before.\n",
-    "\n",
-    "    Returns:\n",
-    "        cobra.Model: modified cobra.Model.\n",
-    "    \"\"\"\n",
-    "    for mod, react_list in solution.items():\n",
-    "        for react in react_list.keys():\n",
-    "            reaction = model.reactions.get_by_id(react)\n",
-    "            if type(reaction) is cobra.Reaction:\n",
-    "                if mod == \"RM\":\n",
-    "                    reaction.delete()\n",
-    "                    print(f\"{reaction.id} is removed.\")\n",
-    "                elif mod == \"RB\":\n",
-    "                    reaction.bounds = (0.0, 1000.0)\n",
-    "                    print(f\"Backward reaction is removed from {reaction.id}.\")\n",
-    "                elif mod == \"RF\":\n",
-    "                    model = reaction.bounds = (1000.0, 0.0)\n",
-    "                    print(f\"Forward reaction is removed from {reaction.id}.\")\n",
-    "                elif mod == \"MR\":\n",
-    "                    model = reaction.bounds = (1000.0, 1000.0)\n",
-    "                    print(f\"{reaction.id} is now reversible.\")\n",
-    "                else:\n",
-    "                    print(f\"{mod} is no viable modification... Something went wrong.\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_egc_path = '/Users/brune/Downloads/iJN746.xml'\n",
-    "test_egc = load_model(test_egc_path,'cobra')\n",
-    "\n",
-    "test_model_path = '/Users/brune/Documents/11_Test_Data/test_SPECIMEN/thesis/Kp_std/03_refinement/step4-smoothing/Kp_std_smooth.xml'\n",
-    "test_model = load_model(test_model_path,'cobra')\n",
-    "\n",
-    "if len(find_egcs(test_model)) == 0:\n",
-    "    print('t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_____________________________________\n",
-      "Try to resolve the following EGCs: ['ATP', 'CTP', 'GTP', 'UTP', 'NADH', 'NADPH', 'FADH2', 'FMNH2', 'Q8H2', 'ACCOA', 'PROTON'] \n",
-      "This might take a while...\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Resolve EGCs: 100%|██████████| 1054/1054 [15:18<00:00,  1.15it/s]\n"
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'HO*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C8H14NOS2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'HO2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C8H16NOS2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C5H6N2O4*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C6H8N2O4*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C7H9N4O5P*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C7H8N4O2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'CO2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C22H35N7O17P3S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C17H27N5O4S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C18H26N5O6S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C15H26O19P3*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C5H11O10P2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C6H8N2O2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C10H12N4O4S2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C10H14N4O4S2*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula '*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'CN*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C10H15N3O6S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'HO4S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C14H22O8P*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C15H24O8P*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C11H17N4O5S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n",
+      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/cobra/core/metabolite.py:129: UserWarning: invalid character '*' found in formula 'C12H17N4O6S*'\n",
+      "  warn(f\"invalid character '*' found in formula '{self.formula}'\")\n"
      ]
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "{'ATP': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RF': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RM': ['ALDD2x_copy2', 'ACKr']},\n",
-       " 'CTP': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RF': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RM': ['ALDD2x_copy2']},\n",
-       " 'GTP': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RF': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RM': ['ALDD2x_copy2', 'ACKr']},\n",
-       " 'UTP': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RF': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RM': ['ALDD2x_copy2']},\n",
-       " 'ITP': {'MR': [], 'RB': [], 'RF': [], 'RM': []},\n",
-       " 'NADH': {'MR': ['MMTSAO',\n",
-       "   'PSD181',\n",
-       "   'OXOADLR',\n",
-       "   'NTD3',\n",
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-       "   '6HNACMO',\n",
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-       "   'NADH16pp',\n",
-       "   'BZALDH',\n",
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-       "   'SGDS',\n",
-       "   'DM_mclPHA_c',\n",
-       "   'GLUTRR',\n",
-       "   'DH3MCHCDH',\n",
-       "   'NTRIR2x',\n",
-       "   '3HAD40',\n",
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-       "   'NFMLDF',\n",
-       "   'OAADC',\n",
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-       "   'FE2abc',\n",
-       "   'EX_leu__L_e',\n",
-       "   'EX_glu__L_e',\n",
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-       "   'SGSAD',\n",
-       "   'DPHAPC141',\n",
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-       "   'EX_octa_e',\n",
-       "   '2DHGLCNkt_tpp',\n",
-       "   'FTHFD',\n",
-       "   'ACACT12',\n",
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-       "   'ACGS',\n",
-       "   'DHPS',\n",
-       "   'NADS2',\n",
-       "   'OXOAEL',\n",
-       "   'FUMt2_2pp',\n",
-       "   'HMSH2',\n",
-       "   'HSK',\n",
-       "   'HISTD',\n",
-       "   'DM_ptal_c',\n",
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-       "   'OMCDC',\n",
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-       "   'HIBDkt',\n",
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-       "   'PROD2',\n",
-       "   'METGL',\n",
-       "   'PRAMPC',\n",
-       "   'GLNTRAT',\n",
-       "   'ILEabcpp',\n",
-       "   'G3PAT180',\n",
-       "   'PGP180abcpp',\n",
-       "   'DAPDC',\n",
-       "   'DHQS',\n",
-       "   'GNK',\n",
-       "   'EX_asp__L_e',\n",
-       "   'EX_orn_e',\n",
-       "   'DM_C120mclPHA_c',\n",
-       "   'PHAPC60',\n",
-       "   'CFAS160E',\n",
-       "   'PYDXPP',\n",
-       "   'NAt3_2pp',\n",
-       "   'EX_confrl_e',\n",
-       "   'FDH',\n",
-       "   'FMNAT',\n",
-       "   'XTSNH',\n",
-       "   'PSSA180',\n",
-       "   'RNDR4',\n",
-       "   'DHFS',\n",
-       "   'FOLD3',\n",
-       "   'ACOADH1',\n",
-       "   'COCOAHA',\n",
-       "   'DM_C100mclPHA_c',\n",
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-       "   'EX_no2_e',\n",
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-       "   'GLUN',\n",
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-       "   'KAS15',\n",
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-       "   'NTD9',\n",
-       "   'PGP161abcpp',\n",
-       "   'UDCPDP',\n",
-       "   'VNTDM',\n",
-       "   'COALDDH',\n",
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-       "   '3HAD160',\n",
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-       "   'ADOCBIK',\n",
-       "   'HISDr',\n",
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-       "   'CS',\n",
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-       "   'CFAS160G',\n",
-       "   'EAR120y',\n",
-       "   'NFMLDF',\n",
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-       "   'LPCOXc',\n",
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-       "   'EAR121y',\n",
-       "   'IG3PS',\n",
-       "   'SERTRS',\n",
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-       "   'TAUDO',\n",
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-       "   '3HAD80',\n",
-       "   'G5SD',\n",
-       "   'MCITS',\n",
-       "   'GLCOAS',\n",
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-       "   'HACD7i',\n",
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-       "   'FTHFD',\n",
-       "   'ACACT12',\n",
-       "   'PSD161',\n",
-       "   'DHPS',\n",
-       "   'NADS2',\n",
-       "   'OXOAEL',\n",
-       "   'FUMt2_2pp',\n",
-       "   'HSK',\n",
-       "   'HISTD',\n",
-       "   'SLCYSS',\n",
-       "   'OMCDC',\n",
-       "   'DPHAPC60',\n",
-       "   'ACACT8',\n",
-       "   'FACOAL140t2pp',\n",
-       "   'PROD2',\n",
-       "   'PRAMPC',\n",
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-       "   'PGP180abcpp',\n",
-       "   'DAPDC',\n",
-       "   'DHQS',\n",
-       "   'GNK',\n",
-       "   'EX_orn_e',\n",
-       "   'DM_C120mclPHA_c',\n",
-       "   'PHAPC60',\n",
-       "   'CFAS160E',\n",
-       "   'PYDXPP',\n",
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-       "   'FDH',\n",
-       "   'FMNAT',\n",
-       "   'PSSA180',\n",
-       "   'DHFS',\n",
-       "   'FOLD3',\n",
-       "   'ACOADH1',\n",
-       "   'COCOAHA',\n",
-       "   'DM_C100mclPHA_c',\n",
-       "   'G3PAT161',\n",
-       "   '3OAS160',\n",
-       "   'EX_tsul_e',\n",
-       "   'NAMNPP',\n",
-       "   'OBDHc',\n",
-       "   'HISTP',\n",
-       "   'UPP3MT',\n",
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-       "   'EAR60y',\n",
-       "   'GLUt4pp',\n",
-       "   'RZ5PP',\n",
-       "   'PTRCabcpp',\n",
-       "   'DHPM1',\n",
-       "   'MBCOAi',\n",
-       "   'PROTRS',\n",
-       "   'COALCDH',\n",
-       "   'AKGDH',\n",
-       "   'ASNN',\n",
-       "   'CYSTL',\n",
-       "   'CHLt2pp',\n",
-       "   'ILETRS',\n",
-       "   'PGSA120',\n",
-       "   'ADSS',\n",
-       "   '3OADPCOAT',\n",
-       "   'EX_glyb_e',\n",
-       "   '3HAD121',\n",
-       "   'EX_gly_e',\n",
-       "   '3OAS121',\n",
-       "   'PABB',\n",
-       "   'PGPP120',\n",
-       "   'L_LACD2',\n",
-       "   'PGPP161pp',\n",
-       "   'FALDDH',\n",
-       "   'NTD9pp',\n",
-       "   'PHAPC140',\n",
-       "   'GLYK',\n",
-       "   'PPND',\n",
-       "   'GLUDC',\n",
-       "   'PYRDOX',\n",
-       "   'GLUN',\n",
-       "   'PSP_L',\n",
-       "   'GLYCTO1',\n",
-       "   'GLYTRS',\n",
-       "   'PGSA181',\n",
-       "   'GF6PTA',\n",
-       "   'PTOLDOX',\n",
-       "   'DMATT',\n",
-       "   'HACD5i',\n",
-       "   'CATDOX',\n",
-       "   'DHAD2',\n",
-       "   'PHAPC120',\n",
-       "   'URCN',\n",
-       "   'MDH2',\n",
-       "   'DPHAPC140',\n",
-       "   'ACACT10',\n",
-       "   'BHMT',\n",
-       "   'ECOAH9ir',\n",
-       "   'PHAPC121',\n",
-       "   'IPDPS',\n",
-       "   'ASPK',\n",
-       "   'EX_fum_e',\n",
-       "   'SADT',\n",
-       "   'FDMOtau',\n",
-       "   'MECDPS',\n",
-       "   'MACCOAT',\n",
-       "   '3OAR121',\n",
-       "   'EAR80y',\n",
-       "   'SPMS',\n",
-       "   'VALabcpp',\n",
-       "   'ADA',\n",
-       "   'EX_T4hcinnm_e',\n",
-       "   'BETALDHx',\n",
-       "   '3HAD181',\n",
-       "   'HXPRT',\n",
-       "   'RBFSb',\n",
-       "   'MALt2_2pp',\n",
-       "   'P5CD',\n",
-       "   'NADK',\n",
-       "   'PPNDH',\n",
-       "   'EAR140y',\n",
-       "   'DASYN181',\n",
-       "   'RIBabcpp',\n",
-       "   'VNDH',\n",
-       "   'HMSD',\n",
-       "   'DM_aacald_c',\n",
-       "   'AASAD3',\n",
-       "   'VNDH_2',\n",
-       "   'NAt3_1p5pp',\n",
-       "   'EX_ethso3_e',\n",
-       "   'PPNCL',\n",
-       "   'EX_lac__D_e',\n",
-       "   '4OD2',\n",
-       "   'PPTGS_KT',\n",
-       "   'FMETTRS',\n",
-       "   'IGPS',\n",
-       "   'EX_glyc_e',\n",
-       "   'DHPPDA',\n",
-       "   'IGPDH',\n",
-       "   'GTPCII',\n",
-       "   'PROabcpp',\n",
-       "   'MEPCT',\n",
-       "   'PRATPP',\n",
-       "   'SELMETAT',\n",
-       "   'EX_chols_e',\n",
-       "   'ACGK',\n",
-       "   'EX_2dhglcn_e',\n",
-       "   'KAT1',\n",
-       "   'PGPP160',\n",
-       "   'MACPD',\n",
-       "   '3OAS80',\n",
-       "   'FRUpts',\n",
-       "   'FMNRx2',\n",
-       "   'EAR181y',\n",
-       "   '34HPPOR',\n",
-       "   'PSSA160',\n",
-       "   'NTD7',\n",
-       "   'ORNDC',\n",
-       "   'ARGDC',\n",
-       "   'SADH',\n",
-       "   'EX_tol_e',\n",
-       "   'NTD6pp',\n",
-       "   'G3PAT181',\n",
-       "   'CHORM',\n",
-       "   '3OAR161',\n",
-       "   '3HBCOAHL',\n",
-       "   'LYSabcpp',\n",
-       "   'HACD2i',\n",
-       "   'DAPAT',\n",
-       "   'NTD5pp',\n",
-       "   'R05224',\n",
-       "   'PDX5POi',\n",
-       "   'PRAIS',\n",
-       "   'QULNS',\n",
-       "   '3OAS120',\n",
-       "   'LEUTRS',\n",
-       "   'SDPDS',\n",
-       "   'EAR180y',\n",
-       "   'LEUabcpp',\n",
-       "   'IZPN',\n",
-       "   'PRMICI',\n",
-       "   'ACLS',\n",
-       "   'PHACOAOR',\n",
-       "   'ASNS1',\n",
-       "   'ANPRT',\n",
-       "   'EX_cit_e',\n",
-       "   'DM_C140mclPHA_c',\n",
-       "   'PPBNGS',\n",
-       "   'G3PD',\n",
-       "   'PGL',\n",
-       "   'TAURabcpp',\n",
-       "   'ASPCT',\n",
-       "   'NTD6',\n",
-       "   'G3PAT160',\n",
-       "   'OIVD2',\n",
-       "   'CHORS',\n",
-       "   'DM_C60mclPHA_c',\n",
-       "   'GLYCK',\n",
-       "   'DPCOAK',\n",
-       "   'PC17M',\n",
-       "   'ARGSS',\n",
-       "   'MCITD',\n",
-       "   'OARGDC',\n",
-       "   'EX_hxa_e',\n",
-       "   'EX_catechol_e',\n",
-       "   '3OAS141',\n",
-       "   '3OAR181',\n",
-       "   'FACOAL120t2pp',\n",
-       "   'CHOLD',\n",
-       "   'IMPD',\n",
-       "   'DHAD1',\n",
-       "   '3OAS60',\n",
-       "   'DASYN161',\n",
-       "   'GNNUC',\n",
-       "   'NTD8pp',\n",
-       "   'EX_p_xyl_e',\n",
-       "   'ADCL',\n",
-       "   'OMAHY',\n",
-       "   'LYSMO',\n",
-       "   'ACACT11',\n",
-       "   '2DHGLCK',\n",
-       "   'PGPP180',\n",
-       "   'GLUTRS',\n",
-       "   'DDPA',\n",
-       "   'GLUPRT',\n",
-       "   'UPPN',\n",
-       "   'DHDPRy',\n",
-       "   'NACHY',\n",
-       "   'DNTPPA',\n",
-       "   'EX_rib__D_e',\n",
-       "   'BZ12DOX',\n",
-       "   '3HAD140',\n",
-       "   'DHPR',\n",
-       "   'GLYCL_2',\n",
-       "   'VALTRS',\n",
-       "   'TYRTRS',\n",
-       "   'SHKK',\n",
-       "   'EAR160y',\n",
-       "   'EX_ser__L_e',\n",
-       "   'EX_ile__L_e',\n",
-       "   'EX_crn_e',\n",
-       "   'RBFK',\n",
-       "   'AMPN',\n",
-       "   'UAGDP',\n",
-       "   'PPRDNDH',\n",
-       "   'UGMDDS',\n",
-       "   '3HAD180',\n",
-       "   'DCTPD2',\n",
-       "   'RNDR3',\n",
-       "   'EX_ac_e',\n",
-       "   'AST',\n",
-       "   'RNDR1',\n",
-       "   'FORGLUIH',\n",
-       "   'PSSA161',\n",
-       "   'CAT23DOX',\n",
-       "   'EX_4hbz_e',\n",
-       "   'UPPDC1',\n",
-       "   'RBFSa',\n",
-       "   'NTD11pp',\n",
-       "   'NTD4pp',\n",
-       "   'EX_glyald_e',\n",
-       "   'MECDPDH',\n",
-       "   'EX_vanln_e',\n",
-       "   'SHCHF',\n",
-       "   'GLUSy',\n",
-       "   'GMPS2',\n",
-       "   '3MBZDH',\n",
-       "   'LYSDC',\n",
-       "   'EX_lac__L_e',\n",
-       "   'PRFGS',\n",
-       "   'CPPPGO2',\n",
-       "   'NNDMBRT',\n",
-       "   'SUCDi',\n",
-       "   'AGPAT181',\n",
-       "   '3HAD141',\n",
-       "   'ASPO1',\n",
-       "   'PNTK',\n",
-       "   'ACOADH2',\n",
-       "   'PSSA181',\n",
-       "   'CYRDAR',\n",
-       "   'FUMAC',\n",
-       "   'DPHAPC100',\n",
-       "   'CDPMEK',\n",
-       "   'GLNS',\n",
-       "   'CYSS',\n",
-       "   'BIOMASS_KT_TEMP',\n",
-       "   'SHSL1',\n",
-       "   'ADPT',\n",
-       "   'PMDPHT',\n",
-       "   'CTPS2',\n",
-       "   'ADCPS2',\n",
-       "   'OP4ENH2',\n",
-       "   'PANTS',\n",
-       "   'G1SAT',\n",
-       "   'PYK',\n",
-       "   'CFAS180E',\n",
-       "   'EAR100y',\n",
-       "   'EAR141y',\n",
-       "   'EX_arg__L_e',\n",
-       "   '3OAS180',\n",
-       "   'EX_thr__L_e',\n",
-       "   'EX_ttdca_e',\n",
-       "   'ACHBS',\n",
-       "   'SUCCt2_2pp',\n",
-       "   'TMDS',\n",
-       "   'EX_his__L_e',\n",
-       "   'ARGDr',\n",
-       "   'UREA',\n",
-       "   'IPPS',\n",
-       "   'TRDR',\n",
-       "   'PGSA161',\n",
-       "   'DADA',\n",
-       "   'PPC',\n",
-       "   'OMPDC',\n",
-       "   'DM_C141mclPHA_c',\n",
-       "   'NTD2',\n",
-       "   'DPHAPC80',\n",
-       "   'DHDPS',\n",
-       "   'ORNabcpp',\n",
-       "   'HMBS',\n",
-       "   'HOPNTAL',\n",
-       "   'GADOX',\n",
-       "   'AGPAT160',\n",
-       "   'APRAUR',\n",
-       "   'PHAPC80',\n",
-       "   'EX_taur_e',\n",
-       "   'GUDBUTNAH',\n",
-       "   'GLCOASYNT',\n",
-       "   'MALS',\n",
-       "   'DXPS',\n",
-       "   'MTHFR3',\n",
-       "   'DHPACCOAHIT',\n",
-       "   'ME1',\n",
-       "   'PSD180',\n",
-       "   'EX_dca_e',\n",
-       "   'FCLT',\n",
-       "   'NFORGLUAH',\n",
-       "   'ALCD2ir',\n",
-       "   'ACS2',\n",
-       "   'ASPt2_2pp',\n",
-       "   '3OAS161',\n",
-       "   'QUIDH',\n",
-       "   'ACACT9',\n",
-       "   'PRAGSr',\n",
-       "   'HOPNTAL2',\n",
-       "   '3OAS140',\n",
-       "   'EX_akg_e',\n",
-       "   'EX_mso3_e',\n",
-       "   'PGPP181',\n",
-       "   'AP4AH',\n",
-       "   '4CMLCL_kt',\n",
-       "   'ORNCD',\n",
-       "   '3MCAT23DOX',\n",
-       "   'XMO',\n",
-       "   'G6PDH2r',\n",
-       "   'CPPPGO',\n",
-       "   'SULabcpp',\n",
-       "   'SULR',\n",
-       "   'ALDD2x_copy1',\n",
-       "   'CYTBDpp',\n",
-       "   'FCOAHA',\n",
-       "   '3OAS181',\n",
-       "   'PSD120',\n",
-       "   'HACD6i',\n",
-       "   'NTD8',\n",
-       "   'DNMPPA',\n",
-       "   'MUCCY_kt',\n",
-       "   'THRD_L',\n",
-       "   'MALMDA',\n",
-       "   'CYTBO3_4pp',\n",
-       "   'CAT',\n",
-       "   'ADOCBLS',\n",
-       "   'HMGL',\n",
-       "   'R05219',\n",
-       "   'LYSTRS',\n",
-       "   'ICL',\n",
-       "   'AGPAT180',\n",
-       "   'GRTT',\n",
-       "   '4MBZALDH',\n",
-       "   'EX_fer_e',\n",
-       "   'DAAD',\n",
-       "   'PSD160',\n",
-       "   '3HAD161',\n",
-       "   'DASYN120',\n",
-       "   'PPCDC',\n",
-       "   'AGMT',\n",
-       "   'NTD10pp',\n",
-       "   'NTD10',\n",
-       "   'ADCYRS',\n",
-       "   'NNDPR',\n",
-       "   'GLXCL',\n",
-       "   'CYO1b',\n",
-       "   'DM_C80mclPHA_c',\n",
-       "   'HEMEOS',\n",
-       "   'MACACI',\n",
-       "   'HACD3i',\n",
-       "   'CYRDAAT',\n",
-       "   'APSR',\n",
-       "   'PHAPC141',\n",
-       "   'DASYN180',\n",
-       "   'MOHMT',\n",
-       "   'VNDH_3',\n",
-       "   'GDBTALDH',\n",
-       "   'EDA',\n",
-       "   'PDH',\n",
-       "   'DB4PS',\n",
-       "   'HACD4i'],\n",
-       "  'RB': ['T4HCINNMtex',\n",
-       "   'MMTSAO',\n",
-       "   'PSD181',\n",
-       "   'SHK3Dr',\n",
-       "   'NTD3',\n",
-       "   'EX_ga_e',\n",
-       "   '3OAR120',\n",
-       "   'THDPS',\n",
-       "   'MGCH',\n",
-       "   'ASPtex',\n",
-       "   'TRPS2',\n",
-       "   'METAT',\n",
-       "   'HMPK1',\n",
-       "   'EX_hdca_e',\n",
-       "   'EX_3oxoadp_e',\n",
-       "   'ISETACtex',\n",
-       "   'VANLTtex',\n",
-       "   'AGPAT120',\n",
-       "   'ILEtex',\n",
-       "   'EX_tyr__L_e',\n",
-       "   'PENTSOtex',\n",
-       "   'UPP3S',\n",
-       "   'CRNabcpp',\n",
-       "   'DUTPDP',\n",
-       "   'SHCHD2',\n",
-       "   'GCALDD',\n",
-       "   '3OAR60',\n",
-       "   'GLUabcpp',\n",
-       "   'SO4t4_2',\n",
-       "   'PPS',\n",
-       "   'SSALy',\n",
-       "   'ASPT',\n",
-       "   '3MBZALDH',\n",
-       "   'PROt4pp',\n",
-       "   'EX_m_xyl_e',\n",
-       "   'HADPCOADH',\n",
-       "   'UPPRT',\n",
-       "   'EX_4abut_e',\n",
-       "   'TKT2',\n",
-       "   'OP4ENH',\n",
-       "   'PIuabcpp',\n",
-       "   'GARFT',\n",
-       "   'NDPK1',\n",
-       "   'ALATRS',\n",
-       "   'ECOAH12',\n",
-       "   'CYSTGL',\n",
-       "   'UAGPT3',\n",
-       "   'R15BPK',\n",
-       "   'PAPPT3',\n",
-       "   'THRS',\n",
-       "   'DASYN160',\n",
-       "   'GTHPi',\n",
-       "   'GLUtex',\n",
-       "   'OCAALD',\n",
-       "   'FDMO',\n",
-       "   'MCCC',\n",
-       "   'DLYSOXGAT',\n",
-       "   'OIVD3',\n",
-       "   'PE181abcpp',\n",
-       "   'DHSKDH',\n",
-       "   'DCTPD',\n",
-       "   'ECOAH4',\n",
-       "   '6HNACMO',\n",
-       "   'UAAGDS',\n",
-       "   'BZALDH',\n",
-       "   'SGDS',\n",
-       "   'DM_mclPHA_c',\n",
-       "   'GLUTRR',\n",
-       "   'UMPK',\n",
-       "   'T2DECAI',\n",
-       "   'NTRIR2x',\n",
-       "   'ARGSL',\n",
-       "   'CRNtex',\n",
-       "   'ORNTAC',\n",
-       "   'HPPK',\n",
-       "   'ACBIPGT',\n",
-       "   'HXAND',\n",
-       "   'PGPP181pp',\n",
-       "   'FDMO3',\n",
-       "   'CHOLSabc',\n",
-       "   'ACOAD2f',\n",
-       "   'ALAALAr',\n",
-       "   'GK1',\n",
-       "   'ISETACabcpp',\n",
-       "   'CAFFCOA',\n",
-       "   'GTHOr',\n",
-       "   'METTRS',\n",
-       "   'EX_isetac_e',\n",
-       "   '34DHCINMtex',\n",
-       "   'ETHSO3abcpp',\n",
-       "   'FACOAL160t2pp',\n",
-       "   'UAPGR',\n",
-       "   '4CMCOAS',\n",
-       "   'NDPK7',\n",
-       "   'NTD9',\n",
-       "   'TYRTA',\n",
-       "   'VNTDM',\n",
-       "   'NDPK2',\n",
-       "   'SK_pqq_c',\n",
-       "   'G3PAT120',\n",
-       "   'GLCabcpp',\n",
-       "   'PGPP161',\n",
-       "   '3OXOADPt_ex',\n",
-       "   'EAR120y',\n",
-       "   'EX_acon_C_e',\n",
-       "   'THRabcpp',\n",
-       "   'FERtex',\n",
-       "   'COBALT2tpp',\n",
-       "   'INSH',\n",
-       "   'PC20M',\n",
-       "   'TRPS3',\n",
-       "   'CYStex',\n",
-       "   'IG3PS',\n",
-       "   'ATPPRT',\n",
-       "   'ACOAD7f',\n",
-       "   '3OXOADPt_pp',\n",
-       "   'PGI',\n",
-       "   'TAUDO',\n",
-       "   'CITt_kt',\n",
-       "   'FRUK',\n",
-       "   'R05217',\n",
-       "   'SO4tex',\n",
-       "   'PDHcr',\n",
-       "   'ARGTRS',\n",
-       "   'MTOLDOX',\n",
-       "   'ARGORNt7pp',\n",
-       "   'GLCOAS',\n",
-       "   'FE2t',\n",
-       "   'TYRtex',\n",
-       "   'EX_val__L_e',\n",
-       "   'HISabcpp',\n",
-       "   'OCOAT1',\n",
-       "   'UAGCVT',\n",
-       "   'EX_nac_e',\n",
-       "   'EX_succ_e',\n",
-       "   'PGPP180pp',\n",
-       "   'FE2abc',\n",
-       "   'EX_leu__L_e',\n",
-       "   'PGSA180',\n",
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-       "   'NTRIR2x',\n",
-       "   '3HAD40',\n",
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-       "   'EAR120y',\n",
-       "   'NFMLDF',\n",
-       "   'OAADC',\n",
-       "   '3HAD60',\n",
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-       "   'TAUDO',\n",
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-       "   'G5SD',\n",
-       "   'MCITS',\n",
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-       "   'ACGS',\n",
-       "   'DHPS',\n",
-       "   'NADS2',\n",
-       "   'OXOAEL',\n",
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-       "   'HSK',\n",
-       "   'HISTD',\n",
-       "   'DM_ptal_c',\n",
-       "   'SLCYSS',\n",
-       "   'OMCDC',\n",
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-       "   'PROD2',\n",
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-       "   'DAPDC',\n",
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-       "   'PSSA180',\n",
-       "   'RNDR4',\n",
-       "   'DHFS',\n",
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-       "   'EX_no2_e',\n",
-       "   '3OAS160',\n",
-       "   'EX_tsul_e',\n",
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-       "   'ARGabcpp',\n",
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-       "   'DHPM1',\n",
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-       "   'NTD9pp',\n",
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-       "   'PYRDOX',\n",
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-       "   'GLYCTO1',\n",
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-       "   'GF6PTA',\n",
-       "   'PTOLDOX',\n",
-       "   'DMATT',\n",
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-       "   'CATDOX',\n",
-       "   'DHAD2',\n",
-       "   'PHAPC120',\n",
-       "   'DPHAPC140',\n",
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-       "   'PHAPC121',\n",
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-       "   'ASPK',\n",
-       "   'SADT',\n",
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-       "   'PPNCL',\n",
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-       "   'SELMETAT',\n",
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-       "   'KAT1',\n",
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-       "   'MACPD',\n",
-       "   '3OAS80',\n",
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-       "   'GND',\n",
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-       "   'R05224',\n",
-       "   'PDX5POi',\n",
-       "   'PRAIS',\n",
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-       "   'QULNS',\n",
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-       "   'ASNS1',\n",
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-       "   'GTPDPK',\n",
-       "   'EX_cit_e',\n",
-       "   'DM_C140mclPHA_c',\n",
-       "   'PPBNGS',\n",
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-       "   'OIVD2',\n",
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-       "   'PC17M',\n",
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-       "   'NTD7pp',\n",
-       "   'ARGSS',\n",
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-       "   'RNDR3',\n",
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-       "   'P5CR',\n",
-       "   'AST',\n",
-       "   'G6PBDH',\n",
-       "   'FORGLUIH',\n",
-       "   'PSSA161',\n",
-       "   'RNDR1',\n",
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-       "   'UMPK',\n",
-       "   'DH3MCHCDH',\n",
-       "   'T2DECAI',\n",
-       "   'NTRIR2x',\n",
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-       "   'ETHSO3abcpp',\n",
-       "   'FACOAL160t2pp',\n",
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-       "   'ALCD19',\n",
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-       "   'CHOLSH',\n",
-       "   'NTD9',\n",
-       "   'ALAR',\n",
-       "   'UDCPDP',\n",
-       "   'TYRTA',\n",
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-       "   'EX_acon_C_e',\n",
-       "   'ANS',\n",
-       "   'COBALT2tpp',\n",
-       "   'BZDH',\n",
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-       "   'INSH',\n",
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-       "   'PC20M',\n",
-       "   'LPCOXc',\n",
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-       "   'CITt_kt',\n",
-       "   'FRUK',\n",
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-       "   'G5SD',\n",
-       "   'SO4tex',\n",
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-       "   'PDHcr',\n",
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-       "   'PGPP180pp',\n",
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-       "   'NO2t2rpp',\n",
-       "   'KARA2',\n",
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-       "   'HISTD',\n",
-       "   'PTPATi',\n",
-       "   'TYRt2rpp',\n",
-       "   'OMCDC',\n",
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-       "   'CLPNS160pp',\n",
-       "   'PROD2',\n",
-       "   'ACOAD4f',\n",
-       "   'METGL',\n",
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-       "   'GHMT2r',\n",
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-       "   '4OT2',\n",
-       "   'ECOAH7',\n",
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-       "   'EX_orn_e',\n",
-       "   'DM_C120mclPHA_c',\n",
-       "   'PHAPC60',\n",
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-       "   'PROTRS',\n",
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-       "   'BHMT',\n",
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-       "   'CSND',\n",
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-       "   'EX_bz_e',\n",
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-       "   'LDH_D',\n",
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-       "   '3HBCOAHL',\n",
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-       "   'HACD2i',\n",
-       "   'DAPAT',\n",
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-       "   'R15BPK',\n",
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-       "   'UNK3',\n",
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-       "   'DM_ptal_c',\n",
-       "   'TYRt2rpp',\n",
-       "   'TOLtpp',\n",
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-       "   'ABTA',\n",
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-       "   'GLCOASYNT',\n",
-       "   'SUCOAS',\n",
-       "   'EX_34dhcinm_e',\n",
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-       "   'GLNTRS',\n",
-       "   'MTHFR3',\n",
-       "   'MXMO',\n",
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-       "   'ME1',\n",
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-       "   'ALCD2ir',\n",
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-       "   'AP4AH',\n",
-       "   '4CMLCL_kt',\n",
-       "   'ORNCD',\n",
-       "   'ACt2rpp',\n",
-       "   '3MCAT23DOX',\n",
-       "   'CONFRLtex',\n",
-       "   'CONFRLtpp',\n",
-       "   'G6PDH2r',\n",
-       "   'GLCNt2rpp',\n",
-       "   'CPPPGO',\n",
-       "   'HMSH',\n",
-       "   'PROt2rpp',\n",
-       "   'SULabcpp',\n",
-       "   'ALDD2x_copy1',\n",
-       "   'CYTBDpp',\n",
-       "   'DPHAPC121',\n",
-       "   'PROtex',\n",
-       "   'CYTK2',\n",
-       "   'FCOAHA',\n",
-       "   'THRD_L',\n",
-       "   'MUCCY_kt',\n",
-       "   'EX_ni2_e',\n",
-       "   'CYTBO3_4pp',\n",
-       "   'CAT',\n",
-       "   'ADOCBLS',\n",
-       "   'HMGL',\n",
-       "   'FUMtex',\n",
-       "   'LYSTRS',\n",
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-       "   'ATPS4rpp',\n",
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-       "   'NTD10',\n",
-       "   'MALEI',\n",
-       "   'SERtex',\n",
-       "   'GLXCL',\n",
-       "   'DM_C80mclPHA_c',\n",
-       "   'MACACI',\n",
-       "   'VNDH_3',\n",
-       "   'GDBTALDH',\n",
-       "   'ICITt_kt',\n",
-       "   'HACD4i']},\n",
-       " 'Q8H2': {'MR': ['MMTSAO',\n",
-       "   'PSD181',\n",
-       "   'PPPGO',\n",
-       "   'NTD3',\n",
-       "   'DXPRIi',\n",
-       "   'NTD2pp',\n",
-       "   'ASPO6',\n",
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-       "   'MGCH',\n",
-       "   'THDPS',\n",
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-       "   'TRPS2',\n",
-       "   'METAT',\n",
-       "   '3HAD120',\n",
-       "   'EX_hdca_e',\n",
-       "   'APENTAMAH',\n",
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-       "   'DUTPDP',\n",
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-       "   'ASPT',\n",
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-       "   'EX_4abut_e',\n",
-       "   'PDHa',\n",
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-       "   '4OD',\n",
-       "   'TSULabcpp',\n",
-       "   'DASYN160',\n",
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-       "   'FDMO',\n",
-       "   'MCCC',\n",
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-       "   'GTPCI',\n",
-       "   'EAR161y',\n",
-       "   '6HNACMO',\n",
-       "   'CFAS180G',\n",
-       "   'NADH16pp',\n",
-       "   'UAAGDS',\n",
-       "   'SGDS',\n",
-       "   'DM_mclPHA_c',\n",
-       "   'GLUTRR',\n",
-       "   '3HAD40',\n",
-       "   'HPPK',\n",
-       "   'EDD',\n",
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-       "   'ISETACabcpp',\n",
-       "   'GTHOr',\n",
-       "   'ACODA',\n",
-       "   'METTRS',\n",
-       "   'ABUTt2pp',\n",
-       "   'CHOLSH',\n",
-       "   'PGP161abcpp',\n",
-       "   'UDCPDP',\n",
-       "   'COALDDH',\n",
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-       "   'G3PAT120',\n",
-       "   'GLCabcpp',\n",
-       "   'FACOAL80t2pp',\n",
-       "   'PGPP161',\n",
-       "   'CFAS160G',\n",
-       "   'EAR120y',\n",
-       "   'NFMLDF',\n",
-       "   '3HAD60',\n",
-       "   '3OAR141',\n",
-       "   'EX_acon_C_e',\n",
-       "   'ANS',\n",
-       "   'THRabcpp',\n",
-       "   'BZDH',\n",
-       "   'EX_ddca_e',\n",
-       "   '3OAS100',\n",
-       "   'EAR121y',\n",
-       "   'IG3PS',\n",
-       "   'SERTRS',\n",
-       "   'TAUDO',\n",
-       "   'DHNPA',\n",
-       "   'R05217',\n",
-       "   '3HAD80',\n",
-       "   'G5SD',\n",
-       "   'MCITS',\n",
-       "   'ARGTRS',\n",
-       "   'GLCOAS',\n",
-       "   'EX_val__L_e',\n",
-       "   'HISabcpp',\n",
-       "   'DM_fald_c',\n",
-       "   'EX_glu__L_e',\n",
-       "   'PGSA180',\n",
-       "   'EX_octa_e',\n",
-       "   'PSD161',\n",
-       "   'ACGS',\n",
-       "   'NADS2',\n",
-       "   'OXOAEL',\n",
-       "   'HMSH2',\n",
-       "   'HSK',\n",
-       "   'HISTD',\n",
-       "   'OMCDC',\n",
-       "   'DPHAPC60',\n",
-       "   'ACACT8',\n",
-       "   'HIBDkt',\n",
-       "   'PGPP120pp',\n",
-       "   'PRAMPC',\n",
-       "   'GLNTRAT',\n",
-       "   'G3PAT180',\n",
-       "   'DAPDC',\n",
-       "   'DHQS',\n",
-       "   'EX_asp__L_e',\n",
-       "   'EX_orn_e',\n",
-       "   'PHAPC60',\n",
-       "   'CFAS160E',\n",
-       "   'PYDXPP',\n",
-       "   'EX_confrl_e',\n",
-       "   'FDH',\n",
-       "   'FMNAT',\n",
-       "   'XTSNH',\n",
-       "   'PSSA180',\n",
-       "   'RNDR4',\n",
-       "   'DHFS',\n",
-       "   'G3PAT161',\n",
-       "   'EX_no2_e',\n",
-       "   '3OAS160',\n",
-       "   'OBDHc',\n",
-       "   'HISTP',\n",
-       "   'UPP3MT',\n",
-       "   'DPR',\n",
-       "   'EAR60y',\n",
-       "   'RZ5PP',\n",
-       "   'FBP',\n",
-       "   'DHPM1',\n",
-       "   'MBCOAi',\n",
-       "   'FDMO2',\n",
-       "   'EX_ptrc_e',\n",
-       "   'COALCDH',\n",
-       "   'AKGDH',\n",
-       "   'ASNN',\n",
-       "   'CYSTL',\n",
-       "   'CHLt2pp',\n",
-       "   'GLYBabcpp',\n",
-       "   'ILETRS',\n",
-       "   'PGSA120',\n",
-       "   'PC6AR',\n",
-       "   'ADSS',\n",
-       "   '3OADPCOAT',\n",
-       "   'COCHL',\n",
-       "   '3HAD121',\n",
-       "   'BZDIOLDH',\n",
-       "   'EX_gly_e',\n",
-       "   '3OAS121',\n",
-       "   'PABB',\n",
-       "   'PGPP120',\n",
-       "   'L_LACD2',\n",
-       "   'PGPP161pp',\n",
-       "   'FALDDH',\n",
-       "   'PPND',\n",
-       "   'GLUDC',\n",
-       "   'PYRDOX',\n",
-       "   'GLUN',\n",
-       "   'KAS15',\n",
-       "   'PSP_L',\n",
-       "   'GLYCTO1',\n",
-       "   'GLYTRS',\n",
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-       "   'HACD1i',\n",
-       "   'PTOLDOX',\n",
-       "   'DMATT',\n",
-       "   'HACD5i',\n",
-       "   'CATDOX',\n",
-       "   'DHAD2',\n",
-       "   'MDH2',\n",
-       "   'DPHAPC140',\n",
-       "   'DM_C121mclPHA_c',\n",
-       "   'ACACT10',\n",
-       "   'IPDPS',\n",
-       "   'FACOAL60t2pp',\n",
-       "   'ASPK',\n",
-       "   'EX_fum_e',\n",
-       "   'SADT',\n",
-       "   'METabcpp',\n",
-       "   'MECDPS',\n",
-       "   'MACCOAT',\n",
-       "   '3OAR121',\n",
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-       "   'SPMS',\n",
-       "   'ADA',\n",
-       "   'EX_T4hcinnm_e',\n",
-       "   'UPPDC2',\n",
-       "   'GLUTCOADHc',\n",
-       "   '3HAD181',\n",
-       "   'HXPRT',\n",
-       "   'RBFSb',\n",
-       "   'PHETHPTOX',\n",
-       "   'P5CD',\n",
-       "   'NTD3pp',\n",
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-       "   'DASYN181',\n",
-       "   'RIBabcpp',\n",
-       "   'DM_aacald_c',\n",
-       "   'AASAD3',\n",
-       "   'EX_ethso3_e',\n",
-       "   'PPNCL',\n",
-       "   'EX_lac__D_e',\n",
-       "   '4MCAT23DOX',\n",
-       "   'UAMAS',\n",
-       "   'PPTGS_KT',\n",
-       "   'FMETTRS',\n",
-       "   'IGPS',\n",
-       "   'DHPPDA',\n",
-       "   'IGPDH',\n",
-       "   'GTPCII',\n",
-       "   'PROabcpp',\n",
-       "   'MEPCT',\n",
-       "   'PRATPP',\n",
-       "   'SELMETAT',\n",
-       "   'CSND',\n",
-       "   'ACGK',\n",
-       "   'KAT1',\n",
-       "   'PGPP160',\n",
-       "   'MACPD',\n",
-       "   '3OAS80',\n",
-       "   'FMNRx2',\n",
-       "   'EAR181y',\n",
-       "   'FERULCOAS',\n",
-       "   'PSSA160',\n",
-       "   'FACOAL100t2pp',\n",
-       "   'NTD7',\n",
-       "   'THRTRS',\n",
-       "   'R1PK',\n",
-       "   'ARGDC',\n",
-       "   'SADH',\n",
-       "   'NTD6pp',\n",
-       "   'ASPTRS',\n",
-       "   'G3PAT181',\n",
-       "   'CHORM',\n",
-       "   '3OAR161',\n",
-       "   '3HBCOAHL',\n",
-       "   'GND',\n",
-       "   'HACD2i',\n",
-       "   'DAPAT',\n",
-       "   'ASAD',\n",
-       "   'R05224',\n",
-       "   'PDX5POi',\n",
-       "   'PRAIS',\n",
-       "   'PMPK',\n",
-       "   'QULNS',\n",
-       "   '3OAS120',\n",
-       "   'LEUTRS',\n",
-       "   'NTD11',\n",
-       "   'SDPDS',\n",
-       "   'PC11M',\n",
-       "   'EAR180y',\n",
-       "   'PCADYOX',\n",
-       "   'IZPN',\n",
-       "   'PRMICI',\n",
-       "   'ACLS',\n",
-       "   'PHACOAOR',\n",
-       "   'ASNS1',\n",
-       "   'ANPRT',\n",
-       "   'EX_cit_e',\n",
-       "   'DM_C140mclPHA_c',\n",
-       "   'PPBNGS',\n",
-       "   'G3PD',\n",
-       "   'GLUCYS',\n",
-       "   'TAURabcpp',\n",
-       "   'ASPCT',\n",
-       "   'G3PAT160',\n",
-       "   'OIVD2',\n",
-       "   'CHORS',\n",
-       "   'DM_C60mclPHA_c',\n",
-       "   'GLYCK',\n",
-       "   'DPCOAK',\n",
-       "   'NTD7pp',\n",
-       "   'ARGSS',\n",
-       "   'HISTRS',\n",
-       "   'EX_hxa_e',\n",
-       "   'EX_catechol_e',\n",
-       "   '3OAS141',\n",
-       "   '3OAR181',\n",
-       "   'CHOLD',\n",
-       "   'IMPD',\n",
-       "   'DHAD1',\n",
-       "   '3OAS60',\n",
-       "   'DASYN161',\n",
-       "   'GNNUC',\n",
-       "   'EX_p_xyl_e',\n",
-       "   'ADCL',\n",
-       "   'OMAHY',\n",
-       "   'LYSMO',\n",
-       "   'ACACT11',\n",
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-       "   'GLUTRS',\n",
-       "   'DDPA',\n",
-       "   'GLUPRT',\n",
-       "   'UPPN',\n",
-       "   'DHDPRy',\n",
-       "   'DNTPPA',\n",
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-       "   'BZ12DOX',\n",
-       "   '3HAD140',\n",
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-       "   'PGP181abcpp',\n",
-       "   'AMPN',\n",
-       "   'UAGDP',\n",
-       "   'PPRDNDH',\n",
-       "   '3HAD180',\n",
-       "   'DCTPD2',\n",
-       "   'RNDR3',\n",
-       "   'EX_ac_e',\n",
-       "   'P5CR',\n",
-       "   'RNDR1',\n",
-       "   'G6PBDH',\n",
-       "   'PSSA161',\n",
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-       "   'UPPDC1',\n",
-       "   'RBFSa',\n",
-       "   'NTD11pp',\n",
-       "   'NTD4pp',\n",
-       "   'MECDPDH',\n",
-       "   'GLU5K',\n",
-       "   'PC8XM',\n",
-       "   'SHCHF',\n",
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-       "   'PDXPP',\n",
-       "   'GMPS2',\n",
-       "   'LYSDC',\n",
-       "   'PRFGS',\n",
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-       "   'CPPPGO2',\n",
-       "   'SUCDi',\n",
-       "   'AGPAT181',\n",
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-       "   'ASPO1',\n",
-       "   'PNTK',\n",
-       "   'PSSA181',\n",
-       "   'CYRDAR',\n",
-       "   'CDPMEK',\n",
-       "   'GLNS',\n",
-       "   'G1PACT',\n",
-       "   'CYSS',\n",
-       "   'BIOMASS_KT_TEMP',\n",
-       "   'PMDPHT',\n",
-       "   'CTPS2',\n",
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-       "   'ADCPS2',\n",
-       "   'PANTS',\n",
-       "   'G1SAT',\n",
-       "   'PYK',\n",
-       "   'CFAS180E',\n",
-       "   'EAR100y',\n",
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-       "   '3OAS180',\n",
-       "   'ACHBS',\n",
-       "   'SUCCt2_2pp',\n",
-       "   'TMDS',\n",
-       "   'ARGDr',\n",
-       "   'UREA',\n",
-       "   'IPPS',\n",
-       "   'TRDR',\n",
-       "   'PGSA161',\n",
-       "   'DADA',\n",
-       "   'PPC',\n",
-       "   'OMPDC',\n",
-       "   'DM_C141mclPHA_c',\n",
-       "   'NTD2',\n",
-       "   'DHDPS',\n",
-       "   'ORNabcpp',\n",
-       "   'HMBS',\n",
-       "   'HOPNTAL',\n",
-       "   'SARCOX',\n",
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-       "   'EX_ala__L_e',\n",
-       "   'APRAUR',\n",
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-       "   'NO2t2rpp',\n",
-       "   '2DHGLCNkt_tpp',\n",
-       "   'FTHFD',\n",
-       "   'ACACT12',\n",
-       "   'H2Otex',\n",
-       "   'DHPS',\n",
-       "   'FUMt2_2pp',\n",
-       "   'DM_ptal_c',\n",
-       "   'TOLtpp',\n",
-       "   'SLCYSS',\n",
-       "   'DPHAPC60',\n",
-       "   'ACACT8',\n",
-       "   'HIBDkt',\n",
-       "   'FACOAL140t2pp',\n",
-       "   'CLPNS160pp',\n",
-       "   'PROD2',\n",
-       "   'METGL',\n",
-       "   'MSULR',\n",
-       "   'GLYCtpp',\n",
-       "   'PPK2',\n",
-       "   'ILEabcpp',\n",
-       "   '3HAACOAT80',\n",
-       "   'CLPNS181pp',\n",
-       "   'PGP180abcpp',\n",
-       "   'GHMT2r',\n",
-       "   'GNK',\n",
-       "   '4OT2',\n",
-       "   'ECOAH7',\n",
-       "   'EX_asp__L_e',\n",
-       "   'DM_C120mclPHA_c',\n",
-       "   'NAt3_2pp',\n",
-       "   'EX_confrl_e',\n",
-       "   'FDH',\n",
-       "   'PRPPS',\n",
-       "   'FERtpp',\n",
-       "   'XTSNH',\n",
-       "   'ACOAD5f',\n",
-       "   'FOLD3',\n",
-       "   'ACOADH1',\n",
-       "   'COCOAHA',\n",
-       "   'DM_C100mclPHA_c',\n",
-       "   'VALtex',\n",
-       "   'EX_tsul_e',\n",
-       "   'LYStex',\n",
-       "   'NAMNPP',\n",
-       "   'GLUt4pp',\n",
-       "   'PACt3',\n",
-       "   'PTRCabcpp',\n",
-       "   'CITtex',\n",
-       "   'ARGabcpp',\n",
-       "   'ADPT2',\n",
-       "   'GTPDPDP',\n",
-       "   'ALATA_L',\n",
-       "   'AOXHEXCYCL',\n",
-       "   'FDMO2',\n",
-       "   'EX_ptrc_e',\n",
-       "   'MUCLI',\n",
-       "   'HCO3E',\n",
-       "   'CO2tex',\n",
-       "   'PROTRS',\n",
-       "   'COALCDH',\n",
-       "   'ACtex',\n",
-       "   'EX_pac_e',\n",
-       "   'HMSH3',\n",
-       "   'HGNTOR',\n",
-       "   'LYSRC',\n",
-       "   '3OADPCOAT',\n",
-       "   'COCHL',\n",
-       "   'EX_glyb_e',\n",
-       "   'PACCOAL',\n",
-       "   'BZDIOLDH',\n",
-       "   'EX_gly_e',\n",
-       "   'FUM',\n",
-       "   'SOTA',\n",
-       "   'L_LACD2',\n",
-       "   'FALDDH',\n",
-       "   'NTD9pp',\n",
-       "   'PE160abcpp',\n",
-       "   'PHAPC140',\n",
-       "   'PACt1',\n",
-       "   'P_XYLtpp',\n",
-       "   'T4HCINNMtpp',\n",
-       "   'GLYK',\n",
-       "   'GLCDpp',\n",
-       "   'DABAAT',\n",
-       "   'PHEt2rpp',\n",
-       "   'PE180abcpp',\n",
-       "   'GLUDy',\n",
-       "   'GLYCTO1',\n",
-       "   'GF6PTA',\n",
-       "   'HACD1i',\n",
-       "   'HXAtex',\n",
-       "   'M_Xylt1',\n",
-       "   'NAtex',\n",
-       "   'TRSARr',\n",
-       "   'DGK1',\n",
-       "   'OIVD1r',\n",
-       "   'CATDOX',\n",
-       "   'PHAPC120',\n",
-       "   'URCN',\n",
-       "   'GLYCtex',\n",
-       "   'MDH2',\n",
-       "   'ACACT10',\n",
-       "   'BHMT',\n",
-       "   'ECOAH9ir',\n",
-       "   'PHAPC121',\n",
-       "   'NI2uabcpp',\n",
-       "   'FACOAL60t2pp',\n",
-       "   'SERt2rpp',\n",
-       "   'EX_co2_e',\n",
-       "   'CATECHOLtex',\n",
-       "   'METabcpp',\n",
-       "   'FDMOtau',\n",
-       "   'RHACOAR60',\n",
-       "   'SPMS',\n",
-       "   'D_LACt2pp',\n",
-       "   'VALabcpp',\n",
-       "   'HDCAtexi',\n",
-       "   'ADA',\n",
-       "   'BETALDHx',\n",
-       "   'PPA',\n",
-       "   'HXPRT',\n",
-       "   'MALt2_2pp',\n",
-       "   'P5CD',\n",
-       "   'MSO3abcpp',\n",
-       "   'PIt2rpp',\n",
-       "   'CACOAHA',\n",
-       "   'RIBabcpp',\n",
-       "   'GUAPRT',\n",
-       "   'VNDH',\n",
-       "   'HMSD',\n",
-       "   'DM_aacald_c',\n",
-       "   'KAS14',\n",
-       "   'VNDH_2',\n",
-       "   'EX_spmd_e',\n",
-       "   'NAt3_1p5pp',\n",
-       "   'EX_ethso3_e',\n",
-       "   '2DHGLCNtex',\n",
-       "   'GLYBtex',\n",
-       "   '4OD2',\n",
-       "   'PE161abcpp',\n",
-       "   'SUCCtex',\n",
-       "   'ORNtex',\n",
-       "   'EX_glyc_e',\n",
-       "   'PROabcpp',\n",
-       "   'EX_chols_e',\n",
-       "   'EX_bz_e',\n",
-       "   'EX_2dhglcn_e',\n",
-       "   'HPYRI',\n",
-       "   'ETHSO3tex',\n",
-       "   'MACPD',\n",
-       "   'FRUpts',\n",
-       "   'FMNRx2',\n",
-       "   'ECOAH2',\n",
-       "   'PE120abcpp',\n",
-       "   '34HPPOR',\n",
-       "   'BDH',\n",
-       "   'FACOAL100t2pp',\n",
-       "   'EX_icit_e',\n",
-       "   'ORNDC',\n",
-       "   'ARGDC',\n",
-       "   'EX_h_e',\n",
-       "   'RHACOAR80',\n",
-       "   'GAt1',\n",
-       "   'SADH',\n",
-       "   'EX_tol_e',\n",
-       "   '34DHCINMtpp',\n",
-       "   'LDH_D',\n",
-       "   'NTD5',\n",
-       "   'LYSabcpp',\n",
-       "   'GND',\n",
-       "   'HACD2i',\n",
-       "   'OMAIS',\n",
-       "   'NTD5pp',\n",
-       "   'ALDD2x_copy2',\n",
-       "   'ECOAH6',\n",
-       "   '4HBZtex',\n",
-       "   'EX_2ddglcn_e',\n",
-       "   'BZtex',\n",
-       "   'CLPNS120pp',\n",
-       "   'MCITL2',\n",
-       "   'DMGDH',\n",
-       "   'L_LACtex',\n",
-       "   'PDX5POi',\n",
-       "   'ICDHyr',\n",
-       "   'PMPK',\n",
-       "   'AKGDa',\n",
-       "   'CYO1_KT',\n",
-       "   'P_Xylt1',\n",
-       "   'NTD11',\n",
-       "   'CYSabcpp',\n",
-       "   'THRtex',\n",
-       "   'PC11M',\n",
-       "   'EX_34dhbz_e',\n",
-       "   'LEUabcpp',\n",
-       "   'IZPN',\n",
-       "   'MSO3tex',\n",
-       "   'THRA2',\n",
-       "   'DCAtex',\n",
-       "   'PHACOAOR',\n",
-       "   '4MBZDH',\n",
-       "   'NADTRHD',\n",
-       "   'TRPS1',\n",
-       "   'GTPDPK',\n",
-       "   'DM_C140mclPHA_c',\n",
-       "   'ECOAH3',\n",
-       "   'PGL',\n",
-       "   'EX_pro__L_e',\n",
-       "   'NTD6',\n",
-       "   'DM_C60mclPHA_c',\n",
-       "   'PC17M',\n",
-       "   'PGK',\n",
-       "   'GLYALDtex',\n",
-       "   'MCITD',\n",
-       "   'OARGDC',\n",
-       "   'PPGPPDP',\n",
-       "   'FACOAL120t2pp',\n",
-       "   '3HAACOAT120',\n",
-       "   'CHOLD',\n",
-       "   'BZt1pp',\n",
-       "   'GNNUC',\n",
-       "   'EX_h2o_e',\n",
-       "   'NTD8pp',\n",
-       "   'TSULtex',\n",
-       "   'LYSMO',\n",
-       "   'PGCD',\n",
-       "   'RNADRr',\n",
-       "   'ACACT11',\n",
-       "   'CBMKr',\n",
-       "   '2DHGLCK',\n",
-       "   '4HBHYOX',\n",
-       "   'EX_glyclt_e',\n",
-       "   'GLYtex',\n",
-       "   'PGP120abcpp',\n",
-       "   'NACHY',\n",
-       "   'EX_h2_e',\n",
-       "   'EX_rib__D_e',\n",
-       "   'BZ12DOX',\n",
-       "   'FBA',\n",
-       "   'PHEtex',\n",
-       "   'GART',\n",
-       "   'GLYCL_2',\n",
-       "   'H2tex',\n",
-       "   'OHPBAT',\n",
-       "   'GLYabcpp',\n",
-       "   'EX_ser__L_e',\n",
-       "   'EX_ile__L_e',\n",
-       "   'QUIN2tex',\n",
-       "   'ACONTb',\n",
-       "   'ARGtex',\n",
-       "   'EX_cobalt2_e',\n",
-       "   'PGP181abcpp',\n",
-       "   'PPRDNDH',\n",
-       "   'UGMDDS',\n",
-       "   'GLUR',\n",
-       "   'AKGtex',\n",
-       "   'E4PD',\n",
-       "   'GLYCLTtex',\n",
-       "   'AST',\n",
-       "   'P5CR',\n",
-       "   'FORGLUIH',\n",
-       "   'H2CO3D',\n",
-       "   'PC',\n",
-       "   'EX_glyald_e',\n",
-       "   'XAND',\n",
-       "   'PPM',\n",
-       "   'EX_vanln_e',\n",
-       "   'NO2tex',\n",
-       "   'GLUSy',\n",
-       "   'EX_quin_e',\n",
-       "   '3MBZDH',\n",
-       "   'EX_lac__L_e',\n",
-       "   'NNDMBRT',\n",
-       "   'TKT1',\n",
-       "   'SUCDi',\n",
-       "   'ACOADH2',\n",
-       "   'CYRDAR',\n",
-       "   'FUMAC',\n",
-       "   'DPHAPC100',\n",
-       "   'PERD',\n",
-       "   'ECOAH1',\n",
-       "   'EX_pentso3_e',\n",
-       "   'SHSL1',\n",
-       "   'ADPT',\n",
-       "   'ACACT1r',\n",
-       "   'NADPHQR2',\n",
-       "   'ACKr',\n",
-       "   'ADCPS2',\n",
-       "   'OP4ENH2',\n",
-       "   'PYK',\n",
-       "   'CHLabcpp',\n",
-       "   'CYSTRS',\n",
-       "   'EX_arg__L_e',\n",
-       "   'EX_thr__L_e',\n",
-       "   'EX_ttdca_e',\n",
-       "   'DH4MCHCDH',\n",
-       "   'SUCCt2_2pp',\n",
-       "   'EX_his__L_e',\n",
-       "   'MALtex',\n",
-       "   'UREA',\n",
-       "   'PGAMT',\n",
-       "   'PPC',\n",
-       "   'DM_C141mclPHA_c',\n",
-       "   'DPHAPC80',\n",
-       "   'GTHS',\n",
-       "   'GADOX',\n",
-       "   'VANLNtex',\n",
-       "   'CO2tpp',\n",
-       "   '3_4DHBZt2',\n",
-       "   'PHAPC80',\n",
-       "   'GUDBUTNAH',\n",
-       "   'GLCOASYNT',\n",
-       "   'MALS',\n",
-       "   'COBALT2tex',\n",
-       "   'EX_34dhcinm_e',\n",
-       "   '1PPDCRc',\n",
-       "   'MTHFR3',\n",
-       "   'MXMO',\n",
-       "   'DHPACCOAHIT',\n",
-       "   'SK_5mthglu_c',\n",
-       "   'ME1',\n",
-       "   'EX_dca_e',\n",
-       "   'AKGt2rpp',\n",
-       "   'ALCD2ir',\n",
-       "   'ASPt2_2pp',\n",
-       "   'QUIDH',\n",
-       "   'ACACT9',\n",
-       "   'ALAtex',\n",
-       "   'EX_akg_e',\n",
-       "   'NACt1pp',\n",
-       "   'EX_mso3_e',\n",
-       "   'HPYRRx',\n",
-       "   'AP4AH',\n",
-       "   'ACONCtex',\n",
-       "   '4CMLCL_kt',\n",
-       "   'ACt2rpp',\n",
-       "   '3MCAT23DOX',\n",
-       "   'G6PDH2r',\n",
-       "   'PROt2rpp',\n",
-       "   'CPPPGO',\n",
-       "   'HMSH',\n",
-       "   'SULabcpp',\n",
-       "   'ALDD2x_copy1',\n",
-       "   'CYTBDpp',\n",
-       "   'DPHAPC121',\n",
-       "   'L_LACt2rpp',\n",
-       "   'THRD_L',\n",
-       "   'MUCCY_kt',\n",
-       "   'CYTBO3_4pp',\n",
-       "   'CAT',\n",
-       "   'ADOCBLS',\n",
-       "   'HMGL',\n",
-       "   'ICL',\n",
-       "   'ACACT13',\n",
-       "   '4MBZALDH',\n",
-       "   'PTRCtex',\n",
-       "   'GLYALDtpp',\n",
-       "   'EX_fer_e',\n",
-       "   'DAAD',\n",
-       "   'GLYALDDr',\n",
-       "   'MUCCYCI',\n",
-       "   'PXMO',\n",
-       "   'ASPabcpp',\n",
-       "   'ATPS4rpp',\n",
-       "   'CHLtex',\n",
-       "   'PGP160abcpp',\n",
-       "   'EX_mal__L_e',\n",
-       "   'ADCPS1',\n",
-       "   'NTD10',\n",
-       "   'MALEI',\n",
-       "   'NTD4',\n",
-       "   'HACD9',\n",
-       "   'GLXCL',\n",
-       "   'TTDCAtexi',\n",
-       "   'MACACI',\n",
-       "   'CYRDAAT',\n",
-       "   'VNDH_3',\n",
-       "   'GDBTALDH',\n",
-       "   'EDA',\n",
-       "   'PDH',\n",
-       "   'ICITt_kt',\n",
-       "   'HACD4i']},\n",
-       " 'MQL8': {'MR': [], 'RB': [], 'RF': [], 'RM': []},\n",
-       " 'DMMQL8': {'MR': [], 'RB': [], 'RF': [], 'RM': []},\n",
-       " 'ACCOA': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2'],\n",
-       "  'RF': ['ALDD2x_copy2'],\n",
-       "  'RM': ['ALDD2x_copy2']},\n",
-       " 'GLU': {'MR': [], 'RB': [], 'RF': [], 'RM': []},\n",
-       " 'PROTON': {'MR': [],\n",
-       "  'RB': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RF': ['ALDD2x_copy2', 'ACKr'],\n",
-       "  'RM': ['ALDD2x_copy2']}}"
+       "<Figure size 640x480 with 4 Axes>"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "with test_egc as t:\n",
-    "    egc_reactions, obj_vals = find_egcs(t)\n",
-    "    results = find_mods_resolve_egcs(t, present_egcs=egc_reactions, chunksize=16)\n",
-    "\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "------\n",
-    "\n",
-    "## EGCs - new try"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/pydantic/_internal/_config.py:322: UserWarning: Valid config keys have changed in V2:\n",
-      "* 'underscore_attrs_are_private' has been removed\n",
-      "  warnings.warn(message, UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from refinegems.analysis.growth import MIN_GROWTH_THRESHOLD\n",
-    "from refinegems.classes.medium import Medium\n",
-    "from refinegems.curation.biomass import test_biomass_presence\n",
-    "from refinegems.analysis.growth import set_bounds_to_default\n",
     "from refinegems.utility.io import load_model\n",
+    "from refinegems.classes.reports import ModelInfoReport\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "import cobra\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "from multiprocess import Pool\n",
-    "from functools import partial\n",
-    "from itertools import product\n",
-    "\n",
-    "from typing import Literal\n",
-    "import warnings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# needed information\n",
-    "\n",
-    "# namespace to use\n",
-    "namespace = 'BiGG'\n",
-    "\n",
-    "# compartment to search for metabolites\n",
-    "compartment = 'c'\n",
-    "compartment_2 = 'p'\n",
-    "\n",
-    "# @TODO\n",
-    "# NOTE: Ammonia in DB is NH3 - this should be NH4 - change it?\n",
-    "# reactions equations to add / factors etc.\n",
-    "DISSIPATION_RXNS = {\n",
-    "    \"ATP\": {\"ATP [Adenosine triphosphate]\": -1, \"Water [H2O]\": -1, \"ADP [Adenosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"CTP\": {\"CTP [Cytidine triphosphate]\": -1, \"Water [H2O]\": -1, \"CDP [Cytidine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"GTP\": {\"GTP [Guanosine triphosphate]\": -1, \"Water [H2O]\": -1, \"GDP [Guanosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"UTP\": {\"UTP [Uridine triphosphate]\": -1, \"Water [H2O]\": -1, \"UDP [Uridine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"ITP\": {\"ITP [Inosine triphosphate]\": -1, \"Water [H2O]\": -1, \"IDP [Inosine diphosphate]\": 1, \"Hydrogen [H(+)]\": 1, \"Phosphate [PO4(3-)]\": 1},\n",
-    "    \"NADH\": {\"NADH [reduced Nicotinamide adenine dinucleotide]\": -1, \"Hydrogen [H(+)]\": 1, \"NAD [oxidized Nicotinamide adenine dinucleotide]\": 1},\n",
-    "    \"NADPH\": {\"NADPH [reduced Nicotinamide adenine dinucleotide phosphate]\": -1, \"Hydrogen [H(+)]\": 1, \"NADP [oxidized Nicotinamide adenine dinucleotide phosphate]\": 1},\n",
-    "    \"FADH2\": {\"FADH2 [reduced Flavin adenine dinucleotide]\": -1, \"Hydrogen [H(+)]\": 2, \"FAD [oxidized Flavin adenine dinucleotide]\": 1},\n",
-    "    \"FMNH2\": {\"FMNH2 [reduced Flavin mononucleotide]\": -1,  \"Hydrogen [H(+)]\": 2, \"FMN [oxidized Flavin mononucleotide]\": 1},\n",
-    "    \"Q8H2\": {\"Ubiquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"Ubiquinol-8\": 1},\n",
-    "    \"MQL8\": {\"Menaquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"Menaquinol-8\": 1},\n",
-    "    \"DMMQL8\": {\"2-Demethylmenaquinone-8\": -1, \"Hydrogen [H(+)]\": 2, \"2-Demethylmenaquinol-8\": 1},\n",
-    "    \"ACCOA\": {\"Acetyl-CoA\": -1, \"Water [H2O]\": -1, \"Hydrogen [H(+)]\": 1, \"Acetate [Acetic acid]\": 1, \"Coenzyme A\": 1},\n",
-    "    \"GLU\": {\"D-Glucose\": -1, \"Water [H2O]\": -1, \"2-Oxoglutarate [Oxoglutaric acid]\": 1, \"Ammonia\": 1, \"Hydrogen [H(+)]\": 2},\n",
-    "    \"PROTON\": {\"Hydrogen [H(+)]\": 1, \"Hydrogen [H(+)] transported\": -1}\n",
-    "}\n",
-    "\n",
-    "ECG_SCORING_MATRIX = {'MR':1, 'RB':3, 'RF':3,'RM':6}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Find the EGCs\n",
-    "\n",
-    "NOTE: combination possibility of `find_egcs` with `find_egcs_only`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# find EGC\n",
-    "# --------\n",
-    "\n",
-    "def add_DISSIPATIONRXNS(model: cobra.Model,\n",
-    "                        namespace:Literal['BiGG']='BiGG',\n",
-    "                        compartment:list=['c','p']) -> cobra.Model:\n",
-    "    \n",
-    "    def check_metab_integration(metabolites: dict[str: int], model: cobra.Model,\n",
-    "                            metab_info:Medium, namespace:Literal['BiGG']='BiGG',compartment:list=['c','p']) -> None|dict:\n",
-    "\n",
-    "        found_ids = {}\n",
-    "\n",
-    "        c1_metab = [_.id for _ in model.metabolites if _.compartment == compartment[0]]\n",
-    "        c2_metab = [_.id for _ in model.metabolites if _.compartment == compartment[1]]\n",
-    "\n",
-    "        for meta in list(metabolites.keys()):\n",
-    "            # get metabolite database annotations\n",
-    "            pos_ids = metab_info.substance_table[metab_info.substance_table['name']==meta][['db_type','db_id']]\n",
-    "\n",
-    "            # check namespace availability \n",
-    "            if not any(namespace in _ for _ in list(pos_ids['db_type'])):\n",
-    "                return None\n",
-    "            # check metabolite availability in model\n",
-    "            id = pos_ids[pos_ids['db_type'].str.contains(namespace)]['db_id']\n",
-    "            for i in id:\n",
-    "                match namespace:\n",
-    "                    # BiGG ID needs the compartment with a '_' as suffix\n",
-    "                    case 'BiGG':\n",
-    "                        if len(metabolites) == 2: # special case proton\n",
-    "                            i_p = i + '_' + compartment[1]\n",
-    "                            i_c = i + '_' + compartment[0]\n",
-    "                            if i_p not in c2_metab or i_c not in c1_metab:\n",
-    "                                return None\n",
-    "                            else:\n",
-    "                                found_ids[i_p] = metabolites['Hydrogen [H(+)] transported']\n",
-    "                                found_ids[i_c] = metabolites['Hydrogen [H(+)]']\n",
-    "                                return found_ids\n",
-    "                        else:\n",
-    "                            i += '_' + compartment[0]\n",
-    "                    # No namespacce given\n",
-    "                    case _:\n",
-    "                        mes = f'Unknown namespace or no namespace given: {namespace}'\n",
-    "                        warnings.warn(mes)\n",
-    "                        return None\n",
-    "\n",
-    "                if i not in c1_metab:\n",
-    "                    return None\n",
-    "                else:\n",
-    "                    found_ids[i] = metabolites[meta]\n",
-    "                    break \n",
-    "\n",
-    "        return found_ids\n",
-    "\n",
-    "    # retrieve information about dissipation reaction metabolites\n",
-    "    metab_info = Medium('dissipation reaction metabolites')\n",
-    "    metab_info.add_subset('DiReM')\n",
-    "    \n",
-    "    # add dissipations reactions to model\n",
-    "    for name, metabolites in DISSIPATION_RXNS.items():\n",
-    "        if \"DISSI_\"+name not in model.reactions:\n",
-    "            ids_in_namespace = check_metab_integration(metabolites, model,\n",
-    "                                       metab_info, namespace,compartment)\n",
-    "            if ids_in_namespace:\n",
-    "                rea = cobra.Reaction(name=name, id=\"DISSI_\"+name)\n",
-    "                model.add_reactions([rea])\n",
-    "                rea.add_metabolites(ids_in_namespace)\n",
-    "\n",
-    "    return model\n",
-    "\n",
-    "\n",
-    "def limit_bounds(model: cobra.Model):\n",
-    "    \"\"\"Limits upper and lower bounds of\n",
-    "    exchange reactions to (0, 0)\n",
-    "    reversible reactions to (-1, 1)\n",
-    "    irreversible reactions to (0, 1)\n",
-    "\n",
-    "    excludes dissipation reactions\n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): cobrapy model\n",
-    "    \"\"\"\n",
-    "    external_comp = cobra.medium.find_external_compartment(model)\n",
-    "    # set fluxes for each reaction within model\n",
-    "    for rea in model.reactions:\n",
-    "        # except dissipation reactions\n",
-    "        if \"DISSI_\" in rea.id:  \n",
-    "            continue\n",
-    "        # turn off exchange reactions\n",
-    "        elif cobra.medium.is_boundary_type(rea,'exchange',external_comp):\n",
-    "            rea.bounds = (0.0, 0.0)\n",
-    "        # limit reversible reactions to [-1, 1] -> flux 0.1\n",
-    "        elif rea.reversibility: \n",
-    "            rea.bounds = (-1.0, 1.0)\n",
-    "        # limit irreversible reactions to [0, 1] -> flux 0.1\n",
-    "        else: \n",
-    "            rea.bounds = (0.0, 1.0)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def find_egcs(model: cobra.Model) -> tuple[dict, dict]:\n",
-    "    \"\"\"Checks a cobra.Model for 15 Energy Generating Cycles (EGCs) specified in \n",
-    "    DISSIPATION_RXN by adding the dissipation reactions turning off exchange reactions\n",
-    "    and limit other reactions to 0.1% flux.\n",
-    "\n",
-    "    Based on Fritzemaier et al. (2017, https://doi.org/10.1371/journal.pcbi.1005494)    \n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): GEM read in with cobra\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple[dict, dict]: (egc_reactions, objective_values)\n",
-    "    \"\"\"\n",
-    "    # add 15 energy dissipation reactions\n",
-    "    with model as mod_model:  \n",
-    "\n",
-    "        # ensure no model modifications\n",
-    "        mod_model = add_DISSIPATIONRXNS(mod_model)\n",
-    "\n",
-    "        # set fluxes for each reaction within model\n",
-    "        mod_model = limit_bounds(mod_model)\n",
-    "\n",
-    "        # set each dissipation reaction as objective & optimize\n",
-    "        egc_reactions = {}\n",
-    "        obj_vals = {}\n",
-    "        for name in DISSIPATION_RXNS.keys():\n",
-    "            rea_id = \"DISSI_\"+name\n",
-    "            if rea_id in mod_model.reactions:\n",
-    "                mod_model.objective = \"DISSI_\"+name\n",
-    "                solution = mod_model.optimize()\n",
-    "                fluxes = solution.fluxes\n",
-    "                objval = solution.objective_value\n",
-    "\n",
-    "                if objval > 0.0:  # optimization > 0 --> EGC detected\n",
-    "                    obj_vals[name] = objval\n",
-    "                    egc_reactions[name] = {}\n",
-    "\n",
-    "                    for rea, flux in fluxes.items():\n",
-    "                        # cutoff for flux 1.0e-10 (=no growth) \n",
-    "                        if  (flux > 1.0e-10 or flux < -1.0e-10) and not rea.startswith(\"DISSI\"):\n",
-    "                            egc_reactions[name][rea] = flux\n",
-    "\n",
-    "    return (egc_reactions, obj_vals)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Identify possible solution\n",
-    "NOTE: based on single changes only -> greedy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_egcs_only(model:cobra.Model):\n",
-    "\n",
-    "    # add 15 energy dissipation reactions\n",
-    "    with model as mod_model:  \n",
-    "\n",
-    "        # ensure no model modifications\n",
-    "        mod_model = add_DISSIPATIONRXNS(mod_model)\n",
-    "\n",
-    "        # set fluxes for each reaction within model\n",
-    "        mod_model = limit_bounds(mod_model)\n",
-    "\n",
-    "        # set each dissipation reaction as objective & optimize\n",
-    "        egcs = []\n",
-    "        for name in DISSIPATION_RXNS.keys():\n",
-    "            rea_id = \"DISSI_\"+name\n",
-    "            if rea_id in mod_model.reactions:\n",
-    "                mod_model.objective = \"DISSI_\"+name\n",
-    "                solution = mod_model.optimize()\n",
-    "\n",
-    "                if solution.objective_value > MIN_GROWTH_THRESHOLD:  # optimization > 0 --> EGC detected\n",
-    "                    egcs.append(name)\n",
-    "\n",
-    "        return egcs\n",
-    "\n",
-    "\n",
-    "def egcs_removed(model: cobra.Model, starting_egcs: dict):\n",
-    "\n",
-    "    current_egcs = find_egcs_only(model)\n",
-    "\n",
-    "    removed = [_ for _ in starting_egcs.keys() if _ not in current_egcs]\n",
-    "    added = [_ for _ in current_egcs if _ not in starting_egcs.keys()]\n",
-    "\n",
-    "    if len(added) > 0:\n",
-    "        \n",
-    "        return []\n",
-    "    else: \n",
-    "        return removed\n",
-    "    \n",
-    "\n",
-    "def check_egc_growth(reac: cobra.Reaction, model: cobra.Model, \n",
-    "                     bounds:tuple ,\n",
-    "                     starting_egcs:dict, threshold:float=MIN_GROWTH_THRESHOLD):\n",
-    "\n",
-    "    # set new reaction bounds\n",
-    "    reac.bounds = bounds\n",
-    "    # check egcs removal and growth\n",
-    "    egc_test = egcs_removed(model, starting_egcs)\n",
-    "    if len(egc_test) > 0 and model.optimize().objective_value > threshold:\n",
-    "        return egc_test\n",
-    "    else:\n",
-    "        None\n",
-    "\n",
-    "\n",
-    "def test_modifications(reaction: cobra.Reaction, model: cobra.Model, \n",
-    "                       present_egc: dict, threshold:float=MIN_GROWTH_THRESHOLD) -> dict:\n",
-    "    \"\"\"Tries four cases for a Reaction:\n",
-    "    1. if reaction is not reversible -> make reaction reversible (MR)\n",
-    "    2. limit backward reaction (RB)\n",
-    "    3. limit forward reaction (RF)\n",
-    "    4. \"delete\" reaction by setting fluxes to 0 (RM)\n",
-    "    -> for each case the EGCs which are present in the model are checked if they are removed\n",
-    "    -> if EGCs are removed we check if the model still grows on optimal medium\n",
-    "    => When both limitations are True reaction is saved to corresponding dictionary\n",
-    "\n",
-    "    Args:\n",
-    "        reaction (cobra.Reaction): Reaction from a cobra.Model\n",
-    "        model (cobra.Model): The corresponding GEM loaded with cobrapy\n",
-    "        present_egc (dict): Dictionary of present EGCs {\"egc\": {}} -> EGCs are keys\n",
-    "        results (dict): \"Empty\" dictionary of dictionary formated of results {\"egc\": {\"MR\":[], \"RB\":[], \"RF\":[], \"RM\":[]}}\n",
-    "\n",
-    "    Returns:\n",
-    "        dict: {\"egc\": {\"MR\":[potential_solutions],\n",
-    "                       \"RB\":[potential_solutions],\n",
-    "                       \"RF\":[potential_solutions],\n",
-    "                       \"RM\":[potential_solutions]}}\n",
-    "    \"\"\"\n",
-    "    results = {\n",
-    "            \"ATP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"CTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"GTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"UTP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"ITP\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"NADH\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"NADPH\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"FADH2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"FMNH2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"Q8H2\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"MQL8\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"DMMQL8\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"ACCOA\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"GLU\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []},\n",
-    "            \"PROTON\": {\"MR\": [], \"RB\": [], \"RF\": [], \"RM\": []}\n",
-    "            }\n",
-    "    \n",
-    "    # skip exchange reactions\n",
-    "    if reaction.boundary:\n",
-    "        return None\n",
-    "    \n",
-    "    # test modifications of the reaction\n",
-    "    with model as m:\n",
-    "        original_bounds = reaction.bounds\n",
-    "        # case 1: make reaction reversible\n",
-    "        if not reaction.reversibility:  # skip if reaction is already reversible\n",
-    "            res = check_egc_growth(reaction,m,\n",
-    "                                   (-1000.0,1000.0), # irreversible -> reversible\n",
-    "                                   present_egc,threshold)  \n",
-    "            if res:\n",
-    "                for egc in res:\n",
-    "                    results[egc][\"MR\"].append(reaction.id)  # MR = make reversible\n",
-    "\n",
-    "        # case 2: limit backward reaction\n",
-    "        if original_bounds != (0.0,1000.0):\n",
-    "            res = check_egc_growth(reaction,m,\n",
-    "                                   (0.0,1000.0), # Remove Backward (RB) reaction\n",
-    "                                   present_egc,threshold)  \n",
-    "            if res:\n",
-    "                for egc in res:\n",
-    "                    results[egc][\"RB\"].append(reaction.id)  \n",
-    "\n",
-    "        # case 3: limit forward reaction\n",
-    "        if original_bounds != (-1000.0, 0):\n",
-    "            res = check_egc_growth(reaction,m,\n",
-    "                                   (-1000.0, 0), # Remove Forward (RF) reaction\n",
-    "                                   present_egc,threshold)  \n",
-    "            if res:\n",
-    "                for egc in res:\n",
-    "                    results[egc][\"RF\"].append(reaction.id)    \n",
-    "\n",
-    "        # case 4: \"delete\" reaction\n",
-    "        if original_bounds != (0.0, 0.0):\n",
-    "            res = check_egc_growth(reaction,m,\n",
-    "                                   (0.0, 0.0),  # Remove (RM) reaction\n",
-    "                                   present_egc,threshold)  \n",
-    "            if res:\n",
-    "                for egc in res:\n",
-    "                    results[egc][\"RM\"].append(reaction.id)   \n",
-    "\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "# @TEST good default for limit and chunksize\n",
-    "# params not optimal and runtime options need rechecking\n",
-    "def find_mods_resolve_egcs_greedy(model: cobra.Model, present_egcs: dict,\n",
-    "                                  threshold:float=MIN_GROWTH_THRESHOLD,\n",
-    "                                  limit:int=8, chunksize:int=12) -> dict:\n",
-    "    \"\"\"Find the (single) modifications to reactions in a cobra.Model and returns these in a dictionary.\n",
-    "    Splits the modification check in multiple processes.\n",
-    "    \n",
-    "\n",
-    "    Args:\n",
-    "        model (cobra.Model): input GEM\n",
-    "\n",
-    "    Returns:\n",
-    "        dict: Dictionary of potential modifications to resolve EGCs\n",
-    "              {\"egc\": {\"MR\":[potential_solutions],\n",
-    "                       \"RB\":[potential_solutions],\n",
-    "                       \"RF\":[potential_solutions],\n",
-    "                       \"RM\":[potential_solutions]}}\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    output_list = []\n",
-    "\n",
-    "    print(\"_____________________________________\")\n",
-    "    print(f\"Try to resolve the following EGCs: {[egc for egc in present_egcs]} \\nThis might take a while...\")\n",
-    "\n",
-    "    # try to find BOF\n",
-    "    pos_bofs = test_biomass_presence(model)\n",
-    "    if pos_bofs:\n",
-    "        bof = pos_bofs[0]\n",
-    "    else:\n",
-    "        mes = 'No growth or biomass objectuve function in model. Cannot solve EGCs.'\n",
-    "        raise KeyError(mes)\n",
-    "    \n",
-    "    with model as m:\n",
-    "        set_bounds_to_default(m)\n",
-    "        m.objective = bof\n",
-    "\n",
-    "        # might limit processes with Pool(process=limit) -> otherwise it consumes all cores\n",
-    "        # limit to half of machine cores -> at least for me no speed increment with more cores\n",
-    "        \n",
-    "        with Pool(processes=limit) as pool:\n",
-    "                    \n",
-    "            # partial -> creates function with fixed variables to call with each iteration\n",
-    "            # needed since pool.imap cannot do that\n",
-    "            part_test_mods = partial(test_modifications,\n",
-    "                                    model=m,\n",
-    "                                    present_egc=present_egcs,\n",
-    "                                    threshold=threshold)\n",
-    "            # increment in chunksize will reduce computation time -> but progressbar update also...\n",
-    "            for res in list(tqdm(pool.imap_unordered(func=part_test_mods, iterable=m.reactions, \n",
-    "                                                     chunksize=chunksize),\n",
-    "                            total=len(m.reactions),\n",
-    "                            desc=\"Resolve EGCs\")):\n",
-    "                if res:\n",
-    "                    output_list.append(res)\n",
-    "        \n",
-    "        \n",
-    "    \n",
-    "    # merge output_list to the final results\n",
-    "    results = {}\n",
-    "    for output_dict in output_list:\n",
-    "        for egc, modifications in output_dict.items():\n",
-    "            for mod, reactions in modifications.items():\n",
-    "                if not egc in results.keys():\n",
-    "                    results[egc] = {}\n",
-    "                if mod in results[egc].keys():\n",
-    "                    results[egc][mod] = list(set(results[egc][mod] + reactions))\n",
-    "                else:\n",
-    "                    results[egc][mod] = list(set(reactions))\n",
-    "                \n",
-    "\n",
-    "    return results\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### find A solution for the results of `find_mods_resolve_egcs_greedy`\n",
-    "\n",
-    "this algorithm is greedy, it does not return the optimal solution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def find_solution_greedy(results):\n",
-    "    \n",
-    "    solved_egcs = set()\n",
-    "    score = 0\n",
-    "    reacs_for_solu = {}\n",
-    "\n",
-    "    # make table and drop EGCs that are not in the model\n",
-    "    solution_table = pd.DataFrame(results)[list(egc_reactions.keys())]\n",
-    "    for col in solution_table.columns:\n",
-    "        solution_table[col] = solution_table[col].apply(lambda x: ', '.join(map(str,x)))\n",
-    "    # find not solvable (with the current code) cyles \n",
-    "    not_solvable = []\n",
-    "    for col in egc_reactions.keys():\n",
-    "        if all(len(_) == 0 for _ in solution_table[col]):\n",
-    "            not_solvable.append(col)\n",
-    "    # report the problems for manual curation\n",
-    "    if len(not_solvable) > 0:\n",
-    "        print(f'The following EGCs cannot be fixed with the current code: {not_solvable}')\n",
-    "        solution_table.drop(not_solvable, axis=1, inplace=True)\n",
-    "    # fix the EGCs that can be fixed with single changes\n",
-    "    solution_table = solution_table.T\n",
-    "    for egc in solution_table.index.to_list():\n",
-    "        # check if sgc has already been taken care of \n",
-    "        if egc in solved_egcs:\n",
-    "            continue\n",
-    "        \n",
-    "        # best solution for this cycle\n",
-    "        for mode in ECG_SCORING_MATRIX.keys():\n",
-    "            if solution_table.loc[egc, mode]:\n",
-    "                reac = solution_table.loc[egc,mode].split(', ')[0]\n",
-    "                newly_solved = [_ for _ in solution_table.index[solution_table[mode].str.contains(reac)].to_list() if _ not in solved_egcs]\n",
-    "                if len(newly_solved) > 0:\n",
-    "                    solved_egcs = set([*solved_egcs,*newly_solved])\n",
-    "                    score += ECG_SCORING_MATRIX[mode] \n",
-    "                    reacs_for_solu[reac] = mode\n",
-    "\n",
-    "    return (reacs_for_solu, score)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Apply solution to model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def apply_modifications(model: cobra.Model, solution: dict):\n",
-    "    \"\"\"Apply the modifications to reactions in solution to the model.\n",
+    "test_model_path = '/Users/brune/Documents/11_Test_Data/test_SPECIMEN/thesis/Kp_std/03_refinement/step4-smoothing/Kp_std_smooth.xml'\n",
+    "test_model = load_model(test_model_path,'cobra')\n",
     "\n",
-    "    4 modifications are possible:\n",
-    "    \"RM\" -> removes the reaction\n",
-    "    \"RB\" -> removes the backwards reaction\n",
-    "    \"RF\" -> removes the forward reaction\n",
-    "    \"MR\" -> makes reaction reversible\n",
+    "rep = ModelInfoReport(test_model)\n",
+    "fig = rep.visualise()\n",
     "\n",
-    "    Args:\n",
-    "        model (cobra.Model): Input model\n",
-    "        solution (dict): Best solution from calculation in `py:func:find_solution_greedy`.\n",
-    "    \"\"\"\n",
-    "    for reac, mode in solution.items():\n",
-    "            reaction = model.reactions.get_by_id(reac)\n",
-    "            if type(reaction) is cobra.Reaction:\n",
-    "                match mode:  \n",
-    "                    case \"RM\":\n",
-    "                        reaction.delete()\n",
-    "                    case \"RB\":\n",
-    "                        reaction.bounds = (0.0, 1000.0)\n",
-    "                    case \"RF\":\n",
-    "                        model = reaction.bounds = (1000.0, 0.0)\n",
-    "                    case \"MR\":\n",
-    "                        model = reaction.bounds = (1000.0, 1000.0)\n",
-    "                    case _:\n",
-    "                        mes = f\"{mode} is no viable modification... Something went wrong.\"\n",
-    "                        warnings.warn(mes)\n",
-    "\n"
+    "plt.show()"
    ]
   },
   {
@@ -12824,102 +132,104 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def solve_egcs(model:cobra.Model):\n",
-    "    \n",
-    "    egc_reactions, obj_vals = find_egcs(model)\n",
-    "    results = find_mods_resolve_egcs_greedy(model, present_egcs=egc_reactions)\n",
-    "    solution,score = find_solution_greedy(results)\n",
-    "    apply_modifications(model,solution)\n",
-    "    still_egc, vals = find_egcs(model)\n",
-    "\n",
-    "    return {'solution':solution, 'score':score, 'remaining egcs':still_egc}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing\n",
-    "\n",
-    "Problem:\n",
-    "currently runtime varies greatly - maybe something to do the the Pool and not reloading the kernel\n",
-    "--> restaring the kernel DEFINITLY improves runtime by a lot"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/brune/miniconda3/envs/sprg/lib/python3.10/site-packages/pydantic/_internal/_config.py:322: UserWarning: Valid config keys have changed in V2:\n",
-      "* 'underscore_attrs_are_private' has been removed\n",
-      "  warnings.warn(message, UserWarning)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-\n",
-      "-\n",
-      "_____________________________________\n",
-      "Try to resolve the following EGCs: ['ATP', 'CTP', 'GTP', 'UTP', 'NADH', 'NADPH', 'FADH2', 'FMNH2', 'Q8H2', 'ACCOA', 'PROTON'] \n",
-      "This might take a while...\n"
+      "{'ATP': {'ALDD2x_copy2': -1.0, 'ACALD': 1.0, 'ACKr': -1.0, 'PTAr': 1.0}, 'CTP': {'ALDD2x_copy2': -1.0, 'ACALD': 1.0, 'ACKr': -1.0, 'NDPK3': 1.0, 'PTAr': 1.0}, 'GTP': {'ALDD2x_copy2': -1.0, 'ACALD': 1.0, 'ACKr': -1.0, 'NDPK1': 1.0, 'PTAr': 1.0}, 'UTP': {'ALDD2x_copy2': -1.0, 'ACALD': 1.0, 'ACKr': -1.0, 'NDPK2': 1.0, 'PTAr': 1.0}, 'ACCOA': {'ALDD2x_copy2': -0.9999999999999997, 'ACALD': 1.0, 'ACKr': -1.0000000000000002, 'HSERTA': -1.0, 'HSK': 1.0, 'AHSERL2': -1.0, 'PTAr': 1.0000000000000002, 'SHSL2r': 1.0, 'SHSL4r': -0.9999999999999999, 'THRD_L': 0.9999999999999998, 'THRS': 1.0000000000000002}, 'PROTON': {'ALDD2x_copy2': -1.0, 'ATPS4rpp': -1.0, 'ACALD': 1.0000000000000004, 'ACKr': -1.0, 'PTAr': 1.0}}\n",
+      "\n",
+      "\n",
+      "\n",
+      "{'ATP': 1.0000000000000002, 'CTP': 1.0, 'GTP': 0.9999999999999997, 'UTP': 1.0, 'ACCOA': 0.9999999999999998, 'PROTON': 4.000000000000104}\n",
+      "              ATP    CTP            GTP  UTP        ACCOA PROTON\n",
+      "RM  [PTAr, test5]    NaN  [PTAr, test5]  NaN       [PTAr]    NaN\n",
+      "RB            NaN  [Clt]          [Clt]  NaN  [PTAr, Clt]  [Clt]\n",
+      "MR            NaN    NaN            NaN  NaN          NaN    NaN\n",
+      "RF            NaN    NaN            NaN  NaN       [PTAr]    NaN\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Resolve EGCs: 100%|██████████| 1054/1054 [01:54<00:00,  9.17it/s]\n"
+     "ename": "TypeError",
+     "evalue": "'float' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 35\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28mprint\u001b[39m(solution_table)\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m solution_table\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m---> 35\u001b[0m     solution_table[col] \u001b[38;5;241m=\u001b[39m \u001b[43msolution_table\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m, \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/sprg/lib/python3.10/site-packages/pandas/core/series.py:4771\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4661\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m   4662\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   4663\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4666\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   4667\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m   4668\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4669\u001b[0m \u001b[38;5;124;03m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4670\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4769\u001b[0m \u001b[38;5;124;03m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4770\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4771\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/sprg/lib/python3.10/site-packages/pandas/core/apply.py:1123\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1120\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_str()\n\u001b[1;32m   1122\u001b[0m \u001b[38;5;66;03m# self.f is Callable\u001b[39;00m\n\u001b[0;32m-> 1123\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/sprg/lib/python3.10/site-packages/pandas/core/apply.py:1174\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1172\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1173\u001b[0m         values \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m)\u001b[38;5;241m.\u001b[39m_values\n\u001b[0;32m-> 1174\u001b[0m         mapped \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1175\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1176\u001b[0m \u001b[43m            \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1177\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1178\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1181\u001b[0m     \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1182\u001b[0m     \u001b[38;5;66;03m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n",
+      "File \u001b[0;32m~/miniconda3/envs/sprg/lib/python3.10/site-packages/pandas/_libs/lib.pyx:2924\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
+      "Cell \u001b[0;32mIn[6], line 35\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28mprint\u001b[39m(solution_table)\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m solution_table\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m---> 35\u001b[0m     solution_table[col] \u001b[38;5;241m=\u001b[39m solution_table[col]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m))\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'float' object is not iterable"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "115.82557320594788"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from refinegems.utility.io import load_model\n",
+    "import pandas as pd\n",
     "from refinegems.curation.egcs import GreedyEGCSolver\n",
+    "from refinegems.utility.io import load_model\n",
+    "\n",
+    "test_egc = load_model('/Users/brune/Downloads/iJN746.xml','cobra')\n",
     "\n",
-    "test_egc_path = '/Users/brune/Downloads/iJN746.xml'\n",
-    "print('-')\n",
-    "test_egc = load_model(test_egc_path,'cobra')\n",
-    "print('-')\n",
     "greedy_solver = GreedyEGCSolver()\n",
-    "greedy_results = greedy_solver.solve_egcs(test_egc)"
+    "    #greedy_results = greedy_solver.solve_egcs(test_egc)\n",
+    "    #print(greedy_results)\n",
+    "egcs, _ = greedy_solver.find_egcs(test_egc, with_reacs=True)\n",
+    "filtered_result2 = {\n",
+    "                        'ATP': {\"RM\": [\"PTAr\", \"test5\"]},\n",
+    "                        'CTP': {'RB': ['Clt']},\n",
+    "                        'GTP': {'RB': ['Clt'], \"RM\": [\"PTAr\", \"test5\"]},\n",
+    "                        'ITP': {\"MR\": [\"2faffa5\"]},\n",
+    "                        'NADH': {'MR': ['check'], \"RM\": [\"asd\", \"t0\"], \"RB\": [\"triple\",  \"check\"]},\n",
+    "                        'FMNH2': {'RB': ['check2']},\n",
+    "                        'ACCOA': {'RB': ['PTAr', 'Clt'], 'RF': ['PTAr'], 'RM': ['PTAr']},\n",
+    "                        'PROTON': {'RB': ['Clt']},\n",
+    "                        \"MQL8\": {\"RM\": [\"test1\"]},\n",
+    "                        \"GLU\": {'RB': ['PTAr', 'Clt'], 'RF': ['PTAr'], 'RM': ['PTAr']},\n",
+    "                        'UTP':{}\n",
+    "                        }\n",
+    "    \n",
+    "print(egcs)\n",
+    "print('\\n\\n')\n",
+    "print(_)\n",
+    "# sol, score = greedy_solver.find_solution_greedy(filtered_result2, egcs)\n",
+    "    \n",
+    "# print(sol, score)\n",
+    "\n",
+    "solution_table = pd.DataFrame(filtered_result2)[list(egcs.keys())]\n",
+    "print(solution_table)\n",
+    "for col in solution_table.columns:\n",
+    "    solution_table[col] = solution_table[col].apply(lambda x: ', '.join(map(str,x)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[]"
+       "False"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "greedy_results['remaining egcs']"
+    "from refinegems.analysis.growth import MIN_GROWTH_THRESHOLD\n",
+    "\n",
+    "-1.0e-16 < MIN_GROWTH_THRESHOLD * -1.0"
    ]
   },
   {
diff --git a/src/refinegems/curation/egcs.py b/src/refinegems/curation/egcs.py
index c4904b30..56f617f1 100644
--- a/src/refinegems/curation/egcs.py
+++ b/src/refinegems/curation/egcs.py
@@ -1,7 +1,7 @@
 """Identify, report and solve energy generating cycles (EGCs).
 """
 
-__author__ = 'Carolin Brune'
+__author__ = 'Carolin Brune and Tobias Fehrenbach'
 
 ################################################################################
 # requirements
@@ -264,12 +264,12 @@ def find_egcs(self, model:cobra.Model, with_reacs:bool=False,
                         fluxes = solution.fluxes
                         objval = solution.objective_value
 
-                        if objval > 0.0:  # optimization > 0 --> EGC detected
+                        if objval > MIN_GROWTH_THRESHOLD:  # optimization > 0 --> EGC detected
                             obj_vals[name] = objval
                             egc_reactions[name] = {}
 
                             for rea, flux in fluxes.items():
-                                # cutoff for flux 1.0e-10 (=no growth) 
+                                # cutoff for flux 
                                 if  (flux > MIN_GROWTH_THRESHOLD or flux < -1.0 * MIN_GROWTH_THRESHOLD) and not rea.startswith("DISSI"):
                                     egc_reactions[name][rea] = flux