Upload medium test

79ba53e4 · João Eduardo Levandoski · c923260a · 79ba53e4
Commit 79ba53e4 authored 2 years ago by João Eduardo Levandoski
--- a/Notebooks/medium_test_cobrapy.ipynb
+++ b/Notebooks/medium_test_cobrapy.ipynb
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install packages"
+      ],
+      "metadata": {
+        "id": "ujCjjfKkmlFM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install cobra --quiet"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lvq377hsei9N",
+        "outputId": "e469be2b-851c-401a-debe-c9b9acb8681b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[K     |████████████████████████████████| 1.2 MB 5.0 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 2.3 MB 41.1 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 147 kB 46.7 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 237 kB 73.9 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 6.8 MB 44.8 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 109 kB 69.0 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 84 kB 3.5 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 44 kB 2.5 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 68 kB 5.4 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 80 kB 7.0 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 54 kB 3.0 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 51 kB 4.6 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 546 kB 47.2 MB/s \n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import libraries"
+      ],
+      "metadata": {
+        "id": "hJnI_X6vmo4X"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IwIG7Ol3eWJL"
+      },
+      "outputs": [],
+      "source": [
+        "import cobra\n",
+        "\n",
+        "from cobra.io import load_matlab_model, save_json_model, load_json_model, write_sbml_model, load_model, read_sbml_model\n",
+        "from cobra.flux_analysis import flux_variability_analysis\n",
+        "\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from scipy.ndimage.filters import gaussian_filter\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Mount drive"
+      ],
+      "metadata": {
+        "id": "5AwUPc_EmyZA"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Here you need to insert the path for yout model"
+      ],
+      "metadata": {
+        "id": "AYFwMybEeZH3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/gdrive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6M79a2Ad347m",
+        "outputId": "1393060e-5060-449f-831c-131521ff9a4f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /gdrive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls /gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis/"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IKzFrefU34vF",
+        "outputId": "5f568c5b-3684-4931-d5b8-afa160581e21"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "41598_2017_6918_MOESM1_ESM.xml\n",
+            "449_2020_2299_MOESM3_ESM.mat\n",
+            "cobrapy_fba_diferentes_meios.ipynb\n",
+            "cobrapy_testes.ipynb\n",
+            "figs\n",
+            "FVA_max_min.gsheet\n",
+            "kinetic_parameters_estimation.ipynb\n",
+            "medium_test_cobrapy_oslo.ipynb\n",
+            "medium_test_cobrapy_oslo_versao_com_so4.ipynb\n",
+            "Resultados_e_todo_list_models.gdoc\n",
+            "results_from_nh4_o2_comb.npy\n",
+            "results_from_nh4_o2_FO_Cellulose_comb.npy\n",
+            "results_from_nh4_so4_FO_biomass.npy\n",
+            "results_from_nh4_so4_FO_Cellulose2.npy\n",
+            "results_from_nh4_so4_FO_Cellulose.npy\n",
+            "Solutions_GKOProblem_70_cellObjs.csv\n",
+            "Solutions_GKOProblem.csv\n",
+            "versao1_cobrapy.ipynb\n",
+            "versao2_cobrapy.ipynb\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "%cd /gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis/"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KiVnPpYS34dG",
+        "outputId": "c5291360-0510-44cd-b379-b97ecbeabfac"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JSFxubrz34Kc",
+        "outputId": "e94ab8c6-b27f-4b79-c3b7-52881640da12"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "41598_2017_6918_MOESM1_ESM.xml\n",
+            "449_2020_2299_MOESM3_ESM.mat\n",
+            "cobrapy_fba_diferentes_meios.ipynb\n",
+            "cobrapy_testes.ipynb\n",
+            "figs\n",
+            "FVA_max_min.gsheet\n",
+            "kinetic_parameters_estimation.ipynb\n",
+            "medium_test_cobrapy_oslo.ipynb\n",
+            "medium_test_cobrapy_oslo_versao_com_so4.ipynb\n",
+            "Resultados_e_todo_list_models.gdoc\n",
+            "results_from_nh4_o2_comb.npy\n",
+            "results_from_nh4_o2_FO_Cellulose_comb.npy\n",
+            "results_from_nh4_so4_FO_biomass.npy\n",
+            "results_from_nh4_so4_FO_Cellulose2.npy\n",
+            "results_from_nh4_so4_FO_Cellulose.npy\n",
+            "Solutions_GKOProblem_70_cellObjs.csv\n",
+            "Solutions_GKOProblem.csv\n",
+            "versao1_cobrapy.ipynb\n",
+            "versao2_cobrapy.ipynb\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Open Model"
+      ],
+      "metadata": {
+        "id": "8UiP5PRum3AD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelo = '449_2020_2299_MOESM3_ESM.mat'"
+      ],
+      "metadata": {
+        "id": "OZWiG8xKev0l"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "tU6ys1z7F7Q_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = load_matlab_model(modelo)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qZU64trH5Ee-",
+        "outputId": "38ea2d40-8811-4cf7-accd-60bfabf1ceb3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:cobra.io.mat:This model seems to have confidenceScores instead of rxnConfidenceScores field. Will use confidenceScores for what rxnConfidenceScores represents.\n",
+            "WARNING:cobra.io.mat:This model seems to have metCharge instead of metCharges field. Will use metCharge for what metCharges represents.\n",
+            "WARNING:cobra.io.mat:No defined compartments in model model. Compartments will be deduced heuristically using regular expressions.\n",
+            "WARNING:cobra.io.mat:Using regular expression found the following compartments:c, e\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.medium"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "98DiucuZfHl5",
+        "outputId": "aaa8f1df-c62e-43ca-8e91-302a6bb01635"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'EX_pi(e)': 1000.0,\n",
+              " 'EX_glc(e)': 10.0,\n",
+              " 'EX_nh4(e)': 1000.0,\n",
+              " 'EX_h(e)': 1000.0,\n",
+              " 'EX_o2(e)': 1000.0,\n",
+              " 'EX_h2o(e)': 1000.0,\n",
+              " 'EX_so4(e)': 1000.0,\n",
+              " 'CYStex': 1000.0}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# save_json_model(model, \"komagataeibacter_xylinus.json\")\n",
+        "# write_sbml_model(model, \"komagataeibacter_xylinus.xml\")"
+      ],
+      "metadata": {
+        "id": "Z4BspK4LUP9B"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(model.reactions))\n",
+        "print(len(model.metabolites))\n",
+        "print(len(model.genes))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-bEaihJzfAFw",
+        "outputId": "1d9e85ec-4968-4d9a-877e-64e2d312bb22"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "918\n",
+            "865\n",
+            "640\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 169
+        },
+        "id": "G0Q0gjJffFNe",
+        "outputId": "5f515cef-d51b-4995-ab10-f4b9c502c461"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Model model at 0x7fce39620590>"
+            ],
+            "text/html": [
+              "\n",
+              "        <table>\n",
+              "            <tr>\n",
+              "                <td><strong>Name</strong></td>\n",
+              "                <td>model</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Memory address</strong></td>\n",
+              "                <td>0x07fce39620590</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Number of metabolites</strong></td>\n",
+              "                <td>865</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Number of reactions</strong></td>\n",
+              "                <td>918</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Number of groups</strong></td>\n",
+              "                <td>74</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Objective expression</strong></td>\n",
+              "                <td>1.0*EX_biomass - 1.0*EX_biomass_reverse_91cf2</td>\n",
+              "            </tr><tr>\n",
+              "                <td><strong>Compartments</strong></td>\n",
+              "                <td>c, e</td>\n",
+              "            </tr>\n",
+              "          </table>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Set the objective function"
+      ],
+      "metadata": {
+        "id": "GDFoxvlBCTO6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.objective = \"EX_biomass\" # CELtexpp\n",
+        "model.objective = \"CELtexpp\""
+      ],
+      "metadata": {
+        "id": "SAPOU8DhCoxr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(model.objective.expression)\n",
+        "print(model.objective.direction)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aq8wpDHeDiKl",
+        "outputId": "518a06e6-beec-4e27-9d6f-4e35213c3574"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.0*CELtexpp - 1.0*CELtexpp_reverse_29bf6\n",
+            "max\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "exchanges_reactions = {'EX_pi(e)',\n",
+        "                'EX_glc(e)',\n",
+        "                'EX_nh4(e)',\n",
+        "                'EX_h(e)',\n",
+        "                'EX_o2(e)',\n",
+        "                'EX_h2o(e)',\n",
+        "                'EX_so4(e)',\n",
+        "                'CYStex'}\n",
+        "for id in exchanges_reactions:\n",
+        "\n",
+        "    r = model.reactions.get_by_id(id)\n",
+        "\n",
+        "    print(id, r.bounds)\n"
+      ],
+      "metadata": {
+        "id": "bKu9f22nDiEE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9230ee39-1bad-4e00-b111-e5054bef9f08"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "EX_pi(e) (-1000.0, 1000.0)\n",
+            "EX_h2o(e) (-1000.0, 1000.0)\n",
+            "EX_o2(e) (-1000.0, 0.0)\n",
+            "CYStex (-1000.0, 1000.0)\n",
+            "EX_glc(e) (-10.0, -10.0)\n",
+            "EX_nh4(e) (-1000.0, 1000.0)\n",
+            "EX_h(e) (-1000.0, 1000.0)\n",
+            "EX_so4(e) (-1000.0, 1000.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "72Vr-LbADh8n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Checking model"
+      ],
+      "metadata": {
+        "id": "nMPzumT2n5Ur"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "solution = model.optimize()\n",
+        "print(solution)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lZyXts_2gp2z",
+        "outputId": "6098b841-6be9-47a7-ed81-e29a7fdbf98d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<Solution 0.709 at 0x7fce31c378d0>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "solution.fluxes"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3uFWWPFk-SzA",
+        "outputId": "31712e86-bbaa-4c4f-d7fa-9bc8123689f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PGMT        -0.106366\n",
+              "GLGC         0.106366\n",
+              "HEX1        10.000000\n",
+              "PGI        -18.024638\n",
+              "FBP          0.000000\n",
+              "              ...    \n",
+              "CYStex       0.000000\n",
+              "CHORt2       0.000000\n",
+              "CYTDt2pp     0.000000\n",
+              "SO4tex       0.159904\n",
+              "5MTRtpp      0.020635\n",
+              "Name: fluxes, Length: 918, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Loops"
+      ],
+      "metadata": {
+        "id": "9sheyXiaoLgk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Fluxes $NH_4$ and $O_2$"
+      ],
+      "metadata": {
+        "id": "EIU6p6kWA6ow"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "b9zNcWFHFueG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ranges_for_uptakes = np.logspace(-3, 3, 100)\n",
+        "ranges = (ranges_for_uptakes, ranges_for_uptakes)\n",
+        "\n",
+        "\n",
+        "combination_limits = np.array(np.meshgrid(*ranges))\n",
+        "combinations = combination_limits.T.reshape(-1, len(ranges))\n",
+        "\n",
+        "full_combinations = np.array([])\n",
+        "lim_glc = 10\n",
+        "\n",
+        "i = 0\n",
+        "for comb in combinations:\n",
+        "    lim_nh4, lim_o2 = comb\n",
+        "\n",
+        "    medium_new = {'EX_pi(e)': 1000,\n",
+        "                    'EX_glc(e)': lim_glc,\n",
+        "                    'EX_nh4(e)': lim_nh4,\n",
+        "                    'EX_h(e)': 1000,\n",
+        "                    'EX_o2(e)': lim_o2,\n",
+        "                    'EX_h2o(e)': 1000,\n",
+        "                    'EX_so4(e)': 0\n",
+        "                    }\n",
+        "\n",
+        "    model.medium = medium_new\n",
+        "\n",
+        "    solution = model.optimize()\n",
+        "        \n",
+        "    reactions_fluxes =  [solution.fluxes.EX_biomass, solution.fluxes.CELtexpp]\n",
+        "\n",
+        "\n",
+        "    uptakes = comb\n",
+        "    outputs = np.array([solution.fluxes.EX_biomass, solution.fluxes.CELtexpp])\n",
+        "\n",
+        "    full_line = np.array([*uptakes, *outputs])\n",
+        "    full_combinations = np.array([*full_combinations,full_line])"
+      ],
+      "metadata": {
+        "id": "Q8EeAASlM7mz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "medium_new"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W9vqgd87LtOS",
+        "outputId": "cb526092-7aee-46fe-a4da-66d22d5d1a78"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'EX_pi(e)': 1000,\n",
+              " 'EX_glc(e)': 10,\n",
+              " 'EX_nh4(e)': 1000.0,\n",
+              " 'EX_h(e)': 1000,\n",
+              " 'EX_o2(e)': 1000.0,\n",
+              " 'EX_h2o(e)': 1000,\n",
+              " 'EX_so4(e)': 1}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# full_combinations_foBC = full_combinations\n",
+        "# full_combinations_foBio = full_combinations"
+      ],
+      "metadata": {
+        "id": "CZExahqSbJd4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Recomendation - Save the results, because take a time to run"
+      ],
+      "metadata": {
+        "id": "ijzqiYDjDFgA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls"
+      ],
+      "metadata": {
+        "id": "ox_uTymPDE9Z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "abd21daa-ffce-48f2-bc79-367863bc1710"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "41598_2017_6918_MOESM1_ESM.xml\n",
+            "449_2020_2299_MOESM3_ESM.mat\n",
+            "cobrapy_fba_diferentes_meios.ipynb\n",
+            "cobrapy_testes.ipynb\n",
+            "figs\n",
+            "FVA_max_min.gsheet\n",
+            "kinetic_parameters_estimation.ipynb\n",
+            "medium_test_cobrapy_oslo.ipynb\n",
+            "medium_test_cobrapy_oslo_versao_com_so4.ipynb\n",
+            "Resultados_e_todo_list_models.gdoc\n",
+            "results_from_nh4_o2_comb.npy\n",
+            "results_from_nh4_o2_FO_Cellulose_comb.npy\n",
+            "results_from_nh4_so4_FO_biomass.npy\n",
+            "results_from_nh4_so4_FO_Cellulose2.npy\n",
+            "results_from_nh4_so4_FO_Cellulose.npy\n",
+            "Solutions_GKOProblem_70_cellObjs.csv\n",
+            "Solutions_GKOProblem.csv\n",
+            "versao1_cobrapy.ipynb\n",
+            "versao2_cobrapy.ipynb\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# np.save(\"results_from_nh4_o2_FO_Cellulose_comb_2.npy\", full_combinations)\n",
+        "\n",
+        "\n",
+        "\n",
+        "full_combinations_foBC = np.load(\"results_from_nh4_o2_FO_Cellulose_comb.npy\") #open previous results\n",
+        "\n",
+        "full_combinations_foBio = np.load(\"results_from_nh4_o2_comb.npy\") #open previous results"
+      ],
+      "metadata": {
+        "id": "jYOvoSouWuw3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Organize data"
+      ],
+      "metadata": {
+        "id": "R0ew7l-YDano"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "columns_bio = int(np.sqrt(len(full_combinations_foBio[:,0])))\n",
+        "\n",
+        "z_bio = full_combinations_foBio[:, 2].reshape(columns_bio, columns_bio)\n",
+        "x_bio, y_bio = full_combinations_foBio[:, 0], full_combinations_foBio[:, 1]"
+      ],
+      "metadata": {
+        "id": "cJwf-1QIFuUi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "columns_bc = int(np.sqrt(len(full_combinations_foBC[:,0])))\n",
+        "\n",
+        "z_bc = full_combinations_foBC[:, 3].reshape(columns_bc, columns_bc)\n",
+        "x_bc, y_bc = full_combinations_foBC[:, 0], full_combinations_foBC[:, 1]"
+      ],
+      "metadata": {
+        "id": "pycojIDCbXmu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Plot the results"
+      ],
+      "metadata": {
+        "id": "DQ42Yx4zDkp_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "xs = np.round_(x_bio[::columns_bio], decimals=4)\n",
+        "ys = np.round_(y_bio[0:columns_bio], decimals=4)\n",
+        "\n",
+        "mat = gaussian_filter(z_bio, sigma=0)\n",
+        "mapa = pd.DataFrame(data=mat, index=ys, columns=xs)\n",
+        "\n",
+        "# plt.savefig('figs/biomass_production.png', dpi=300, format='png')\n",
+        "\n",
+        "fig = plt.figure(figsize=(15, 10))\n",
+        "ax = fig.add_subplot(1, 1, 1)\n",
+        "mesh = ax.pcolormesh(xs, ys, mapa, cmap='hot')\n",
+        "\n",
+        "ax.set_xlabel('NH4_Flux [mmol/gDCW.h]')\n",
+        "ax.set_ylabel('O2_Flux [mmol/gDCW.h]')\n",
+        "ax.set_yscale('log')\n",
+        "ax.set_xscale('log')\n",
+        "# ax.xaxis.set_major_locator(ticker.MultipleLocator(25))\n",
+        "# ax.yaxis.set_major_locator(ticker.MultipleLocator(.05))\n",
+        "\n",
+        "ax.set_title('Medium Influence in specific growth rate')\n",
+        "# plt.savefig('figs/biomass_production.png', dpi=300, format='png')\n",
+        "\n",
+        "cbar = fig.colorbar(mesh, ax=ax)\n",
+        "cbar.set_label('Specific growth rate [1/h]')\n",
+        "# cbar.locator = ticker.MultipleLocator(.05)\n",
+        "# cbar.update_ticks()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 626
+        },
+        "id": "ejSsMJQ1gTj7",
+        "outputId": "475048d3-7435-4fc3-9be1-e9cceeb3127a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 2 Axes>"
+            ],
+            "image/png": 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5DyZJkiRp2XWuQiRJkiRpPtcQDWIhRZIkSdLMskIkSZIkdZ4VokGsEEmSJEmaWVaIJEmSpM4r4IZJBzGVrBBJkiRJmllWiCRJkqTOcw3RIFaIJEmSJM0sK0SSJEnSTLBCtBArRJIkSZJmlhUiSZIkqfNcQzSIFSJJkiRJM8uESJIkSdLMcsqcJEmS1HlOmRukcwlRkvXAektfkiRJkkbpXEJUVZuBzWuTEyYdiyRJkjQdrBANYiFFkiRJ0szqXIVIkiRJ0nxWiAaxQiRJkiRpZlkhkiRJkmaCFaKFWCGSJEmSNLOsEEmSJEmd5xqiQawQSZIkSZpZVogkSZKkzrNCNIgVIkmSJEkzywqRJEmS1HlWiAaxQiRJkiRpZlkhkiRJkjrPCtEgVogkSZIkzSwTIkmSJEljlWRdki8kuSzJCwec8/gklyS5OMnbVioWp8xJkiRJM2E6pswlWQOcAhwLbAcuSLKpqi7pO+dQ4EXAg6rqO0lut1LxWCGSJEmSNE5HApdV1baqug44E3jsvHNOAE6pqu8AVNWVKxWMFSJJkiSp86aqqcL+wOV929uBo+adc1eAJB8H1gAvrar3r0QwJkSSJEmSltO+Sbb2bW+sqo2LvMbNgEOBo4EDgI8l+ZWq+u4yxbjTjSRJkiR12lgrRDuq6oghx68ADuzbPqDZ12878H+r6qfAV5J8kV6CdMGyRopriCRJkiSN1wXAoUkOSbI7cBywad4576FXHSLJvvSm0G1biWCsEEmSJEmdNz1riKrq+iQnAufQWx90WlVdnOQkYGtVbWqOPSLJJcANwPOr6qqViMeESJIkSdJYVdUWYMu8fS/ue1/AnzavFbUqEqIkvwU8CtgTOLWqPjDhkCRJkqRV5oZJBzCVJraGKMlpSa5M8rl5+2/y1Nqqek9VnQA8Hfi9ScQrSZIkqXsm2VThdGBd/46+p9Y+EjgMOD7JYX2n/EVzXJIkSVJrc2uIxvFaXSaWEFXVx4Cr5+1e8Km16fk74Oyq+tSgaybZkGRrkq0/W7nQJUmSJHXEtK0hGvTU2mcCxwB7JblLVb1hocHNA582AqxNaoVjlSRJklaJ6ekyN22mLSFaUFWdDJw86TgkSZIkdcu0JURtnlorSZIkaVGsEA0yyaYKC2nz1NqhkqxPstE1RJIkSZJGmWTb7TOATwJ3S7I9ydOq6npg7qm1lwLvqKqLF3PdqtpcVRumLdOTJEmSNH0mNmWuqo4fsP8mT62VJEmStCucMjeIhRRJkiRJM2vamipIkiRJWnZWiAbpXEKUZD2w3tKXJEmSpFE6lxBV1WZg89rkhEnHIkmSJE0PK0QLsZAiSZIkaWZ1rkIkSZIkaT7XEA1ihUiSJEnSzOpchcimCpIkSdJ8VogG6VzeUFWbq2pD5z6YJEmSpGXXuQqRJEmSpPmsEA1iIUWSJEnSzLJCJEmSJHWeFaJBrBBJkiRJmlmdqxDZZU6SJElaiBWihXQub7DLnCRJkqS2zBskSZIkzazOTZmTJEmSNJ9NFQaxQiRJkiRpZlkhkiRJkjrPCtEgVogkSZIkzazOVYhsuy1JkiTNZ4VokM7lDbbdliRJktRW5ypEkiRJkuazQjSIhRRJkiRJM8sKkSRJkjQTbph0AFPJCpEkSZKkmWWFSJIkSeo81xANYoVIkiRJ0syyQiRJkiR1nhWiQTqXEPlgVkmSJEltdS4hqqrNwOa1yQmTjkWSJEmaDlaIBrGQIkmSJGlmmRBJkiRJmlmdmzInSZIkaSFOmVuIFSJJkiRJM8sKkSRJktR5NlUYxAqRJEmSpJllhUiSJEnqPCtEg1ghkiRJkjSzrBBJkiRJnWeFaJDOJURJ1gPrLX1JkiRJGqVzCVFVbQY2r01OmHQskiRJ0tSoGyYdwVSykCJJkiRpZnWuQiRJkiRpAT+bdADTyQqRJEmSpJllhUiSJEnqugJcQrQgK0SSJEmSZpYVIkmSJKnrrBANZIVIkiRJ0swyIZIkSZI0s5wyJ0mSJM0C224vyAqRJEmSpJllhUiSJEnqOpsqDGSFSJIkSdLMskIkSZIkzQLXEC3ICpEkSZKkmdW5ClGS9cB6Mz1JkiSp4RqigTqXN1TV5qra0LkPJkmSJHVEknVJvpDksiQvXOD4U5J8O8lFzeuPViqWzlWIJEmSJC1gSipESdYApwDHAtuBC5JsqqpL5p369qo6caXjsZAiSZIkaZyOBC6rqm1VdR1wJvDYSQVjQiRJkiR1XdHrMjeOF+ybZGvfa8O8aPYHLu/b3t7sm+9xSf6/JGclOXBXPv4wTpmTJEmStJx2VNURu3iNzcAZVfWTJP8LeDPw8F0P7aZMiCRJkqRZMCVriIArgP6KzwHNvp+rqqv6Nt8IvHKlgnHKnCRJkqRxugA4NMkhSXYHjgM29Z+Q5A59m48BLl2pYKwQSZIkSV03Rc8hqqrrk5wInAOsAU6rqouTnARsrapNwLOSPAa4HrgaeMpKxWNCJEmSJGmsqmoLsGXevhf3vX8R8KJxxGJCJEmSJM2Cn006gOnkGiJJkiRJM8uESJIkSdLMcsqcJEmS1HVT1FRh2lghkiRJkjSzrBBJkiRJs8CmCguyQiRJkiRpZlkhkiRJkrrONUQDWSGSJEmSNLOsEEmSJEldZ4VoICtEkiRJkmaWFSJJkiRpFthlbkFWiCRJkiTNLCtEkiRJUte5hmigVVEhSnKnJKcmOWvSsUiSJEnqjoklRElOS3Jlks/N278uyReSXJbkhQBVta2qnjaZSCVJkqQOuGFMr1VmkhWi04F1/TuSrAFOAR4JHAYcn+Sw8YcmSZIkaRZMLCGqqo8BV8/bfSRwWVMRug44E3js2IOTJEmSuqTodZkbx2uVmbY1RPsDl/dtbwf2T3LbJG8ADk/yokGDk2xIsjXJ1lX4ZyFJkiRpzFZFl7mqugp4eovzNgIbAdYmtdJxSZIkSVrdpi0hugI4sG/7gGafJEmSpF2xChsejMO0TZm7ADg0ySFJdgeOAzZNOCZJkiRJHTXJtttnAJ8E7pZke5KnVdX1wInAOcClwDuq6uJFXnd9ko2uIZIkSZIaNlUYaGJT5qrq+AH7twBbduG6m4HNa5MTlnoNSZIkSbNh2tYQSZIkSVoJriFa0LStIZIkSZKkselchSjJemC9mZ4kSZLUKKwQDdC5vKGqNlfVhs59MEmSJEnLrnMVIkmSJEkLWIUd4MbBQookSZKkmWWFSJIkSeo61xANZIVIkiRJ0szqXIXILnOSJEnSAqwQLahzeYNd5iRJkiS11bkKkSRJkqR5CrvMDWBCJEmSpth7Jx2ApI5zZpkkSZKkmWWFSJIkSZoFNlVYUOcSIrvMSZIkSWqrc3mDXeYkSZKkeeaaKozjtcp0rkIkSZJWmw8s8Zgk7ToTIkmSJGkWuIZoQc4skyRJkjSzrBBJkiRJXVdYIRrACpEkSZKkmdW5CpFttyVJkqQFrMIOcOPQuYSoqjYDm9cmJ0w6FkmS1PjCsINXjCsKSbqJziVEkiRJkubp4BqiJH/a4rRrqupfhp3gzDJJkiRJq9HzgVsBtx7yeu6oiwytECXZp0UgP6uq77Y4T5IkSdIkdLBCBLy1qk4adkKSW466yKgpc99oXhlyzhrgjqNuJEmSJEnLpapesBznjEqILq2qw4edkOTTo24iSZJmwLYlHpM0Hh3tMpfk5sDjgIPpy29GVY/mjEqIHtjiGm3OkSRJkqSV8F7ge8CFwE8WO3hoQlRV1869T7IG+CV2zrq+3n+OJEmSJI3ZAVW1bqmDW7XdTvJM4CXAt7ix2FbAvZZ645Xig1klSZKkebrZVGHOJ5L8SlV9dimD2z6H6NnA3arqqqXcZJx8MKskSZLUfUk+Sy/Vuxnwh0m20ZsyF6CqqlXxpm1CdDm9eXmSJEmSVqPuNVV49HJcZNRziOae/roN+GiS99G3UKmqXrUcQUiSpFVk+yL3S9LKeBdwHnA28NGl9jYYVSG6dfPfrzev3ZuXJEmSpNWim2uIjgIeDKwD/irJVcA5wNlV9cW2FxnVZe6vdilESZIkSVoBVXU98NHmRZL96CVHL0tyF+D8qvqTUddpu4boJpJsqKqNSx0vSZIkaYy6VyHaSVV9AzgNOC3JbrR8XurQ7tRJ9h52uH14kiRJkjQ2b6iqj7c5cVSF6AtJdgAfBz4BfHxuPl5V/cuuxShJkqbWN5Z4TNJ0KjrXZS7JPoMOAb/Z9jqj1hDdLsldgV9tXs9N8ovA+fSSo1e2vZEkSZIkLaNvA19j55lr1Wzfru1FRq4haipCXwROT3JnetnWs4FHACZEkiRJ0mrQvTVE24Bfr6qvzz+Q5PK2Fxn1HKK5ytADgQObm54PPBH41GKiHZck64H1QxdHSZIkSVrtXgPsTe/xQPO1LtyMqhCdRy/xeTXw7qr6UevwJqSqNgOb1yYnTDoWSZIkaSpM2RqiJOuA1wJrgDdW1SsGnPc44Czg/lW1tf9YVZ0y6PpV9U9tYxlVSNkP+FvgcOD9ST6R5HVJfj/JndreRJIkSZIAkqwBTgEeCRwGHJ/ksAXOuzW9pTr/d8B17tviXiPPGdVU4X+AdzUvktwCeCrwV8Ah9DI6SZK0Gv3PEo9JWp2mZw3RkcBlVbUNIMmZwGOBS+ad99fA3wHPH3CdNyU5muGPAzqVXnFnoFFriPait35obi3R4cCXgM30WnFLkiRJUr99k/RPb9tYVRv7tvcH+psebAeO6r9AU9k5sKrel2RQQrQXcCHDE6Jvjwp21Bqiy4BPNq+TgAuq6sejLipJkiRpZu2oqiOWOjjJbsCrgKcMO6+qDl7qPfqNSogOAm5VVVf272yeRfSDqrp2OYKQJEmStIKKaZoydwW9DtZzDmj2zbk1cE/go0kAbg9sSvKY+Y0VlsOopgqvAR68wP4H0+s8J0mSJEmLcQFwaJJDkuwOHAdsmjtYVd+rqn2r6uCmCnQ+sCLJEIyuEN2vqjbM31lV707yspUISJIkLZ8f/HTIwavHFoakaTAlbber6vokJwLn0GvSdlpVXZzkJGBrVW0afoXlNSohusWQYz77VJIkSdKiVdUWYMu8fS8ecO7Rw66V3ry63wfuVFUnJbkjcPuq+u82sYxKaq5McuQCN70/LTo2SJIkSZoCc2uIxvEav9fT64x9fLP9A3rPOWplVIXo+cA7kpxOr6UdwBHAk+nN9ZMkSZKkSTqqqu6b5NMAVfWdZm1SK6MezPrfSY4C/oQb295d3Nz0yoEDJUmSJE2P6eoyt9x+mmQNvU851xG79YqpURUiqupbwEuaC1NVTpWTJEmSNC1OBt4N3C7J3wC/C/xl28FDE6JmgdJLgGfQ6wBBkhuAf6qqk5YasSRJWl7fW+R+STNoSrrMLbeq+rckFwK/DgT4raq6tO34UU0VngM8CDiyqvapqn2Ao4AHJXnOUoOWJEmSpOWQ5K1V9fmqOqWqXldVlyZ5a9vxo6bMPQk4tqp2zO2oqm1Jngh8gCl8OGuS9cB6e4JLkiRJjW6vIbpH/0aznuh+bQePyhvW9idDc5p1RGvb3mScqmpzVW0wIZIkSZK6K8mLkvwAuFeS7yf5QbN9JfDettcZVSG6bonHJEmSJE2Tjq0hqqqXAy9P8vKqetFSrzMqIbp3ku8vsD/AHku9qSRJWrzvLvGYJHVZVb0oyd7AofTlKFX1sTbjRz2HaM2uhSdJkiRp4jq8hijJHwHPBg4ALgIeAHwSeHib8a2W2iTZZ4HXVK4hkiRJkjRTng3cH/haVT0MOJxFFM7b9h74FPBt4IvAl5r3X03yqSStOzhIkiRJ0jK7tqquBUhy86r6PHC3toNHrSGa80HgrKo6p7nRI4DHAW8CXk/v2USSJEmSplVHp8wB25PcBngP8MEk3wG+1nZw24ToAVV1wtxGVX0gyT9U1f9KcvPFxStJkiRJy6Oqfrt5+9IkHwH2At7fdnzbhOibSf4MOLPZ/j3gW81DjzrWwE+SpMm5ZonHJGmoopO/tTf5yMVVdXeAqjp3sddou4boCfS6NrwHeDdwYLNvDfD4xd5UkiRJknZVVd0AfCHJHZd6jaEVoiQvAt5fVZ8GnjngtMuWenNJkiRJY9LdNUR7Axcn+W/6iulV9Zg2g331k/UAACAASURBVEdNmdsGPDvJvYHPAGcDH6iq7ywxWEmSJElaTn+5K4NHPZj17cDbAZIcDqwD3tXM1fsQverRf+9KAJIkSZJWWIcfzLqUdUP92jZVoJk292ng5Un2BI4F/ggwIZIkaRGuXeIxSdLyG7WG6HeGHK6q2rDM8UiSJElaCR3sMrccRlWI1g85VsC7ljEWSZIkSRqrUWuI/nBcgUiSJElaIR1eQ5TkQcBLgYPo5TehN5vtTm3Gt1pDlGQv4CXAQ5pd5wInVdX3FhuwJEmSJC2jU4HnABeyhLSvbVOF04DPceNDWJ8EvAkYtsZIkiRJ0rTo7hqi71XV2Usd3DYhunNVPa5v+6+SXLTUm0qSNAuuW+R+SVJ7Se7bvP1Ikr+n19/gJ3PHq+pTba7TNiH6cZIHV9V5zc0fBPx4EfFKkiRJmpRuriH6x3nbR/S9L+DhbS7SNiH6Y+DNzVqiAFcDT2k5VpIkSZKWVVU9DCDJnapqW/+xJK0aKkDLhKiqLgLu3TyQlar6/iJilSRJkqSVchZw33n73gncr83gtl3mbgM8GTgYuFkSAKrqWW2j3BVJbgm8nt60649W1b+N476SJElSZ3RsylySuwP3APZK0t/sbU9gj7bXaTtlbgtwPvBZlqk/RZLTgEcDV1bVPfv2rwNeC6wB3lhVr6DXze6sqtqc5O2ACZEkaSr8dMgxn00hSSvqbvTyidsA6/v2/wA4oe1F2iZEe1TVn7aPrZXTgdcBb5nbkWQNcApwLLAduCDJJuAAeskYdC63lSRJklZY0bm221X1XuC9SR5QVecv9Tq7tTzvrUlOSHKHJPvMvZZ6U4Cq+hi95gz9jgQuq6ptVXUdcCbwWHrJ0QGjYk6yIcnWJFs79uctSZIkaWH/muTjSV6R5FFNI7jW2laIrgP+HvhzevklzX9bd29oaX/g8r7t7cBRwMnA65I8Ctg8aHBVbQQ2AqxNatB5kiRJ0szp6DyrqrpLkjsCvwY8CjglyXer6j5txrdNiJ4L3KWqdiwxzl1SVdcAfziJe0uSJEmaXkkOAB5ELyG6N3AxcF7b8W0TosuAHy06usW7Ajiwb/uAZp8kSZKkpermg1nnfB24APjbqnr6Yge3TYiuAS5K8hHgJ3M7V6Dt9gXAoUkOoZcIHQc8YTEXSLIeWN92cZQkSaMM+x3imrFFIUka4HDgwcATkrwQ+BJwblWd2mZw24ToPc1r2SQ5Azga2DfJduAlVXVqkhOBc+i13T6tqi5ezHWrajOweW3SutWeJEmS1Hkd7TpWVZ9J8mXgy/SmzT0ReCiwfAlRVb15yREOvubxA/ZvoffcI0mSJEkaKslW4ObAJ4D/Ah5SVV9rO75VQpTk0cBfAwc1YwJUVe256IglSZIkjVe31xA9sqq+vdTBbafMvQb4HeCzVWU7a0mSJEnT4rokrwIe0myfC5xUVd9rM7htQnQ58LnVkAzZVEGStBTD/uH0B2OLQpJWSNHZNUTAacDngMc3208C3kSvoDNS24ToBcCWJOeyc5e5V7WPczxsqiBJkiTNlDtX1eP6tv8qyUVtB7dNiP4G+CGwB7D7IoKTJEmSNA26u4box0keXFXnASR5EPDjtoPbJkT7VdU9lxKdJEmSJK2gpwNvSbJXs/0d4A/aDm6bEG1J8oiq+sBio5MkSZKklZBkDfCkqrp3kj0Bqur7i7lG24Toj4HnJfkJ8FOmuO22TRUkSZKkeTradruqbkjy4Ob9ohKhOW0fzHrrpVx8EmyqIElaimsnHYAkaak+nWQT8E7gmrmdVfWuNoOHJkRJbl9V/7Or50iSJEmasO623d4DuAp4eN++AnY9IQK2APddhnMkSZIkadlV1R/uyvhRCdG9kwybixdgSXP1JEmSJI1JR9cQASQ5eYHd3wO2VtV7R40fmhBV1ZqlBiZJkiRJY7AHcHd6a4gAHgd8hV5x52FV9b+HDW7bZW7VsMucJGkYmydImlkdrRAB9wIeVFU3ACT5Z+C/gAcDnx01uHN5Q1VtrqoNnftgkiRJkhayN3Crvu1bAvs0CdJPRg3uXIVIkiRJ0jxFl7vMvRK4KMlH6fU4eAjwt0luCXxo1OBWhZQkxyyw7w8WF6ckSZIkLa+qOhX4VeA9wLuBB1fVG6vqmqp6/qjxbStEL07yOOB59MpRb6RXfnrz0sKWJEmSNFbdXUNEVX0TGNlRbiFtE6KHAs8FLmq2X1xVZyzlhpIkrbTrJh2AJGnVaJsQ7Q0cCXwZOAA4KEmqqlYsMkmSJEnLo9triHZJ22Zs5wPvr6p1wP2B/YCPr1hUkiRJktRCkgckuXXf9p5Jjmo7vm2F6Jiq+jpAVf0YeFaShywu1PHwOUSSJEnSArq7huifgfv2bf9wgX0DtU2IDk5y8KLCmpCq2gxsXpucMOlYJEmSJK24nZbyVNXPkrR+vFDbE/vb1e1Bbz3RhcDD295IkiRJklbAtiTPolcVAvgTYFvbwa0Soqpa37+d5EDgNW1vIknSchs286O7s0IkaYmKLv/l+HTgZOAv6H3S/wQ2tB3cupQ0z3bgl5c4VpIkSZKWRVVdCRy31PGtEqIk/0Qv24JeZ7r7AJ9a6k0lSZIkjdkUtd1Osg54LbAGeGNVvWLe8acDz6BX1/ohsKGqLpl3zguq6pXzcpWfq6pntYmlbYVoa9/764Ezqsq225IkSZIWJcka4BTgWHozzy5IsmlewvO2qnpDc/5jgFcB6+Zdau78reyCtmuI3rwrN5EkSZI0QdO1huhI4LKq2gaQ5EzgsdyY4FBV3+87/5YsUAECfg/4D+A2VfXapQYzNCFK8tkBN08vzrrXUm+8UnwOkSR1x7DZHVM080OStLN9k/RXbTZW1ca+7f2By/u2twM3eZBqkmcAfwrszsLdre+XZD/gqUneQi9H+bmqurpNsKMqRI9uc5Fp4nOIJEmSpHnGWyHaUVVH7OpFquoU4JQkT6DXQe4P5p3yBnod5e5E75FA/QlRNftHGpUQ3aGqzm8VsSRJkiSNdgVwYN/2Ac2+Qc7kxmcM/VxVnQycnOSfq+qPlxrMqJllr597k+STS72JJEmSpAn72Zheo10AHJrkkCS702uZvan/hCSH9m0+CvjS/Isk2bN5++dJ9pn/ahUJoytE/WWnPdpeVJIkSZIWUlXXJzkROIde2+3TquriJCcBW6tqE3BikmOAnwLf4abT5QDeRm+Jz4X0psityJS53ZLsTa+SNPf+5zdqu1BJkiRJ0gRNV5c5qmoLsGXevhf3vX92i2s8uvnvIbsSy6iEaC92XqDU/zDW1lmXJElL8dNJByBJmnpJfhv4cFV9r9m+DXB0Vb2nzfihCVFVHdwyiHtU1cVtzpUkSZI0Ad19XsFLqurdcxtV9d0kLwFaJUTL9biety7TdSRJkiRpMRbKaUbNhFv8iSNk9CmSJEmSJmLK1hAts61JXgWc0mw/g96yn1aWq0JUy3QdSZIkSVqMZwLXAW+n98yia+klRa0sV4VIkqQluW7SAUiSVrWqugZ4YZJbNu8XZbkqRFPz/7Mk65Ns7O6aMUmSJGkJbhjTa8yS/GqSS4BLm+17J3l92/GtEqIkaxfYt+/c+6p6QNsbrrSq2lxVG5Yr05MkSZI01V4N/AZwFUBVfQZ4SNvBQ/OGJA9Lsh34ZpIPJDm47/AHFh2qJEmSpPErem23x/GagKq6fN6u1rWqUYWUVwK/UVX7AhuBDyaZqwbZWU6SJEnSpF2e5FeBSrI2yfNops+1Maqpwu5zD1ytqrOSXAq8K8mfYWc5SZIkafXobtvtpwOvBfYHvgGcwzJ2mftpkttX1f8AVNXFSX4d+A/gzkuLV5I0i2x2I0laCVW1A/j9pY4fNWXuhcAvzbvhduBo4BVLvakkSZKkMZp7MGs3u8zdKcnmJN9OcmWS9ya5U9vxQxOiqvpQVX0myR5J7tm89qiq71bV3+x6+JIkSZK0S94GvAO4A7Af8E7gjLaDR3WZu1mSVwLbgTcDb6G3aOmVC7XiliRJkjSluttl7hZV9daqur55/SuwR9vBo6bM/T2wD3BIVd2vqu5Lb+3QbYB/WHLIkiRJkrQ8zk7ywiQHJzkoyQuALUn2SbLPqMGpGtwsLsmXgLvWvJOSrAE+X1WH7mr0K2VtUntPOghJGmDNEsYM+xespVxv2LhhUwBWIg5JWq0ugQur6ohJxzHKETdLbd1rPPfK1eP9TpJ8Zcjhqqqh64lGdZmr+clQs/OGJLbdliRJkjRRVXXIrowfNWXukiRPnr8zyROBz+/KjSVJkiSNUcfWECW5f5Lb920/uekwd3KbqXJzRlWInkHvQaxPBS5s9h0B/ALw24sNWpIkSZKWyb8AxwAkeQi9xwI9E7gPsBH43TYXGZoQVdUVwFFJHg7co9m9par+c4lBS5IkSRq3uecQdcuaqrq6ef97wMaq+nfg35Nc1PYioypEAFTVh4EPLz5GSZIkSVoRa5LcrKquB34d2NB3rFWes6gTV4sk64H1oxZHSZKWX/f+8VGSOqR7f0mfAZybZAfwY+C/AJLcBfhe24t0LiGqqs3A5rXJCZOORZIkSdLKqKq/SfKfwB2AD/R1x96N3lqiVjqXEEmSJEmaDVV1/gL7vriYa5gQSZIkSV1XjLUl9mriUhtJkiRJM8sKkSRpUfwHRklapbrXVGFZWCGSJEmSNLOsEEmSJEld180Hsy4LK0SSJEmSZpYVIkmSJGkWuAh0QVaIJEmSJM0sK0SSpJtwmrkkdY9/ty/MCpEkSZKkmWWFSJIkSeo4m8wNZoVIkiRJ0syyQiRJkiTNAJvMLcwKkSRJkqSZZYVIkiRJ6jjXEA1mhUiSJEnSzDIhkiRJkjSznDInSZIkzQCbKizMCpEkSZKkmWWFSJJ0E/4roiR1i00VBlsVFaIkd0pyapKzJh2LJEmSpO5Y8YQoyWlJrkzyuXn71yX5QpLLkrxw2DWqaltVPW1lI5UkSZK6aa5CNI7XajOOKXOnA68D3jK3I8ka4BTgWGA7cEGSTcAa4OXzxj+1qq4cQ5ySJEmSZsyKJ0RV9bEkB8/bfSRwWVVtA0hyJvDYqno58OiVjkmSJEmaNa4PXdik1hDtD1zet7292begJLdN8gbg8CQvGnLehiRbk2z1D1ySJEnSKKuiy1xVXQU8vcV5G4GNAGuTWum4JEmSpNXALnODTapCdAVwYN/2Ac0+SZIkSRqbSVWILgAOTXIIvUToOOAJE4pFkiRJ6jwrRAsbR9vtM4BPAndLsj3J06rqeuBE4BzgUuAdVXXxMt1vfZKNriGSJEmSNEqqurnUZm1Se086CEkaYM0Sxgz7F6ylXG9XxkmSer4KF1bVEZOOY5R7JfW+Md3rjqvkO5mzKpoqSJJWhtMnJEmzzoRIkiRJmgH+I9jCJtVlTpIkSZImrnMVoiTrgfVmepIkSZJG6VzeUFWbq2pD5z6YJEmStEQF/GxMr9XGvEGSJEnSzOrclDlJkiRJN2VThYVZIZIkSZI0szpXIbKpgiRJkrSzwgrRIJ3LG2yqIEmSJKmtzlWIJEmSJN3UauwANw4mRJLUAU6DkCStJknWAa8F1gBvrKpXzDv+p8AfAdcD3waeWlVfW4lYnFkmSZIkddzcGqJxvEZJsgY4BXgkcBhwfJLD5p32aeCIqroXcBbwyiV87FZMiCRJkiSN05HAZVW1raquA84EHtt/QlV9pKp+1GyeDxywUsF0bsqcXeYkSZKknU1Zl7n9gcv7trcDRw05/2nA2SsVTOfyBrvMSZIkSRO1b5Ktfa8NS71QkicCRwB/v3zh7axzFSJJkiRJNzXGLnM7quqIIcevAA7s2z6g2beTJMcAfw48tKp+srwh3shCiiRJkqRxugA4NMkhSXYHjgM29Z+Q5HDgX4DHVNWVKxmMFSJJkiSp46ZpDVFVXZ/kROAcem23T6uqi5OcBGytqk30psjdCnhnEoCvV9VjViIeEyJJkiRJY1VVW4At8/a9uO/9MeOKxSlzkiRJkmZW5ypEtt2WJEmSbmqMTRVWlc7lDbbdliRJktRW5ypEkiRJknY2TU0Vpo2FFEmSJEkzywqRJEmSNAOsEC3MCpEkSZKkmWWFSJIkSeq4wi5zg1ghkiRJkjSzrBBJkiRJM8A1RAvrXELkg1klSZIktdW5vMEHs0qSJEk7m3sO0Theq415gyRJkqSZ1bkpc5LUVXYHkiTtCv8/sjArRJIkSZJmlhUiSZIkqePm1hDppqwQSZIkSZpZJkSSJEmSZpZT5iRJkqQZYFOFhVkhkiRJkjSzrBBJkiRJHWdThcE6lxAlWQ+st/QlSZIkaZTO5Q1VtbmqNnTug0mSJEm74IYxvVYb8wZJkiRJM6tzU+YkSZIk7aywy9wgVogkSZIkzSwrRJIkSdIMWI3re8bBCpEkSZKkmWWFSJIkSeo4n0M0mBUiSZIkSTPLCpEkSZI0A+wytzArRJIkSZJmlhUiSZIkqeNcQzSYFSJJkiRJM8uESJIkSdLMcsqcJEmS1HGFTRUGsUIkSZIkaWZ1rkKUZD2w3kxPkiRJupFNFRbWubyhqjZX1YbOfTBJkiRJy65zFSJJkiRJO7Pt9mAWUiRJkiTNLCtEkiRJ0gywy9zCrBBJkiRJmllWiCRJkqSOcw3RYFaIJEmSJM0sK0SSJEnSDLBCtDArRJIkSZJmlhUiSZIkqeMKu8wNYoVIkiRJ0syyQiRJkiTNANcQLcwKkSRJkqSZZUIkSZIkaWY5ZU6SJEnqOJsqDGaFSJIkSdLMskIkSZIkzQCbKizMCpEkSZKkmWWFSJIkSeq4wgrRIFaIJEmSJM0sK0SSJEnSDLDL3MKsEEmSJEmaWVaIJEmSpI5zDdFgqyIhSvJbwKOAPYFTq+oDEw5JkiRJUgeseEKU5DTg0cCVVXXPvv3rgNcCa4A3VtUrBl2jqt4DvCfJ3sA/ACZEklY1/5VOkjROVogGG0eF6HTgdcBb5nYkWQOcAhwLbAcuSLKJXnL08nnjn1pVVzbv/6IZJ0mSJEm7bMUToqr6WJKD5+0+ErisqrYBJDkTeGxVvZxeNWknSQK8Aji7qj416F5JNgAbwG4RkiRJUj+7zC1sUmuI9gcu79veDhw15PxnAscAeyW5S1W9YaGTqmojsBFgbVLLFKskSZKkjloVTRWq6mTg5EnHIUmSJK1GriEabFIzy64ADuzbPqDZJ0mSJKnjkqxL8oUklyV54QLHH5LkU0muT/K7KxnLpBKiC4BDkxySZHfgOGDThGKRJEmSNCZ9DdYeCRwGHJ/ksHmnfR14CvC2lY5nHG23zwCOBvZNsh14SVWdmuRE4Bx6neVOq6qLl+l+64H1NlWQJEmSbjRFTRUWbLAGXDJ3QlV9tTm24mGPo8vc8QP2bwG2rMD9NgOb1yYnLPe1JUmSJI20b5Ktfdsbm+ZncxbbYG1FrYqmCpIkSZKWbsxNFXZU1RHju92ucWaZJEmSpHGaqgZrnasQuYZIkiRJuqkparv98wZr9BKh44An/P/t3X/wZXVdx/HnqwVsJIdMskaxFg3JHVBCWxWdRrMxSH44hgqilYMSmWROTumMlTk2WjOODkkKKq2ZigyTtoBJiW46SAooyQJDrYSKWeKPKDVR4N0f9yD3++Xe7/fe3fvrnPN8zNzZe875nHM+9773s/e+933O5y6rM53LG6rq4qo6o3MvTJIkSeqAqroTuGeCtRuBC6vq+iSvTXIiQJKfbyZkezZwbpKZTMA2SucqRJIkSZLWKlZqlrmRE6xV1R8NPb+KwaV0c2chRZIkSVJvWSGSJEmSemCF7iFaKVaIJEmSJPVW5ypEzjInSZIkrbXg3yFqlc7lDc4yJ0mSJGlSnasQSZIkSbqvVZplbpVYSJEkSZLUW1aIJEmSpI7zHqLxrBBJkiRJ6q3OVYicZU6SJEnSpDqXNzjLnCRJknRfdy/o0TbmDZIkSZJ6q3OXzEmSJElay0kVxrNCJEmSJKm3rBBJkiRJPWCFaDQrRJIkSZJ6q3MVIqfdliRJktYq2jkD3CJ0Lm9w2m1JkiRJk+pchUiSJEnSfXkP0WgWUiRJkiT1lhUiSZIkqeP8HaLxrBBJkiRJ6i0rRJIkSVLHOcvceFaIJEmSJPWWFSJJkiSpB7yHaLTOJUT+MKskSZKkSXUub/CHWSVJkiRNqnMVIkmSJElrOanCeBZSJEmSJPWWFSJJkiSpB5xUYTQrRJIkSZJ6ywqRJEmS1HGFFaJxrBBJkiRJ6i0rRJIkSVIPOMvcaFaIJEmSJPWWFSJJkiSp47yHaLzOJURJTgBOsPQlSZIkaTOdyxuq6uKqOqNzL0ySJEnaB3ct6NE25g2SJEmSeqtzl8xJkiRJWqtwlrlxrBBJkiRJ6i0rRJIkSVIPtPH+nkWwQiRJkiSpt6wQSZIkSR3nPUTjWSGSJEmS1FsmRJIkSZJ6y0vmJEmSpB5wUoXRrBBJkiRJ6i0rRJIkSVLHFVaIxrFCJEmSJKm3rBBJkiRJPeC026NZIZIkSZLUW52rECU5ATjBTE+SJEka8B6i8TqXN1TVxVV1RudemCRJkqSZ61yFSJIkSdJaVojGs5AiSZIkqbesEEmSJEk94Cxzo1khkiRJktRbVogkSZKkjvMeovGsEEmSJEnqLStEkiRJUg94D9FoVogkSZIk9ZYJkSRJkqTe8pI5SZIkqeOcVGE8K0SSJEmSessKkSRJktQDVohGs0IkSZIkaaGSHJvkpiR7krxyxPb7JXl/s/1TSbbOqy8mRJIkSVLHFYNptxfx2EySLcA5wHHANuDUJNvWNTsd+GZV/QzwJuDP9uJlT8SESJIkSdIibQf2VNXNVfU94ALgpHVtTgLe1Ty/CHhaksyjM95DJEmSJPXAAu8hOjjJ1UPL51XVeUPLDwW+NLR8K/D4dcf4QZuqujPJ7cCDgK/NurMmRJIkSZJm6WtV9bhld2JSJkSSJElSx63Y7xB9GXjY0PIhzbpRbW5Nsh9wEPD1eXTGe4gkSZIkLdJVwGFJDk1yAHAKsHNdm53ArzfPTwY+WlU1j85YIZIkSZJ6YJIZ4BahuSfopcBlwBbg/Kq6PslrgauraifwTuDdSfYA32CQNM1F5pRoLd3+ST1w2Z2QJElSp90G17Thfpn9knrAgs713y15T+5hhUiSJEnquBW7h2iltCIhSvIo4GXAwcDlVfXWzfa5G/jOvDsm9dQq3Hy4ZQ7HnPXrmkcfJUnSbM39e02S85N8NcnudeuPTXJTkj1JXrnRMarqxqo6E3gO8KR59leSJEnqorsX9GibRfxH7w7g2OEVSbYA5wDHAduAU5NsS3JkkkvWPR7c7HMicCnwoQX0WZIkSVIPzP2Suar6eJKt61ZvB/ZU1c0ASS4ATqqq1wPHjznOTmBnkkuB945qk+QM4Ixm8Y5vw+5R7RboIOD2FTjeNPtN0najNtNuG7XuYObwK8R7oW3x29fYbbR9mvVdjF9fxh6sRvzaNvYmaevYW+yxVnnsjVu/CvHr4tjbrM0s/u08fJPzr4S74bJvD/6eLcKy/y5Pp6rm/gC2AruHlk8G3jG0/ALgLRvs/xTgbOBc4LcnPOfVi3htm/ThvFU43jT7TdJ2ozbTbhuzbumxa2P89jV2G22fZn0X49eXsbcq8Wvb2JukrWOvvbHbrM3ebFvV+HVx7M06fqsaOx/79mjFpApVtQvYteRu7I2LV+R40+w3SduN2ky7bdbv0Sy1LX77GruNtk+7fhXMsm+OvcVq29ibpK1jb7HHWuWxN+k5l6GLY2+zNl36t1N7aSG/Q9RcMndJVR3RLD8ReE1V/XKz/CqAGlwyN6tzXl0tmv9c9zJ27Wb82s34tZexazfj117Grv2WNXvuVcBhSQ5NcgCDX57dOeNznDfj42lxjF27Gb92M37tZezazfi1l7FrublXiJK8j8E9QAcD/wX8cVW9M8mvAG9m8FMd51fVn861I5IkSZK0zkIumZMkSZKkVbQKPzgvSZIkSUthQiRJkiSpt0yIJEmSJPVW7xKiJI9K8rYkFyX5rWX3R9NJ8swkb0/y/iRPX3Z/NJ0kD0/yziQXLbsv2lySA5O8qxlzpy27P5qO4629/KxrN79rtk+rEqIk5yf5apLd69Yfm+SmJHuSvHKjY1TVjVV1JvAc4Enz7K/WmlH8PlhVLwbOBJ47z/5qrRnF7+aqOn2+PdVGpozjs4CLmjF34sI7q/uYJn6Ot9UyZez8rFsxU8bP75ot06qECNgBHDu8IskW4BzgOGAbcGqSbUmOTHLJuseDm31OBC4FPrTY7vfeDmYQv8arm/20ODuYXfy0PDuYMI7AIcCXmmZ3LbCPGm8Hk8dPq2UH08fOz7rVsYMp4ud3zXbZb9kdmEZVfTzJ1nWrtwN7qupmgCQXACdV1euB48ccZyewM8mlwHvn12MNm0X8kgR4A/D3VfWZ+fZYw2Y1/rRc08QRuJVBUnQt7fsPtE6aMn43LLZ32sg0sUtyI37WrZRpx57fNdulCx9wD+Xe/8GEwQf4Q8c1TvKUJGcnORez9lUwVfyAs4BfAk5OcuY8O6aJTDv+HpTkbcDPJXnVvDuniY2L498Cv5rkrcDFy+iYJjIyfo63Vhg39vysa4dxY8/vmi3TqgrRLFTVLmDXkruhvVRVZwNnL7sf2jtV9XUG18SrBarq28ALl90P7R3HW3v5Wdduftdsny5UiL4MPGxo+ZBmndrB+LWb8esG49huxq+9jF27Gb+O6EJCdBVwWJJDkxwAnALsXHKfNDnj127GrxuMY7sZv/Yydu1m/DqiVQlRkvcBVwKHJ7k1yelVdSfwUuAy4Ebgwqq6fpn91GjGr92MXzcYx3Yzfu1l7NrN+HVbqmrZfZAkSZKkpWhVhUiSJEmSZsmESJIkSVJvmRBJkiRJ6i0TIkmSJEm9ZUIkSZIkqbdMiCRJkiT1lgmRJEmSpN4yIZKkIUkqyRuHHmFqdQAABPJJREFUll+R5DXN89ckecW69rckOXhoeUuSzya5ZJPz7EpyU5Jrm8fJzfpvzeh13JLkuiSPm8XxJjznmvcnyROSvH2Tfe7p53VJbkjyuiQ/PLT9kUk+lOTfknwmyYVJfqJ5j49q2uyX5FtJnj+03zVJjh5a/o0kbxnTh481+y/svZIkrQ4TIkla6w7gWcNJzpRexuAXyydxWlUd1Twu2svzbeSpVXX1HI47qeOAD0/Q7qlVdSSwHXg4cC5AkxhdCry1qg6rqqOBvwR+HLgCOKbZ/zHAv96znORA4BHAv0zSyap6KrDM90mStEQmRJK01p3AecDLp90xySHAM4B37GsnkjxluMqU5C1NleOgprJ0eLP+fUlePMHxbkny+qYadXWSo5NcluTzSc4cOuc/Jfm7JDcneUOS05J8uqngPKJptzXJR5N8LsnlSX5qzGmfBnwkyf2bys4NST6Q5FOjqjFV9S3gTOCZSX4MeB5wZVVdPNRmV1XtBj7JvQnRMcDbgKOa5e3ANVV117pTPCTJh5tq059v9p5JkvrBhEiS7usc4LQkB43Y9vKhy9yuBR4ytO3NwO8Dd094nvcMHetBk+xQVbcDLwV2JDkFeGBVbXhZ2pAvVtVRwCeAHcDJwBOAPxlq8xgGScmjgBcAj6yq7QySvLOaNn8BvKuqHg28Bzh7/YmaCtv3m/6+BPhmVW0D/hB47Aav73+AfwcOA44ArhnTdLhCdAzwceCOJA9olj85Yp+jgOcCRwLPTfKwcf2QJPWHCZEkrdN8Kf9r4HdGbH7T0GVuRwH/AZDkeOCrVTXuC/wow5fMfX2K/v0jcB2DxO1FU5xvZ/PndcCnqup/q+o2BonEjzbbrqqqr1TVHcDngX8Y2mdr8/yJwHub5+8GnjziXE8f2vfJwAVN33cDn9ukn9nshVTVF4ADkvwk8LPATcBVwOMZJERXjNjt8qq6vaq+C9wA/PRm55EkdZ8JkSSN9mbgdODACds/CTgxyS0Mvvz/YpK/2Yfz38naf6OHJxr4IQYVnO8AD5zimHc0f9499Pye5f3WtVnfbrjNJCa9f2iNpsKzlcE9QdezQTWJQRXo2cBXqqqAf2YQh+3AlSPaD7+2u5ju9UiSOsqESJJGqKpvABcySIomaf+qqjqkqrYCpwAfrarnb7LbRr4AbEtyv6Z687ShbS9nMHHD84C/SrL/Ppxnb3ySwWsEOI3BJXg/kCTAo4Frm1VXAM9ptm1jcMnafST5EQaTJnywqr7JoAp1TJJnDLX5hSRHDPXjd7k3+bkS+DXgP5tL9SRJ2pQJkSSN90Zgb2eb2ydV9SUGCdnu5s/PAjSTKbwI+L2q+gSDe2deveDunQW8MMnnGNxn9LJ12x8LfLap2kAzM1ySG4DXMaj8DCcsH0uyG/g08EXgNwGq6v+A44GzmokQbmBwP9JtzX5XMJiV7sqm/VeALTT3DyU5MclrZ/aqJUmdlHs/ryRJXdFcuve4qvraEs79amBPVV3QLG8B9q+q7zYz1X0EOLyqvrfovo2TZBfwiiVPUy5JWgKvn5akbroNuDzJ6Yv+kl9Vr1u36v4MqkD7M5gw4SUrlgx9jEGl6fvL7oskafGsEEnSHCX5AHDoutV/UFWXLaM/kiRpLRMiSZIkSb3lpAqSJEmSesuESJIkSVJvmRBJkiRJ6i0TIkmSJEm99f9uDzU63TA+zwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "xs = np.round_(x_bc[::columns_bio], decimals=4)\n",
+        "ys = np.round_(y_bc[0:columns_bio], decimals=4)\n",
+        "\n",
+        "mat = gaussian_filter(z_bc, sigma=0)\n",
+        "mapa = pd.DataFrame(data=mat, index=ys, columns=xs)\n",
+        "\n",
+        "fig = plt.figure(figsize=(15, 10))\n",
+        "ax = fig.add_subplot(1, 1, 1)\n",
+        "mesh = ax.pcolormesh(xs, ys, mapa, cmap='hot')\n",
+        "\n",
+        "ax.set_xlabel('NH4_Flux [mmol/gDCW.h]')\n",
+        "ax.set_ylabel('O2_Flux [mmol/gDCW.h]')\n",
+        "ax.set_yscale('log')\n",
+        "ax.set_xscale('log')\n",
+        "# ax.xaxis.set_major_locator(ticker.MultipleLocator(25))\n",
+        "# ax.yaxis.set_major_locator(ticker.MultipleLocator(.05))\n",
+        "\n",
+        "ax.set_title('Medium Influence in Cellulose Production')\n",
+        "# plt.savefig('figs/biomass_production.png', dpi=300, format='png')\n",
+        "\n",
+        "cbar = fig.colorbar(mesh, ax=ax)\n",
+        "cbar.set_label('Cellulose [mmol/gDCW.h]')\n",
+        "# cbar.locator = ticker.MultipleLocator(.05)\n",
+        "# cbar.update_ticks()\n"
+      ],
+      "metadata": {
+        "id": "kFTxrMw_D5fS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 626
+        },
+        "outputId": "14d1c298-a886-400d-bfe4-f71a0fc71add"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "dSlIGpwphuy2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
+%% Cell type:markdown id: tags:
+
+# Install packages
+
+%% Cell type:code id: tags:
+
+``` 
+!pip install cobra --quiet
+```
+
+%% Output
+
+    [K     |████████████████████████████████| 1.2 MB 5.0 MB/s
+    [K     |████████████████████████████████| 2.3 MB 41.1 MB/s
+    [K     |████████████████████████████████| 147 kB 46.7 MB/s
+    [K     |████████████████████████████████| 237 kB 73.9 MB/s
+    [K     |████████████████████████████████| 6.8 MB 44.8 MB/s
+    [K     |████████████████████████████████| 109 kB 69.0 MB/s
+    [K     |████████████████████████████████| 84 kB 3.5 MB/s
+    [K     |████████████████████████████████| 44 kB 2.5 MB/s
+    [K     |████████████████████████████████| 68 kB 5.4 MB/s
+    [K     |████████████████████████████████| 80 kB 7.0 MB/s
+    [K     |████████████████████████████████| 54 kB 3.0 MB/s
+    [K     |████████████████████████████████| 51 kB 4.6 MB/s
+    [K     |████████████████████████████████| 546 kB 47.2 MB/s
+    [?25h
+
+%% Cell type:markdown id: tags:
+
+# Import libraries
+
+%% Cell type:code id: tags:
+
+``` 
+import cobra
+
+from cobra.io import load_matlab_model, save_json_model, load_json_model, write_sbml_model, load_model, read_sbml_model
+from cobra.flux_analysis import flux_variability_analysis
+
+
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from scipy.ndimage.filters import gaussian_filter
+```
+
+%% Cell type:markdown id: tags:
+
+# Mount drive
+
+%% Cell type:markdown id: tags:
+
+## Here you need to insert the path for yout model
+
+%% Cell type:code id: tags:
+
+``` 
+from google.colab import drive
+drive.mount('/gdrive')
+```
+
+%% Output
+
+    Mounted at /gdrive
+
+%% Cell type:code id: tags:
+
+``` 
+!ls /gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis/
+```
+
+%% Output
+
+    41598_2017_6918_MOESM1_ESM.xml
+    449_2020_2299_MOESM3_ESM.mat
+    cobrapy_fba_diferentes_meios.ipynb
+    cobrapy_testes.ipynb
+    figs
+    FVA_max_min.gsheet
+    kinetic_parameters_estimation.ipynb
+    medium_test_cobrapy_oslo.ipynb
+    medium_test_cobrapy_oslo_versao_com_so4.ipynb
+    Resultados_e_todo_list_models.gdoc
+    results_from_nh4_o2_comb.npy
+    results_from_nh4_o2_FO_Cellulose_comb.npy
+    results_from_nh4_so4_FO_biomass.npy
+    results_from_nh4_so4_FO_Cellulose2.npy
+    results_from_nh4_so4_FO_Cellulose.npy
+    Solutions_GKOProblem_70_cellObjs.csv
+    Solutions_GKOProblem.csv
+    versao1_cobrapy.ipynb
+    versao2_cobrapy.ipynb
+
+%% Cell type:code id: tags:
+
+``` 
+
+%cd /gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis/
+```
+
+%% Output
+
+    /gdrive/MyDrive/mestrado/iGEM_unicamp/fba_analysis
+
+%% Cell type:code id: tags:
+
+``` 
+!ls
+```
+
+%% Output
+
+    41598_2017_6918_MOESM1_ESM.xml
+    449_2020_2299_MOESM3_ESM.mat
+    cobrapy_fba_diferentes_meios.ipynb
+    cobrapy_testes.ipynb
+    figs
+    FVA_max_min.gsheet
+    kinetic_parameters_estimation.ipynb
+    medium_test_cobrapy_oslo.ipynb
+    medium_test_cobrapy_oslo_versao_com_so4.ipynb
+    Resultados_e_todo_list_models.gdoc
+    results_from_nh4_o2_comb.npy
+    results_from_nh4_o2_FO_Cellulose_comb.npy
+    results_from_nh4_so4_FO_biomass.npy
+    results_from_nh4_so4_FO_Cellulose2.npy
+    results_from_nh4_so4_FO_Cellulose.npy
+    Solutions_GKOProblem_70_cellObjs.csv
+    Solutions_GKOProblem.csv
+    versao1_cobrapy.ipynb
+    versao2_cobrapy.ipynb
+
+%% Cell type:markdown id: tags:
+
+# Open Model
+
+%% Cell type:code id: tags:
+
+``` 
+modelo = '449_2020_2299_MOESM3_ESM.mat'
+```
+
+%% Cell type:code id: tags:
+
+``` 
+```
+
+%% Cell type:code id: tags:
+
+``` 
+model = load_matlab_model(modelo)
+```
+
+%% Output
+
+    WARNING:cobra.io.mat:This model seems to have confidenceScores instead of rxnConfidenceScores field. Will use confidenceScores for what rxnConfidenceScores represents.
+    WARNING:cobra.io.mat:This model seems to have metCharge instead of metCharges field. Will use metCharge for what metCharges represents.
+    WARNING:cobra.io.mat:No defined compartments in model model. Compartments will be deduced heuristically using regular expressions.
+    WARNING:cobra.io.mat:Using regular expression found the following compartments:c, e
+
+%% Cell type:code id: tags:
+
+``` 
+model.medium
+```
+
+%% Output
+
+    {'EX_pi(e)': 1000.0,
+     'EX_glc(e)': 10.0,
+     'EX_nh4(e)': 1000.0,
+     'EX_h(e)': 1000.0,
+     'EX_o2(e)': 1000.0,
+     'EX_h2o(e)': 1000.0,
+     'EX_so4(e)': 1000.0,
+     'CYStex': 1000.0}
+
+%% Cell type:code id: tags:
+
+``` 
+# save_json_model(model, "komagataeibacter_xylinus.json")
+# write_sbml_model(model, "komagataeibacter_xylinus.xml")
+```
+
+%% Cell type:code id: tags:
+
+``` 
+print(len(model.reactions))
+print(len(model.metabolites))
+print(len(model.genes))
+```
+
+%% Output
+
+    918
+    865
+    640
+
+%% Cell type:code id: tags:
+
+``` 
+model
+```
+
+%% Output
+
+    <Model model at 0x7fce39620590>
+
+%% Cell type:markdown id: tags:
+
+# Set the objective function
+
+%% Cell type:code id: tags:
+
+``` 
+model.objective = "EX_biomass" # CELtexpp
+model.objective = "CELtexpp"
+```
+
+%% Cell type:code id: tags:
+
+``` 
+print(model.objective.expression)
+print(model.objective.direction)
+```
+
+%% Output
+
+    1.0*CELtexpp - 1.0*CELtexpp_reverse_29bf6
+    max
+
+%% Cell type:code id: tags:
+
+``` 
+exchanges_reactions = {'EX_pi(e)',
+                'EX_glc(e)',
+                'EX_nh4(e)',
+                'EX_h(e)',
+                'EX_o2(e)',
+                'EX_h2o(e)',
+                'EX_so4(e)',
+                'CYStex'}
+for id in exchanges_reactions:
+
+    r = model.reactions.get_by_id(id)
+
+    print(id, r.bounds)
+```
+
+%% Output
+
+    EX_pi(e) (-1000.0, 1000.0)
+    EX_h2o(e) (-1000.0, 1000.0)
+    EX_o2(e) (-1000.0, 0.0)
+    CYStex (-1000.0, 1000.0)
+    EX_glc(e) (-10.0, -10.0)
+    EX_nh4(e) (-1000.0, 1000.0)
+    EX_h(e) (-1000.0, 1000.0)
+    EX_so4(e) (-1000.0, 1000.0)
+
+%% Cell type:code id: tags:
+
+``` 
+```
+
+%% Cell type:markdown id: tags:
+
+# Checking model
+
+%% Cell type:code id: tags:
+
+``` 
+solution = model.optimize()
+print(solution)
+```
+
+%% Output
+
+    <Solution 0.709 at 0x7fce31c378d0>
+
+%% Cell type:code id: tags:
+
+``` 
+solution.fluxes
+```
+
+%% Output
+
+    PGMT        -0.106366
+    GLGC         0.106366
+    HEX1        10.000000
+    PGI        -18.024638
+    FBP          0.000000
+                  ...
+    CYStex       0.000000
+    CHORt2       0.000000
+    CYTDt2pp     0.000000
+    SO4tex       0.159904
+    5MTRtpp      0.020635
+    Name: fluxes, Length: 918, dtype: float64
+
+%% Cell type:markdown id: tags:
+
+# Loops
+
+%% Cell type:markdown id: tags:
+
+## Fluxes $NH_4$ and $O_2$
+
+%% Cell type:code id: tags:
+
+``` 
+```
+
+%% Cell type:code id: tags:
+
+``` 
+ranges_for_uptakes = np.logspace(-3, 3, 100)
+ranges = (ranges_for_uptakes, ranges_for_uptakes)
+
+
+combination_limits = np.array(np.meshgrid(*ranges))
+combinations = combination_limits.T.reshape(-1, len(ranges))
+
+full_combinations = np.array([])
+lim_glc = 10
+
+i = 0
+for comb in combinations:
+    lim_nh4, lim_o2 = comb
+
+    medium_new = {'EX_pi(e)': 1000,
+                    'EX_glc(e)': lim_glc,
+                    'EX_nh4(e)': lim_nh4,
+                    'EX_h(e)': 1000,
+                    'EX_o2(e)': lim_o2,
+                    'EX_h2o(e)': 1000,
+                    'EX_so4(e)': 0
+                    }
+
+    model.medium = medium_new
+
+    solution = model.optimize()
+
+    reactions_fluxes =  [solution.fluxes.EX_biomass, solution.fluxes.CELtexpp]
+
+
+    uptakes = comb
+    outputs = np.array([solution.fluxes.EX_biomass, solution.fluxes.CELtexpp])
+
+    full_line = np.array([*uptakes, *outputs])
+    full_combinations = np.array([*full_combinations,full_line])
+```
+
+%% Cell type:code id: tags:
+
+``` 
+medium_new
+```
+
+%% Output
+
+    {'EX_pi(e)': 1000,
+     'EX_glc(e)': 10,
+     'EX_nh4(e)': 1000.0,
+     'EX_h(e)': 1000,
+     'EX_o2(e)': 1000.0,
+     'EX_h2o(e)': 1000,
+     'EX_so4(e)': 1}
+
+%% Cell type:code id: tags:
+
+``` 
+# full_combinations_foBC = full_combinations
+# full_combinations_foBio = full_combinations
+```
+
+%% Cell type:markdown id: tags:
+
+### Recomendation - Save the results, because take a time to run
+
+%% Cell type:code id: tags:
+
+``` 
+!ls
+```
+
+%% Output
+
+    41598_2017_6918_MOESM1_ESM.xml
+    449_2020_2299_MOESM3_ESM.mat
+    cobrapy_fba_diferentes_meios.ipynb
+    cobrapy_testes.ipynb
+    figs
+    FVA_max_min.gsheet
+    kinetic_parameters_estimation.ipynb
+    medium_test_cobrapy_oslo.ipynb
+    medium_test_cobrapy_oslo_versao_com_so4.ipynb
+    Resultados_e_todo_list_models.gdoc
+    results_from_nh4_o2_comb.npy
+    results_from_nh4_o2_FO_Cellulose_comb.npy
+    results_from_nh4_so4_FO_biomass.npy
+    results_from_nh4_so4_FO_Cellulose2.npy
+    results_from_nh4_so4_FO_Cellulose.npy
+    Solutions_GKOProblem_70_cellObjs.csv
+    Solutions_GKOProblem.csv
+    versao1_cobrapy.ipynb
+    versao2_cobrapy.ipynb
+
+%% Cell type:code id: tags:
+
+``` 
+# np.save("results_from_nh4_o2_FO_Cellulose_comb_2.npy", full_combinations)
+
+
+
+full_combinations_foBC = np.load("results_from_nh4_o2_FO_Cellulose_comb.npy") #open previous results
+
+full_combinations_foBio = np.load("results_from_nh4_o2_comb.npy") #open previous results
+```
+
+%% Cell type:markdown id: tags:
+
+#### Organize data
+
+%% Cell type:code id: tags:
+
+``` 
+columns_bio = int(np.sqrt(len(full_combinations_foBio[:,0])))
+
+z_bio = full_combinations_foBio[:, 2].reshape(columns_bio, columns_bio)
+x_bio, y_bio = full_combinations_foBio[:, 0], full_combinations_foBio[:, 1]
+```
+
+%% Cell type:code id: tags:
+
+``` 
+columns_bc = int(np.sqrt(len(full_combinations_foBC[:,0])))
+
+z_bc = full_combinations_foBC[:, 3].reshape(columns_bc, columns_bc)
+x_bc, y_bc = full_combinations_foBC[:, 0], full_combinations_foBC[:, 1]
+```
+
+%% Cell type:markdown id: tags:
+
+#### Plot the results
+
+%% Cell type:code id: tags:
+
+``` 
+xs = np.round_(x_bio[::columns_bio], decimals=4)
+ys = np.round_(y_bio[0:columns_bio], decimals=4)
+
+mat = gaussian_filter(z_bio, sigma=0)
+mapa = pd.DataFrame(data=mat, index=ys, columns=xs)
+
+# plt.savefig('figs/biomass_production.png', dpi=300, format='png')
+
+fig = plt.figure(figsize=(15, 10))
+ax = fig.add_subplot(1, 1, 1)
+mesh = ax.pcolormesh(xs, ys, mapa, cmap='hot')
+
+ax.set_xlabel('NH4_Flux [mmol/gDCW.h]')
+ax.set_ylabel('O2_Flux [mmol/gDCW.h]')
+ax.set_yscale('log')
+ax.set_xscale('log')
+# ax.xaxis.set_major_locator(ticker.MultipleLocator(25))
+# ax.yaxis.set_major_locator(ticker.MultipleLocator(.05))
+
+ax.set_title('Medium Influence in specific growth rate')
+# plt.savefig('figs/biomass_production.png', dpi=300, format='png')
+
+cbar = fig.colorbar(mesh, ax=ax)
+cbar.set_label('Specific growth rate [1/h]')
+# cbar.locator = ticker.MultipleLocator(.05)
+# cbar.update_ticks()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` 
+xs = np.round_(x_bc[::columns_bio], decimals=4)
+ys = np.round_(y_bc[0:columns_bio], decimals=4)
+
+mat = gaussian_filter(z_bc, sigma=0)
+mapa = pd.DataFrame(data=mat, index=ys, columns=xs)
+
+fig = plt.figure(figsize=(15, 10))
+ax = fig.add_subplot(1, 1, 1)
+mesh = ax.pcolormesh(xs, ys, mapa, cmap='hot')
+
+ax.set_xlabel('NH4_Flux [mmol/gDCW.h]')
+ax.set_ylabel('O2_Flux [mmol/gDCW.h]')
+ax.set_yscale('log')
+ax.set_xscale('log')
+# ax.xaxis.set_major_locator(ticker.MultipleLocator(25))
+# ax.yaxis.set_major_locator(ticker.MultipleLocator(.05))
+
+ax.set_title('Medium Influence in Cellulose Production')
+# plt.savefig('figs/biomass_production.png', dpi=300, format='png')
+
+cbar = fig.colorbar(mesh, ax=ax)
+cbar.set_label('Cellulose [mmol/gDCW.h]')
+# cbar.locator = ticker.MultipleLocator(.05)
+# cbar.update_ticks()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` 
+```