iUL959_JupyterNotebook.ipynb

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  "metadata": {
    "colab": {
      "name": "iUL959-JupyterNotebook",
      "version": "0.3.2",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/uliebal/iUL959/blob/master/iUL959_JupyterNotebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "a8Mag-0F9SzA",
        "colab_type": "code",
        "colab": {}
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      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv_Iyuda-xnr",
        "colab_type": "text"
      },
      "source": [
        "# Genome scale model reconstruction of the methylotrophic yeast *Ogataea polymorpha*: Reproduction of simulations of iUL959 with CobraPy Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdZhhX8A-3f6",
        "colab_type": "text"
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      "source": [
        "## Introduction\n",
        "\n",
        "This notebook is an extension to the article by Liebal et al., (2019) and reproduces the comparison of experimental growth rates with predictions by the genome scale metabolic model (GSMM) of *Ogataea polymorpha* iUL959.\n",
        "\n",
        "\n",
        "## Requirements\n",
        "\n",
        "Make sure that the required files are present in the notebook. Only dependencies not already present in the jupyter/scipy-noteboook are listed (see here for the default list: https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html) \n",
        "\n",
        "Files:\n",
        "* iUL959.xml: SBML file, Genome scale metabolic model of O. polymorpha\n",
        "* iUL959_GSMM-addinfo.xlsx: Excel file, contains the literature growth rates and uptake rate in sheet 'Literature_Chemostat'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKd3r3HOATOg",
        "colab_type": "text"
      },
      "source": [
        "## Set-up compute environment \n",
        "\n",
        "In the following cell the python environment is set up. If pandas, xlrd, or CobraPy are not already availabel as package (e.g. in Binder), use the following command to download the packages from the cell:\n",
        "!{sys.executable} -m pip install pandas\n",
        "!{sys.executable} -m pip install xlrd\n",
        "!{sys.executable} -m pip install cobra"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "outputId": "d8c7451b-07fa-4644-81b7-58c1d92966c5"
      },
      "source": [
        "#  If pandas, xlrd, or CobraPy are not already availabel as package (e.g. in Binder), use the following command to download the packages from the cell.\n",
        "import sys # loading commands to control/navigate within the system architecture\n",
        "!{sys.executable} -m pip install pandas\n",
        "!{sys.executable} -m pip install xlrd\n",
        "!{sys.executable} -m pip install matplotlib\n",
        "!{sys.executable} -m pip install cobra"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (0.24.2)\n",
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            "Requirement already satisfied: xlrd in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Collecting cobra\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/64/961ce89b50edbfe44a198e5867bfb788d135cc6b92a16dcdaadba6716815/cobra-0.15.4-py2.py3-none-any.whl (1.6MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/37/b0375c9e9a1263820637050cdf8f9bf5cedff4e7fafd2f7f393b26c14150/swiglpk-4.65.0-cp36-cp36m-manylinux1_x86_64.whl (627kB)\n",
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            "Collecting python-libsbml-experimental==5.18.0 (from cobra)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b6/8c/9116eb297e2d713382e1934651b3a6ee2dbe82c47944428db30b99f0cab9/python_libsbml_experimental-5.18.0-cp36-cp36m-manylinux1_x86_64.whl (43.9MB)\n",
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            "Collecting ruamel.yaml>=0.15 (from cobra)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8f/8c/37f9fc75cf24535534446e592578443d47577aff38b519cef397c4d73de4/ruamel.yaml-0.16.0-py2.py3-none-any.whl (122kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/1d/a3/c5d91c1e91a0e3c61c19563e224bda4f22fc24b6a86ef1c7e00f66ea9c4e/depinfo-1.5.1-py2.py3-none-any.whl\n",
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            "Collecting ruamel.yaml.clib; platform_python_implementation == \"CPython\" and python_version < \"3.8\" (from ruamel.yaml>=0.15->cobra)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/90/d8/3ce70adb2154ccbbb5796724a48ed4d60ec2b6ba9a00de1122c79125b631/ruamel.yaml.clib-0.1.0-cp36-cp36m-manylinux1_x86_64.whl (549kB)\n",
            "\u001b[K     |████████████████████████████████| 552kB 44.5MB/s \n",
            "\u001b[?25hCollecting pipdeptree>=0.13.2 (from depinfo->cobra)\n",
            "  Downloading https://files.pythonhosted.org/packages/12/64/26c7df3ad833cd6e8b9735c5958b25d6aef1617c915b2731baedfbeee712/pipdeptree-0.13.2-py3-none-any.whl\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.6/dist-packages (from sympy>=1.0.0->optlang>=1.4.2->cobra) (1.1.0)\n",
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            "Installing collected packages: swiglpk, optlang, python-libsbml-experimental, ruamel.yaml.clib, ruamel.yaml, pipdeptree, depinfo, cobra\n",
            "Successfully installed cobra-0.15.4 depinfo-1.5.1 optlang-1.4.4 pipdeptree-0.13.2 python-libsbml-experimental-5.18.0 ruamel.yaml-0.16.0 ruamel.yaml.clib-0.1.0 swiglpk-4.65.0\n"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InLQFiau9ghT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading pandas, a library for data manipulation\n",
        "#!{sys.executable} -m pip install pandas\n",
        "import pandas as pd\n",
        "import xlrd\n",
        "\n",
        "# Loading numpy, a library fo manipulation of numbers\n",
        "import numpy as np\n",
        "\n",
        "# loading matplotlib, a library for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# loading cobrapy, a library dedicated to the analysis of genome scale metabolic models\n",
        "from cobra.io import read_sbml_model\n",
        "from cobra.io import write_sbml_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hY2k6iAAOz-",
        "colab_type": "text"
      },
      "source": [
        "## Data loading\n",
        "\n",
        "Now, the external data is integrated. In Google, you can use the upload link below. For Binder, upload them manually. The excel-sheet is parsed and the GSMM file is converted to a model variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tUuJf_-Anuj",
        "colab_type": "code",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "outputId": "2f8bda0f-d2de-47db-fc18-67ff457bd158"
      },
      "source": [
        "# Load Excel sheet: iUL959_GSMM-addinfo.xlsx (located in Manuscript-AddDat)\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2a703220-8b34-4c3f-b69a-0b5be9f6b152\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2a703220-8b34-4c3f-b69a-0b5be9f6b152\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving iUL959_GSMM-addinfo.xlsx to iUL959_GSMM-addinfo.xlsx\n",
            "User uploaded file \"iUL959_GSMM-addinfo.xlsx\" with length 35760 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1jrdmwd_RNF",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "outputId": "e7b67e5a-87b8-4e2d-806d-ac9296b5a88e"
      },
      "source": [
        "# Load SBML file: iUL959.xml (located in Manuscript-AddDat)\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-af5282a4-d8cf-4d9e-bfb0-e6f2537ccc5e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-af5282a4-d8cf-4d9e-bfb0-e6f2537ccc5e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving iUL959.xml to iUL959.xml\n",
            "User uploaded file \"iUL959.xml\" with length 5281702 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZ1QrNNHFiZt",
        "colab_type": "text"
      },
      "source": [
        "## Data manipulation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vp4W-w7AFosa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "95aa8c4a-3ec0-473b-8f00-1207614662e1"
      },
      "source": [
        "myxls = 'iUL959_GSMM-addinfo.xlsx'\n",
        "x1 = pd.ExcelFile(myxls)\n",
        "print(x1.sheet_names)\n",
        "df1 = x1.parse('Literature_Chemostat')\n",
        "df1.head(9)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Reactions with single GPR', 'missing Homologs from iMT1026', 'New Reactions', 'New Genes', 'Literature_Chemostat', 'FSEOF']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Substrate</th>\n",
              "      <th>Exchange</th>\n",
              "      <th>uptake rate (mmol/gCDW/h)</th>\n",
              "      <th>growth rate (/h)</th>\n",
              "      <th>yield (gCDW/gMeOH)</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Glucose</td>\n",
              "      <td>Ex_glc_D</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>0.600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lehnen et al. (10.1016/j.meteno.2017.07.001)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>2.343750</td>\n",
              "      <td>0.024</td>\n",
              "      <td>0.32</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>2.935606</td>\n",
              "      <td>0.031</td>\n",
              "      <td>0.33</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>4.285714</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.35</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>5.827703</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.37</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>6.578947</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.38</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>9.868421</td>\n",
              "      <td>0.120</td>\n",
              "      <td>0.38</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Methanol</td>\n",
              "      <td>Ex_meoh</td>\n",
              "      <td>14.285714</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.35</td>\n",
              "      <td>van Dijken et al. (Fig.6)(10.1007/BF00446560)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Glycerol</td>\n",
              "      <td>Ex_glyc</td>\n",
              "      <td>7.125407</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.64</td>\n",
              "      <td>growth rate: de Koning et al. (10.1007/BF00456...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Substrate  ...                                             source\n",
              "0   Glucose  ...       Lehnen et al. (10.1016/j.meteno.2017.07.001)\n",
              "1  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "2  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "3  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "4  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "5  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "6  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "7  Methanol  ...      van Dijken et al. (Fig.6)(10.1007/BF00446560)\n",
              "8  Glycerol  ...  growth rate: de Koning et al. (10.1007/BF00456...\n",
              "\n",
              "[9 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFppbhjTHM8h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "outputId": "b3788c9f-f925-4ddc-bddb-4ded42963ecb"
      },
      "source": [
        "# generating cobra variable from SBML/xml file\n",
        "model = read_sbml_model('iUL959.xml')\n",
        "model"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <table>\n",
              "            <tr>\n",
              "                <td><strong>Name</strong></td>\n",
              "                <td>COBRAModel</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Memory address</strong></td>\n",
              "                <td>0x07ff598f53f60</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of metabolites</strong></td>\n",
              "                <td>1756</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of reactions</strong></td>\n",
              "                <td>2329</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of groups</strong></td>\n",
              "                <td>39</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Objective expression</strong></td>\n",
              "                <td>1.0*BIOMASS - 1.0*BIOMASS_reverse_69053</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Compartments</strong></td>\n",
              "                <td>c, n, r, g, m, i, v, x, e</td>\n",
              "            </tr>\n",
              "          </table>"
            ],
            "text/plain": [
              "<Model COBRAModel at 0x7ff598f53f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCoO_eIFAP-s",
        "colab_type": "text"
      },
      "source": [
        "## Function definitions\n",
        "\n",
        "We are testing three different growth conditions with substrates methanol, glycerol, and glucose. For each of the substrates, the corresponding exchange rate has to be activated, the approriate biomass composition formula has to be selected and exchange reactions for non-used substrates need to be closed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWLfIcNlET3C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def AdaptMethanol(model, meoh_up):\n",
        "  # setting uptake reactions right\n",
        "  model.reactions.Ex_glc_D.lower_bound = 0\n",
        "  model.reactions.Ex_glyc.lower_bound = 0\n",
        "  model.reactions.Ex_meoh.lower_bound = -np.abs(meoh_up)\n",
        "  # setting additional biomass composition\n",
        "  model.reactions.LIPIDS_meoh.upper_bound = 1000\n",
        "  model.reactions.PROTEINS_meoh.upper_bound = 1000\n",
        "  model.reactions.STEROLS_meoh.upper_bound = 1000\n",
        "  model.reactions.BIOMASS_meoh.upper_bound = 1000\n",
        "  # deactivating Glc-based biomass composition\n",
        "  model.reactions.LIPIDS.upper_bound = 0\n",
        "  model.reactions.PROTEINS.upper_bound = 0\n",
        "  model.reactions.STEROLS.upper_bound = 0\n",
        "  model.reactions.BIOMASS.upper_bound = 0  \n",
        "  model.objective = 'BIOMASS_meoh'\n",
        "  return model\n",
        "\n",
        "def AdaptGlucose(model, glc_up):\n",
        "  # setting uptake reactions right\n",
        "  model.reactions.Ex_meoh.lower_bound = 0\n",
        "  model.reactions.Ex_glyc.lower_bound = 0\n",
        "  model.reactions.Ex_glc_D.lower_bound = -np.abs(glc_up)\n",
        "  # setting additional biomass composition\n",
        "  model.reactions.LIPIDS.upper_bound = 1000\n",
        "  model.reactions.PROTEINS.upper_bound = 1000\n",
        "  model.reactions.STEROLS.upper_bound = 1000\n",
        "  model.reactions.BIOMASS.upper_bound = 1000  \n",
        "  # deactivating Glc-based biomass composition\n",
        "  model.reactions.LIPIDS_meoh.upper_bound = 0\n",
        "  model.reactions.PROTEINS_meoh.upper_bound = 0\n",
        "  model.reactions.STEROLS_meoh.upper_bound = 0\n",
        "  model.reactions.BIOMASS_meoh.upper_bound = 0\n",
        "  model.objective = 'BIOMASS'\n",
        "  return model\n",
        "\n",
        "def AdaptGlycerol(model, glyc_up):\n",
        "  # setting uptake reactions right\n",
        "  model.reactions.Ex_meoh.lower_bound = 0;\n",
        "  model.reactions.Ex_glc_D.lower_bound = 0;\n",
        "  model.reactions.Ex_glyc.lower_bound = -np.abs(glyc_up)\n",
        "  # setting additional biomass composition\n",
        "  model.reactions.LIPIDS.upper_bound = 1000\n",
        "  model.reactions.PROTEINS.upper_bound = 1000\n",
        "  model.reactions.STEROLS.upper_bound = 1000\n",
        "  model.reactions.BIOMASS_glyc.upper_bound = 1000  \n",
        "  # deactivating Glc-based biomass composition\n",
        "  model.reactions.LIPIDS_meoh.upper_bound = 0\n",
        "  model.reactions.PROTEINS_meoh.upper_bound = 0\n",
        "  model.reactions.STEROLS_meoh.upper_bound = 0\n",
        "  model.reactions.BIOMASS_meoh.upper_bound = 0\n",
        "  model.objective = 'BIOMASS_glyc'\n",
        "  return model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivb9lHHTAW9r",
        "colab_type": "text"
      },
      "source": [
        "## Simulation and visualization\n",
        "\n",
        "Here, we start the simulation loop. For all substrates we use the substrate uptake rate and use the functions defined previously to update the model. The final growth rate is stored, and is then used in a plot versus the experimentally observed growth."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pqra-K5XhnPS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "83f8e60c-f387-495f-ba1a-4a277a2cdc63"
      },
      "source": [
        "test_model = model.copy()\n",
        "growth_simulated = [];\n",
        "for index, row in df1.iterrows():\n",
        "  print(index)\n",
        "  if row['Substrate'] == 'Methanol':\n",
        "    test_model = AdaptMethanol(test_model, row['uptake rate (mmol/gCDW/h)'])\n",
        "  elif row['Substrate'] == 'Glucose':\n",
        "    test_model = AdaptGlucose(test_model, row['uptake rate (mmol/gCDW/h)'])\n",
        "  elif row['Substrate'] == 'Glycerol':\n",
        "    test_model = AdaptGlycerol(test_model, row['uptake rate (mmol/gCDW/h)'])\n",
        "  else:\n",
        "    print('substrate not considered')      \n",
        "  test_model.optimize()\n",
        "  growth_simulated.append(test_model.objective.value)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBB6GXj5rQn2",
        "colab_type": "code",
        "outputId": "0a1ae82e-1f9c-4d15-a9c8-ae7c43102e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.rc('axes', labelsize=12) \n",
        "plt.rc('xtick', labelsize=10) \n",
        "plt.rc('ytick', labelsize=10) \n",
        "plt.rc('legend', fontsize=12) \n",
        "# plt.figure(figsize=(4,3))\n",
        "plt.scatter(df1['growth rate (/h)'][1:8], growth_simulated[1:8], s=100, c='k', marker='o', );\n",
        "plt.scatter(df1['growth rate (/h)'][8], growth_simulated[8], s=100, c='k', marker='s');\n",
        "plt.scatter(df1['growth rate (/h)'][0], growth_simulated[0], s=100, c='k', marker='x');\n",
        "plt.xlabel('Experiment (/h)');\n",
        "plt.ylabel('Simulation (/h)');\n",
        "myline = np.linspace(0,np.max(growth_simulated),10);\n",
        "plt.plot(myline,myline,'k--');\n",
        "# plt.title('Growth rate comparison');\n",
        "plt.legend(['Optimum','Methanol (van Dijken)','Glycerol (deKoning, Moon)','Glucose (Lehnen)'], loc=2);\n",
        "plt.style.use('seaborn-paper')\n",
        "plt.savefig('iOpol_growth_exp-vs-sim.png', bbox_inches='tight')\n",
        "# plt.savefig('iOpol_growth_exp-vs-sim.pdf', bbox_inches='tight')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LSy+9RMuWLaldu3a276t79+7s2LHDshwUFMTGjRv54YcfiIuLY/LkyWnOjmwt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\nzKzUP56h74QdS8UMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymnBrgxgsjJL",
        "colab_type": "text"
      },
      "source": [
        "### Adding reactions for itaconate production"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJjYloqxsoHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from cobra.core import Reaction, Model, Metabolite\n",
        "\n",
        "Model_itaconate = model.copy()\n",
        "\n",
        "# assuming that citrate is in quick equilibrium with aconitate to ignore this reaction\n",
        "# first reaction is the conversion of cytoplasmic citrate to itaconate\n",
        "# itaconate is exported and exchanged\n",
        "reaction1 = Reaction('CAD')\n",
        "reaction1.name = 'cis-aconitate decarboxylase'\n",
        "reaction1.lower_bound = 0.\n",
        "reaction1.upper_bound = 1000.\n",
        "#reaction1.objective_coefficient = 0\n",
        "reaction1.reaction_reversibility = 0\n",
        "\n",
        "R1metCit = Model_itaconate.metabolites.get_by_id('cit__91__c__93__')\n",
        "R1metCO2 = Model_itaconate.metabolites.get_by_id('co2__91__c__93__')\n",
        "R1metIta = Metabolite('Ita_c', name='Itaconate, cytoplasm', formula='C5H6O4', compartment='c')\n",
        "reaction1.add_metabolites({R1metCit: -1.0,\n",
        "                           R1metCO2: 1.0,\n",
        "                           R1metIta: 1.0})\n",
        "\n",
        "# for testing purpose we add the exchange reaction directly to the cytoplasmic itaconate\n",
        "reaction2 = Reaction('Ex_ita')\n",
        "reaction2.name = 'Exchange, itaconate cytoplasm'\n",
        "reaction2.lower_bound = 0.\n",
        "reaction2.upper_bound = 1000.\n",
        "#reaction2.objective_coefficient = 0\n",
        "reaction2.reaction_reversibility = 1\n",
        "reaction2.add_metabolites({R1metIta: -1.0})\n",
        "\n",
        "Model_itaconate.add_reactions([reaction1, reaction2])\n",
        "# Model_itaconate.reactions.get_by_id('Ex_ita')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exSGKts_ZFbJ",
        "colab_type": "text"
      },
      "source": [
        "### Testing itaconate production"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sQWPJs81fyD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Model_itaconate.objective = 'Ex_ita'\n",
        "Model_itaconate.optimize()\n",
        "Model_itaconate.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}