models_solutions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "models_solutions.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gibbons-Lab/isb_course_2021/blob/main/models_solutions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXWCzV6yAz-D"
      },
      "source": [
        "# 🦠 Metabolic modeling of bacterial isolates 🧫\n",
        "\n",
        "This notebook will accompany the second day of the of the [2021 ISB Virtual Microbiome series](https://isbscience.org/microbiome2021/). The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2021/models). \n",
        "\n",
        "Save your own local copy of this notebook by using `File > Save a copy in Drive`. At some point you may be promted to certify the notebook is safe. We promise that it is 🤞\n",
        "\n",
        "**Disclaimer:**\n",
        "\n",
        "The Google Colab notebook environment will interpret any command as Python code by default. If we want to run bash commands we will have to prefix them by `!`. So any command you see with a leading `!` is a bash command and if you wanted to run it in your terminal you would omit the leading `!`. For example, if in the Colab notebook you ran `!wget` you would just run `wget` in your terminal. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6I42MY9QWpfi"
      },
      "source": [
        "## Setup\n",
        "\n",
        "The setup is a bit simpler this time. We will install a small Python package that was built for the very purpose of this course and installing it will also pull in some additional commands and packages we need, for instance the metabolic modeling package [COBRAPY](https://opencobra.github.io/cobrapy) which we will use extensively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JS-fUTqAAwlk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25b298d6-b7d2-4e87-d673-1b743087310b"
      },
      "source": [
        "!pip install -q git+https://github.com/gibbons-lab/2021_microbiome_course_utils\n",
        "!bootstrap"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2;36m[20:22:53]\u001b[0m\u001b[2;36m \u001b[0m🔨 Set up \u001b[32mprodigal\u001b[0m.                                   \u001b]8;id=231463;file:///usr/local/lib/python3.7/dist-packages/course/bootstrap.py\u001b\\\u001b[2mbootstrap.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:28\u001b[0m\n",
            "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m🔨 All clean now.                                     \u001b]8;id=363889;file:///usr/local/lib/python3.7/dist-packages/course/bootstrap.py\u001b\\\u001b[2mbootstrap.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:31\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3GbbjGOWgnI"
      },
      "source": [
        "That's it. We are ready to start.\n",
        "\n",
        "# 🧬 From Genome to Model "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kL85Tq0Lnq0F"
      },
      "source": [
        "## The Assembly\n",
        "For our first step we will get our assembled genome. As noted before, these assemblies will not give us a singe contigious piece of DNA, but rather a set of fragmented *contigs*.\n",
        "\n",
        "We will use the course Python package to get our own personal bacterial assembly. **Before running the code below, substitute `[YOUR NAME]` with your full name or nickname.** You can call yourself whatever you'd like, just make sure it's fairly unique. This name will be used to randomly assign you a specific genome assembly from the database. \n",
        "\n",
        "What organism do you get?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVf8zp5NDha2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "25849f67-c532-451f-f8de-d54e00ab47fb"
      },
      "source": [
        "from course import gimme_genome\n",
        "\n",
        "assembly = gimme_genome(\"Arthur Dent\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Hi, <span style=\"color: #5f5fff; text-decoration-color: #5f5fff; font-weight: bold\">Arthur Dent</span>! Welcome to the course 🎉\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Hi, \u001b[1;38;5;63mArthur Dent\u001b[0m! Welcome to the course 🎉\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Saved the assembly to <span style=\"color: #008000; text-decoration-color: #008000\">SRR9205608.asm1.fna</span>.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Saved the assembly to \u001b[32mSRR9205608.asm1.fna\u001b[0m.\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Wow! Your assembly is <span style=\"color: #008000; text-decoration-color: #008000\">SRR9205608.asm1</span> which is <span style=\"color: #008000; text-decoration-color: #008000\">Bacteroides ovatus</span> 🤯 \n",
              "By the way, the full GTDB taxonomic assignment is Bacteria | Bacteroidota | Bacteroidia | \n",
              "Bacteroidales | Bacteroidaceae | Bacteroides | Bacteroides ovatus 🤓\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Wow! Your assembly is \u001b[32mSRR9205608.asm1\u001b[0m which is \u001b[32mBacteroides ovatus\u001b[0m 🤯 \n",
              "By the way, the full GTDB taxonomic assignment is Bacteria | Bacteroidota | Bacteroidia | \n",
              "Bacteroidales | Bacteroidaceae | Bacteroides | Bacteroides ovatus 🤓\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QtV1SRhYYP1"
      },
      "source": [
        "This will download your assembled genome and tell you which organism you are dealing with. Let's take a peak at your assembly. What do you expect it to look like?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFaxWGXWGBlF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e80ec907-198a-43a8-f58b-79b62b19bf12"
      },
      "source": [
        "!head *.fna"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">k141_0 flag=0 multi=98.5629 len=5073\n",
            "GCTACATCATTCAATCCGAGCATCCGAGCCATTTCACTGTATCCGGCCACTCCCATGATCGCTTTCACGGAAAGATTGGCATTATGAGCCCAATGTCCGGCGAAGTCATCCGTGCAAAGTTGGTTTTCCGGGTCCTGTCCAGACTAAACTGCCATTTATCAATTGTTTGAAATACAGATGATTATCTTTGCTCTGTATTGTCTTTGTGTTGTTATGGCTATTAAATAAAGTACAAAATTCTAACTTATGAGCAGACTTTTAATAAATAAAAAGTTAATGGATAAATGTAGTGGTAGTCTAAGATGAGAAATGGACTAATCTGAAAAAATGAATATTTTTCCTCTCTCTCTTTTCATTTATCCATGTAATTTCTTTTATGACATAAAAACTAGCAACATTTTAAGATATTAAAAATCAAATAGATTGAATAATTACTCTTGTTGAATGAATTCGACGTAAATAAAACTTTCTGGAATGATTTATCTATCTTTTTATGCTATTTTGCAGAGAAATGTTATTGTGTGATAGCTTAAGGTAATAAGGAAAGTAGATGTTAGAACCCCATTCAGGAGTCCATAGTGTGGTTTATTTTAAACCAATAAGTAGTTTCTTTTAAAGGTAGGAAATGTTCCTTTTAAACCAACGACTTGTTTCATTATATCAAAACTTGTTTTCGATAGTAGGAAAAAATACATGTTTATAAAGAATTATGCTATATATATTCAATAAAAGGAATATATATAATGTAAATGTGACTTTCTTTTTTCTATTTAGCAATTCAATAGAATCTTAATTCTTCTGCATATATGAAAAATAGAACATGATTTGAAATAACTAAGATGAAATATTTCAATCTTTACCTGGCAAGTTTAGCTCGAAGTTCTTTTAGTCTTTTTGCAGCCTGTAGATTATCAACCTTTATATATCTCATGAATGCAGATGGACTCTTATGCCCGGATATGCGCATAAGTTCTCCGGCAGGAAAGCCGGATAAATACATATTTGTGCAAAACGATCGTCGGCAAGTATGAGAAGAGATCATCTCCCATTTTTCATAGACTCTCTTTTCCCGTTCTTTCCCTTTCTTGCGAACGACTTCGATCTTCTGTTTTAGTTTTGCTCTTCGCCCTAATTCTTTAACCCTTTCGTTGAATATATATCTAGGAATTTTAGGTAAATCATAGTTGTACTTCTTGAGTATTTCCGGTACATAATCGATCATAGGGATTATGACAGCTTTATGCACTTTTCTCTGTTTGAGATTTATAATCTCATTATCTAAGTCAATATGTTCGGGACTTAATGTTGAGAGATCACTATATCGTAGTCCTGTAAAACACCCAGTAATAAAAACATCTCTAATCTCTTCCAATTGCTTATCATCTGACAAGTCTAACTCGTGTATTGTTGAAAGTTCCTTTTCACTTAAATAAATAGCATCAGTTTCTTCTTCTACGACCTTAAAATGCTTCAAGTCAATTGGAGGAATAACTCCTTTATCTATTTGATAATTAACAAATCCCTTTAACAAACGTATAATTTTTCCTGCAGAATTAGTGAGCAAGCCAACACTCCCATCGGGCTTTATAACCTTATAAAATAAATAGTCCATGAATTCATTGTAAAATATAAGATTCAGGTTATGAAAAGTAATTGGCTGCGATGAATGTTCTTTGAAAGCGATCAGATGTTTACGAAGTGTTCGGTAGTCTTTTATTTGGTCTGCAGAAACAACAAGAGCTTTTTCTGTAATATATCTTTCTAATGCGTCAAATATATCTACCCGATTAGGACGTAATTCATATTCTCTATTTTTTTTTAATTCAAGTAAAACAAAATCTACGGTTGGACTTATTCCGTTTATCTTAGCATATGTAAGAATTTCTCCAAGCTTGGACGTTATTCTAATCAGGCAGGCATCAATTTCATCATAGTTTGGACAAGCGCGCTTAATCCATCTCTTTTTAGAATCCCAGTGTTCCGGTTTAATACTGTATCCAGTAGAGATGAGCGTACGCTTAGTTCGATTGTAGTTATATCGAACATAGAGGACAGTAGTCCCCTCTCGAGTGACGTACTTTGGTTTAATATATGGAGTATATTTTGCGATAGTAAATCTGTATTGCTTTCGTATTGCAAAATTACAAAAGCTATTCGATATGAGCAATATCTCATCGTTAAAAGCTAGAACCTCCAAAAACATTGTACTATCTTAATAAAAGTGGATAAATATTGCTCAAAAAGGAAAAAAACTACTCAAAGCACACTGTAAGAGACTTATTTTGCACTTTAAAAACACTCAAAATATGAAAAAAGAATAGATTTTGTCCTTTCTTGAACACAGATTTTCCTAATATTTAGCATTAATTCCATTATTTTGTAGCGTTACAAATAAAATGCTATGGAAAAACAAATGGATTTGTATGAATCCTGGGTCCTACCTGAAACACACAATTCTATATAAACTTTTCCTCTAGCATTTATAAATATTCGACAGTACTGATTTTAATATATTATTTAGGTATAGCGATAAGAAAGGTAAGATTGCATTTAAAAGGTAATAGTAAGAAGGTAAAAAGAGAAATAAAAGAAAACGTAGGGTTTGTTTTTCTTTATAGGTAAACAAGATTGTAGATGGGTAACCTTAAAATTGTAGATTAATTAGAAAATATTTATGAAGAAACTACTAACAGTAATGGCCTTTTCGGTCGGTTTATTCGCAAATGCTCAAACCGGCAACTTATTTAAACCAGTAAAAGAGGTAGCGCTACGTACTCCATCGGTACCTATCGTTGTTTCAGATCCTCACTTCTCTATCTGGTCTCCTTATGATAAATTAATGGAAGGAAGTACAGAACACTGGACTACAGCTAAGAAGCCATTAGTAGGGGCTTTACGCGTAGATGGTAAAGTGTATCGTTTCCTGGGTAAAGATCAAGTAGCTCTAATACCTATTGCTCCCATGACAAATGTGGAACGTTGGGAGGCTGCATATACTAATAGTCAACCAGCAAATGGATGGCAAGAATTCCAGTTTGATGATAGTAGTTGGAAAAAAGGTAAAGCAGCTTTCGGTAGCCGTGATATGCCACGTGTTCGCACAGAATGGAAAGGAGATAATACAGATATTTATATCCGACGTACATTTGAAATAAATGATTTGGATTTAACAGAGAATATTTTTCTTATTTATTCTCATGACGATGTATTCGAATTATATTTAAATGGAGAAAAACTGGTTGCTACAGATCTCGTATGGAAGAATAATGTAAATCTAAAACTTTCAGACGAAGCCAAGAAGAAGCTTCGTAATGGTAAAAATGTGATTGCAGCACATTGTCATAACACGACAGGAGGATCTTATGTGGATTTTGGTTTATATCGTGAAAAGAAAAATGCAGTAACATTTGAAAATGAGGCTGTACAGAAATCGGTAGATGTACTAGCCACTTCCAGTTACTATACATTCACTTGCGGACCAGTCGAATTAGATGTCGTATTTACAGCACCTCAATTGATAGATGACTTGGATTTGTTGTCAACTCCGATTAACTACATCTCCTATCGTGTGCGTCCTCTTGATAAAAAAGAGCATGATGTACAATTCTATATAGAAACGACTCCGGTATTAGCAGTGAATGAGACTACTCAACCTACTATTGCACGTACATTATCGAAAAATGGTATTAGTTATGTAGAAGCTGGCACCATCAACCAGCCGATATGTGATCGTAAAGGTGATTTGATTTGTGCTGACTGGGGATATGTATATCTTGGTAGTGTAAATGGAGCAGGAAAATCTATAAGTTTGGGTGACTATAGCGGAATGAAGGAAGCTTTTGTTAAAAATGGAACTTTAGCTTCAAGTAAAAGCAAATGGATTACTCGTCGGGAAGAAAATACTCCTGCAATGGCTTATGTACATAATTTTGGAACTGTTACCAAAGATGGTAAAGATGGTTTCTTGATGATCGGTTATGATGACATTTATTCCATTGAATATATGTATGAGAAACGTATGGGATATTGGAAGCATGATGGTAAAGTAACGATTTTTGATGCTTTCGAAAAACTAAGAGATAACTATCAATCCATCATGGAGCGTTGTCGTGCACTTGATGAGCTTATTTATAGCGATGCAGAAAAAGCTGGTGGTAAAAAATATGCAGAAATCTGTTCTGCAGCCTATCGTCAAGTTATTTCTGCTCACAAACTATTTACAGACAAAGAAGGCAATCTGATGTGGTTCTCTAAAGAGAATAACAGTAATGGTTGTATCAATACAGTCGACCTGACCTATCCGTCTGCTCCCCTGTTTTTGGTCTATAATCCTGATTTACAAAAGGCAATGATGACTAGTATCTTTGAATATAGTGCTAGCGGTCGCTGGGATAAACCTTTCGCAGCACATGACCTTGGCACTTATCCAATAGCTAATGGACAAGTTTATGGTGGTGACATGCCGATTGAAGAAAGTGGTAATATGGTGATTCTAACTGCAGCTATTTCGAAAATTGAAGGTAATGCAGATTATGCTAAGAAATATTGGGACATTCTAACGACATGGACCAATTATTTAGTAGAGTATGGACAGGACCCAGAGAACCAACTTTGTACAGATGATTTTGCAGGCCATTGGGCCCATAATGCCAATCTTTCTGTAAAAGCTATCATGGGAGTAGCTGGGTACAGCGAGATGGCTCGTATGCTTGGAATGAGTGATGTAGCCGAAAAATATGCAGCAATAGCTAAGAAAATGGCGGTGAAATGGGAGGAAATGGCAAATGAGGGCGACCACTATCGCTTGGCTTTCGACCGTAAGAATACCTGGAGCCAGAAGTATAATATGGTTTGGGACAAATTGTGGAACCTGAATTTATTTCCTAATAATGTAATTGGAAAAGAGCTCAATTATTATCTAACAAAACAGAATCCTTATGGACTCCCTTTGGATTCTCGTAAAGAATACACAAAATCCGACTGGATCATGTGGACGGCAGCTATGTCATCAGATAAGGAAACCTTCCAGAAATTCTCTGATCCTGTCTATAAATATATCAATGAAACAGTATC\n",
            ">k141_2 flag=0 multi=222.9366 len=1623\n",
            "TATTAAAGAATTTCGGTTATCGATGGGGATGCGTTCCATTAGTTTGTTGGCGGGGTAACGGCCCACCAAGACTACGATGGATAGGGGTTCTGAGAGGAAGGTCCCCCACATTGGAACTGAGACACGGTCCAAACTCCTACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGGCGAGAGCCTGAACCAGCCAAGTAGCGTGAAGGATGAAGGCTCTATGGGTCGTAAACTTCTTTTATATGGGAATAAAGTTTTCCACGTGTGGAATTTTGTATGTACCATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTCACTGACACTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGATACCCTGGTAGTCCACACAGTAAACGATGAATACTCGCTGTTTGCGATATACAGTAAGCGGCCAAGCGAAAGCATTAAGTATTCCACCTGGGGAGTACGCCGGCAACGGTGAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGAGGAACATGTGGTTTAATTCGATGATACGCGAGGAACCTTACCCGGGCTTAAATTGCAACAGAATATATTGGAAACAGTATAGCCGTAAGGCTGTTGTGAAGGTGCTGCATGGTTGTCGTCAGCTCGTGCCGTGAGGTGTCGGCTTAAGTGCCATAACGAGCGCAACCCTTATCTTTAGTTACTAACAGGTTATGCTGAGGACTCTAGAGAGACTGCCGTCGTAAGATGTGAGGAAGGTGGGGATGACGTCAAATCAGCACGGCCCTTACGTCCGGGGCTACACACGTGTTACAATGGGGGGTACAGAAGGCGGCTACCTGGTGACAGGATGCTAATCCCAAAAACCTCTCTCAGTTCGGATCGAAGTCTGCAACCCGACTTCGTGAAGCTGGATTCGCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCAAGCCATGAAAGCCGGGGGTACCTGAAGTACGTAACCGCAAGGAGCGTCCTAGGGTAAAACTGGTAATTGGGGCTAAGTCGTAACAAGGTAGCCGTACCGGAAGGTGCGGCTGGAACACCTCCTTTCTGGAGCGATGTCGTAACTGTCACAACCGTGACAGTCACTTATAGACTTTAAAGGTTCTGTTTTTTGTACTACTGGTACTTGTTTATTTATAAATATAGATCAACCATCTATAGTATTATAGATAGAGATAAACAAGAGAAACAAGAAGCCGAGTCTAGACGAAAGGTAGACAAGGTTGAACTAGTCCTATAGCTCAGTTGGTTAGAGCGCTACACTGATAATGTAGAGGTCGGCAGTTCAACTCTGCCTGGGACTACTTCGAGAATGTTGATGATTGTGTGGTAGTTGTAAGTTTGACTCTCAA\n",
            ">k141_3 flag=0 multi=12660.0118 len=4649\n",
            "CTTTGGCAAGATCGTACTCCAGTGCTGCCGTGCCATGAGATATGCTCTTTCCTTTTGCTATCATCGTGAAAGTATCTTTTTAAGTTCATTGATTAGTGCCTGGTTCTCTTCGAACACTTCCCTGTACTGGCGCCCACCGAAGTAGTTATTCAGAAGCTGGAGTGTTCCTTTCAGTCGGGCTATCTCCCGAAACAGTTCCCTTTCTTCTTCCGTGTACCTCTCCTTGGGTCGTCCGCCCAGTGCGAGTGTACGGCAGTACTCCGATGTGCTGATGCCGCATCGGGCTGCCTGTTCCGCAAGTGCCGCCTTTTCAAGTGCGGTGCATCGGAATGTTATCTTTTTCGTTCGATTTACTTCCATGTCGCAAAGGTAATCTTAAAATCCTTTTGAGCAAAGCGAGAAAGGCAAGAATGTCCGACAAGGACACTTCTTGCTATATATACATACCGCATGGGAATACACACAGTGCTAACACACTGCATAGCCACACTTTTTTTCTTTTTTCCCTGCCATTTCCACAACGGAACACGGAGAGCGGTCAAGGGTTCGAGCGGAAAAAACCGCACAGCGAAGCTGCTCAGGATTTTTTCAGATCCGAACTTGCGAAGCCCTTGACAGCTTCGGAGTGTGGAGTTACCTTTGCAGACCGAAAAGAGAAAAATCATTTATCGCTGTAATGCCCTTCTGCGGATATGACAAACACATTCACAACATCTTCATAAATTTCATAGACAAGCCTATCCTTCTTGTTCACCTCCCTTGAATATACGTTTCCTCCGTATCCTTTCAATGGCTCTGGATGCCCAGTCCCAGTACGAGGATGTTTGTGCAGTTCTTCGATGAATTTCGTAATTTTCTTAAAAGATGCAGGGTTTGACCTTTTAAATTTCGCAATGGAAGCCAAGGCTTCATCACTATACGTTACACTATACATAAACTACAATGATTCAAGTAAATCCATAGATTGCTCAATAGTCTTACATGTCACACCTTTTCCTGCCTTGTAATCCTCTCTGGCTTTTGAAATCTTCTTTCTAAATTCGTCACTGATGGTATAATCATCTTCATGGATTGGGACAATCATATAAGACTTCCTTATTCCGCGATGAATTACAATGTCTTCACCAGCATCAGCCTTATCCAAAATTAAAGCCTGTTTTTCCCTTAATTCTCTTGTTGTGTAGTCGAATGTTGCCATGATTTCACTGTTTTTAGGTTTACTCTGCAAAGATAAGAAAATATATCGGTGCTACCAAATGGTACGCGGCAAAAAAAATCTATAACCACACAATCCCCCAACCCCCTCACGAGGGGGCTTTGTCTTTGTTTTCCCTCTTGTTTTTCATCCGCTTTTTCCGAAAGTCGGGAAGCATCTCCAGCTTTATGAGCCTGTTTCTATCTTAACATCGACTATCTATGTCCTATCCTCGCGCGCGCGCATTATGCGAGAAAAAAGCCCTCTAAGTACGACATTTTTGATACTTTAATACGACTTTTTTGATACTTTTTTAAAGGGGTAAAAACGACATTTTTGATACTTTATGCGTCTATCTATTGCATTAGTCGTTTTTAATAGATACCTTTGCCTTAAACTTTTAAATCAGCACTTATGAAATCAACTACCTACATTAATTGGGACGGTCTGAAAGACATTCCGTTCTTCTACTGCGATACAAAGGAAGATGAAGAAAACAGCATCTATGGCAAGAACTATAATAAACAATAAGCAAGTCTTTCAATCTTATGTCCTAACTACTGCTAAATATGATTTTTCGCCATATGAAAAGCGCATAATGTATAGACTAATTGAGCTTGCTCAAAAAGAAATTGAAGGGATAAAATTAAAAGATAACCTCCGAAAAATTGCTCCAACAAATTTCGGCAGAGAAATAACCATGCCTGTATCGGACATACTGAAAGACGAACAAGACAAGCACTACGCTATTGCCAAAGCTGCATTCAGAAGCCTATCTGAAAAGCATATAGAGTACGAAGATGATGAAGTATGGTCATACACTCCTATCATAACTGCACCTGAAATAAAGAAGAATAGTGGCTCTGTAAAGTTCTATGTTTTCGATAATATTTGGAGGTGCTTGCTTGATTTTACCAAAGGATTTAAGAAGTATGAACTTATTACCGCCATGAAATTTAAAAGTGCTTATGCTATGCGGTTCTATGAACTAATGTCTGGACAAACAAAGCCCTTATTTGTGCCATTAGAAGGACCTGATGGGCTTCGTGAACGATTCTATTTGCAAGGAAAGTATGAAAAAGTAAACGACTTTCGCAGAAAGGTTATCGATGTAGCCAAAAAAGAACTCGACGAATCTTCTCCTTATTCGTTCGTAGCTAAGGAGGAAAAAGCTGGAAAAAAAATTATTGGCTGGACTTTCTTCCCTGTATTTTATGAAAACAGAGAAGACCCTGCGCTGCAGGAACAAGCGCGCATGGCTAAAGTTACAGCACGGCTCCAGCTGGAAAACAACGTATATGACTACATTAAATTCTCCTTTGACTTCAAGTCTGATGAAATCAACAAAAACAAGAAAACCCTTATCGAGGGACAAAACCGTATTCCCGATTTTATGGGGTTCTTGGGAGAATTGAAGAAAGGCGCACGACTGGCAGAAAACCCCAAAGGATATGTTATCGGCGCTATTAAGAGAAAACTAAAAGAAATCTAACAGAAAGAGGACAACATAACAAATAATTTCTTAAATTTGCCATATCTAAACTTTAAATGTATACTAATATGAAAAAGTCTTTATTATCATTAGCTACTGCTTTTTCTTTTGTGTTATTCTTAGGGTCATGTACCAATGAAGAGGACATAAACAACAACGGTTATTCCGATAAAGAAAAAGCTAAAATCGAAACATTGATGAATTTGTTCGATTCTTATGGTTGGGAACTTGACACGACTGTTAGTATAGAACAGCGTAACAAAGAACTTTTAGAAATGGATTACGAAAAGACTAAAAGTTTCTTGGAGTACATGAGTAATGGTATTGAGTTTGATAATTTTGAACCAACTCAACAAAGTGAAGACAATGCACCAAAAGCCTTAAGCAACACAAGATCTACTATGACCTTTCCGATATATGGTAGTCATAGTAGCGCTGTCGCTTCATCGCAAACCACTATGATTTTATCTTATGACGGTCCAAAACCTTCCAGTGTGACGATACAAAGTACATCAGTATCTTCAAACCCTGCAACAACTTGGACTCCTGATGAATACGGTTCTTTTAACTTTTCAGGGAACAAATGTGATAATATAAAAGCCACAGGAATGATTAAATATGGCAGTATCTATAAACACAAATACGAAATGGTAGGGTGGTGTTCAAAAAATAGTTCAGGTATTGTTGATGACGGAAAAATAACTGGTTTTCATGCAATATAGCCAAATGATAAAATATATTATAAACAAAAAAGCGGAGAATACTACTCTCCGCTTTTTTGTTTATCCATTTCTTGAAAATACTCATAATACCTCTTGTAATTGTCCCTTTCCTTGGCTGCATCCACCCACAGATAACACATCAGACAGGCAAACAATATGGAGAAAACAGCACAAAGGATTGAGTACATTGAAACTTTCTTGTCCACATAGCTACCGTTTTTCATTCTGTTTCCCTGCCGTTCAAAGTCTGCCAATACGCTTTTTAGGCGATTTTCGGCACTTTCAAGACTTCCTCTGTCTATTTCCACCTTTTTAGCTGTTGCGTCCTCTATGCGCTTCGTATAGGCTTCTACACGCTTCAATTCTTGAAACACCAGTTCTACCGCAAGTTGCGGATTTACTTTGGGTGTGGCATCTTTTCTCTTTCGGGGGCTGTACTGCCCCTGCTTATAGGTGTAATCCTCCGTCTTTGAGCTTGAGGGTGTTTCCATAATTCCTGCTGATTGAGTTATACTTTAAGTTTTTCTGTGTCTTTTCGATGTGGGCAAGCGTATATCCTTTCCCTATCTCGCTTGCCTTGTATTCCGTTCCGCTCCGGGCTTCCACATAGTAGCCGTTAAGTTTGCCGGTACTCGCACGGGCTTCCCTCACCTTGAAGCCCAGTTTTCCGAGTTCACGGCTGAATCCGGCAAGGTCAAACCCCTGCATCCTTGTAAGGACACTGTCCATAGCCTGTTTTATCTCTTCCCTGTTGGCTTTGCCTATGTCCTTTGACTGTACCAAGTTCCGTTCTCTGGCTATGCCGTTGGCGGCTTCCGTGGCTCTCTTCCCTATCCAGTTGTCTTTGTAGAGTCCCCCCGATAACGACACACGGTTTGCCAGTATATGCAGGTGAGCTTGTCTGCTGTTCTTCTCCGTCCCGCTGTGCTTCACGATGATGTACTGGTGATTCATCAAGCCCATGCGCTGCATGAAGTCGTTGCCGAGCTTCGCCCAGTCCGCATCGGTCATGCCTGCACTTTCTTCCACTGAAGGGCTGACCTCAAACCGAAGGCAGTTGTTCTTTACGTTCGGAAAATCAACAAAGTAAGGTTTCATTTCCTGCACCATCTCTTCACCTGTACAGCCGAAAAGCTCATGGCGGTGTATCTCGGTGGCTGCTGCCTCCCCGTTTATCTCTTTGGCAAGATCGTACTCCAGTGCTGCCGTGCCATGAGATATGCTCTTTCCTTTTGCTATC\n",
            ">k141_4 flag=0 multi=11328.6185 len=5882\n",
            "CTTTGGCAAGATCGTACTCCAGTGCTGCCGTGCCATGAGATATGCTCTTTCCTTTTGCTATCATCGTGAAAGTATCTTTTTAAGTTCATTGATTAGTGCCTGGTTCTCTTCGAACACTTCCCTGTACTGGCGCCCACCGAAATAGTTGTTCAGACGCTGGAGTGTTCCTTTCAGTCGGGCTATCTCCCGAAACAGTTCCCTTTCTTCTTCCGTGTACCTCTCTTTGGGGCGTCCGCCCAGTGCGAGTGTACGGCAGTACTCCGATGTACTGATGCCGCATCGGGCTGCCTGTTCCGCAAGTGCCGCCTTTTCAAGTGCCGTGCAGCGGAATGTTATCTTCTCCGTTCGATTTACTTCCATGTCGCAAAGGTAATCTTAAAATCCTTTTGAGCAAAGCGAGAAAGGCAAGAATGTCCGACAAGGACACTTCTTGCTATATATACACATGCCGCATGGGAATACACACAACGCTGACACGCTGCATAGGCACACATTTTTTTCTCTTTTTCCTGCCTTTTTCACAACGGAATACGGAGAACTGTCAAGGGGACGGGCTGGAAAAACCGCACGAGCCTGCGAGCGCAGGATTTTTCCGGATCCGGACTTGCGAAGCCCTTGACGGTTACGGAGTATGGAGTTACATTTGCAGACAAAAAAGAGAAAAATTACTTATTCAGTAATACTTTTTCTTTCGCGGTTAAGTTCTCCAATTCTTCATCACTTATAGGAATAACAGTATAGGTTTTCCCCTCGTGATGAATAAGTACATTGTCGCCTTTACTCGCCAAATCAAGCATTTCCTCTATTCGGTTAGCAAATTCCTCGGATGTAAGATTTATAACTGCCATATCTTTAATTTTTACATTGTTTTTTTACGTCATTTATCGTCATAGTGCCCCCAAACAGAAATAACAAAAACAACAATGGTGGTTTCGTTCACCGTATATCTAATACGGTTTTTCTTATCAAGTTGCCTTGACCATACACCCTGCAAATACTTCAACTGTTCGGGGTGTCCAGTCCCGGTGTATGGATGTTCTTTCAGCTCATCCAATAGCCTCTCTAACTTTTTGAAGAGTTGCGGAGAGGACTTTCTTAGCAACAGTACATGTTTTGCAGCTTCTTTTGAAAATTCTATCTCATACATTGCACTCATCCTCTATACGGTTCAAAAAATCATCTAATGATTCATCCTTCCGCATTTTAAAAGTTCGTCCCTGCTTAATATCCTCAATACCCCTCTGAATAGCCTCCAATTCTTCCCTTGAGGGGAAATCCTCTTCCGTAGCCGCATATACGACTATGGGGGCGGCATTACGACGGGAAACAAAGATTGTCTCTTTTTCAGCCAGTTCAAAATATTTCTTCTGATTGGCACGAAATTCACGCGTTGTAATTATCTGCATAATATTTATATATTTGATTTACACCGCAAATATAGCTATAAAAGTTCAACCTCCAAACTATTTGTGTACCTATTTGTGTACACGCCATTAAAAGCCATATTCAAGCCTACCGCCCGTTGGTTGGTGTTGCTTTGCTTTTTCCCCTCTTTTCGAGTTGATTTTCAGCCTTTTTCCCTCTAAATTCCCCTTGTCTGTTATAAGCATCGTCTGTAATATGCTCTATCACTTTTGCGGAAACACCGATAAATAAAGGGATTAAAACATGCGTTTCCCTACACTTTGGGAGTTTTAATCCTACACTTTGGGAGTTTTTAAGACGAACTTTCCCTACACTTTGGGAGTTTTACCCTTCTTTTTTTGTTTATATGTAGGTATTTTATATCTTTGCAGCAAATCAAATAGTTATGAAAAAGAAGCTACCTATTACCAAAAATAAAGACGTAGTTGTATCATGGGTATATACATGGTCCAAACAGCAAGACATGTCCATACACGAACAAAGAATAGTTCTTCGCATACTGGAAGCCTGCCAGGCTGAACTAAAGGGAGTAAAGCTGAAAGATTATGCAGGCACGAAACGCAAATTTGAGCATGGACTTTGGGATGTTGATGCGCAAATGCATGTGTCTGACGTTATTTTTTCCGGACGTGACTACAACGAAATCATAGCTGCACTCGATTCACTGGCTGGACGTTTTTTCACTTACGAAGATGATGAGGAATGGTGGAAATGTGGATTTATATCTAACCCGAAATACAAAAAGCATACCGGTATTATAACATTTCGTGTATCTAATGACCTTTGGGACGTGTTTACCAAATTCGCAAAAGGATATAGGGAATTTGAACTAAACAAGGCTCTTGCGCTGCCTACAGGCTATTCACTTAGGTTTTATATGCTCATGAGTGGGCAGGTGTACCCTTTGGATATATCACTGGACAACTTAAAAGAACGACTTGGCATACCTGCGGACAAATACAAAGACAAAAACGGAAAAGATAGAATCGACAACTTCGAGGAAAGAGTATTAAAACCAGCAAAGGCTGCGCTTGATGAAAGCTGCCCATACACATTCAACTACGTAAAAGTGCGTGAAAACCCTAACAACAAACGTAGTAAAGTAACTGGATTTAGATTTTACCCTGTATATCAACCTCAATTCAGGGATGAAGAACTGGAAGTAAAAGAACTTCAAGCAAAAGTAACGGCACGTCACCAAATAGATAGCCATGTGTATGAATACCTCCGTTATTCCTGCGGTTTCACTTCAGAAGAAATAAACCGGAACAAGGAAACATTTATAACTGCACAAGAAAATATTACTGACCTTATAAGAGAACTGGCTATCCTAAACGGAAAATCACGAGAAAAGAATAATCCAAAAGGTTGGATTATCAATGCACTAAAGGGGAAAATAAAGGAGTATAGTGCTTGATTTTCAAAAGGAATTGCTATATTTGTGGATATTAACTTTAAATTATACCATTATGAAAAAGTTTTATTATGTTATTTTAAGCATGATAGCAATAGCTTTAGTATCATGCACGTCTGAATTAGACGAGATAAATAACACTGTTCATCAGCAAGAAACCTTATCCGGAAATGAACTTGGTGCTAATCTAATGAAATCTTTTCAAAATGCAGTATCTCGAAGTAGTGAGATAAAGCATTTATCATATCCTTCATATTATGGAGGAGCCTATTTAAACAAAGAAGGCAAACTGGTTGTAAAGGTAGTCAATAAAACTTCGGAAGAGATAGAAAAAGATTTAATCACTCGATGTGGTGGAAATGGATCCATTGTGGATATCTGTGAATATTCTTATTCAGAACTATTAAATGCAGCAGAAAAGATGGATAACTATTTGTTATCTAAAAAGAATGCAGATAATCCTTTTGAATTTTATGGTTTTTCTATCTGCGATACAGACAACAACATAGAAGTCTATTTAGGTGACATTTCAGAATCAAATATACAAGACTTCAAAAAAGAAGTTTTAGAAGAACCCTTCTTGAAGTTTGTGAAATCAGAAAAACCAGCTTTCCTAAGCGAAATACTAACAGGGCAAAGTATTGTTTCCGGAACACGTTCATATGGTTCTGTCGGATTCCGAGCTAAGCGAAAAGATAGTCATGTTGTACCTGTTACAGGATTTGTTACCGCAGGACACGTTGTACAAAAAGCTGGAACTTATGTATACGAAAATGAATCGATGAGTACTCCTATAGGTTGTGCTGAAATTTCACAAACGTCAGGAGATACGGATGCTGCCTTTTGTTATTCAATGAATGGATATACATTTTCAAATCATTTATATAGCGATTTTTTATCTGTTAATCCAAAACTATCTTCTTATTTAGTTAATTCAAAAGTCGCAATTCGTGGTAGGCATAGCGCAAGTACTGGTTATATTAACTCCACTTATGCAACATCCACTTTTAAATGGCCCTCTCAAGGCATATCAGTTACTTTAACTGGCATAGTAAAAATGACCTGTAATTCACAAAGCGGAGATAGCGGATGCATTGTTTACACTCCTGATGATATGAATATAGCCGGAACATTAATAGGAGGTGTCACAAATTCCAATCCTTCATATTTTATGCCTGCTTCTCGTACCGTGGAAAAATATGGTTTGGAATTGTATTAGAATTATAAGCTTTGCTGTTTTCATTATATCCTGTTCGAATAAAAAAAACAGTAATTATATAGACACGTTAACTGTAAATGAAGTAATTTCCAATTGGAAGAATTCTATTAAAGAGAAAGGAGAACCACCTTATTTAGGAGGTATGTATGTGGAAAATGGAATTGTCTACTTTTGTATTACAAACGACACATACAATATACGGAATGATATTTTTGAAAGATGTAAAAGTTCCAATGGAGTAATTATAAAAATGTGTGGAAACAGTAAAGAGTCCTTAATGCAGCAAATAAAGATTTTAGACTCCTTATTACTTGAAAAAGATTTTACAAAATTAAAGTATTATGGTCACTATTTAGATGAAAGAAGGAATAAAGTAGTCATAATGTTGGGCGACACATCTACAAGTAATATTGCCTCCTTTAGAAAAAGTGTTATAGACTCTCCCAATTTAATTTTTGAGAAATCAGAAGAAATGTTTTTTGAATAAAGAGCAAGCCGCCGGAACTCCATAGATTCCCGGCGGCTTTTATATATAGGAATGGCATCAGTGCCATTTAAGAGTTTGCAAAGAATTACTATCTTTGCAAATATTTGGTTTTCTACCCATTTAACTCGTACATTTCTATTCCTTCCCTTCTTTCTGCTCTTGCATCTGCTGGTAATATTCATAAAAGGGCTGGTATCTGTCCCGTTCCTTGGCAGCATCCACCCACAAATAACACATGAGGCAGGCAAACAAGAGGGAGATAACGGCACAAAGAATGGAGTAGAACGAAACCCGTTTATCCACATAGCCGCCATTTTTCATTCTGTTCCCCAGCCGTTCAAATTCCGCCAATACGCTTTTTAGGCGATTTTCGGCACTTTCAAGGCTTCCTCTATCTACTTCTACCTTTTTGGCTGTTGCGTCCTCTATGCGCTTCGTATAGGATTCTACGCGCTTCAACTCTTGAAACACCAGCTCTACCGCAAGTTGCGGATTTACTTTGGGCGTGGTGTCCTTTCTCTTTCGGGGGCTGTACTGTCCCTGCTTATAGGTGTAATCCTCCGTCTTTGGGTTTGAGGGTGTTTCCATAATTCCTGCTGATTGAGTTATACTTTAAGTTTTTCTGTGTCTTTTCGATGTGGGCAAGCGTATAGCCTTTCCCTATCTCGCTTGCCTTGTATTCCGTTCCGCTCCGGCTCGTTACATAGTAGCCGTTAAGTTTGCCGGTACTCGCACGGGCTTCCCTTACCCTGAAGCCCAGTTTTTCGAGTTCCCGGCTGAATCCGGCAAGGTCAAATCCCTGCATCCTTGCAAGGATACCGTCCATAGCCTGTTTTATCTCTTCCCTGTTGGCTTTGCCTATGTCCTTTGACTGTACCAAGTTCCGCTCTCTGGCTATACCGTTGGCGGCTTCCGTTGCTCTCTTCCCTATCCAGTTGTCTTTGTAGAGTTCTCCCGATAACGACACACGGTTTGCCAGTATATGCAGGTGGGCTTGCCTGCGGTTCTTCTCCGTTCCGCTGTGCTTCACAATGATGTACTGGTGGTTCATCAAGCCCATGCGCTGCATGAAATCGTTGCCGAGCTTCGCCCAGTCCGCATCGGTCATGCCTGCACTTTCTTCCACTGAAGGGCTGACCTCAAACCGAAGGCAGTTGTTCTTTACGTTCGGAAAATCAACAAAGTAAGGTTTCATCTCCTGCACCATCTCTTCACCTGTACAGCCGAAAAGCTCATGGCGGTGTATCTCGGTGGCTGCTGCCTCCCCGTTTATCTCTTTGGCAAGATCGTACTCCAGTGCTGCCGTGCCATGAGATATGCTCTTTCCTTTTGCTATC\n",
            ">k141_36 flag=0 multi=69.8296 len=17955\n",
            "TAGGTTGACTCCGCAAGATTAATCAATTCCTCACAAGCTTCATGACGAAGACTTTCTTCCTGAAGAGCACGTTGAAGCTTGCGATTTTCGGCACGCAATGCAGCGAGTTCTTTCTCCAGTTCATTGAGAGACTCATCAGAGGCTTTGCTTAAAGAGGGTTGTTCTGTACTCATAACTTTATCATTTATATGATACTTATCAAGCCAAAGAATCAGCAAACGACGGCAAATACCCGTACGCTTGGCAAACTGATACTTCGTTTCGGAAGTCGATAAATACTCACGAATATACGACATTCGTTCAGATTCTGTGTAAGGAGTGTACTTCTTTTTTAATTCTTCCATGTGAATTTCGAATTTAAGAAGTGTCAACTTATTCAGTACACTACAAAACGACAGTTCCCCGCCTGGAAACGGGAAGTTAATGTATACAATAAACTTTTGCACTCATGGAGAATTAGCTTATGTAAGGTTTATACTGTGTTGCAGATACTTTATCTGCAACTTATCTGCCGCAGGTATCTGCAACGCTCCAACTATTTATCAACTAGTGCATTACACGCTTTTCGTTGCAGGTGGCAGATGTTTTCATGTTTTTTCAACTTTATGGTAGGAGGATTACGCATCAAAATTATGCTCACAACCATAAGTCATAAAACAAATTCACCTCTGTGCCCTCCGTGTCCTCTGTGGTGAAAAGAATCAAATCTGTGCAGAAACAAAGTAGTCAAAGTTAGCAATAGGAGACAAATTAGCGCAGTTCACTACTAATCGTTGTCAGATTTCGGAGCATTGTTACTAAGGGACGAGGTATCATCTGACTTTTTATCTCCTCCCAGCCGACTATACAATAAGCCTATAATTTTGTCTTCTCGCATTACAGGTTCTTGCTTATACAATTCTTCAATACATTTACGTGCACAATTTTTAAATTTTTGTATATTGTTGATTTTTTGTTTACCCTGCAGCCCCAAAGATGCACAAATAGTACTAGCCTTTATATCTGGTATAAAGTATAATACAGCAGCATAAAAGCGGATATCACTTCTCTTTTTTTGCATAAAGGACAATTTTCTTAGTTGAGAATATGCATTGACAAAGTCATCGCATCCTTTTTTGACCATCCTGGAGGATTGCCTTCCATCAACTTTCTCCTTCAAGGATTTTATTTTCTCTTCATGCCCAACAGCTGCATACTGTTCGTAAAAAGTTTGGCTATAGGGAGCTATGCAATCCGCCAAACCATTTTCTAATTTTAATAGCTTCTCTTTGAGTTTATCTATTTGTGTTGTCGTTTGATATTCGATATCTGTTCTTATCTTGTCTTTATCTTCAATAATCTTTTTCAATGATTTTTCAAGCTCATTTGTTCTTATTTTAGGCAGGCTCATTATATTTTTTATACGTTGCTCTACTTCTGCAAGACCCTTCTCGATGTCGGCTGTTGTCATTCCGTCCATACGATTGCGACAAGAAACCATATTGTCCGCCAGATCTCTTTCCAATATCAACAGTTTCTGTTTGAGTGAGTTAATATGCTTTATCGTTTTATATTCGGCATCAACACTTATCCTTTTTTGATATTCGATGATTTCTTCCAATAAATTCTCCTGTTCCTCTTTCTCTATTTTAGGCAGATTCTTCATTATTTCTATTCGTTGAGCTACTTCTTCCAAACTCATTTCGATATATTCATTTAAGGTCAAAGCCTTTTTGAAAAATTCGGTTCTCAACTCATTTACATTCTCTTTCATTTCCCTGACAAAAATACCGGATATATATTCCCCTTGCTTTCGCAGGAGCAGTTTGCCTATAAGATCATCAAATTTCATCAAAAGTGCTTCTCTGTCTTCTTTCTTCACTAAAAGAGATACTTGCGACTTTAACGCATTAACTTCATCCATATATTCAATCAGCTTATTGCTTCTGTATTGCTGTTTCTTTTTCTTTATATAAAAACAATATATTACTGCTCCGCCAATAAGCAACAAAAGCCCCATGATAAAAATATATAAGGTAGTATAGTCTTTTTCTTCTTGTATCGTAATAGGAGATAACGCAGACTGGTCAAACAAGAACAGTCCTAACCGACTGCCCAAAACACAACTTTCCCCTTTGACTGCGATACAGGCCTTACTGAAAGCTATGTCACTGAACAAAGGAAAGGGGCTAACTCGTTTTTCAATCAAGCTATGTGTCAGGCCTTGATAATGGATGCCATGATAATACAGAATAAAGATTCCGTCGCTGGAATTATCCTCATGTACTCTGTAAATCGCCTTAATATCCGCTTTATCAAGAAAGCTCAAAGTCTTGATGTCATCGTTATAAATAATGCCTTCCGATGTATTCAACAGCAAATCCTCTTTACCAGTTATTGCCAACCCGTATGTGCTACCATATTTTTCAGAATGCCAAATTTTCTCAAATTTTCCCTGTTTTCCTGTCTTCTGAAATAACCCTTTATTCAATGTAGCCACATATATGCCGTCATCCCTGCCTGAAATATCAACTACATACAAATCGGTGGTATCCATATCGGCTATGGATTCAATGGTGTATTCAGATGGTACTTTTCTTCCACTGCCACAAGCAATGCTCCCCAGATATCGCCTCGTCCCTATATACAGTTTCTGCTCATCAGGGGAGAAATACAATGCACTGAAATCATTATCATTGTCCATTTTATTAAAATCCACAATGCACTCGGCAGTATTCCCTTTTATCTTATAAAGATTCTTTTTCGTTGCTAAAAAGAAAACGTTACTACCTGTATGTATAAATTTCACTATTTCATCGTGCACATTCAATCCCTTTAGTTGTCCCATGGGTTCGGAATTATACTTGTCTTTTGAGGTATTATCGAAACGATATTTATGCAATTTAAAATCGCTGGTGATAAAATAGATCCTATCGTCACCGCCGGTTCTGTCGCTCACTGCGATGACACTACTTGCCAATCCCTGTGTATTTTGTCTCAATGGAAATGCCAACAACAATTCCCTGTAAGCAATATAATAGAAATCATTATCCATTAAACCAACCTGTTTGCTACTTGGAGTTATGCCGGAAGGAAGTTTATGTTTATATGTTATTCCTTTTTTCTTTAAAAATACATTCCGTCCGGAAAGAAGCCATTCTTCATTGTCCGGTGTTTTACTGTAAGAGTAGAACTCTCCTGAAGCGATGGTCTTATATACAGAGTCGTTTGTTTGGTAAGGATTGTCTATCTTGACGATAGAATTTTGAGTTAATGCATATATCGTATCATTATCCAGTATTACATTATTGACTCTGACGCCTTTTATAGGATTAAAAAATCGAGAAGGGGATTTGCTGATAAAAAGCCCCGGATCTCCGGCCACATACAAGGTGCTATCCCTAAATAAAACTTTATGAATACCATCCTGTTTTTTATCTCCTAATTCCACACGTTCGAGCATTACCAAACCGGGGTTGTTTTTCAGATCCATCTTGAATAATCCGTTACTGGTTCCGAAATAAAGAATCCGGTTGATACTATCCTCGGCAATACTATACACAGAATAATTCAAATCTTTACTTTCGATATTGAAGTCTTCCGATTTAACACACGAATCATTTTTGAGTGTTATCAATTTTAAGCCACCGTCTCTTGTCCCTATAAACAACGAGTCCTTATTATGTTCCCAAATATCGTATATGATTCGTTTGTCTCCTCCTGAATAAACGTGCCGTTTATCTCCTTCCATCTTCACTATAGAACCATTTTCCAAACCGACATATAATCTGCCGGAGTTACCCACCGATTTAGAAATGGCATGGACAGGTGCTTCAAATGTATTGATAGTGCATGCCAGTTCTATTTCTACAACATCATTTGTGACTTTACCACATGCTGACAACAAAATGATGCCCCCTGCTATAACGATGAAAATGTTTTTCATATCTATCAAGATAAAAGGTTCTTTGGCAAAAATACACAATAATCTTTATATTCATAGAGTTAGCTATAAAAACGTATGAACTAATTTTGTGTAATTAGATAATCCAAATACCATCCAAGACAGTCTACCGGAAATGTTAAGAGATGATCAATGACCTTAGAACTCTTATAAACTCGTCTACATCTTCTTCTGTCGTATCAAAAGAAGTTACCAGACGGATTTCATTCCTCTCTTCGTTCCAGAAATAAAAGAAATAAGACTCCAGCATGCGGTCGATAACCGGGCGGGGCATGGTGAGGAATAACTGGTTACTTTCGGCTTTTTGAGTGAAAGTTACTTCGGGCAGTTTCTTCAATTCAGCATAAAGTTTGGTAGTCATTGCATTGGCATGGTTGGCATTCTTCAGCCAGAGGTTATCTGTGAGGTATGCTGTAAACTGGCAAGAGAGGTAACGCATCTTCGAAGCGAGCTGGGCGGATTGCTTACGAATGAAACGGGCTTCCGGTTGCAGGTCTTTATTAAATACAATCACGCACTCTCCCATCATCAGTCCGTTCTTCGTTCCGCCAAAACTAAGAATATCGACTCCGCAGTCTACTGTCAGTTCTTTAAGGGAAAGATTCAGGGCGGCACAGGCATTGGCTATTCTGGCTCCATCCATGTGTACATACATACCATTCAGATGGGCAAAGTCAGTCAGACGTTTTAACTCTTCGGGAGTATAGATGGTGCCAAGTTCGGTACATTGGGAAATATAGAGAGCTCTGGGTTGCGAATGGTGCTGATCGCCGAAACCATGCAGATAGGGTTGCATCAATTCCGGGGTAAGCTTACCGTCGGGAGTAGCAATCGGACGAATCTGACAACCTGTCATTTTCACAGGAGAGCCGCATTCATCTACATAGATATGAGCCGTTTCCGCACAAAAGATAGAATGATACGGACGGGTCATCAGCTGCAATGCTACCACGTTGCTACCTGTTCCGTTAAATACAAACAAGGGGACACAGTTGGGCGTAAAGGTTTCCTTTATTTTCGCTACGGCTTCTTCTGTCCATTTATCATTGCCATATCCTAAAGAATGGTCTATATTAGCCCGGTTCAATGCTTCCATTACCAACGGATGCACACCGGAATTATTGTCGGAGGCAAAACTTCTCATTGTTTCTTGCATTTTAATTCGTGATGCAAAGTAACAGAATTATTTCCGGAAATCCATTGAAAGGAAGATACATAAAGCAGAAAAAGGATAAAAAACGAGAATCTCCGAAGTGCTGACTATGCTGTCATTCCTCCGGAGATTCTTCTGTTCTCGTTAAAGCGTCAAACTCTTTCAGCCAATATAAACATTCACACAGAGATATACATAAAGCTCTTCATCGCACTGATCGAACTCTATTTTGCTCTCTCTTGACAACCACCCCAAAGCGGCACCCAGCTCTTTATCTTTCAGCCCGGATTTTCTCTTCAAAAGTCCATAGCTCCACTTCTCATTATTGCTAAGCAGTTGCCAAACCTTACCGGCATTCACACCGATTTCATTTCTGTTCATTTTTCTAGAGTAATTAAGAGGTTTAATACATTAGGTTAAGATTTCGCCTATAAAGATAACAAAAGGATATTGCAATTGTTTATACATCTTTACTAAAAAGTTGTTACAGAATTAATTTTAACATAAAAGCGGTGATGTGTCCCGACACAAAGGCGTTATTAGCATATGTTAAATTAGAACTTTTCTTTGCAGCCATCTGTTATTACATAAAAATCAATCCTAAACCAATTACAGCTATGGCAAAAAAAATAGCAGAACAGTTGATAGATACTCTGGTAAAATCCGGAGTTGAACGCATCTACGCCGTAACAGGCGACAGTCTGAATGAAGTAAACGAAGCAGTCAGAAAAAACGATCAAATAAAATGGATACATGTGCGCCACGAAGAAACAGGGGCGTATGCCGCTGCGGCAGAAGCACAACTGACCGGTCGCCCGGGATGCTGCGCGGGAAGTAGCGGGCCGGGACATGTTCATCTGATTAACGGCTTGTATGACGCACACCGATCCGGCGCACCGGTAATCGCCATCGCCTCTACAATTCCTACCGGAGAGTTCGGTACGGAATATTTTCAGGAGACGAATACCATCAAACTTTTTAACGATTGCAGTTATTATAATGAAGTAGCCACTACTCCCACACAATTTCCACGTATGCTCCAGTCGGCCATACAAACGGCTGTCACCCGCAAAGGGGTGTCCGTTATCGGGCTTCCGGGTGACTTGGCAAAGGCTTCTGCCGTTGCTGTCGACTCGTCGGTTATCAACTATCCTGCCCCACCGGAAGTTTGTCCGTCGGAAGAAGATCTGGCTCAATTAGCTGACATGTTAAACAAACATACGCGTATCACTCTTTTCTGCGGCATCGGTTGCCGGGGAGCTCACGAGGAAGTGATTGCTCTTTCCGAGAAGCTGAACGCTCCGGTGGTATATACTTTCAAAGGAAAGATGGAGGTGCAATATGAAAATCCGTATGAAGTGGGTATGACCGGATTGCTGGGAATGCCGTCGGGCTATTACAGTATGCATGAAGCCGAAGTGCTTCTCATGCTCGGCACAGACTTTCCTTATTCCGCTTTCCTGCCGGATGACATCAAGATAGCACAGATTGATATAAAGCCCGAACGTCTGGGACGTCGTGCAAAGGTGGATATCGGACTTTGCGGTGATGTTAAACTGAGCATCCAAAGTCTATTGCGTATGCTGAATCCGAAGACAGATGATTCGTTCCTGTTGAAACAACTCAAAAGATATGAAGGCGTAAAGAAGGATCTGGCTGCTTACACGGAAGATAAAGGCGATGTAAATAAGATTCATCCCGAATATGTGATGTCCGAAATAGACAAACTAAGTTCGGACGATGCTGTGTTCACAGTAGATACGGGAATGACTTGTGTGTGGGGAGCACGTTATCTGCAAGCGACAGGCAAACGCCACATGCTGGGTTCTTTCAATCATGGTTCTATGGCAAATGCTTTGCCGCAAGCCATCGGTGCCGCACTAGCTTATCCGGATCGCCAGGTAGTGGCGCTTTGCGGAGATGGTGGCTTGTCTATGACTTTAGGAGACTTGGAAACCGTTGTACAATATAAGTTACCAATTAAAATTATCGTATTCAACAACCGTTCATTGGGAATGGTGAAGTTGGAAATGGAAGTGGACGGATTGCCGGACTGGCAGACAAATATGTTGAATCCAGACTTTGCTCAAGTGGCGGAAGCAATGGGAATGACCGGATTTAATGTCAGTGATCCGGAAGAAGTGTTGACTACCTTACTGAATGCATTTGAGTTGGACGGTCCGGTACTTGTTAATATCATGACTGATCCGAACGCACTGGCCATGCCTCCCAAAATTGAATTCGGTCAAATGGTGGGCTTTGCACAATCAATGTATAAGTTGTTAATCAACGGTCGTTCACAAGAAGTAATTGATACAATCAATTCTAACTTTAAGCATATACGGGAGGTATTTTAAGTTATTCTATTTTGAATATTTTCATAACCTCTCTCAAGGTATGACCTACTATTATTGTCAATTTCAATAAGGTAAGTCATACCTTTTTATAGTCTATCTTGAGGTGATAATCATTTTATTTTCTTATGTTTGTAATCATCAAAAAGCAAAAACACATGGAAAAGACCGACTCTTCGCCTTTATCCCGACAAGCGCTGTACGCAGATAAAAAACAATGGAATCAATTCCTTTCTATTTTTTTATTGGCAGTGGGCGTTGGTTTTACAGTGGCAGGAATTATCTTCTTCTTTGCCTACAACTGGGAGGAATTACCCAAATTTGCAAAGTTAGGAATAGTAGAAGTGTTATTAGTAGCTTCTGTCCTGCTCGCTACATTCACGCATTGGAATAAGCTCGTCAAACAAATTCTCCTGACCGGAGCTACATTCCTGATAGGCACGCTTTTCGCTGTCTTCGGACAAATCTACCAAACGGGAGCCGATGCTTACGATCTTTTCCTGGGATGGACTCTCTTCACTATTCTCTGGGCGGTTGCTATCCGTTTCGCTCCGTTATGGCTGACGTTCATCGGATTACTTTGTACTACGATATGGTTGTACAATATACAGATTGCCAGTGCCAACTCCTGGGAAATGACTTTGCTGGCCAATGCAGTAACCTGGATATGTGCCTTAACAACGATCATCACGGAATGGATGAGTGTAAAAGGTCATTTGGACAGAAACAATCGCTGGTTTGTCAGTCTTCTTTCACTGGCTACTATTATACATACCAGTTTTCTGTTAATGATGGCAATCTGCGAAGAAAATGCCATATTGTCTGTTCCTCTCATAAGCACCCTCCTTTTATTCTCTGCGGGACTTTGGTATGGATGGAAAGTAAAAAGCCTGTTCTATCTGGCTATCATTCCGTTTGCTGCATTAATGATTCTATTGACTACATTCATATCACAAAGCGGTCTTAGGGATGTTCAGATATTCTTCTACGGAGGGGTTATTGTCATCACCGGAACAACTTTACTCATTTACATCATTCTTCATCTAAAAAAGCAATGGTATGGCACAGAAGCATAACCTCACCATTCAAGCCGTATCTATTATCGGAGGAATCCTGACGGCTATTTTCTTTCTGGGATTTCTGGCACTTGCCAGAATCCTCCGCTCCGATATTTCTTGCCTGATTGTAGGAAGTATATTAATTCTCACTACCCTAACAATCAGCCGCATGGTGATTCGGTCTTTTCTGGATGCAATGAACATCACTTTATACATAGCCGGGTGTGTATTGATTGGTTTTGGTATAAATGCCAGCATTAATATCCTCTTCACTACACTTATGGGAATCAGTATTCTCACATTCTTGTTATCAAGAGGGTTTATCCTCCCCTTTCTTTCAGTCATTCTGTTTAATATATCTTTCTTCGGAGAAGCCGCCCATGTCTTTTCTTCGTTTTATCCCTTGCAAATAGCTGTAGTTCCTATCCTTGCACTATTTTTATTTGCCAACATCTTTGAAACTAAGTTGTTTGAATGTATAGGAACAGAGAATTACTTTTCCAAATACAAACCATTTCATTTCGGATTATTCATATCCGGTATCGTCTCACTGGGAGGATTATCAATCAATTATCTGATATCAGAAACAAATAGCTGGCTTGTATCCTGCATACTGTCTGTTTGTATATGGATCGGTATTCTTATAATGGTTCAACGAATCATGCAGGTCATGAAAGTGGATCATCCGGTGAACCAGATTGGTATCTATATTCTTTGTATTGTTATCTGTCTGCCTACCGTGTTTGCTCCTTATTTATCCGGCAGTTTGTTGCTGATTCTAATATGTTTCCATTATGGCTACAAAGCGGAGTGTGCTGCCTCGCTTCTTCTTTTTATCTATGCCGTTTCCAAATACTATTATGATCTGAATTTAAGCCTGCTCACCAAATCCATGACACTTTTCTTCATCGGAATTGCATGTATTACAGCCTGGTATTTCTTCACTCAAAAAAGAACAAGACATGAAAAAGTATAGCCGAATATTGATTATTGCAAACCTGATACTGCTACTGGGATATTTCAACTGGTCGGTTTATAAAAAAGAGCAAACTTTGAAAGACGGACAGTTGATTTTGTTACAGCTAGCTCCCGTCGATCCCCGCTCTCTTATGCAAGGAGACTATATGAGGCTTAGCTACAAAGAAGCTTCATCAGATCTGCTAGACCAACAAACTGCCATACGCGGTTATGCCATACTTCAAATTGACAGTAATCAAGTAGGAAAAATAGTGCGGCTGCAAAACGCTTTAGAGCCTGTAAACGACAATGAGCTAGTCATCAAATATAAAATAGTACGTCACCGGATCTTCCTCGGTGCCGAGTCTTTCTTCTTTGAAGAGGAACAAGATACTCTTTATCAAAAAGCCGTATATGGCGGGCTGAAAGTGGATGGTAAAGGCCAAAGCCTGCTCGTAGGACTGTATGACGAGAACTTCCATTATATTCAATCCGATAAATAAATAATAATGAAAAAGCCCGTACTTTCGCAAGCCCGGGCTTTCTATATTCTCAAAACATCTCCTTAATCTATAGGGAAATAAGGGACTAGCAAATTGGTTTCTAGTGCAAATAAGGAGATGTTATGAAATAAAAAAAAGGGATACCTGTGGGAAATAAGGGAGGTATCCCTTTCTATAAGAAGAACAGAATTGGAAGGATTGACTTTCACAAGCCAAATCCTTCACTCTGTTTCTTCTCGGCGTATTCTTCACACAAGGTGAAGAAGGGTTTTATTATTCTGAATTTGGAGTAGTTTCAACTGTAATACAATCCTTTGCTTATATAAAGCATTAAGAGGTTTGAAGCAACTCTCCAATTTTTCTTCTTATTCTGCCGGAATAGTTACTGTAATTGTATTCTTTCCTTCATAATTCTTAATTGTACCCCACTTAGATGTAGCTTTTACTTCTACAGTATAAGTTACAGCTTCGTAGAAATGGTGAGATTTACCTTCTGCTGTAAAGGTCACCTTTCCTGTAGCATCATCAAGAGACAGATAAGTTGACGCAGCTGCTTCTTTGAATTCAAAAACAGGAGCTGCCAGACCATAGATATCAAGAGGATTAGTAACTGAATTTGCAAATCCATTGCTTTCTGTTCCTGCTACCACAGCACCATCCTTCCACATAGTCTTACCTGCCAAATCATTCCAAGCAAAACCTGTTGAAACATTATATTCTACAGACTTATCAGATAAAGAAAGAGTCGTTGCAGTCGGAGCAACCCAAGTTCCACTAATATCTTTCACTTCAACAATACCTTCAATCGTATCCTCAACTTTACCATTATAGCTAGCTTTAATAGTAAACTTAACTACAGCAGGTACAGTCTTACCACCAACAGTCATCTTACCAGTATAGAGATCTTTATCAAACGTAAACTTAGCCTCGTTTTCATCATAGCTAACGCCAGCAATGCTATTTTCCGTAATGTTAGTAGTAATTACGAAAGTACCACCTTTAGCCAAAACAGCAGCTTTAACAGCATCAAAGTTGGAGAATATAGTAGCAAGCTTAAATTCGGAAGTAATAGCAGTAGCCGCTGTCTCAGAATCTTTTGTCGGAGTAAAGCCTGTACGAGTTTGATCTGAAGACCACATTTCCGACACTCTAGCCAATTTAGCCAAAGTGATAGGCTTAATCTTAACTGGAACATTTACAACAAGCGTATAGCCATCTCCTTCGAACTTAGCTGTTGCGGTATAATCACCAGCAGCTGTATTTTTAGGAATGATTAAAGTCAAGGCATTATTTGCACCATTTTCTAAGCGTATACCTGTTGTAGGAAGAAGTTCAACCAAACTCAAACTTTCATAAGCTGATGGGCGGATATTCACAGCCGGATCGTTATAAATGTCAGCAACATTTAGAACTACTCTCTCCTCTGCAGCAACTGCTGTTTCATTAGTCCAGTCACGTTCTACCAAAGCATACGCATGTGTAAGTTCATCAATAGTACGTGTTACTTTTACAGTTCCTAACTTTTTAGGATTATCAGAACTAGTTTCCAAATAGAAACCTGGAGCCGTTACTTTAGCCATAACATTAGCAGTTTTATCCAAAGAAGCAATCAGACCGGATGTTTTTAGACTAACGCTACCTGCAGTAGAAACTTCAAATAGATTTGCATCTGTTCCAGTCAAGCTATAAGTCGTCGCAAAGATATTCTCTACATTCAGTTCTTCTAATGTTTTTGTAAATGCTGTGCTTCCACTTGCAGTTGTTGTATATGAAACCGTCAAAGTTCCAACTCCTGAATAATCAACCGGTTGGGGAGCATCATAGATAATCTCGCCTGCTTTCTCTGTTGCTACCTCATAATAAGCAGTCTTCAGATAATAACTAGACTGTATCACTGCGATATAATCAGAATTAACATTTGTCGGAGTCAATCCTTCTTTACCTTCCGCATCTTTTGTACTTACAATATTCAAAGATATCGCATGGCTCTTTTCGCTTGAAGTTGTTAATGATACTGTAAGAACACCAGCTTCACAGTTCTTCACTTCTACGGCAAACGGTTCAGCAGCACGAGTCCAGTTACGTTCTTCAGCATTTAACTTGATTTCGTAGTTTTTATTGAAATCCTCCAATGTCATAGCAGCAGAAGCCGGAGAAATACGGAATTGCAATTCTTTTGCATCTGCACTCTTCGCTGCAACAACATAAGAAGATGAGGTTGAAGTTTTCTTAGCATACAAAGTATAGAAATCTACGCTACGATCTATGCTTGTAGGAATATATACTACACTCTGAATCATACCTTTAATAGCAACACCCAGTTTTGCAAGGTCATCTTCTAACGTATCTATACGGCTATTTACAGTCGTCAGTTCTGCTGAAACAATCGCCTTGACTTTTTCACTGAATTCTGCGCTTGCATAAAGACCGGAAAGAGTCGTTTTAAGAGATTTCACCTCTGCAATAACGGCTTCTACACTCTCGAAACCATTTTCTACCGCTTTATCTTCCAATGCTGTTTGATAGTCTTCCAGCGCAGTCAGTTTAGCAAGGATACCAGTTTTATCAGCATTAATAGCAGCATACAATTCTTTAATAACATCTGCTACAGTTGCATCCTCTCCGGCAAGAGCAGCAACTTCCTTCATACTGCCAAAGTCTGCAAGAACCTTTGTTTGAATAGCATCATTTATTGCAGCGGCAATCTCCCCATCTTCTCCCAAAGCTGTGGCAATTTCATCACTGATAAGTCCTGACAGTTTTGCAGATTCAACGTAGTTCTTCAGCTCTTCTTTTGTAGCGAAATTGACTTTTAAACCATCCAATTCCTCTTTAAGGGCTTCCAGCTTTACAATTCTTGCTGTCAAACCATCAATATCAGACTTTTGAAGTTTCTTCAGTGCTTCTAAATCATTCTTAGCTGTTGCCAAGTCTTTTGTCAGCTGGTCGACCGTTGCCTTATCAGCAGTCTTTGACAATTTGTCCTCCAAATCAGTAATTTTCAGACCCAAATCTTTAATTTGAGAATCCTTATTCTCCAATTTTGTCTTTAAATCAGCCAACTGACTTTCCAAAGCACTTTTAGCCACATTAATGGCAGTTTGCATGTCTCCCACGCTTACAGGATTGTTTGACTTGATCTGGTCAATTTGTTCCTGTACACTTTTTATGTCATCATCATAGTCCTTACAACCTACATAGGTGACAGTAGAAAGCGCCAACGCCCCGAAGAACATCACTTTTACAAATTTTCTTTTCATAACTACTTAAAAATTACATTAATAATATATTATTAAACACTAAGTTTCAATAATAGCACAACCCGTTACTCTGTAGGTAACAGAGAAACCTTTGTGCCTGCTCTTCTGGAGTAGTCGATACTGAAAAATGATACCAAATGTGATGAAATTTTTTTACCCCTGCTTCTGAAAGGCAGTAGAGGGGAAGGAAATCTTTTGCAAAATTTAGAAGTTAACAAAGCCCTAATACAAAGAATCCGCGTGTTTTATTTGTCCTAGTTCTACTTTTGAATGCTTTATTAGAAAAAATGTGTGATTTCGTTTGGTTGTTTTAAGAAATATGCTCATATTTGCACCGATATTCATTCCCGTTACCTACAGAGTAACAGAATAATTTTCAAGCACTTACACCTGTCACTGAACGCTTTACAAAATCGAGAACTTGCTTATTTGCTTCATCAATTTTCTTATTCCTGAAAGGCTTCAGATAAGTTTCAGTGACGGTGATAGACGAATGTCCCATCGCTTCGGAAATAATACCCGGATGGATTTCACAATAATAAGCCGTCGTAGCCCAGGTATGACGGGCAGTGTATGAACTAAGTTTATCTCCCAATCCGAGCAATTCACCCAATAACATCAGCTGTTGGTTAAAACTACGTAATGCAAGCTGATATTCGCGATAGGCTTCTTTAGTTCCTTCCCGACTTCTCAACAATGGAAATAAATAAGGAGAAGAAGAATCACGATTCATGTATTTCTTTACCAAAGCCATTGCTTCCGGAGTCAATGTCACCGACAATGGTCGCCCGGTTTTACGTCTGCGATACGTTATTACATTACCGCGCAAATCACTCTTACGCAAATAAGCAAGATCGACAAACGGCATACCACGAAGCGAAAACATCAGGATGAATAGCTCCTGCGCCTGATATACCGCTAATGGTACACCCGATGCACGAGATAATTTAGTAAACACCTTCTTCATATCGTCATCACCCAACGCACGTTTATGGTCAGCCCGCGTTCCGGTATAAACAGAACGAAATAAATGAGGAACATAAGGAGCCTTCTGAAGATCGACCGCACGATTGTAAACCGCACGAAAGGTCCGTAAATAGGTGGAAACGGTGTTCCAACTACATCCACGACTGCGAAGATATACTTCGAACCCTTTCAACCACTCCGAAGTCACTTCACTAAAAACAAAATCATTTTTCCCACGATAAGCAATGATGGCATTCAGACTACTACGATAGACATGTGCAGTCCCAAAGTTTCCCTCCATCTGTAACCCAGTGGCTACCTGCTTCATAAATGCTAGTACTTTCAACATCTCATAATTTATTAATAATTTAGACGATAAATATACGAGTATTATCCCATCAATGGAAATACCCCGCATGAACAAACAAAGGAAATAGCTAAGATGTTCCAAAGACAAAAGAGCACGAAAAGCCCGGACTTTCACAAGCCCGGGCTTTCTATACTCTCAAAACATCTCCTTAATCTATAGGGAAATAAGGGACTAGCAAATTGGTTCTAGTGCAAATAAGAAGATGTAATGAGACAAAAAAAGGGATACCTGTGGGAAATAAGGGAGGTATCCCTTCCAAGAGACAAACATTTGAGAAAACATACTTTCACAAGCATAATTTTCAGTCTATCTCTTAGCATATTTCTTTTTAAAGGATTTATTATCCTATATTACAACTTGTAAAGAGAAGGCATCTTAAGTCTAAGAAGAATTAGGATGCCCTCAATTACAAATCCTTTAATCCTGATTACTTATTTACTTTTACGTAGATTGATACAGATTTAGTTCTGCCCCACTTATCAAGAATATTAACATTTACTTTACAAGTAGGAGGAGTTAGGATTACGGCACCCTCCTTCTTAGATATAACAACTTTCTTTGGATCATCAGTAAGATTACCATCGTTCTTCGCCAAATAAGTTGTAGCGTCATCATCTGCTAATACAACTGAAACAGACTCTATTCTGTTATCGCTGAATTCATAGGAATTACCACGAGCATCTATTCTCTTGAAATCTTTAGCTGATATTTCTACAGTATTTCCTTCGATTATGAGCGGATTTGCTTCTGTTCCTATAATTTTCTTATCTACTTCTTTGGTATATTCGAAAGTACCTTCAAAGATTTCTGATAGTATTGTCAAATTGAATCTATCAGTAATAGCTTTCAAACGAGAATTTCCGAATGGTGCATAAGACACAGTAATATAACGACCAGTATAAGCACCACCCCAATTTTCATCAGTTGCTGGCTTAGAAACATAAGGGAAAACTTTAATTTTTGAATTCTTTGTTGGTTGTGGGAGAGTCTCATCCAACCATGCAGGAGCAAACTGCTTTTTACCACTTACCACTCTAGAAGGTTTCTCTTCAGTGAATGTAATAGTATTCTGTTTATCCGCTGATATAGAACCTTCTTTATATAGGGTGTAAAGATCGAAGCCAATCTTTGTATCAGCAGTAGCAGCAAGAACTGCAGTAGTCGGTACTGTACCATAAGCCTTGGCATTATCACCATCAAAGTAAGCAGTTGCACGTTTAAATGTGAACAATTTATCATTCTGCTGATCTACAGTAATCTTGAATGTAGTTTCATACAAAAAGTCATTACCCAAACCTGCTTGGGAATCCTTATTTTTGAATTTAACTGTAGCAGTATGAGATACGGCAGCCACATTTTCTTCGTCAAATGTAGCTTGGATTACAAATTTAGTAATTTTAGCTATATCAGCTTCACTTGTTCCTGATACAGGCTTTCCGTCTTTATCCAGACCTACCAATTTTAACTGAATACTACCATCCATATAAGACTGAACCACCTTTCCATTGATCATTGCCTTATCATCAGTATAAGCAATAGTCGCAACTATTGGGTCTGACAGTTTAATTTGTCCTTTAATCTCATCTACTGAAGGACCTACTATTTCAGAAGTAGCTGTCTTATTGCTAGCATCTACGACCCAAGTCATATCTTTGATTTCAGCTTTCTTAGACACATAAGTAAACGTCAATTCAACTTCAGGATCTTGAGTACTTCCATCAACAAGAAGACAGTGAATCTTAACTTTCACCTGACCCTCCTTATTTGCTAGAATTGTATTCTTCTCTTTATCAAATTTTGCATCAGCTTTTTTAGCTTCTTCATCAGTTACCTCATAATAATAAGCTACCACCTTATCCAATTCACTACCAAACAATTCATCCAAATTATAGGTAGTTTGGTAAGGCATAGGTTCAGGAGCAGTAAAATTAACATCCGGTATATTTTGGCTGCTAGCCTTAATTTTCACACCATACTGGGAAATGATTTCATTACCCCATGCATCTTTTGTTGTAAGCGCATAAGCAGTTTCAGCACTTTCCAAAGCTGTCAATTCGTCCTTAGTCAATCCGTCTACAAACTTCAGAGTCAAATCGTATACACCTTTATTTGCTGTAGGCTCTTTTTCCGGATCCGCTGCACGAGTCAAAGCATCTATACTAAAATTATCTGCTATATTTGCAACAGCAAAATTAGTATTACCCTTAGAGTCCGTCAAGCCTATTTCATAAGCTTGAATATCTGCTGCTTTCAAGTTTACAGGATTAATCAATGCATGAATCTTGCTGCCACCTCTAGCAACAAGCAAATCCCCTTTCTTCTTGAAAGTTTTGTTTCCATTAAACACAATATCTTTGCCAGCTATGCTATAATATAAAGTAGTTTCAGCCTCTGTAGAACCAACATCAACAGAACCATCGCCTTTTATACCAAGCACACTCATACTAGTGATACGAGCCGATTTCGGCATAGAAACCGTTTCCCATTCTTTAGTCTCCTTATTGAAAATATTCAATATCCAAACCAATGGATTACTAGCATCAGCAGCTACATAAATATCAGTAGCAGCTTTAATTTTTGTCTCTACAGGTTTTTTAGTTTCAGCATCCCAAACCATCCAGAATCCATTTTCTGAAATTTGAGGTGCATATCCGTCTTCTCCTTTACCACCCGGAGATCCTGCAGCGCCCGGTTTGCCATCAGCACCTTTCTTTCCTTCTGCACAAATACCAAGATCATTATCATTGACAATCCAGTTCTTTGTTACCGGATCAATCTTAATAGTCGTTGGTGTAGCATCCTTACCGCTTGTAATAGAATAACTTTTATTATCATTAAAAGTTATCTTAAATCCATCGGTAATTTGTTCAACATTGGTAACCCATTTACCTTCTTTCACAAAATTCTGCAACTCGGCTATACTAGCTTTGTTTGCGTCAATCTGTGTTTGCAGATTGTCGATGTCATCATCGTAGTCTTTACAGCCTACATAGGTGACAGTAGAAAGTGCCAGCGCCCCGAAGAACATCACTTTTACAAATTTTCTTTTCATAACTACTTAAAAATTACATTAATAATATATTATTAAACACTAAGTTTCAATAATAGACTGCCCGTTACTTTGTAGGTAACGGAGAAACCTTTGCGCCTGCTCTTCGGGAGTAGTCAATACTGAAAAATAATACGATATGCAATGAAACTTTTTTACCCCTGCTTCTGAAAGGCAGTAGAGGGAAGGAGATCTTTTGCAAAAATTTAGAAGTTAACAAAGCCCTAATACAAAGAATCCGCGTGTTTTATTTGGTTAATTCCTGTTTTGAGTAGTTTATTAGAAAAAATGTATGATTTCGTTTGGTTGTTTTAAGAAATATGCTCATATTTGCACCGATAT\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZVa-6SiY4Zd"
      },
      "source": [
        "This output is divided into the ID of the contig and some additional information. `flag` denotes the contig's shape in the contig graph `flag=1` denotes the contig is standalone, `flag=2` denotes a looped path, and `flag=0` is for all other contigs. `multi` is an approximation of the coverage of the contig (how many reads cover each base on average). And `len`, unsurprisingly, is the length of the contig. Your organism will have a whole bunch of these contigs.\n",
        "\n",
        "## The Genes\n",
        "\n",
        "To map our genome to enzymes we will have to identify the genes encoding for these enzymes. For this, the first step is to identify all of the open reading frames (ORFs), or genes, in the genome. Luckily, this is not super hard for bacterial genomes and we can use [prodigal](https://doi.org/10.1186/1471-2105-11-119).\n",
        "\n",
        "What is this weird `${ASSEMBLY}`? Well this is a variable that will be filled in automatically with the ID of your assembly. This way we don't have to copy and paste the name of the assembly everytime."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZvpMJfBYyFm"
      },
      "source": [
        "!prodigal -p single -i ${ASSEMBLY}.fna \\\n",
        "    -o ${ASSEMBLY}.gff -a ${ASSEMBLY}.faa -q"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1ypzoh6l0dM"
      },
      "source": [
        "This will predict coding regions in the genome and generate a genome feature file (GFF), along with the amino acid sequences for the predicted proteins. This is what we will use to look for enzymes. Can you guess why we are using the protein sequences and not the nucleotide sequences?\n",
        "\n",
        "Let's have a look at the proteins."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujgbNx33Z9Ht",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "148eb2a6-b9d6-4ae0-9e55-24145d99986c"
      },
      "source": [
        "!head ${ASSEMBLY}.faa"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">k141_0_1 # 857 # 2203 # -1 # ID=1_1;partial=00;start_type=ATG;rbs_motif=TAA;rbs_spacer=9bp;gc_cont=0.350\n",
            "MFLEVLAFNDEILLISNSFCNFAIRKQYRFTIAKYTPYIKPKYVTREGTTVLYVRYNYNR\n",
            "TKRTLISTGYSIKPEHWDSKKRWIKRACPNYDEIDACLIRITSKLGEILTYAKINGISPT\n",
            "VDFVLLELKKNREYELRPNRVDIFDALERYITEKALVVSADQIKDYRTLRKHLIAFKEHS\n",
            "SQPITFHNLNLIFYNEFMDYLFYKVIKPDGSVGLLTNSAGKIIRLLKGFVNYQIDKGVIP\n",
            "PIDLKHFKVVEEETDAIYLSEKELSTIHELDLSDDKQLEEIRDVFITGCFTGLRYSDLST\n",
            "LSPEHIDLDNEIINLKQRKVHKAVIIPMIDYVPEILKKYNYDLPKIPRYIFNERVKELGR\n",
            "RAKLKQKIEVVRKKGKEREKRVYEKWEMISSHTCRRSFCTNMYLSGFPAGELMRISGHKS\n",
            "PSAFMRYIKVDNLQAAKRLKELRAKLAR*\n",
            ">k141_0_2 # 2675 # 5071 # 1 # ID=1_2;partial=01;start_type=ATG;rbs_motif=TAGAA;rbs_spacer=7bp;gc_cont=0.385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZ_eNnSBmirU"
      },
      "source": [
        "The FASTA headers now indicate where in the contig the gene starts and ends and gives some more info about the coding region.\n",
        "\n",
        "## The Reconstruction\n",
        "\n",
        "Now we have our proteins and can start building the metabolic model. Let's recall the the basics of metabolic reconstruction and what we'll need.\n",
        "\n",
        "![reconstruction methods](https://github.com/Gibbons-Lab/isb_course_2021/raw/main/docs/models/assets/reconstruction.png)\n",
        "\n",
        "Roughly, we need to run the following steps:\n",
        "\n",
        "1. map the identified proteins to our universal model\n",
        "2. find the model most consistent with the genomic evidence that can also produce biomass \n",
        "3. curate the model with various growth media\n",
        "\n",
        "CARVEME can run all of these steps, but we will need to supply the required input data:\n",
        "\n",
        "1. a list of protein sequences found within the genome\n",
        "2. the specific growth medium we want to gapfill the model on\n",
        "\n",
        "But what media should we use for curation? Well, the isolates all come from the human gut, so having a description of the available nutrients there might be helpful. We've chosen to use a medium based on the standard European diet (i.e. a 'Western diet'). Additionally, these isolates were grown in some medium in the lab in order to pick individual colonies for sequencing. Therefore, we will use the anaerobic M9 medium, which is a fairly rich medium commonly used for picking gut isolates. We have pre-prepared these media for you, so let's get them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfIBOHRPaIy2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2fb02f5f-d701-45f9-b438-7c689979ac4b"
      },
      "source": [
        "from course import gimme_media\n",
        "\n",
        "media = gimme_media()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Okay, downloaded media to <span style=\"color: #008000; text-decoration-color: #008000\">carveme_media.tsv</span>.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Okay, downloaded media to \u001b[32mcarveme_media.tsv\u001b[0m.\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnyyoFKtrhSC"
      },
      "source": [
        "Let's take a look what we got here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a50rIKxEHC0q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "24ab480c-6145-4ce2-e830-39c4f6e0405a"
      },
      "source": [
        "media"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>compound</th>\n",
              "      <th>medium</th>\n",
              "      <th>description</th>\n",
              "      <th>reaction</th>\n",
              "      <th>flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26dap__M</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_26dap__M_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ac</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_ac_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>acgam</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ala__L</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_ala__L_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amp</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_amp_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>nh4</td>\n",
              "      <td>M9[-O2]</td>\n",
              "      <td>M9 minimal medium (anaerobic)</td>\n",
              "      <td>EX_nh4_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>ni2</td>\n",
              "      <td>M9[-O2]</td>\n",
              "      <td>M9 minimal medium (anaerobic)</td>\n",
              "      <td>EX_ni2_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>pi</td>\n",
              "      <td>M9[-O2]</td>\n",
              "      <td>M9 minimal medium (anaerobic)</td>\n",
              "      <td>EX_pi_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>so4</td>\n",
              "      <td>M9[-O2]</td>\n",
              "      <td>M9 minimal medium (anaerobic)</td>\n",
              "      <td>EX_so4_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>zn2</td>\n",
              "      <td>M9[-O2]</td>\n",
              "      <td>M9 minimal medium (anaerobic)</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>145 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     compound            medium  ...       reaction flux\n",
              "0    26dap__M  western_diet_gut  ...  EX_26dap__M_e  0.1\n",
              "1          ac  western_diet_gut  ...        EX_ac_e  0.1\n",
              "2       acgam  western_diet_gut  ...     EX_acgam_e  0.1\n",
              "3      ala__L  western_diet_gut  ...    EX_ala__L_e  0.1\n",
              "4         amp  western_diet_gut  ...       EX_amp_e  0.1\n",
              "..        ...               ...  ...            ...  ...\n",
              "140       nh4           M9[-O2]  ...       EX_nh4_e  0.1\n",
              "141       ni2           M9[-O2]  ...       EX_ni2_e  0.1\n",
              "142        pi           M9[-O2]  ...        EX_pi_e  0.1\n",
              "143       so4           M9[-O2]  ...       EX_so4_e  0.1\n",
              "144       zn2           M9[-O2]  ...       EX_zn2_e  0.1\n",
              "\n",
              "[145 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGMPySIbsBhk"
      },
      "source": [
        "So we actually have two environmental conditions here. One describes the metabolites expected to be present in the human gut after an average European diet has passed through the small intestine. The other is a well-defined anaerobic M9 medium. We also have some flux bounds that limit the maximum uptake per hour for each of these media.\n",
        "\n",
        "So we got our proteins and the growth media. This is all we need to build the reconstruction...right? \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Houston, we have a problem 🚀**\n",
        "\n",
        "Well it turns out we cannot run CARVEME on Google Colab, because it uses the commercial [CPLEX](https://www.ibm.com/analytics/cplex-optimizer) solver from IBM. IBM does provide free licenses to students and faculty through the [Academic Inititative](https://www.ibm.com/academic/faqs/faqs), but we can't add it to the Colab environment due to licensing restrictions 😭 \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "But no worries, we have an awesome robot friend (🤖) that can impersonate CARVEME very well. For instance, the following command looks indistinguishable from the actual command you'd normally run in CARVEME:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6akG9jOr6w6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14b1bff3-9eba-4adb-e272-3e9eab907649"
      },
      "source": [
        "!carve -h"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🤖 I'm not the real \u001b[34mCARVEME\u001b[0m, but will pretend that I am now\u001b[33m...\u001b[0m\n",
            "usage: carve [-h] [--dna | --egg | --refseq] [--diamond-args DIAMOND_ARGS]\n",
            "             [-r] [-o OUTPUT] [-u UNIVERSE | --universe-file UNIVERSE_FILE]\n",
            "             [--cobra | --fbc2] [-n ENSEMBLE] [-g GAPFILL] [-i INIT]\n",
            "             [--mediadb MEDIADB] [-v] [-d] [--soft SOFT] [--hard HARD]\n",
            "             [--reference REFERENCE]\n",
            "             INPUT [INPUT ...]\n",
            "\n",
            "Reconstruct a metabolic model using CarveMe\n",
            "\n",
            "positional arguments:\n",
            "  INPUT                 Input (protein fasta file by default, see other options for details).\n",
            "                        When used with -r an input pattern with wildcards can also be used.\n",
            "                        When used with --refseq an NCBI RefSeq assembly accession is expected.\n",
            "\n",
            "optional arguments:\n",
            "  -h, --help            show this help message and exit\n",
            "  --dna                 Build from DNA fasta file\n",
            "  --egg                 Build from eggNOG-mapper output file\n",
            "  --refseq              Download genome from NCBI RefSeq and build\n",
            "  --diamond-args DIAMOND_ARGS\n",
            "                        Additional arguments for running diamond\n",
            "  -r, --recursive       Bulk reconstruction from folder with genome files\n",
            "  -o OUTPUT, --output OUTPUT\n",
            "                        SBML output file (or output folder if -r is used)\n",
            "  -u UNIVERSE, --universe UNIVERSE\n",
            "                        Pre-built universe model (default: bacteria)\n",
            "  --universe-file UNIVERSE_FILE\n",
            "                        Reaction universe file (SBML format)\n",
            "  --cobra               Output SBML in old cobra format\n",
            "  --fbc2                Output SBML in sbml-fbc2 format\n",
            "  -n ENSEMBLE, --ensemble ENSEMBLE\n",
            "                        Build model ensemble with N models\n",
            "  -g GAPFILL, --gapfill GAPFILL\n",
            "                        Gap fill model for given media\n",
            "  -i INIT, --init INIT  Initialize model with given medium\n",
            "  --mediadb MEDIADB     Media database file\n",
            "  -v, --verbose         Switch to verbose mode\n",
            "  -d, --debug           Debug mode: writes intermediate results into output files\n",
            "  --soft SOFT           Soft constraints file\n",
            "  --hard HARD           Hard constraints file\n",
            "  --reference REFERENCE\n",
            "                        Manually curated model of a close reference species.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5diMu84t8BN"
      },
      "source": [
        "So let's use it. We will have to supply the protein FASTA file, the growth media database, and the names of all the growth conditions we want to enable our bacterium to grow on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn_-mRjFtsLd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "925b281a-524e-4dec-fbf3-31cf33f023e7"
      },
      "source": [
        "!carve \\\n",
        "    --mediadb carveme_media.tsv --gapfill \"western_diet_gut,M9[-O2]\" \\\n",
        "    --fbc -o ${ASSEMBLY}.xml ${ASSEMBLY}.faa "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🤖 I'm not the real \u001b[34mCARVEME\u001b[0m, but will pretend that I am now\u001b[33m...\u001b[0m\n",
            "Running diamond\u001b[33m...\u001b[0m\n",
            "diamond blastp -d bigg_proteins.dmnd -q SRR9205608.asm1.faa -o \n",
            "SRR9205608.asm1.tsv -p \u001b[1;36m2\u001b[0m\n",
            "Loading universe model\u001b[33m...\u001b[0m\n",
            "Loading media library\u001b[33m...\u001b[0m\n",
            "Scoring reactions\u001b[33m...\u001b[0m\n",
            "Reconstructing a single model\n",
            "Gap filling for western_diet_gut, M9\u001b[1m[\u001b[0m-O2\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m\n",
            "Added \u001b[1;36m0\u001b[0m reactions and \u001b[1;36m0\u001b[0m metabolites\n",
            "Done.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWlYNlMa1fc4"
      },
      "source": [
        "Our CARVEME robot did a great job! Looks just like the real deal. And you know, if it walks like a 🦆 and talks like a 🦆...\n",
        "\n",
        "This generated a file ending in `.xml` which is an SBML file. [SBML](https://sbml.org) is the community standard for sharing metabolic models. And while we are talking about community standards let's also look into model checking a bit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9iQxKKNna6z"
      },
      "source": [
        "## The Model Check\n",
        "\n",
        "We talked a bit about model curation and validation and learned that the best way to test a model is using real-world data. But, believe us, there are **plenty** of ways the model can be wrong even before you compare it to data. \n",
        "\n",
        "This has been known in the metabolic modeling community for quite some time, and has been discussed extensively [here](https://www.embopress.org/doi/full/10.15252/msb.20156157) and [here](https://doi.org/10.1038/s41587-020-0446-y), for example. \n",
        "\n",
        "Luckily, based on this prior feedback from the community, there is now a nice tool called [MEMOTE](https://memote.io), which allows you to upload an SBML model and run it through a whole battery of checks and tests. If we were to do this during the course, we would spend most of our time waiting around, so for now, we have supplied you with the pre-computed MEMOTE report for your reconstruction. To find it, look through the files on the left (📁) and download the file ending in `.html`. You can open it with any web browser and each section has a detailed explanation of what was tested and why. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AjEfZYGptgr"
      },
      "source": [
        "# 💻 From Model to Fluxes\n",
        "\n",
        "Okay, now we have our metabolic reconstruction, so let's finally get to some actual metabolic modeling! We will make use of the [COBRAPY](https://opencobra.github.io/cobrapy/) package, which allows us to run a bunch of different analyses and simulations with our metabolic model.\n",
        "\n",
        "COPRAPY is already installed in your notebook. If you need help with COBRAPY in the future, you can check out [its documentation](https://cobrapy.readthedocs.io/en/latest/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDDbGUtprZUM"
      },
      "source": [
        "## Metabolic models and COBRAPY\n",
        "\n",
        "Before we actually start simulating the model let's have a look at it. We have learned that FBA builds on top of a lot of inferred information. So let's try to find this in the model we built. Here a short overview what ingredients we need to cook up an FBA result:\n",
        "\n",
        "![flux balance analysis](https://github.com/Gibbons-Lab/isb_course_2021/raw/main/docs/models/assets/fba.png)\n",
        "\n",
        "The first thing we have to to is to actually read the model into memory, and maybe inspect it a bit to see what is going on. **Before running the code cell, substitute `[YOUR ASSEMBLY]` with the ID of your assembly (including the `.asm1` part).** You can find the file name ending in .xml on the left (📁)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zm0N7BvXUthu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "outputId": "284c0feb-3605-4285-f816-7bdffcc84702"
      },
      "source": [
        "from cobra.io import read_sbml_model\n",
        "\n",
        "model = read_sbml_model(\"SRR9205608.asm1.xml\")\n",
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <table>\n",
              "            <tr>\n",
              "                <td><strong>Name</strong></td>\n",
              "                <td>SRR9205608_asm1_xml</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Memory address</strong></td>\n",
              "                <td>0x07fbfa6011190</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of metabolites</strong></td>\n",
              "                <td>1500</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of reactions</strong></td>\n",
              "                <td>2488</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Number of groups</strong></td>\n",
              "                <td>0</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Objective expression</strong></td>\n",
              "                <td>1.0*Growth - 1.0*Growth_reverse_699ae</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Compartments</strong></td>\n",
              "                <td>cytosol, periplasm, extracellular space</td>\n",
              "            </tr>\n",
              "          </table>"
            ],
            "text/plain": [
              "<Model SRR9205608_asm1_xml at 0x7fbfa6011190>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXHsl3zdsJv6"
      },
      "source": [
        "Okay, this gives us some very basic info about the model, like the number of metabolites and reactions, and the model objective, which in our case is growth. The model has accessions for metabolites and reactions, which can be used to look up a whole bunch of information and annotations. \n",
        "\n",
        "The objective is described, a bit opaquely, as growth. We talked a bit before about the biomass composition and we can have a look at this by taking a peak at the biomass reaction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzSjdmaAhsOa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "fab690d0-f000-462d-ce28-46a23ad47e3a"
      },
      "source": [
        "model.reactions.Growth.reaction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.000223 10fthf_c + 0.513689 ala__L_c + 0.000223 amet_c + 0.295792 arg__L_c + 0.241055 asn__L_c + 0.241055 asp__L_c + 54.124831 atp_c + 0.005205 ca2_c + 0.005205 cl_c + 0.000576 coa_c + 0.0001 cobalt2_c + 0.133508 ctp_c + 0.000709 cu2_c + 0.09158 cys__L_c + 0.026166 datp_c + 0.027017 dctp_c + 0.027017 dgtp_c + 0.026166 dttp_c + 0.000223 fad_c + 0.006715 fe2_c + 0.007808 fe3_c + 0.26316 gln__L_c + 0.26316 glu__L_c + 0.612638 gly_c + 0.215096 gtp_c + 48.601527 h2o_c + 0.094738 his__L_c + 0.290529 ile__L_c + 0.195193 k_c + 0.450531 leu__L_c + 0.343161 lys__L_c + 0.153686 met__L_c + 0.008675 mg2_c + 0.000223 mlthf_c + 0.000691 mn2_c + 0.0001 mql8_c + 0.001831 nad_c + 0.000447 nadp_c + 0.185265 phe__L_c + 0.221055 pro__L_c + 0.000223 pydx5p_c + 0.000223 ribflv_c + 0.215792 ser__L_c + 0.004338 so4_c + 0.000223 thf_c + 0.000223 thmpp_c + 0.253687 thr__L_c + 0.056843 trp__L_c + 0.137896 tyr__L_c + 0.1 uaagmda_c + 0.144104 utp_c + 0.423162 val__L_c + 0.000341 zn2_c --> 53.95 adp_c + 53.95 h_c + 53.945662 pi_c + 0.773903 ppi_c'"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOfC6dl9iL-E"
      },
      "source": [
        "This looks like what we'd expect. From the previous MEMOTE report, we know that the total consumed mass here should add up to something around 1gDW (± 0.05gDW). You can also see that the reactions are balanced. For instance, the consumed ATP is converted into ADP. \n",
        "\n",
        "The short names can be a bit cryptic. There are fairly human-readable here, but if we had used gapseq or ModelSEED, these features might have looked like `cpd00027_c`. Let's get some more info on, for instance, the 42nd metabolite in the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "debRN0JgsC81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "a861f737-a2a7-4f41-e1e8-bf80ae6cec9e"
      },
      "source": [
        "model.metabolites[42]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <table>\n",
              "            <tr>\n",
              "                <td><strong>Metabolite identifier</strong></td><td>25drapp_c</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Name</strong></td><td>...</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Memory address</strong></td>\n",
              "                <td>0x07fbfa5fabe90</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Formula</strong></td><td>C9H14N5O8P</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Compartment</strong></td><td>C_c</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>In 2 reaction(s)</strong></td><td>\n",
              "                    DHPPDA2, GTPCII2</td>\n",
              "            </tr>\n",
              "        </table>"
            ],
            "text/plain": [
              "<Metabolite 25drapp_c at 0x7fbfa5fabe90>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eP6Qa4lpjZn7"
      },
      "source": [
        "Okay, this is much more informative. You get the full name of the metabolite, the molecular formula, and the reactions in the model that include this metabolite. The same kind of output can be generated for individual reactions in the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuQ2SOPfsmoG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cea8aa3e-dccd-4bcb-95e6-e17dbcdee797"
      },
      "source": [
        "model.reactions[42]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <table>\n",
              "            <tr>\n",
              "                <td><strong>Reaction identifier</strong></td><td>2S6HCCi</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Name</strong></td><td>2 succinyl 6 hydroxy 2 4 cyclohexadiene 1 carboxylate synthase</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Memory address</strong></td>\n",
              "                <td>0x07fbfa5632990</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Stoichiometry</strong></td>\n",
              "                <td>\n",
              "                    <p style='text-align:right'>akg_c + h_c + ichor_c <=> 2shchc_c + co2_c + pyr_c</p>\n",
              "                    <p style='text-align:right'>2-Oxoglutarate + H+ + Isochorismate <=> 2-Succinyl-6-hydroxy-2,4-cyclohexadiene-1-carboxylate + CO2 CO2 + Pyruvate</p>\n",
              "                </td>\n",
              "            </tr><tr>\n",
              "                <td><strong>GPR</strong></td><td>k141_43_172</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Lower bound</strong></td><td>-1000.0</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Upper bound</strong></td><td>1000.0</td>\n",
              "            </tr>\n",
              "        </table>\n",
              "        "
            ],
            "text/plain": [
              "<Reaction 2S6HCCi at 0x7fbfa5632990>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJeyrFUxs-4l"
      },
      "source": [
        "This gives us a lot of information about the reaction, including the substrates, products, reversibility, and bounds. The GPR is the *Gene-Protein-Reaction* rule and it states under what conditions the enzyme is functional.\n",
        "\n",
        "Do those `k*_*_*` look familiar? Exactly, those come from our assembly and gene predicton output. For instance, `k79_237_3` indicates the 3rd gene from the contig named k79_237. So we can track our contigs and genes all the way through the metabolic model.\n",
        "\n",
        "If you want more information about a reaction you can also check out the annotations (see below)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkFvkFNJsyKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49bfc550-c5cd-44b1-9f34-ebb983fb9720"
      },
      "source": [
        "model.reactions[42].annotation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ec-code': ['2.2.1.9', '2.5.1.64', '4.1.1.71', '4.1.3.-', '4.2.99.20'],\n",
              " 'metanetx.reaction': 'MNXR134758',\n",
              " 'sbo': 'SBO:0000176',\n",
              " 'seed.reaction': 'rxn04675'}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xavKRjIRtJtj"
      },
      "source": [
        "This will give you feature IDs for other databases, if available, which can be super-helpful when trying to map results back to experimental data. This is why the presence of sufficient annotations is a major criterion for MEMOTE scoring. \n",
        "\n",
        "## Flux Balance Analysis\n",
        "\n",
        "Okay, can we finally get to an actual simulation, please? 🥺 Yes! Running an FBA here is really simple. Just call `optimize()` on your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndbuS4ohtbXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "2c5af694-6333-4404-d8f6-ed83605a80dc"
      },
      "source": [
        "fba_solution = model.optimize()\n",
        "fba_solution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<strong><em>Optimal</em> solution with objective value 69.341</strong><br><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>fluxes</th>\n",
              "      <th>reduced_costs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12DGR120tipp</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.087704e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12DGR140tipp</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12DGR160tipp</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12DGR161tipp</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-4.894667e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12DGR180tipp</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_xylan8_e</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_xylb_e</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_zn2_e</th>\n",
              "      <td>-0.023645</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Growth</th>\n",
              "      <td>69.341109</td>\n",
              "      <td>4.093914e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ATPM</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.263111e-02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2488 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "<Solution 69.341 at 0x7fbfa3dfc710>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei3iO5eeuiPA"
      },
      "source": [
        "There is a lot of stuff going on under the hood here, so it's kind of cool that it runs so fast. \n",
        "\n",
        "Here you see the predicted growth rate and the fluxes. We won't spend too much time on the reduced costs here, but they are metrics that can be helpful sometimes. The two things to know about reduced costs are: \n",
        "\n",
        "1. They are only different from zero if the flux is zero.\n",
        "2. They quantify by how much the growth rate would be reduced if we force a flux through this reaction.\n",
        "\n",
        "So they are kind of the cost in growth rate the organism would have to pay to get flux through that previously shut-off reaction.\n",
        "\n",
        "This output may be a bit complex. For any solution we can also get a summary output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UA7rK6E0uVXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2faaead8-e73f-4b24-8cbb-081c8bdd7e64"
      },
      "source": [
        "model.summary(fba_solution)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<h3>Objective</h3><p>1.0 Growth = 69.34110900781552</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>3amp_e</td>\n",
              "      <td>EX_3amp_e</td>\n",
              "      <td>14.17</td>\n",
              "      <td>10</td>\n",
              "      <td>1.05%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3cmp_e</td>\n",
              "      <td>EX_3cmp_e</td>\n",
              "      <td>11.13</td>\n",
              "      <td>9</td>\n",
              "      <td>0.74%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>acald_e</td>\n",
              "      <td>EX_acald_e</td>\n",
              "      <td>1000</td>\n",
              "      <td>2</td>\n",
              "      <td>14.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>arg__L_e</td>\n",
              "      <td>EX_arg__L_e</td>\n",
              "      <td>20.51</td>\n",
              "      <td>6</td>\n",
              "      <td>0.91%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>asn__L_e</td>\n",
              "      <td>EX_asn__L_e</td>\n",
              "      <td>16.72</td>\n",
              "      <td>4</td>\n",
              "      <td>0.50%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ca2_e</td>\n",
              "      <td>EX_ca2_e</td>\n",
              "      <td>0.3609</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cl_e</td>\n",
              "      <td>EX_cl_e</td>\n",
              "      <td>0.3609</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cobalt2_e</td>\n",
              "      <td>EX_cobalt2_e</td>\n",
              "      <td>0.006934</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cu2_e</td>\n",
              "      <td>EX_cu2_e</td>\n",
              "      <td>0.04916</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>dgmp_e</td>\n",
              "      <td>EX_dgmp_e</td>\n",
              "      <td>1.873</td>\n",
              "      <td>10</td>\n",
              "      <td>0.14%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>dxyl_e</td>\n",
              "      <td>EX_dxyl_e</td>\n",
              "      <td>76.35</td>\n",
              "      <td>5</td>\n",
              "      <td>2.83%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe2_e</td>\n",
              "      <td>EX_fe2_e</td>\n",
              "      <td>0.4656</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe3_e</td>\n",
              "      <td>EX_fe3_e</td>\n",
              "      <td>0.5414</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fol_e</td>\n",
              "      <td>EX_fol_e</td>\n",
              "      <td>0.04639</td>\n",
              "      <td>19</td>\n",
              "      <td>0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>g3pg_e</td>\n",
              "      <td>EX_g3pg_e</td>\n",
              "      <td>1000</td>\n",
              "      <td>6</td>\n",
              "      <td>44.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gln__L_e</td>\n",
              "      <td>EX_gln__L_e</td>\n",
              "      <td>320.5</td>\n",
              "      <td>5</td>\n",
              "      <td>11.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gua_e</td>\n",
              "      <td>EX_gua_e</td>\n",
              "      <td>14.91</td>\n",
              "      <td>5</td>\n",
              "      <td>0.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2_e</td>\n",
              "      <td>EX_h2_e</td>\n",
              "      <td>302.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2o2_e</td>\n",
              "      <td>EX_h2o2_e</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>his__L_e</td>\n",
              "      <td>EX_his__L_e</td>\n",
              "      <td>6.569</td>\n",
              "      <td>6</td>\n",
              "      <td>0.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>hxa_e</td>\n",
              "      <td>EX_hxa_e</td>\n",
              "      <td>60.62</td>\n",
              "      <td>6</td>\n",
              "      <td>2.70%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ile__L_e</td>\n",
              "      <td>EX_ile__L_e</td>\n",
              "      <td>20.14</td>\n",
              "      <td>6</td>\n",
              "      <td>0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>indole_e</td>\n",
              "      <td>EX_indole_e</td>\n",
              "      <td>3.942</td>\n",
              "      <td>8</td>\n",
              "      <td>0.23%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>k_e</td>\n",
              "      <td>EX_k_e</td>\n",
              "      <td>13.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lys__L_e</td>\n",
              "      <td>EX_lys__L_e</td>\n",
              "      <td>23.8</td>\n",
              "      <td>6</td>\n",
              "      <td>1.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>malthx_e</td>\n",
              "      <td>EX_malthx_e</td>\n",
              "      <td>6.821</td>\n",
              "      <td>36</td>\n",
              "      <td>1.82%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>meoh_e</td>\n",
              "      <td>EX_meoh_e</td>\n",
              "      <td>0.03994</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>met__L_e</td>\n",
              "      <td>EX_met__L_e</td>\n",
              "      <td>10.68</td>\n",
              "      <td>5</td>\n",
              "      <td>0.40%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mg2_e</td>\n",
              "      <td>EX_mg2_e</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mn2_e</td>\n",
              "      <td>EX_mn2_e</td>\n",
              "      <td>0.04791</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>no3_e</td>\n",
              "      <td>EX_no3_e</td>\n",
              "      <td>322</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>o2_e</td>\n",
              "      <td>EX_o2_e</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>phe__L_e</td>\n",
              "      <td>EX_phe__L_e</td>\n",
              "      <td>12.85</td>\n",
              "      <td>9</td>\n",
              "      <td>0.86%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ppi_e</td>\n",
              "      <td>EX_ppi_e</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pro__L_e</td>\n",
              "      <td>EX_pro__L_e</td>\n",
              "      <td>15.33</td>\n",
              "      <td>5</td>\n",
              "      <td>0.57%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ribflv_e</td>\n",
              "      <td>EX_ribflv_e</td>\n",
              "      <td>0.03093</td>\n",
              "      <td>17</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>s_e</td>\n",
              "      <td>EX_s_e</td>\n",
              "      <td>229.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ser__D_e</td>\n",
              "      <td>EX_ser__D_e</td>\n",
              "      <td>537.1</td>\n",
              "      <td>3</td>\n",
              "      <td>11.96%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>so4_e</td>\n",
              "      <td>EX_so4_e</td>\n",
              "      <td>0.3008</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thm_e</td>\n",
              "      <td>EX_thm_e</td>\n",
              "      <td>0.01546</td>\n",
              "      <td>12</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thr__L_e</td>\n",
              "      <td>EX_thr__L_e</td>\n",
              "      <td>17.59</td>\n",
              "      <td>4</td>\n",
              "      <td>0.52%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>tyr__L_e</td>\n",
              "      <td>EX_tyr__L_e</td>\n",
              "      <td>9.562</td>\n",
              "      <td>9</td>\n",
              "      <td>0.64%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>zn2_e</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>0.02365</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>2mpa_e</td>\n",
              "      <td>EX_2mpa_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>4</td>\n",
              "      <td>38.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>co2_e</td>\n",
              "      <td>EX_co2_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>1</td>\n",
              "      <td>9.52%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glyc_e</td>\n",
              "      <td>EX_glyc_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>3</td>\n",
              "      <td>28.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glyclt_e</td>\n",
              "      <td>EX_glyclt_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>2</td>\n",
              "      <td>19.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2s_e</td>\n",
              "      <td>EX_h2s_e</td>\n",
              "      <td>-223.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h_e</td>\n",
              "      <td>EX_h_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lac__L_e</td>\n",
              "      <td>EX_lac__L_e</td>\n",
              "      <td>-169.9</td>\n",
              "      <td>3</td>\n",
              "      <td>4.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>nh4_e</td>\n",
              "      <td>EX_nh4_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>no2_e</td>\n",
              "      <td>EX_no2_e</td>\n",
              "      <td>-185.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pi_e</td>\n",
              "      <td>EX_pi_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<cobra.summary.model_summary.ModelSummary at 0x7fbfa3da3ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG1NNm9PxAs3"
      },
      "source": [
        "This is still fairly long, but definitely looks nicer. We now see what your bacterium eats (uptake) and what proportion of the total carbon influx comes from that particular uptake (C-Flux). We get the same output for secretion. \n",
        "\n",
        "What is the major carbon source in for your organism in this simulation? \n",
        "\n",
        "Does the organism secrete carbon-rich metabolites? If yes, which ones?\n",
        "\n",
        "<br>\n",
        "\n",
        "---\n",
        "\n",
        "**Break time ☕  🥪** \n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmfv6xayBVxD"
      },
      "source": [
        "# 🌍 Comparative Reverse Ecology\n",
        "\n",
        "Why are we interested in those uptake fluxes? Well, they are the recipe for an organism's growth, and thus, related to its evolutionary history (i.e. to the environmental conditions that it has adapted to live in). They are not solely what is present in the organism's surroundings, but the actual consumption preferences of that organism within a particular environment.\n",
        "\n",
        "When we started constructing our models, we had a description of the (metabolic) environment (the available nutrients) and our organism's genome. But now, suddenly, we learn something about how our organism interacts with the environment. And that means we learned something about its **ecology** 🤯\n",
        "\n",
        "Inferring information about an organism's ecology from genomic data is called [Reverse Ecology](https://en.wikipedia.org/wiki/Reverse_ecology). Check these [slides](https://gibbons-lab.github.io/isb_course_2021/models/#/26) for some more discussion on this point.\n",
        "\n",
        "Before we get there, however, we need to curate our model a bit..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvdGPq8uz-_t"
      },
      "source": [
        "## Getting realistic: PFBA and media\n",
        "\n",
        "As we noted, the particular flux solutions are often not unique and there may be many other solutions. To alleviate this let's use parsimonious FBA (pFBA), which minimizes the total import flux and the amount of enzymes required for growth.\n",
        "\n",
        "![parsimonious FBA](https://github.com/Gibbons-Lab/isb_course_2021/raw/main/docs/models/assets/pfba.png)\n",
        "\n",
        "pFBA is part of COBRAPY and will give us a new solution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dc-zLuH92Pju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "680dacaf-b594-494b-dc99-90b8c7d3ca24"
      },
      "source": [
        "from cobra.flux_analysis import pfba\n",
        "\n",
        "pfba_solution = pfba(model)\n",
        "model.summary(pfba_solution)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<h3>Objective</h3><p>1.0 Growth = 69.3411090078155</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>3amp_e</td>\n",
              "      <td>EX_3amp_e</td>\n",
              "      <td>14.17</td>\n",
              "      <td>10</td>\n",
              "      <td>1.05%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3cmp_e</td>\n",
              "      <td>EX_3cmp_e</td>\n",
              "      <td>11.13</td>\n",
              "      <td>9</td>\n",
              "      <td>0.74%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>acald_e</td>\n",
              "      <td>EX_acald_e</td>\n",
              "      <td>1000</td>\n",
              "      <td>2</td>\n",
              "      <td>14.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>arg__L_e</td>\n",
              "      <td>EX_arg__L_e</td>\n",
              "      <td>20.51</td>\n",
              "      <td>6</td>\n",
              "      <td>0.91%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>asn__L_e</td>\n",
              "      <td>EX_asn__L_e</td>\n",
              "      <td>16.72</td>\n",
              "      <td>4</td>\n",
              "      <td>0.50%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ca2_e</td>\n",
              "      <td>EX_ca2_e</td>\n",
              "      <td>0.3609</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cl_e</td>\n",
              "      <td>EX_cl_e</td>\n",
              "      <td>0.3609</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cobalt2_e</td>\n",
              "      <td>EX_cobalt2_e</td>\n",
              "      <td>0.006934</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cu2_e</td>\n",
              "      <td>EX_cu2_e</td>\n",
              "      <td>0.04916</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>dgmp_e</td>\n",
              "      <td>EX_dgmp_e</td>\n",
              "      <td>1.873</td>\n",
              "      <td>10</td>\n",
              "      <td>0.14%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>dxyl_e</td>\n",
              "      <td>EX_dxyl_e</td>\n",
              "      <td>76.35</td>\n",
              "      <td>5</td>\n",
              "      <td>2.83%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe2_e</td>\n",
              "      <td>EX_fe2_e</td>\n",
              "      <td>0.4656</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe3_e</td>\n",
              "      <td>EX_fe3_e</td>\n",
              "      <td>0.5414</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fol_e</td>\n",
              "      <td>EX_fol_e</td>\n",
              "      <td>0.04639</td>\n",
              "      <td>19</td>\n",
              "      <td>0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>g3pg_e</td>\n",
              "      <td>EX_g3pg_e</td>\n",
              "      <td>1000</td>\n",
              "      <td>6</td>\n",
              "      <td>44.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gln__L_e</td>\n",
              "      <td>EX_gln__L_e</td>\n",
              "      <td>320.5</td>\n",
              "      <td>5</td>\n",
              "      <td>11.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gua_e</td>\n",
              "      <td>EX_gua_e</td>\n",
              "      <td>14.91</td>\n",
              "      <td>5</td>\n",
              "      <td>0.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2o2_e</td>\n",
              "      <td>EX_h2o2_e</td>\n",
              "      <td>41.88</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2o_e</td>\n",
              "      <td>EX_h2o_e</td>\n",
              "      <td>206.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>his__L_e</td>\n",
              "      <td>EX_his__L_e</td>\n",
              "      <td>6.569</td>\n",
              "      <td>6</td>\n",
              "      <td>0.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>hxa_e</td>\n",
              "      <td>EX_hxa_e</td>\n",
              "      <td>60.62</td>\n",
              "      <td>6</td>\n",
              "      <td>2.70%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ile__L_e</td>\n",
              "      <td>EX_ile__L_e</td>\n",
              "      <td>20.14</td>\n",
              "      <td>6</td>\n",
              "      <td>0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>indole_e</td>\n",
              "      <td>EX_indole_e</td>\n",
              "      <td>3.942</td>\n",
              "      <td>8</td>\n",
              "      <td>0.23%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>k_e</td>\n",
              "      <td>EX_k_e</td>\n",
              "      <td>13.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lys__L_e</td>\n",
              "      <td>EX_lys__L_e</td>\n",
              "      <td>23.8</td>\n",
              "      <td>6</td>\n",
              "      <td>1.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>malthx_e</td>\n",
              "      <td>EX_malthx_e</td>\n",
              "      <td>6.821</td>\n",
              "      <td>36</td>\n",
              "      <td>1.82%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>meoh_e</td>\n",
              "      <td>EX_meoh_e</td>\n",
              "      <td>0.03994</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>metox__R_e</td>\n",
              "      <td>EX_metox__R_e</td>\n",
              "      <td>10.68</td>\n",
              "      <td>5</td>\n",
              "      <td>0.40%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mg2_e</td>\n",
              "      <td>EX_mg2_e</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mn2_e</td>\n",
              "      <td>EX_mn2_e</td>\n",
              "      <td>0.04791</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>no3_e</td>\n",
              "      <td>EX_no3_e</td>\n",
              "      <td>136.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>o2_e</td>\n",
              "      <td>EX_o2_e</td>\n",
              "      <td>979.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>phe__L_e</td>\n",
              "      <td>EX_phe__L_e</td>\n",
              "      <td>12.85</td>\n",
              "      <td>9</td>\n",
              "      <td>0.86%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ppi_e</td>\n",
              "      <td>EX_ppi_e</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pro__L_e</td>\n",
              "      <td>EX_pro__L_e</td>\n",
              "      <td>15.33</td>\n",
              "      <td>5</td>\n",
              "      <td>0.57%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ribflv_e</td>\n",
              "      <td>EX_ribflv_e</td>\n",
              "      <td>0.03093</td>\n",
              "      <td>17</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>s_e</td>\n",
              "      <td>EX_s_e</td>\n",
              "      <td>101.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ser__D_e</td>\n",
              "      <td>EX_ser__D_e</td>\n",
              "      <td>537.1</td>\n",
              "      <td>3</td>\n",
              "      <td>11.96%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>so4_e</td>\n",
              "      <td>EX_so4_e</td>\n",
              "      <td>0.3008</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thm_e</td>\n",
              "      <td>EX_thm_e</td>\n",
              "      <td>0.01546</td>\n",
              "      <td>12</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thr__L_e</td>\n",
              "      <td>EX_thr__L_e</td>\n",
              "      <td>17.59</td>\n",
              "      <td>4</td>\n",
              "      <td>0.52%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>tyr__L_e</td>\n",
              "      <td>EX_tyr__L_e</td>\n",
              "      <td>9.562</td>\n",
              "      <td>9</td>\n",
              "      <td>0.64%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>zn2_e</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>0.02365</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>2mpa_e</td>\n",
              "      <td>EX_2mpa_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>4</td>\n",
              "      <td>38.06%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>co2_e</td>\n",
              "      <td>EX_co2_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>1</td>\n",
              "      <td>9.52%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glyc_e</td>\n",
              "      <td>EX_glyc_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>3</td>\n",
              "      <td>28.55%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glyclt_e</td>\n",
              "      <td>EX_glyclt_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>2</td>\n",
              "      <td>19.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2s_e</td>\n",
              "      <td>EX_h2s_e</td>\n",
              "      <td>-95.49</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h_e</td>\n",
              "      <td>EX_h_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lac__L_e</td>\n",
              "      <td>EX_lac__L_e</td>\n",
              "      <td>-169.9</td>\n",
              "      <td>3</td>\n",
              "      <td>4.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>nh4_e</td>\n",
              "      <td>EX_nh4_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pi_e</td>\n",
              "      <td>EX_pi_e</td>\n",
              "      <td>-1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<cobra.summary.model_summary.ModelSummary at 0x7fbfa3da3e50>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmTF8uIZ1k9z"
      },
      "source": [
        "We got our first real results -- awesome! But are they realistic? Remember that we stated that the biomass flux is essentially equivalent to the growth rate. We can convert the growth rate $\\mu$ to the doubling time with the formula\n",
        "\n",
        "$$\n",
        "\\tau = \\ln(2) / \\mu\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsVweGSl0JN3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ecd43f7-ce73-4bfa-f33c-71ab7ec9273f"
      },
      "source": [
        "from math import log\n",
        "\n",
        "tau = log(2) / fba_solution.objective_value\n",
        "print(f\"The predicted dubling time is {tau:.2g} hours ({tau*60*60:.2g} seconds).\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The predicted dubling time is 0.01 hours (36 seconds).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFfgwEuc1b9s"
      },
      "source": [
        "That probably looks a bit fast, right? So if you complained that this growth rate was too high in the Slack chat (or to yourself when we ran the first FBA), then you should take a few seconds to find the mirror closest to you and tell yourself that you are, indeed, a smart cookie, because you were absolutely right! No bacterium doubles that fast.\n",
        "\n",
        "But why is it predicted to grow so fast? \n",
        "\n",
        "## The Environment and Nutrient Preferences\n",
        "\n",
        "Well, we did not impose particular environmental conditions yet. So what did the model use? Let's look at the first uptake reaction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXcXT-831KOX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "614b2081-dd80-4d12-e30d-f51fe39a7921"
      },
      "source": [
        "model.exchanges[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <table>\n",
              "            <tr>\n",
              "                <td><strong>Reaction identifier</strong></td><td>EX_12ppd__R_e</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Name</strong></td><td>R_EX_12ppd__R_e</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Memory address</strong></td>\n",
              "                <td>0x07fbfa3e41650</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Stoichiometry</strong></td>\n",
              "                <td>\n",
              "                    <p style='text-align:right'>12ppd__R_e <=></p>\n",
              "                    <p style='text-align:right'>(R)-Propane-1,2-diol <=></p>\n",
              "                </td>\n",
              "            </tr><tr>\n",
              "                <td><strong>GPR</strong></td><td></td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Lower bound</strong></td><td>-1000.0</td>\n",
              "            </tr><tr>\n",
              "                <td><strong>Upper bound</strong></td><td>1000.0</td>\n",
              "            </tr>\n",
              "        </table>\n",
              "        "
            ],
            "text/plain": [
              "<Reaction EX_12ppd__R_e at 0x7fbfa3e41650>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrVULjR623kW"
      },
      "source": [
        "You see that the reaction is formulated into the export reaction (i.e. positive values denote the rate at which the compounds disappears). So, a negative flux denotes import and the lower bound dictates the largest possible import, which is 1000 mmol/(gDW h), in this case. So one mole per hour per gram of bacteria. So about $6\\cdot 10^{23}$ molecules per hour, hmmmmmm 🤨\n",
        "\n",
        "But we gapfilled the model with realistic media! So let's use those this time. Since the gut environment is probably a bit more interesting let's use this one. We will read in the environmental conditions using the `pandas` package and then format it a bit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBBIMkBs2ygl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "bd0c7286-2539-45a9-d96d-5edb5b6fe886"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "media_db = pd.read_csv(\"carveme_media.tsv\", sep=\"\\t\")\n",
        "media_db.index = media_db.reaction\n",
        "western_diet = media_db[media_db.medium == \"western_diet_gut\"]\n",
        "western_diet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>compound</th>\n",
              "      <th>medium</th>\n",
              "      <th>description</th>\n",
              "      <th>reaction</th>\n",
              "      <th>flux</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reaction</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>EX_26dap__M_e</th>\n",
              "      <td>26dap__M</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_26dap__M_e</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ac_e</th>\n",
              "      <td>ac</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_ac_e</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_acgam_e</th>\n",
              "      <td>acgam</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ala__L_e</th>\n",
              "      <td>ala__L</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_ala__L_e</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_amp_e</th>\n",
              "      <td>amp</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_amp_e</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_amylose_e</th>\n",
              "      <td>amylose</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_amylose_e</td>\n",
              "      <td>0.000047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_glc__D_e</th>\n",
              "      <td>glc__D</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_glc__D_e</td>\n",
              "      <td>0.014899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_pect_e</th>\n",
              "      <td>pect</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_pect_e</td>\n",
              "      <td>0.000033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_xylan4_e</th>\n",
              "      <td>xylan4</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_xylan4_e</td>\n",
              "      <td>0.000032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_xylan8_e</th>\n",
              "      <td>xylan8</td>\n",
              "      <td>western_diet_gut</td>\n",
              "      <td>average European diet adjusted for adsorption ...</td>\n",
              "      <td>EX_xylan8_e</td>\n",
              "      <td>0.000032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>126 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               compound            medium  ...       reaction      flux\n",
              "reaction                                   ...                         \n",
              "EX_26dap__M_e  26dap__M  western_diet_gut  ...  EX_26dap__M_e  0.100000\n",
              "EX_ac_e              ac  western_diet_gut  ...        EX_ac_e  0.100000\n",
              "EX_acgam_e        acgam  western_diet_gut  ...     EX_acgam_e  0.100000\n",
              "EX_ala__L_e      ala__L  western_diet_gut  ...    EX_ala__L_e  0.100000\n",
              "EX_amp_e            amp  western_diet_gut  ...       EX_amp_e  0.100000\n",
              "...                 ...               ...  ...            ...       ...\n",
              "EX_amylose_e    amylose  western_diet_gut  ...   EX_amylose_e  0.000047\n",
              "EX_glc__D_e      glc__D  western_diet_gut  ...    EX_glc__D_e  0.014899\n",
              "EX_pect_e          pect  western_diet_gut  ...      EX_pect_e  0.000033\n",
              "EX_xylan4_e      xylan4  western_diet_gut  ...    EX_xylan4_e  0.000032\n",
              "EX_xylan8_e      xylan8  western_diet_gut  ...    EX_xylan8_e  0.000032\n",
              "\n",
              "[126 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsKO8vZN6he_"
      },
      "source": [
        "Here, the `flux` columns denotes the upper bound for the uptake flux. You can see that those are much smaller than 1000 mmol/(gDW h).\n",
        "\n",
        "Let's apply this medium to our model. It's easy! We just set the model medium to the flux bounds. However, some of the metabolites or transport reactions may not be present in the model. So we will first filter it by the exchange reactions that can be found in the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GY4pywaI6d-s"
      },
      "source": [
        "exchange_ids = [r.id for r in model.exchanges]\n",
        "western_diet_found = western_diet[western_diet.reaction.isin(exchange_ids)]\n",
        "\n",
        "model.medium = western_diet_found.flux"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhkbV9Ml7SvF"
      },
      "source": [
        "Now let's run the PFBA again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXjiBadI61fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e3f39d8d-67e1-491b-f224-ae3c7b37ac2d"
      },
      "source": [
        "diet_sol = pfba(model)\n",
        "model.summary(diet_sol)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<h3>Objective</h3><p>1.0 Growth = 0.03204335567039064</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>acgam_e</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>0.1</td>\n",
              "      <td>8</td>\n",
              "      <td>12.94%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ade_e</td>\n",
              "      <td>EX_ade_e</td>\n",
              "      <td>0.006546</td>\n",
              "      <td>5</td>\n",
              "      <td>0.53%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ala__D_e</td>\n",
              "      <td>EX_ala__D_e</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ala__L_e</td>\n",
              "      <td>EX_ala__L_e</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.85%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>arab__L_e</td>\n",
              "      <td>EX_arab__L_e</td>\n",
              "      <td>0.01788</td>\n",
              "      <td>5</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>arg__L_e</td>\n",
              "      <td>EX_arg__L_e</td>\n",
              "      <td>0.009478</td>\n",
              "      <td>6</td>\n",
              "      <td>0.92%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>asn__L_e</td>\n",
              "      <td>EX_asn__L_e</td>\n",
              "      <td>0.0225</td>\n",
              "      <td>4</td>\n",
              "      <td>1.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>asp__L_e</td>\n",
              "      <td>EX_asp__L_e</td>\n",
              "      <td>0.0225</td>\n",
              "      <td>4</td>\n",
              "      <td>1.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ca2_e</td>\n",
              "      <td>EX_ca2_e</td>\n",
              "      <td>0.0001668</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cellb_e</td>\n",
              "      <td>EX_cellb_e</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>12</td>\n",
              "      <td>14.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cit_e</td>\n",
              "      <td>EX_cit_e</td>\n",
              "      <td>0.0005004</td>\n",
              "      <td>6</td>\n",
              "      <td>0.05%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cl_e</td>\n",
              "      <td>EX_cl_e</td>\n",
              "      <td>0.0001668</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cobalt2_e</td>\n",
              "      <td>EX_cobalt2_e</td>\n",
              "      <td>3.204E-06</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>csn_e</td>\n",
              "      <td>EX_csn_e</td>\n",
              "      <td>0.01062</td>\n",
              "      <td>4</td>\n",
              "      <td>0.69%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>cu2_e</td>\n",
              "      <td>EX_cu2_e</td>\n",
              "      <td>2.272E-05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>drib_e</td>\n",
              "      <td>EX_drib_e</td>\n",
              "      <td>0.01788</td>\n",
              "      <td>5</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe2_e</td>\n",
              "      <td>EX_fe2_e</td>\n",
              "      <td>0.0002152</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fe3_e</td>\n",
              "      <td>EX_fe3_e</td>\n",
              "      <td>0.0002502</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fol_e</td>\n",
              "      <td>EX_fol_e</td>\n",
              "      <td>2.144E-05</td>\n",
              "      <td>19</td>\n",
              "      <td>0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fru_e</td>\n",
              "      <td>EX_fru_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fuc__L_e</td>\n",
              "      <td>EX_fuc__L_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>fum_e</td>\n",
              "      <td>EX_fum_e</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4</td>\n",
              "      <td>6.47%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glc__D_e</td>\n",
              "      <td>EX_glc__D_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glcn_e</td>\n",
              "      <td>EX_glcn_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gln__L_e</td>\n",
              "      <td>EX_gln__L_e</td>\n",
              "      <td>0.018</td>\n",
              "      <td>5</td>\n",
              "      <td>1.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gly_e</td>\n",
              "      <td>EX_gly_e</td>\n",
              "      <td>0.045</td>\n",
              "      <td>2</td>\n",
              "      <td>1.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>glyc_e</td>\n",
              "      <td>EX_glyc_e</td>\n",
              "      <td>0.18</td>\n",
              "      <td>3</td>\n",
              "      <td>8.73%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gua_e</td>\n",
              "      <td>EX_gua_e</td>\n",
              "      <td>0.007758</td>\n",
              "      <td>5</td>\n",
              "      <td>0.63%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2s_e</td>\n",
              "      <td>EX_h2s_e</td>\n",
              "      <td>0.002953</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>his__L_e</td>\n",
              "      <td>EX_his__L_e</td>\n",
              "      <td>0.003036</td>\n",
              "      <td>6</td>\n",
              "      <td>0.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ile__L_e</td>\n",
              "      <td>EX_ile__L_e</td>\n",
              "      <td>0.00931</td>\n",
              "      <td>6</td>\n",
              "      <td>0.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>indole_e</td>\n",
              "      <td>EX_indole_e</td>\n",
              "      <td>0.001821</td>\n",
              "      <td>8</td>\n",
              "      <td>0.24%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>k_e</td>\n",
              "      <td>EX_k_e</td>\n",
              "      <td>0.006255</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lcts_e</td>\n",
              "      <td>EX_lcts_e</td>\n",
              "      <td>0.007449</td>\n",
              "      <td>12</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>leu__L_e</td>\n",
              "      <td>EX_leu__L_e</td>\n",
              "      <td>0.01444</td>\n",
              "      <td>6</td>\n",
              "      <td>1.40%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>lys__L_e</td>\n",
              "      <td>EX_lys__L_e</td>\n",
              "      <td>0.015</td>\n",
              "      <td>6</td>\n",
              "      <td>1.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>man_e</td>\n",
              "      <td>EX_man_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>meoh_e</td>\n",
              "      <td>EX_meoh_e</td>\n",
              "      <td>1.846E-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>met__L_e</td>\n",
              "      <td>EX_met__L_e</td>\n",
              "      <td>0.004932</td>\n",
              "      <td>5</td>\n",
              "      <td>0.40%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mg2_e</td>\n",
              "      <td>EX_mg2_e</td>\n",
              "      <td>0.000278</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>mn2_e</td>\n",
              "      <td>EX_mn2_e</td>\n",
              "      <td>2.214E-05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>no2_e</td>\n",
              "      <td>EX_no2_e</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>o2_e</td>\n",
              "      <td>EX_o2_e</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>phe__L_e</td>\n",
              "      <td>EX_phe__L_e</td>\n",
              "      <td>0.005937</td>\n",
              "      <td>9</td>\n",
              "      <td>0.86%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pi_e</td>\n",
              "      <td>EX_pi_e</td>\n",
              "      <td>0.0316</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>pro__L_e</td>\n",
              "      <td>EX_pro__L_e</td>\n",
              "      <td>0.007083</td>\n",
              "      <td>5</td>\n",
              "      <td>0.57%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>raffin_e</td>\n",
              "      <td>EX_raffin_e</td>\n",
              "      <td>0.004702</td>\n",
              "      <td>18</td>\n",
              "      <td>1.37%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>rib__D_e</td>\n",
              "      <td>EX_rib__D_e</td>\n",
              "      <td>0.01788</td>\n",
              "      <td>5</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>ribflv_e</td>\n",
              "      <td>EX_ribflv_e</td>\n",
              "      <td>1.429E-05</td>\n",
              "      <td>17</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>rmn_e</td>\n",
              "      <td>EX_rmn_e</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>6</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>so4_e</td>\n",
              "      <td>EX_so4_e</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thm_e</td>\n",
              "      <td>EX_thm_e</td>\n",
              "      <td>7.146E-06</td>\n",
              "      <td>12</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>thr__L_e</td>\n",
              "      <td>EX_thr__L_e</td>\n",
              "      <td>0.008129</td>\n",
              "      <td>4</td>\n",
              "      <td>0.53%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>tre_e</td>\n",
              "      <td>EX_tre_e</td>\n",
              "      <td>0.07449</td>\n",
              "      <td>12</td>\n",
              "      <td>14.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>tyr__L_e</td>\n",
              "      <td>EX_tyr__L_e</td>\n",
              "      <td>0.004419</td>\n",
              "      <td>9</td>\n",
              "      <td>0.64%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xyl__D_e</td>\n",
              "      <td>EX_xyl__D_e</td>\n",
              "      <td>0.01788</td>\n",
              "      <td>5</td>\n",
              "      <td>1.45%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xylan4_e</td>\n",
              "      <td>EX_xylan4_e</td>\n",
              "      <td>3.21E-05</td>\n",
              "      <td>26</td>\n",
              "      <td>0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>xylan8_e</td>\n",
              "      <td>EX_xylan8_e</td>\n",
              "      <td>3.21E-05</td>\n",
              "      <td>52</td>\n",
              "      <td>0.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>zn2_e</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>1.093E-05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Metabolite</th>\n",
              "      <th>Reaction</th>\n",
              "      <th>Flux</th>\n",
              "      <th>C-Number</th>\n",
              "      <th>C-Flux</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>12ppd__S_e</td>\n",
              "      <td>EX_12ppd__S_e</td>\n",
              "      <td>-0.0298</td>\n",
              "      <td>3</td>\n",
              "      <td>1.86%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2mpa_e</td>\n",
              "      <td>EX_2mpa_e</td>\n",
              "      <td>-0.7109</td>\n",
              "      <td>4</td>\n",
              "      <td>59.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>co2_e</td>\n",
              "      <td>EX_co2_e</td>\n",
              "      <td>-1.411</td>\n",
              "      <td>1</td>\n",
              "      <td>29.29%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>gal_e</td>\n",
              "      <td>EX_gal_e</td>\n",
              "      <td>-0.01215</td>\n",
              "      <td>6</td>\n",
              "      <td>1.51%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2_e</td>\n",
              "      <td>EX_h2_e</td>\n",
              "      <td>-0.7771</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h2o_e</td>\n",
              "      <td>EX_h2o_e</td>\n",
              "      <td>-0.1507</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>h_e</td>\n",
              "      <td>EX_h_e</td>\n",
              "      <td>-0.4118</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>nh4_e</td>\n",
              "      <td>EX_nh4_e</td>\n",
              "      <td>-0.4264</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>succ_e</td>\n",
              "      <td>EX_succ_e</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>4</td>\n",
              "      <td>8.30%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<cobra.summary.model_summary.ModelSummary at 0x7fbfa60c5a90>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PbeEhFc7yly"
      },
      "source": [
        "This should have changed the doubling time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QP0cBOp74e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f02f638-10b2-4b09-8a5a-1f645d8711f1"
      },
      "source": [
        "tau = log(2) / diet_sol.fluxes[\"Growth\"]\n",
        "print(f\"The new doubling time is {tau:.2g} h.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The new doubling time is 22 h.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iz-stBB88jAP"
      },
      "source": [
        "That changed quite a bit, didn't it? What are the major carbon sources this time? \n",
        "\n",
        "We can also subet the fluxes so we get only the uptake fluxes.\n",
        "\n",
        "**Remember:** In our model, uptakes are exchange reactions with negative flux values. \n",
        "\n",
        "In practice the solver will not be able to distinguish very small values from zero (due to [floating-point arithmetic](https://docs.python.org/3/tutorial/floatingpoint.html)). Therefore, we will consider uptakes to be 'active' if they are smaller than -1e-6. \n",
        "\n",
        "Calling `head()` will only show the first few fluxes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDs7B4Zw8Fu3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e1a430e-e985-481c-c508-d884ad390c9a"
      },
      "source": [
        "uptake = diet_sol.fluxes[diet_sol.fluxes.index.isin(exchange_ids) & (diet_sol.fluxes < -1e-6)]\n",
        "uptake.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EX_acgam_e     -0.100000\n",
              "EX_ade_e       -0.006546\n",
              "EX_ala__D_e    -0.100000\n",
              "EX_ala__L_e    -0.100000\n",
              "EX_arab__L_e   -0.017878\n",
              "Name: fluxes, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbLgcZrI-NS5"
      },
      "source": [
        "This looks interesting and quantifies the expected uptake of metabolites in the gut to achieve the optimal growth rate. These import values are actual proportions of consumed nutrients, which may be quite different for each individual organism in the gut. \n",
        "\n",
        "Let's make this a clean table and also add in the human-readable metabolite names."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruWOa8gX_Q8T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "df0f753d-5acc-4697-ac9e-146446a6c26f"
      },
      "source": [
        "uptake_nice = uptake.to_frame()\n",
        "uptake_nice[\"reaction\"] = uptake.index\n",
        "uptake_nice[\"metabolite\"] = [model.reactions.get_by_id(rid).reactants[0].name for rid in uptake.index]\n",
        "uptake_nice.sort_values(by=\"fluxes\").head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>fluxes</th>\n",
              "      <th>reaction</th>\n",
              "      <th>metabolite</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>EX_glyc_e</th>\n",
              "      <td>-0.179966</td>\n",
              "      <td>EX_glyc_e</td>\n",
              "      <td>Glycerol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_acgam_e</th>\n",
              "      <td>-0.100000</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>N-Acetyl-D-glucosamine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ala__D_e</th>\n",
              "      <td>-0.100000</td>\n",
              "      <td>EX_ala__D_e</td>\n",
              "      <td>D-Alanine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_ala__L_e</th>\n",
              "      <td>-0.100000</td>\n",
              "      <td>EX_ala__L_e</td>\n",
              "      <td>L-Alanine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EX_fum_e</th>\n",
              "      <td>-0.100000</td>\n",
              "      <td>EX_fum_e</td>\n",
              "      <td>Fumarate</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               fluxes     reaction              metabolite\n",
              "EX_glyc_e   -0.179966    EX_glyc_e                Glycerol\n",
              "EX_acgam_e  -0.100000   EX_acgam_e  N-Acetyl-D-glucosamine\n",
              "EX_ala__D_e -0.100000  EX_ala__D_e               D-Alanine\n",
              "EX_ala__L_e -0.100000  EX_ala__L_e               L-Alanine\n",
              "EX_fum_e    -0.100000     EX_fum_e                Fumarate"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5aTWeser4pE"
      },
      "source": [
        "## Comparative Reverse Ecology\n",
        "\n",
        "Now, let's compare all the uptake and secretion rates that everyone in the class has estimated. For that, let's just collect everybody's exchange results across the entire course. Easy peasy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0q_O8DIC9Wv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "7cef60c9-8242-445a-8db2-868d42d557a8"
      },
      "source": [
        "from course import gimme_exchanges\n",
        "\n",
        "exchanges = gimme_exchanges()\n",
        "exchanges"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">🔨 Sure, let me collect them from everybody real quick<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "🔨 Sure, let me collect them from everybody real quick\u001b[33m...\u001b[0m\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Okay, summarized all exchange fluxes to <span style=\"color: #008000; text-decoration-color: #008000\">all_exchanges.csv</span>.\n",
              "</pre>\n"
            ],
            "text/plain": [
              "Okay, summarized all exchange fluxes to \u001b[32mall_exchanges.csv\u001b[0m.\n"
            ]
          },
          "metadata": {}
        },
        {
          "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>assembly</th>\n",
              "      <th>reaction</th>\n",
              "      <th>flux</th>\n",
              "      <th>metabolite</th>\n",
              "      <th>species</th>\n",
              "      <th>classification</th>\n",
              "      <th>genus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_12ppd__S_e</td>\n",
              "      <td>0.014899</td>\n",
              "      <td>(S)-Propane-1,2-diol</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_2mpa_e</td>\n",
              "      <td>1.063572</td>\n",
              "      <td>2 methylpropanoic acid</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_4abut_e</td>\n",
              "      <td>0.018000</td>\n",
              "      <td>4-Aminobutanoate</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>-0.007224</td>\n",
              "      <td>N-Acetyl-D-glucosamine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_ala__D_e</td>\n",
              "      <td>-0.100000</td>\n",
              "      <td>D-Alanine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75084</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_ura_e</td>\n",
              "      <td>-0.010706</td>\n",
              "      <td>Uracil</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75085</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_val__L_e</td>\n",
              "      <td>-0.018000</td>\n",
              "      <td>L-Valine</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75086</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_xyl__D_e</td>\n",
              "      <td>-0.017878</td>\n",
              "      <td>D-Xylose</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75087</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_xylan4_e</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>Xylan (4 backbone units, 1 glcur side chain)</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75088</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>-0.000011</td>\n",
              "      <td>Zinc</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>75089 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              assembly  ...        genus\n",
              "0      SRR9205533.asm1  ...    Alistipes\n",
              "1      SRR9205533.asm1  ...    Alistipes\n",
              "2      SRR9205533.asm1  ...    Alistipes\n",
              "3      SRR9205533.asm1  ...    Alistipes\n",
              "4      SRR9205533.asm1  ...    Alistipes\n",
              "...                ...  ...          ...\n",
              "75084  SRR9275565.asm1  ...  Bacteroides\n",
              "75085  SRR9275565.asm1  ...  Bacteroides\n",
              "75086  SRR9275565.asm1  ...  Bacteroides\n",
              "75087  SRR9275565.asm1  ...  Bacteroides\n",
              "75088  SRR9275565.asm1  ...  Bacteroides\n",
              "\n",
              "[75089 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWgWTC3WUu4L"
      },
      "source": [
        "Note that this includes uptake and secretion fluxes. As discussed before, transport reactions are formulated in the export direction, so negative fluxes denote uptake and positive fluxes denote secretion.\n",
        "\n",
        "Let's visualize this. We can start by drawing a heatmap of all the uptake fluxes. For this we will filter the table by uptake and use the absolute values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6036zmWR349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "531352fa-97ca-45be-cf42-3788a834084a"
      },
      "source": [
        "uptake = exchanges[exchanges.flux < 0].copy()\n",
        "uptake.flux = uptake.flux.abs()\n",
        "uptake"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>assembly</th>\n",
              "      <th>reaction</th>\n",
              "      <th>flux</th>\n",
              "      <th>metabolite</th>\n",
              "      <th>species</th>\n",
              "      <th>classification</th>\n",
              "      <th>genus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_acgam_e</td>\n",
              "      <td>0.007224</td>\n",
              "      <td>N-Acetyl-D-glucosamine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_ala__D_e</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>D-Alanine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_ala__L_e</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>L-Alanine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_arab__L_e</td>\n",
              "      <td>0.017878</td>\n",
              "      <td>L-Arabinose</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>EX_arg__L_e</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>L-Arginine</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Alistipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75084</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_ura_e</td>\n",
              "      <td>0.010706</td>\n",
              "      <td>Uracil</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75085</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_val__L_e</td>\n",
              "      <td>0.018000</td>\n",
              "      <td>L-Valine</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75086</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_xyl__D_e</td>\n",
              "      <td>0.017878</td>\n",
              "      <td>D-Xylose</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75087</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_xylan4_e</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>Xylan (4 backbone units, 1 glcur side chain)</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75088</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>EX_zn2_e</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>Zinc</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>Bacteroides</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63991 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              assembly  ...        genus\n",
              "3      SRR9205533.asm1  ...    Alistipes\n",
              "4      SRR9205533.asm1  ...    Alistipes\n",
              "5      SRR9205533.asm1  ...    Alistipes\n",
              "6      SRR9205533.asm1  ...    Alistipes\n",
              "7      SRR9205533.asm1  ...    Alistipes\n",
              "...                ...  ...          ...\n",
              "75084  SRR9275565.asm1  ...  Bacteroides\n",
              "75085  SRR9275565.asm1  ...  Bacteroides\n",
              "75086  SRR9275565.asm1  ...  Bacteroides\n",
              "75087  SRR9275565.asm1  ...  Bacteroides\n",
              "75088  SRR9275565.asm1  ...  Bacteroides\n",
              "\n",
              "[63991 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OB_lRhfzV-eg"
      },
      "source": [
        "Now let's convert this output into a matrix that we can visualize as a heatmap. One issue is that flux values between different metabolites can vary across many orders of magnitude. For instance water (H₂O) can often just diffuse passively into the cell with very large fluxes, but importing large molecules will always be much slower. There are many way to normalize these fluxes. For instance, one could scale it by molecular weight of a compound. However, a very easy way is to just log-transform the fluxes. We will stick with that for now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkrCt-QVR8_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d343d513-c3ef-4f5c-f1af-b220c6d4a6a5"
      },
      "source": [
        "from seaborn import clustermap\n",
        "from numpy import log10\n",
        "\n",
        "uptake_matrix = uptake.pivot_table(\n",
        "    index=\"metabolite\", \n",
        "    columns=\"assembly\", \n",
        "    values=\"flux\", \n",
        "    fill_value=1e-6\n",
        ")\n",
        "clustermap(\n",
        "    log10(uptake_matrix), \n",
        "    cmap=\"magma\", \n",
        "    xticklabels=False, \n",
        "    yticklabels=True, \n",
        "    figsize=(20, 20)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7fbfa2a51090>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhDLdHGzX3Hh"
      },
      "source": [
        "There are several things we can learn from this heatmap. Rows denote individual metabolites and columns denote individual isolates. The column dendrogram groups organisms with similar uptake fluxes together (i.e. organisms with similar metabolic requirements) and the row dendrogram groups metabolites that behave similarly across organisms (i.e. metabolites that tend to be taken up together).\n",
        "\n",
        "\n",
        "You can see that even though all isolates were subjected to the same environmental conditions they use very different strategies to produce biomass. Rows that are very \"patchy\" denote metabolites that are only necessary for a small number of isolates, while homogeneously colored rows denote metabolites that are required by all isolates to form biomass.\n",
        "\n",
        "One way to interpret the set of uptake fluxes for a single organism is as the realized growth niche of that organism in the gut of a human consuming an average European diet. One thing we could expect is that this niche is fairly conserved within a species. In ecology this is known as [*phylogenetic niche conservatism*](https://en.wikipedia.org/wiki/Phylogenetic_niche_conservatism). To investigate this phenomenon, let's have a look at *Phocaeicola vulgatus*, which is the recently renamed `Bacteroides vulgatus`, one of the most abundant taxa in the human gut. We will rotate the heatmap into a horizontal layout this time, so that the rows now denote isolates and columns denote metabolites. \n",
        "\n",
        "For this, we first generate a table that contains the annotatons for all isolates with the same order as our uptake matrix. We can then use this to filter the matrix by specific taxa."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-qgt4yQYAgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "78e2f146-89bf-4a97-bf41-69b0a5d62431"
      },
      "source": [
        "isolates = uptake[[\"assembly\", \"genus\", \"species\", \"classification\"]].drop_duplicates()\n",
        "isolates.index = isolates.assembly\n",
        "isolates = isolates.loc[uptake_matrix.columns, :]\n",
        "\n",
        "phocaeicola = uptake_matrix.loc[:, isolates.species == \"Phocaeicola vulgatus\"]\n",
        "clustermap(\n",
        "    log10(phocaeicola.T), \n",
        "    cmap=\"magma\", \n",
        "    xticklabels=True, \n",
        "    yticklabels=True, \n",
        "    figsize=(22, 10)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7fbfa2ab0910>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1584x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVjYM2nNzZfR"
      },
      "source": [
        "At first glance, we see that the majority of uptakes seem to be identical for *Phocaeicola vulgatus* isolates. However, there are still some differences, indicating that an organisms' exact niche preferences can be strain-specific. \n",
        "\n",
        "\n",
        "\n",
        "But this is only one species in our data set. It would be much more interesting to look at all of them, so can we do better?\n",
        "\n",
        "## Niche maps\n",
        "\n",
        "Probably, but we might have to drop a bit of information in the visualization. If we move away from individual metabolites we can reduce our question to the following:\n",
        "\n",
        "> How close are organismal growth niches to one another?\n",
        "\n",
        "Just like a map can help you find the nearest grocery store to your house, we can build a map of niche distances for our growth utpake fluxes to identify organisms that are closest to one another in niche space. Thinking back to the course from yesterday, we already talked a bit about how to visualize multi-dimensional beta-diversity distances. \n",
        "\n",
        "We will employ a similar approach today, using a method called UMAP (\"map\" is even in the name). This is a non-linear approach that will try to estimate the best possible arrangement of uptake profiles in two dimensions (2D) that is consistent with actual multi-dimensional niche distances. Let's reduce each uptake profile to two coordinates in 2D space (like GPS coordinates). UMAP has a few parameters that can alter the visualization which are [explained here](https://umap-learn.readthedocs.io/en/latest/parameters.html). We will only adjust the minimum distance between points in the final projection because we already know that many uptake profiles are very similar and we don't want them to be piled on top of each other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfXqD1O3aBuo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "cc85f116-7098-496b-dbff-b37ce7e736c2"
      },
      "source": [
        "from umap import UMAP\n",
        "\n",
        "results = UMAP(min_dist=1).fit_transform(log10(uptake_matrix.T))\n",
        "reduced = pd.DataFrame({\"UMAP 1\": results[:, 0], \"UMAP 2\": results[:, 1]}, index=uptake_matrix.columns)\n",
        "reduced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: \u001b[1mThe TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\u001b[0m\n",
            "  warnings.warn(problem)\n"
          ]
        },
        {
          "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>UMAP 1</th>\n",
              "      <th>UMAP 2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assembly</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SRR9205533.asm1</th>\n",
              "      <td>21.496937</td>\n",
              "      <td>14.757705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205534.asm1</th>\n",
              "      <td>-4.805403</td>\n",
              "      <td>-32.081341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205536.asm1</th>\n",
              "      <td>6.935532</td>\n",
              "      <td>27.996593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205537.asm1</th>\n",
              "      <td>-4.152474</td>\n",
              "      <td>-32.216091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205538.asm1</th>\n",
              "      <td>6.986981</td>\n",
              "      <td>28.166555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275558.asm1</th>\n",
              "      <td>4.282793</td>\n",
              "      <td>3.298841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275559.asm1</th>\n",
              "      <td>7.115716</td>\n",
              "      <td>0.494637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275562.asm1</th>\n",
              "      <td>7.883384</td>\n",
              "      <td>0.982833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275564.asm1</th>\n",
              "      <td>9.755206</td>\n",
              "      <td>-11.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275565.asm1</th>\n",
              "      <td>10.437782</td>\n",
              "      <td>-10.571477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>979 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    UMAP 1     UMAP 2\n",
              "assembly                             \n",
              "SRR9205533.asm1  21.496937  14.757705\n",
              "SRR9205534.asm1  -4.805403 -32.081341\n",
              "SRR9205536.asm1   6.935532  27.996593\n",
              "SRR9205537.asm1  -4.152474 -32.216091\n",
              "SRR9205538.asm1   6.986981  28.166555\n",
              "...                    ...        ...\n",
              "SRR9275558.asm1   4.282793   3.298841\n",
              "SRR9275559.asm1   7.115716   0.494637\n",
              "SRR9275562.asm1   7.883384   0.982833\n",
              "SRR9275564.asm1   9.755206 -11.025300\n",
              "SRR9275565.asm1  10.437782 -10.571477\n",
              "\n",
              "[979 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0ZVAp9g1-5Z"
      },
      "source": [
        "Now we will merge this with the taxonomy information from the assemblies/isolates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LruTiRqaaKGC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "e1e6c140-2156-4d31-a46e-62c69e8c5df2"
      },
      "source": [
        "reduced = pd.concat([isolates, reduced], axis=1)\n",
        "reduced"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>assembly</th>\n",
              "      <th>genus</th>\n",
              "      <th>species</th>\n",
              "      <th>classification</th>\n",
              "      <th>UMAP 1</th>\n",
              "      <th>UMAP 2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assembly</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SRR9205533.asm1</th>\n",
              "      <td>SRR9205533.asm1</td>\n",
              "      <td>Alistipes</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>21.496937</td>\n",
              "      <td>14.757705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205534.asm1</th>\n",
              "      <td>SRR9205534.asm1</td>\n",
              "      <td>Alistipes</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>-4.805403</td>\n",
              "      <td>-32.081341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205536.asm1</th>\n",
              "      <td>SRR9205536.asm1</td>\n",
              "      <td>Alistipes</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>6.935532</td>\n",
              "      <td>27.996593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205537.asm1</th>\n",
              "      <td>SRR9205537.asm1</td>\n",
              "      <td>Alistipes</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>-4.152474</td>\n",
              "      <td>-32.216091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9205538.asm1</th>\n",
              "      <td>SRR9205538.asm1</td>\n",
              "      <td>Alistipes</td>\n",
              "      <td>Alistipes onderdonkii</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>6.986981</td>\n",
              "      <td>28.166555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275558.asm1</th>\n",
              "      <td>SRR9275558.asm1</td>\n",
              "      <td>Bifidobacterium</td>\n",
              "      <td>Bifidobacterium longum</td>\n",
              "      <td>d__Bacteria;p__Actinobacteriota;c__Actinomycet...</td>\n",
              "      <td>4.282793</td>\n",
              "      <td>3.298841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275559.asm1</th>\n",
              "      <td>SRR9275559.asm1</td>\n",
              "      <td>Bifidobacterium</td>\n",
              "      <td>Bifidobacterium longum</td>\n",
              "      <td>d__Bacteria;p__Actinobacteriota;c__Actinomycet...</td>\n",
              "      <td>7.115716</td>\n",
              "      <td>0.494637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275562.asm1</th>\n",
              "      <td>SRR9275562.asm1</td>\n",
              "      <td>Bifidobacterium</td>\n",
              "      <td>Bifidobacterium longum</td>\n",
              "      <td>d__Bacteria;p__Actinobacteriota;c__Actinomycet...</td>\n",
              "      <td>7.883384</td>\n",
              "      <td>0.982833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275564.asm1</th>\n",
              "      <td>SRR9275564.asm1</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>9.755206</td>\n",
              "      <td>-11.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SRR9275565.asm1</th>\n",
              "      <td>SRR9275565.asm1</td>\n",
              "      <td>Bacteroides</td>\n",
              "      <td>Bacteroides uniformis</td>\n",
              "      <td>d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__...</td>\n",
              "      <td>10.437782</td>\n",
              "      <td>-10.571477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>979 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                        assembly            genus  ...     UMAP 1     UMAP 2\n",
              "assembly                                           ...                      \n",
              "SRR9205533.asm1  SRR9205533.asm1        Alistipes  ...  21.496937  14.757705\n",
              "SRR9205534.asm1  SRR9205534.asm1        Alistipes  ...  -4.805403 -32.081341\n",
              "SRR9205536.asm1  SRR9205536.asm1        Alistipes  ...   6.935532  27.996593\n",
              "SRR9205537.asm1  SRR9205537.asm1        Alistipes  ...  -4.152474 -32.216091\n",
              "SRR9205538.asm1  SRR9205538.asm1        Alistipes  ...   6.986981  28.166555\n",
              "...                          ...              ...  ...        ...        ...\n",
              "SRR9275558.asm1  SRR9275558.asm1  Bifidobacterium  ...   4.282793   3.298841\n",
              "SRR9275559.asm1  SRR9275559.asm1  Bifidobacterium  ...   7.115716   0.494637\n",
              "SRR9275562.asm1  SRR9275562.asm1  Bifidobacterium  ...   7.883384   0.982833\n",
              "SRR9275564.asm1  SRR9275564.asm1      Bacteroides  ...   9.755206 -11.025300\n",
              "SRR9275565.asm1  SRR9275565.asm1      Bacteroides  ...  10.437782 -10.571477\n",
              "\n",
              "[979 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bR-Tx9yF2Gk3"
      },
      "source": [
        "This output can now be visualized on a 2D map and we can color the points using some additional information. For example, let's use the genus annotations of the isolates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUTWni3Mas3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "outputId": "54ea4afd-ffa5-4391-c8d7-6f1a469b4d13"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15, 15))  # <-- this makes the figure larger\n",
        "\n",
        "reduced[\"genus\"] = reduced.species.str.split(\" \").str[0]\n",
        "sns.scatterplot(\n",
        "    x=\"UMAP 1\", \n",
        "    y=\"UMAP 2\", \n",
        "    hue=\"genus\", \n",
        "    data=reduced\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf44253d10>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOip4EEq3IYz"
      },
      "source": [
        "So we see that there is definitely a tendency for niches to be similar within a genus. But not always. Let's have a look at the genus *Bacteroides*, for instance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEka6Jnkl8uB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "fa1528cf-4197-4530-a69e-d0922370c5d6"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.scatterplot(\n",
        "    x=\"UMAP 1\", \n",
        "    y=\"UMAP 2\", \\\n",
        "    hue=\"species\", \n",
        "    data=reduced[reduced.genus == \"Bacteroides\"]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf44de3e10>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GOB7RyI3udI"
      },
      "source": [
        "As you can see there is a pretty discrete set of growth niches, but they do not separate strictly by species. Looking at this plot, what would be your prediction for the coexistence of these species in a single individual's gut?\n",
        "\n",
        "**Hint:** What happens if you and four of your friends all want the last chocolate chip cookie?\n",
        "\n",
        "**However**, there is a major drawback here that we need to consider. What could it be?\n",
        "\n",
        "# Exercise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNqAdUdB5Iy2"
      },
      "source": [
        "## Niche variability\n",
        "\n",
        "Let's finish by putting some hard numbers on the degree of niche conservatism across taxa. On the first day of the course we already learned how to run statistical tests on differences in beta-diversity distances between groups of samples: PERMANOVA. We can't rely on Qiime 2 here, so we will use [scikit-bio](http://scikit-bio.org/). \n",
        "\n",
        "We will start by actually calculating the distances between uptake profiles for each isolate (i.e. the equivalent to beta-diversity in metabolic niche space). here we use the `pdist` function from [scipy](https://www.scipy.org/) which uses Euclidean distances by default. So it's probably best to log-transform again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YG1FReX95oSC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae56bf5c-de24-40e6-b259-1130eefd51ff"
      },
      "source": [
        "from scipy.spatial.distance import pdist\n",
        "from skbio.stats.distance import DistanceMatrix\n",
        "\n",
        "isolates.index = isolates.assembly\n",
        "D = DistanceMatrix(\n",
        "    pdist(log10(uptake_matrix).T), \n",
        "    ids=uptake_matrix.columns\n",
        ")\n",
        "D.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(979, 979)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1NDkKWE6awX"
      },
      "source": [
        "Now we can run our PERMANOVA. Let's test whether the genus explains a significant fraction of variability in the growth niches. The `scikit-bio` PERMANOVA does not calculate the explained variance (R²) by default, so we will add it in here with a helper function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW4XKU3f533s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3c3e72f-f0a3-46f2-e0fb-a01680d9a704"
      },
      "source": [
        "from skbio.stats.distance import permanova\n",
        "from course import add_r_squared\n",
        "\n",
        "perm_results = permanova(D, grouping=isolates, column=\"genus\")\n",
        "add_r_squared(perm_results)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "method name               PERMANOVA\n",
              "test statistic name        pseudo-F\n",
              "sample size                     979\n",
              "number of groups                 32\n",
              "test statistic              73.1804\n",
              "p-value                       0.001\n",
              "number of permutations          999\n",
              "R²                         0.712267\n",
              "Name: PERMANOVA results, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpZkB8T3XM1N",
        "outputId": "bb0b3f30-3f6e-42ef-d109-af8d77ac436f"
      },
      "source": [
        "perm_results = permanova(D, grouping=isolates, column=\"species\")\n",
        "add_r_squared(perm_results)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "method name               PERMANOVA\n",
              "test statistic name        pseudo-F\n",
              "sample size                     979\n",
              "number of groups                 61\n",
              "test statistic              80.0303\n",
              "p-value                       0.001\n",
              "number of permutations          999\n",
              "R²                         0.841865\n",
              "Name: PERMANOVA results, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTrVqTgi84Qk"
      },
      "source": [
        "How do you interpret the result? Also substitute the tested rank to species by changing the `column=\"...\"` part in the code above. \n",
        "\n",
        "> What do you observe for the R²? \n",
        "\n",
        "As we can see explained variance increases from 71% to 84% when using the species instead of the genus level.\n",
        "\n",
        "> How do you intepret the results when comparing the genus and species level?\n",
        "\n",
        "The results are mostly consistent with phylogenetic niche conservatism because going to a finer taxonomic level also explains more of the variation in inferred niches across genomes. However, in very strict niche conservatism we would expect that the niches are even more similar than the species. Bacterial species tend to share [>95% identity in the core genome](https://www.nature.com/articles/s41467-018-07641-9). Overall, while we find evidence for phylogenetic niche conservatism, our results also indicate that some of the variation in niche is not correlated with the phylogeny. \n",
        "\n",
        "> What is your interpretation of the unexplained variance 1-R²? \n",
        "\n",
        "Every single niche has an associated strain, so the strains necessarily will explain 100% of the niche variance. This means, that about 16% of the niche variability happens on the strain level (i.e. beyond the species level). This is still quite a bit of variation. So how is this possible if strains in the same species share >95% of the core genome? Well, one reason is that small differences can have large impact. A single missing enzyme might block an entire pathway, thus shifting the overall nutrient preferences of an organism. The other reason is that a lot of metabolic genes are often located in the assessory genome. In some instance strains from the same species might differ in up to 30% of their enzymes just because of genes in the assessory genome. Furthermore, unrelated strains can sometimes obtain the same enzymes or transporters through horizontal gene transfers."
      ]
    }
  ]
}