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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating MDV at Steady State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metabolic flux analysis involves simulating the labeling pattern of metabolites at a defined flux distribution in a given metabolic network. In this tutorial, we will demonstrate how to simulate the labeling pattern of metabolites using FreeFlux. We will use a [toy model](https://github.com/Chaowu88/freeflux/tree/main/models/toy) to illustrate the process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by loading the metabolic model from a tab-separated values (tsv) [file](https://github.com/Chaowu88/freeflux/blob/main/models/toy/reactions.tsv). We then create a Simulator object for steady-state simulation ('ss'). We call the `set_target_EMUs method` to specify the EMUs whose MDVs need to be simulated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from freeflux import Model\n",
    "\n",
    "MODEL_FILE = 'path/to/reactions.tsv'\n",
    "\n",
    "model = Model('demo')\n",
    "model.read_from_file(MODEL_FILE)\n",
    "\n",
    "sim = model.simulator('ss')\n",
    "sim.set_target_EMUs({\n",
    "    'Glu': [[1,2,3], '12345'],  # EMU \"Glu_123\" and \"Glu_12345\"\n",
    "    'AKG': [2,3],               # EMU \"AKG_23\"\n",
    "    'Cit': '12345'              # EMU \"Cit_12345\"\n",
    "})           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we specify the labeling strategy for the metabolic network. In this toy model, the network uptakes 25% (mol%) C2 labeled and 25% fully labeled acetyl-CoA with both 100% purities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.set_labeling_strategy(\n",
    "    'AcCoA', \n",
    "    labeling_pattern = ['01', '11'], \n",
    "    percentage = [0.25, 0.25], \n",
    "    purity = [1, 1]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "<b>Note:</b> <br></br> Call this method for each substrate if multiple substrates are used. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can input the defined flux distribution using the `set_flux` method or read it from a .tsv or .xslx [file](https://github.com/Chaowu88/freeflux/blob/main/models/toy/fluxes.tsv) using the `set_fluxes_from_file` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fluxes = {'v1': 10,\n",
    "          'v2': 10,\n",
    "          'v3': 5,\n",
    "          'v4': 5,\n",
    "          'v5': 5,\n",
    "          'v6_f': 12.5,   # \"_f\" denotes forward flux\n",
    "          'v6_b': 7.5,    # \"_b\" denotes backward flux\n",
    "          'v7': 5}\n",
    "for fluxid, value in fluxes.items():\n",
    "    sim.set_flux(fluxid, value)\n",
    "    \n",
    "# or read from file\n",
    "FLUXES_FILE = 'path/to/fluxes.tsv'\n",
    "sim.set_fluxes_from_file(FLUXES_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to simulate the labeling patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulated MDVs\n",
      "Glu_123: MDV([0.671, 0.21, 0.102, 0.017])\n",
      "Glu_12345: MDV([0.328, 0.276, 0.274, 0.088, 0.03, 0.004])\n",
      "AKG_23: MDV([0.693, 0.262, 0.044])\n",
      "Cit_12345: MDV([0.328, 0.276, 0.274, 0.088, 0.03, 0.004])\n"
     ]
    }
   ],
   "source": [
    "sim.prepare()\n",
    "res = sim.simulate()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `with` statement can also be used to perform the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulated MDVs\n",
      "Glu_123: MDV([0.671, 0.21, 0.102, 0.017])\n",
      "Glu_12345: MDV([0.328, 0.276, 0.274, 0.088, 0.03, 0.004])\n",
      "AKG_23: MDV([0.693, 0.262, 0.044])\n",
      "Cit_12345: MDV([0.328, 0.276, 0.274, 0.088, 0.03, 0.004])\n"
     ]
    }
   ],
   "source": [
    "with model.simulator('ss') as sim:\n",
    "    sim.set_target_EMUs({\n",
    "        'Glu': [[1,2,3], '12345'], \n",
    "        'AKG': [2,3], \n",
    "        'Cit': '12345'\n",
    "    })\n",
    "    sim.set_labeling_strategy(\n",
    "        'AcCoA', \n",
    "        labeling_pattern = ['01', '11'], \n",
    "        percentage = [0.25, 0.25], \n",
    "        purity = [1, 1]\n",
    "    )\n",
    "    sim.set_fluxes_from_file(FLUXES_FILE)\n",
    "    sim.prepare()\n",
    "    res = sim.simulate()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more complex example of steady state simulation using an *E. coli* model, you can refer to the script \"[tutorial_synechocystis_inst_simulation.py](https://github.com/Chaowu88/freeflux/tree/main/tutorials)\""
   ]
  }
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