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"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating and simulating a DFBA model\n",
"This tutorial demonstrates how to build and simulate a DFBA model corresponding to the growth of a single strain of _Escherichia coli_ (based on the iJR904 genome-scale model) under anaerobic conditions with glucose and xylose as limiting carbon substrates. \n",
"\n",
"This example can be adapted to other DFBA models by modifying the choice of genome-scale model, relevant kinetic variables, and exchange fluxes. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from os.path import dirname, join, pardir\n",
"\n",
"from cobra.io import read_sbml_model\n",
"\n",
"from dfba import DfbaModel, ExchangeFlux, KineticVariable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dfba model\n",
"Specify path for loading file containing genome-scale metabolic model as [cobra.Model](https://cobrapy.readthedocs.io/en/latest/building_model.html) object and set GLPK as LP solver of choice. After that, instantiate object of class `DfbaModel` with cobrapy model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path_to_model = join(pardir, \"sbml-models\", \"iJR904.xml.gz\")\n",
"fba_model = read_sbml_model(path_to_model)\n",
"fba_model.solver = \"glpk\"\n",
"dfba_model = DfbaModel(fba_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Kinetic Variables\n",
"Instantiate kinetic variables to appear in model. Default initial conditions are $0.0$, but can be set here if wanted (e.g., Oxygen). The last command adds kinetic variables to the model."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"X = KineticVariable(\"Biomass\")\n",
"Gluc = KineticVariable(\"Glucose\")\n",
"Xyl = KineticVariable(\"Xylose\")\n",
"Oxy = KineticVariable(\"Oxygen\", initial_condition=0.24)\n",
"Eth = KineticVariable(\"Ethanol\")\n",
"\n",
"dfba_model.add_kinetic_variables([X, Gluc, Xyl, Oxy, Eth])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Exchange fluxes\n",
"Instantiate exchange fluxes to appear in model, with ids corresponding to exchange reactions of the cobrapy FBA model. The last command adds exchange fluxes to the model."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"mu = ExchangeFlux(\"BiomassEcoli\")\n",
"v_G = ExchangeFlux(\"EX_glc(e)\")\n",
"v_Z = ExchangeFlux(\"EX_xyl_D(e)\")\n",
"v_O = ExchangeFlux(\"EX_o2(e)\")\n",
"v_E = ExchangeFlux(\"EX_etoh(e)\")\n",
"\n",
"dfba_model.add_exchange_fluxes([mu, v_G, v_Z, v_O, v_E])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Rhs expressions \n",
"Provide symbolic expression for calculating the time derivative of each kinetic variable currently in the model."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dfba_model.add_rhs_expression(\"Biomass\", mu * X)\n",
"dfba_model.add_rhs_expression(\"Glucose\", v_G * 180.1559 * X / 1000.0)\n",
"dfba_model.add_rhs_expression(\"Xylose\", v_Z * 150.13 * X / 1000.0)\n",
"dfba_model.add_rhs_expression(\"Oxygen\", v_O * 16.0 * X / 1000.0)\n",
"dfba_model.add_rhs_expression(\"Ethanol\", v_E * 46.06844 * X / 1000.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Lower/upper bound expressions \n",
"Add symbolic expressions for calculating lower/upper bounds of selected exchange fluxes\n",
"currently in the model. Here convention is that both lower and upper bound expressions have \n",
"positive signs, whereas lower bounds values are typically negative in cobrapy. \n",
"\n",
"In many applications, vector components (e.g., concentrations)\n",
"in the true solution are always positive or non-negative, though at times very\n",
"small. In the numerical solution, however, small negative (hence unphysical)\n",
"values can then occur. To prevent these from interfering with the simulation,\n",
"the user can supply a symbolic expression that must be non-negative for\n",
"correct evaluation of lower/upper bounds."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dfba_model.add_exchange_flux_lb(\n",
" \"EX_glc(e)\", 10.5 * (Gluc / (0.0027 + Gluc)) * (1 / (1 + Eth / 20.0)), Gluc\n",
")\n",
"dfba_model.add_exchange_flux_lb(\"EX_o2(e)\", 15.0 * (Oxy / (0.024 + Oxy)), Oxy)\n",
"dfba_model.add_exchange_flux_lb(\n",
" \"EX_xyl_D(e)\",\n",
" 6.0\n",
" * (Xyl / (0.0165 + Xyl))\n",
" * (1 / (1 + Eth / 20.0))\n",
" * (1 / (1 + Gluc / 0.005)),\n",
" Xyl,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Add initial conditions to the model and launch the simulation\n",
"Initial values for each kinetic variable are provided in dictionary form. \n",
"\n",
"The model is simulated using the `simulate` method. This simulation covers the interval $[0.0, 25.0]$ hours, with results stored every $0.1$ hours. Results (trajectories of kinetic variables) will be returned as [pandas.DataFrame](https://pandas.pydata.org/). Optionally, the user can also provide a list of reaction ids whose flux trajectories will also be returned as a separate [pandas.DataFrame](https://pandas.pydata.org/), in this case three exchange fluxes in the model."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"dfba_model.add_initial_conditions(\n",
" {\n",
" \"Biomass\": 0.03,\n",
" \"Glucose\": 15.5,\n",
" \"Xylose\": 8.0,\n",
" \"Oxygen\": 0.0,\n",
" \"Ethanol\": 0.0,\n",
" }\n",
")\n",
"concentrations, trajectories = dfba_model.simulate(0.0, 25.0, 0.1, [\"EX_glc(e)\", \"EX_xyl_D(e)\", \"EX_etoh(e)\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Plotting the results\n",
"\n",
"\n",
"**Note:** In order to plot the results, one of [plotly](https://plot.ly/python/) or [matplotlib](https://matplotlib.org/) should be used. See [Visualization](plotting.rst) to install them along with\n",
"dfba.\n",
"\n",
"
\n",
"\n",
"\n",
"\n",
"### Plotly\n",
"The functions for plotting with `plotly` returns a `plotly.graph_object`. Users of this library may plot the output with the usual functions."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from dfba.plot.plotly import *\n",
"\n",
"import plotly.io as pio\n",
"\n",
"# in plotly version 4, default version is different than in 3\n",
"pio.templates.default = \"plotly_white\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_trajectories(trajectories)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Writing the results to file\n",
"\n",
"Particularly when running a simulation using [Docker](https://www.docker.com/) (see [Installation](installation.rst)), it can be useful to write \n",
"results to a csv file for downstream analysis. This can be achieved using the [pandas.DataFrame](https://pandas.pydata.org/) method `to_csv()`\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"concentrations.to_csv(\"concentrations.csv\")\n",
"trajectories.to_csv(\"trajectories.csv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}