{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stochastic simulation of Boolean network with MaBoSS\n", "\n", "This notebook show the basic features of the `maboss` Python module by reproducing part of the official MaBoSS tutorial at https://maboss.curie.fr/pub/TutorialMaBoSS_2_0.pdf" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import maboss\n", "import ginsim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model\n", "\n", "The input model is a simple Boolean network created with GINsim. We download it from MaBoSS website and display its regulatory graph using the `ginsim` Python module:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Downloading https://maboss.curie.fr/pub/example.zginml" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lrg = ginsim.load(\"https://maboss.curie.fr/pub/example.zginml\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ginsim.show(lrg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building the `Simulation` object\n", "\n", "A GINsim model can be converted to MaBoSS using the `to_maboss` function:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masim = ginsim.to_maboss(lrg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MaBoSS allows to specify some nodes as \"internal\": the value of internal nodes is hidden in the output of simulations. \n", "By default, (if the network was built from scratch or if the `.cfg` file did not contain the information for a given node), all nodes have the attribute `is_internal` set to `False`. This can be changed within the script" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masim.network['DNAdam'].is_internal = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can change other MaBoSS parameters with the function update_parameters:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masim.update_parameters(time_tick=0.1, max_time=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once all the parameters are set, you can run the simulation. The following line will create two temporary files that contains everything MaBoSS needs to know to run properly. The result of the simulation are stored in a `Result` object.\n", "\n", "To run the simulation, use the command below. This command can be quickly written by going to MaBoSS -> Simulation -> run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performing simulations\n", "\n", "The `run` method of the simulation object will call MaBoSS to perfom the requested simulations. A result object is then returned." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res = masim.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Result visualizations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result object provides access to the simulation output, and provides simple plotting functions.\n", "\n", "The `plot_trajectory` method displays the mean probability of each state (composed of non-internal nodes) along time:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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xT5t4OHjU2blvS7GukbtKBYGPw68fQ/plcPa0dI8sprwRdm0pK/IXMEf+lnRw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\nQCZ3nFzu8hdPJPPbN7GkXslEY6PCu60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FNoVwqFRuoV6/m3r9Hibq91AoHDz/TYyBr30S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\n3v6Wci0i6m5dV0YIKNUDKpMBlckClamAal5WJgtUJgOKVR8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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res.plot_trajectory()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot_piechart` method displays the proportion of states in which each simulation ended, providing an approximation of the probability to reach the different attractors." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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mzJlpEomkHQAKCgoMNRoNACA3N9e4u7ubwefzewar/+bNmyZyuZyjVqtx+vRp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\n9mkNTYh82b5yICxDQJtT15wU1ymO9HHpVOyroGhZEZ0usfq7dJrQiYp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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res.plot_piechart(autopct=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulating mutations\n", "\n", "The `maboss` Python module allow to modify a network model to perform the mutation of a node, that is forcing its value to be always 1 (\"ON\") or always 0 (\"OFF\").\n", "\n", "In the following cell, we copy the initial model and configure a gain of function mutant for the node `Mdm2nuc`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mutsim = masim.copy()\n", "mutsim.mutate(\"Mdm2nuc\", \"ON\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting model can then be simulated:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mutres = mutsim.run()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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