{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import boolsim\n", "from colomoto_jupyter import tabulate # for display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input from a GINsim model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import ginsim" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Downloading https://zenodo.org/record/3719029/files/SuppMat_Model_Master_Model.zginml" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lrg = ginsim.load(\"https://zenodo.org/record/3719029/files/SuppMat_Model_Master_Model.zginml\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 26.4 ms, sys: 6.89 ms, total: 33.3 ms\n", "Wall time: 1.92 s\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " AKT1 AKT2 Apoptosis CDH1 CDH2 CellCycleArrest CTNNB1 DKK1 \\\n", "5 0 0 0 1 0 0 0 0 \n", "1 0 0 1 1 0 1 0 0 \n", "8 0 0 1 1 0 1 0 0 \n", "3 0 0 1 1 0 1 0 0 \n", "0 0 0 1 1 0 1 0 0 \n", "2 0 1 0 0 1 1 0 0 \n", "4 0 1 0 0 1 1 0 0 \n", "6 0 1 0 0 1 1 0 1 \n", "7 0 1 0 0 1 1 0 1 \n", "\n", " DNAdamage ECMicroenv EMT ERK GF Invasion Metastasis Migration \\\n", "5 0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 0 0 0 0 \n", "8 1 0 0 0 0 0 0 0 \n", "3 1 1 0 0 0 0 0 0 \n", "0 1 1 0 0 0 0 0 0 \n", "2 0 0 1 1 1 0 0 0 \n", "4 1 0 1 1 1 0 0 0 \n", "6 0 1 1 1 1 1 1 1 \n", "7 1 1 1 1 1 1 1 1 \n", "\n", " miR200 miR203 miR34 NICD p21 p53 p63 p73 SMAD SNAI1 SNAI2 \\\n", "5 0 0 0 0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 1 0 1 1 0 0 0 \n", "8 1 1 0 0 1 1 0 0 0 0 0 \n", "3 1 0 0 0 1 0 1 1 0 0 0 \n", "0 1 1 0 0 1 1 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 0 0 1 1 \n", "4 0 0 0 0 0 0 0 0 0 1 1 \n", "6 0 0 0 1 0 0 0 0 1 1 1 \n", "7 0 0 0 1 0 0 0 0 1 1 1 \n", "\n", " TGFbeta TWIST1 VIM ZEB1 ZEB2 \n", "5 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "8 0 0 0 0 0 \n", "3 1 0 0 0 0 \n", "0 1 0 0 0 0 \n", "2 0 1 1 1 1 \n", "4 0 1 1 1 1 \n", "6 1 1 1 1 1 \n", "7 1 1 1 1 1 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time A = boolsim.attractors(lrg)\n", "tabulate(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input from a `minibn.BooleanNetwork` object" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from colomoto.minibn import BooleanNetwork" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "bn = BooleanNetwork({\n", " \"a\": \"(a|b)&!(a&b)\",\n", " \"b\": \"(a|b)&!(a&b)\"\n", "})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
a b
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" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = boolsim.attractors(bn)\n", "tabulate(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The attractor `1` is a fixed point:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'a': 0, 'b': 0}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The attractor `0` is a complex attractor, being represented as a list of hypercubes:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'a': 0, 'b': 1}, {'a': 1, 'b': '*'}]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }