experiment/SEDML_Fig5_v2.ipynb

{"nbformat":4,"nbformat_minor":0,"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"},"colab":{"name":"SEDML_Fig5_v2.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"bsLgwPexSto2"},"source":["**Reproduction of Figure 5A from Bachmann *et al.***\n","\n","This Colaboratory notebook creates an SED-ML file to partially reproduce  Figure 5A from Bachmann *et al.* 2011([Link](https://doi.org/10.1038/msb.2011.50)).\n","\n","Version: 2.0\n","Date: 2021-06-28"]},{"cell_type":"code","metadata":{"id":"_vB1YTQm9zB6"},"source":["# install Tellurium\n","!pip3 install tellurium"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MGdWTlKa9zCA","executionInfo":{"status":"ok","timestamp":1624961143859,"user_tz":-120,"elapsed":1920,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}}},"source":["# import required packages\n","import os\n","import tellurium as te\n","import phrasedml"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XSoD33rr_Mnf","executionInfo":{"status":"ok","timestamp":1624961162240,"user_tz":-120,"elapsed":17036,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}},"outputId":"aee919e2-f93b-4e16-bc4e-0a65fd6afa52"},"source":["# mount Google Drive if necessary\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gzc8ajMyCrhX","executionInfo":{"status":"ok","timestamp":1624961174903,"user_tz":-120,"elapsed":2001,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}}},"source":["# set working directory to a subfolder \"experiment\" of the folder containing this file, change file path for workingDir as required\n","workingDir = '/content/drive/My Drive/bachmann_archiv/Group4_GenerationSEDML/Tellurium/experiment/'\n","os.chdir(workingDir)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"RoxxzRCd9zCB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1624961177171,"user_tz":-120,"elapsed":935,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}},"outputId":"fed92d0c-acf2-43d3-ff38-4ceaf9b1786e"},"source":["# retrieve SBML model from model folder and set as reference for phraSEDML\n","sbml = \"../model/Bachmann2011.xml\"\n","ant_str = te.sbmlToAntimony(sbml)\n","sbml_str = te.antimonyToSBML(ant_str)\n","phrasedml.setReferencedSBML('../model/Bachmann2011.xml', sbml_str)\n","# print(sbml_str)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"7ydqZzUW9zCD","executionInfo":{"status":"ok","timestamp":1624961229592,"user_tz":-120,"elapsed":360,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}}},"source":["# create phraSEDML string as template for SED-ML\n","phrasedml_str = \"\"\"\n","    model1 = model \"{}\"\n","    model2 = model model1 with SOCS3oe=1\n","    model3 = model model1 with CISoe=1\n","    \n","    sim1 = simulate uniform(0, 80, 1000)\n","    task1 = run sim1 on model1\n","    task2 = run sim1 on model2\n","    task3 = run sim1 on model3\n","\n","    plot \"Figure 5A Nuclear pSTAT5\" task1.time vs task1.npSTAT5, task2.npSTAT5, task3.npSTAT5\n","\n","    report \"Report Figure 5A\" task1.time, task1.npSTAT5, task2.npSTAT5, task3.npSTAT5\n","\n","\"\"\".format('../model/Bachmann2011.xml')"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"P-z7KegV9zCE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1624961238116,"user_tz":-120,"elapsed":467,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}},"outputId":"3db34804-495a-44a0-d0d6-4f14819aa797"},"source":["# convert to SED-ML and create SED-ML file in working directory\n","sedml_str = phrasedml.convertString(phrasedml_str)\n","if sedml_str == None:\n","    raise RuntimeError(phrasedml.getLastError())\n","print(sedml_str)\n","\n","with open(os.path.join(workingDir, 'Fig5.sedml'), 'wb') as f:\n","    f.write(sedml_str.encode('utf-8'))"],"execution_count":8,"outputs":[{"output_type":"stream","text":["<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n","<!-- Created by phraSED-ML version v1.1.1 with libSBML version 5.19.1. -->\n","<sedML xmlns=\"http://sed-ml.org/sed-ml/level1/version3\" level=\"1\" version=\"3\">\n","  <listOfModels>\n","    <model id=\"model1\" language=\"urn:sedml:language:sbml.level-2.version-1\" source=\"../model/Bachmann2011.xml\"/>\n","    <model id=\"model2\" language=\"urn:sedml:language:sbml.level-2.version-1\" source=\"model1\">\n","      <listOfChanges>\n","        <changeAttribute target=\"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='SOCS3oe']/@value\" newValue=\"1\"/>\n","      </listOfChanges>\n","    </model>\n","    <model id=\"model3\" language=\"urn:sedml:language:sbml.level-2.version-1\" source=\"model1\">\n","      <listOfChanges>\n","        <changeAttribute target=\"/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='CISoe']/@value\" newValue=\"1\"/>\n","      </listOfChanges>\n","    </model>\n","  </listOfModels>\n","  <listOfSimulations>\n","    <uniformTimeCourse id=\"sim1\" initialTime=\"0\" outputStartTime=\"0\" outputEndTime=\"80\" numberOfPoints=\"1000\">\n","      <algorithm kisaoID=\"KISAO:0000019\"/>\n","    </uniformTimeCourse>\n","  </listOfSimulations>\n","  <listOfTasks>\n","    <task id=\"task1\" modelReference=\"model1\" simulationReference=\"sim1\"/>\n","    <task id=\"task2\" modelReference=\"model2\" simulationReference=\"sim1\"/>\n","    <task id=\"task3\" modelReference=\"model3\" simulationReference=\"sim1\"/>\n","  </listOfTasks>\n","  <listOfDataGenerators>\n","    <dataGenerator id=\"plot_0_0_0\" name=\"task1.time\">\n","      <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n","        <ci> task1_____time </ci>\n","      </math>\n","      <listOfVariables>\n","        <variable id=\"task1_____time\" symbol=\"urn:sedml:symbol:time\" taskReference=\"task1\" modelReference=\"model1\"/>\n","      </listOfVariables>\n","    </dataGenerator>\n","    <dataGenerator id=\"plot_0_0_1\" name=\"task1.npSTAT5\">\n","      <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n","        <ci> task1_____npSTAT5 </ci>\n","      </math>\n","      <listOfVariables>\n","        <variable id=\"task1_____npSTAT5\" target=\"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='npSTAT5']\" taskReference=\"task1\" modelReference=\"model1\"/>\n","      </listOfVariables>\n","    </dataGenerator>\n","    <dataGenerator id=\"plot_0_1_1\" name=\"task2.npSTAT5\">\n","      <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n","        <ci> task2_____npSTAT5 </ci>\n","      </math>\n","      <listOfVariables>\n","        <variable id=\"task2_____npSTAT5\" target=\"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='npSTAT5']\" taskReference=\"task2\" modelReference=\"model2\"/>\n","      </listOfVariables>\n","    </dataGenerator>\n","    <dataGenerator id=\"plot_0_2_1\" name=\"task3.npSTAT5\">\n","      <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n","        <ci> task3_____npSTAT5 </ci>\n","      </math>\n","      <listOfVariables>\n","        <variable id=\"task3_____npSTAT5\" target=\"/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='npSTAT5']\" taskReference=\"task3\" modelReference=\"model3\"/>\n","      </listOfVariables>\n","    </dataGenerator>\n","  </listOfDataGenerators>\n","  <listOfOutputs>\n","    <plot2D id=\"plot_0\" name=\"Figure 5A Nuclear pSTAT5\">\n","      <listOfCurves>\n","        <curve id=\"plot_0__plot_0_0_0__plot_0_0_1\" logX=\"false\" xDataReference=\"plot_0_0_0\" logY=\"false\" yDataReference=\"plot_0_0_1\"/>\n","        <curve id=\"plot_0__plot_0_0_0__plot_0_1_1\" logX=\"false\" xDataReference=\"plot_0_0_0\" logY=\"false\" yDataReference=\"plot_0_1_1\"/>\n","        <curve id=\"plot_0__plot_0_0_0__plot_0_2_1\" logX=\"false\" xDataReference=\"plot_0_0_0\" logY=\"false\" yDataReference=\"plot_0_2_1\"/>\n","      </listOfCurves>\n","    </plot2D>\n","    <report id=\"report_1\" name=\"Report Figure 5A\">\n","      <listOfDataSets>\n","        <dataSet id=\"plot_0_0_0_dataset\" label=\"task1.time\" dataReference=\"plot_0_0_0\"/>\n","        <dataSet id=\"plot_0_0_1_dataset\" label=\"task1.npSTAT5\" dataReference=\"plot_0_0_1\"/>\n","        <dataSet id=\"plot_0_1_1_dataset\" label=\"task2.npSTAT5\" dataReference=\"plot_0_1_1\"/>\n","        <dataSet id=\"plot_0_2_1_dataset\" label=\"task3.npSTAT5\" dataReference=\"plot_0_2_1\"/>\n","      </listOfDataSets>\n","    </report>\n","  </listOfOutputs>\n","</sedML>\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":523},"id":"K5IwwSmx9zCE","executionInfo":{"status":"ok","timestamp":1624961243161,"user_tz":-120,"elapsed":777,"user":{"displayName":"Jessica Vasseur","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjUqIFyj6Pm18v4Iia0CnnlYkWN5mgVVaTAeL4=s64","userId":"04335986834050352204"}},"outputId":"08901639-7afa-413a-f10b-0060aa92dc46"},"source":["# run the SED-ML file based on the referenced SBML file in ../models/\n","workingDir = '/content/drive/My Drive/bachmann_archiv/Group4_GenerationSEDML/Tellurium/experiment/'\n","os.chdir(workingDir)\n","te.executeSEDML(sedml_str, workingDir)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/roadrunner/roadrunner.py:3944: UserWarning:\n","\n","Simulation requested end point (80.0) not reached, because the maximum number of steps reached.  Possible solutions include:\n","  * Setting an explicit number of points (i.e. r.simulate(0.0, 80.0, 1001)\n","  * Setting r.integrator.variable_step_size to 'False'\n","* Setting \n","\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"KISUzP0N9zCF"},"source":[""],"execution_count":null,"outputs":[]}]}