SimpleSEDML 0.1.0

Python Subnet Discovery for Systems Biology

SimpleSEDML

A simple API for creating directives in the Simulation Experiment Description Markup Language (SED-ML) community standard for describing simulation experiments.

The project provides a python interface to generate SED-ML based on the abstractions provided by phraSED-ML to describe simulation experiments. These absractions are: (a) models (including changes in values of model parameters); (b) simulations (including deterministic, stochastic, and steady state); (c) tasks (which specify simulations to run on tasks and repetitions for changes in parameter values); and (d) output for data reports and plots.

SimpleSEDML generalizes the capabilities of PhraSEDML and simplifies its usage by exploiting the Python environment:

• A model source can be a file path or URL and may be in the Antimony language as well as SBML;
• Repeated tasks are defined more simply by the use of a pandas DataFrame.
• Task oriented convenience methods are provided to simplify the API that provides both plots a data (as a pandas DataFrame).
  • Running a time course for a single model file
  • Running a time course for multiple model files, comparing the results

Example

See this Jupyter notebook for a detailed example. It is also available as a pdf file.

Consider the model below in the Antimony language.

MODEL_ANT = '''
model myModel
    J1: S1 -> S2; k1*S1
    J2: S2 -> S3; k2*S2
    
    S1 = 10
    S2 = 0
    k1 = 1
    k2 = 1

    S1 is "species1"
    S2 is "species2"
end
'''

We want to simulate this model and do a time course plot of all floating species in the model.

import SimpleSEDML as ss

smtc = ss.makeSingleModelTimeCourse(MODEL_ANT, title="My Plot")

The SED-ML generated by this statement can be viewed with

print(smtc.getSEDML())

This generates the following SED-ML:

<?xml version="1.0" encoding="UTF-8"?>
<!-- Created by phraSED-ML version v1.3.0 with libSBML version 5.19.5. -->
<sedML xmlns="http://sed-ml.org/sed-ml/level1/version4" xmlns:sbml="http://www.sbml.org/sbml/level3/version2/core" level="1" version="4">
<listOfModels>
    <model id="time_course_model" language="urn:sedml:language:sbml.level-3.version-2" source="/Users/jlheller/home/Technical/repos/SimpleSEDML/examples/time_course_model"/>
</listOfModels>
<listOfSimulations>
    <uniformTimeCourse id="time_course_sim" initialTime="0" outputStartTime="0" outputEndTime="5" numberOfSteps="50">
    <algorithm name="CVODE" kisaoID="KISAO:0000019"/>
    </uniformTimeCourse>
</listOfSimulations>
<listOfTasks>
    <task id="time_course_task" modelReference="time_course_model" simulationReference="time_course_sim"/>
</listOfTasks>
<listOfDataGenerators>
    <dataGenerator id="plot_0_0_0" name="time">
    <math xmlns="http://www.w3.org/1998/Math/MathML">
        <ci> time </ci>
    </math>
    <listOfVariables>
        <variable id="time" symbol="urn:sedml:symbol:time" taskReference="time_course_task" modelReference="time_course_model"/>
    </listOfVariables>
    </dataGenerator>
    <dataGenerator id="plot_0_0_1" name="S1">
    <math xmlns="http://www.w3.org/1998/Math/MathML">
        <ci> S1 </ci>
    </math>
    <listOfVariables>
        <variable id="S1" target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S1']" taskReference="time_course_task" modelReference="time_course_model"/>
    </listOfVariables>
    </dataGenerator>
    <dataGenerator id="plot_0_1_1" name="S2">
    <math xmlns="http://www.w3.org/1998/Math/MathML">
        <ci> S2 </ci>
    </math>
    <listOfVariables>
        <variable id="S2" target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S2']" taskReference="time_course_task" modelReference="time_course_model"/>
    </listOfVariables>
    </dataGenerator>
    <dataGenerator id="plot_0_2_1" name="S3">
    <math xmlns="http://www.w3.org/1998/Math/MathML">
        <ci> S3 </ci>
    </math>
    <listOfVariables>
        <variable id="S3" target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S3']" taskReference="time_course_task" modelReference="time_course_model"/>
    </listOfVariables>
    </dataGenerator>
</listOfDataGenerators>
<listOfOutputs>
    <plot2D id="plot_0" name="My Plot">
    <listOfCurves>
        <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"/>
        <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"/>
        <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"/>
    </listOfCurves>
    </plot2D>
</listOfOutputs>
</sedML>

The PhraSED-ML to generate the above SED-ML is displayed below (obtained using smtc.getPhraSEDML()). It is considerably more text than the one-line API call.

time_course_model = model "/Users/jlheller/home/Technical/repos/SimpleSEDML/examples/time_course_model" 
time_course_sim = simulate uniform(0, 5, 50)
time_course_sim.algorithm = CVODE
time_course_task = run time_course_sim on time_course_model
plot "My Plot" time vs S1, S2, S3

Executing this SED-ML is done by

smtc.executeSEDML()

which generates the following plot:

Restrictions

1. If there are multiple task directives and/or there is a repeated task directive AND there is a report directive, SimpleSEDML.execute only returns the results of the last simulation. You can circumvent this by iterating in python to obtain the desired reports.

Versions

• 0.1.0 6/3/2025

  • MultipleModel constructors have model_refs as optional
  • Many bug fixes

• 0.0.8

  • MultipleModelParameterScan
  • Refactored to create MultipleModelSimpleSEDML, common code for MultipleModelParameterScan and MultipleModelTimeCourse

• 0.0.7 5/30/2025

  • Single model parameter scan, but cannot execute for steadystate.
  • Display variables are used on plots.

• 0.0.6 5/27/2025

  • Time courses simulate onestep, stochastic, steadystate
  • Refactored API.

• 0.0.5 5/24/2025

  • Added ".xml" to SBML files
  • Model files are created in a target directory
  • Files created during tests are eliminated
  • Create separate test module for testing SingleModelTimeCourse
  • __init__ exposes makeSingleModelTimeCourse, makeMultipleModelTimeCourse, getModelInformtation, SimpleSEDML.
  • Create an OMEX file and validate it