Simple SED-ML example¶
This notebook creates a simple model in SED-ML version 1 level 4. It builds a model of metabolite A degrading.
1) Including libraries¶
In [6]:
from combine_notebooks import RESULTS_DIR
import libsedml
import tellurium as te
import tempfile, os
2) Declaring the SED-ML model¶
First we create a SED-ML document at version 1 level 4.
In [7]:
doc = libsedml.SedDocument(1, 4)
Create a first model referencing an sbml file.
In [8]:
model = doc.createModel()
model.setId("model1")
model.setSource("sbml.xml")
model.setLanguage("urn:sedml:language:sbml.level-3.version-1")
Out[8]:
0
Create a simulation and set the important parameters (e.g. inital time, number of steps). We also specify that we want to use the CVODE algorithm, which is a method for integrating ordinary differential equations.
In [9]:
# create simulation
tc = doc.createUniformTimeCourse()
tc.setId("sim1")
tc.setInitialTime(0.0)
tc.setOutputStartTime(0.0)
tc.setOutputEndTime(5.0)
tc.setNumberOfSteps(100)
# need to set the correct KISAO Term
alg = tc.createAlgorithm()
alg.setName("CVODE")
alg.setKisaoID("KISAO:0000019")
Out[9]:
0
Create a task that uses the simulation and the model above.
In [10]:
task = doc.createTask()
task.setId("task1")
task.setModelReference("model1")
task.setSimulationReference("sim1")
Out[10]:
0
Add three DataGenerator to hold the outputs.
In [11]:
dg = doc.createDataGenerator()
dg.setId("plot_0_0_0")
dg.setName("time")
var = dg.createVariable()
var.setId("time")
var.setTaskReference("task1")
var.setModelReference("model1")
var.setSymbol("urn:sedml:symbol:time")
dg.setMath(libsedml.parseFormula("time"))
dg = doc.createDataGenerator()
dg.setId("plot_0_0_1")
dg.setName("S1")
var = dg.createVariable()
var.setId("S1")
var.setTaskReference("task1")
var.setModelReference("model1")
var.setTarget("/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S1']")
dg.setMath(libsedml.parseFormula("S1"))
dg = doc.createDataGenerator()
dg.setId("plot_0_1_1")
dg.setName("S2")
var = dg.createVariable()
var.setId("S2")
var.setTaskReference("task1")
var.setModelReference("model1")
var.setTarget("/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S2']")
dg.setMath(libsedml.parseFormula("S2"))
Out[11]:
0
Finally, we will create a 2d plot with two curves.
In [12]:
plot = doc.createPlot2D()
plot.setId("plot_0")
plot.setName("Figure 1")
curve = plot.createCurve()
curve.setId("plot_0__plot_0_0_0__plot_0_0_1")
curve.setLogX(False)
curve.setLogY(False)
curve.setXDataReference("plot_0_0_0")
curve.setYDataReference("plot_0_0_1")
curve = plot.createCurve()
curve.setId("plot_0__plot_0_0_0__plot_0_1_1")
curve.setLogX(False)
curve.setLogY(False)
curve.setXDataReference("plot_0_0_0")
curve.setYDataReference("plot_0_1_1")
Out[12]:
0
3) Write and print the generated model¶
And we're done creating the basic model. Now save a text string containing the model into a SED-ML file.
In [13]:
sedml_loc = str(RESULTS_DIR) + '/hello_world_sedml.sedml'
os.makedirs(os.path.dirname(sedml_loc), exist_ok=True)
libsedml.writeSedML(doc, sedml_loc)
sedml_str = open(sedml_loc).read()
print(sedml_str)
<?xml version="1.0" encoding="UTF-8"?>
<sedML xmlns="http://sed-ml.org/sed-ml/level1/version4" level="1" version="4">
<listOfModels>
<model id="model1" language="urn:sedml:language:sbml.level-3.version-1" source="sbml.xml"/>
</listOfModels>
<listOfSimulations>
<uniformTimeCourse id="sim1" initialTime="0" outputStartTime="0" outputEndTime="5" numberOfSteps="100">
<algorithm name="CVODE" kisaoID="KISAO:0000019"/>
</uniformTimeCourse>
</listOfSimulations>
<listOfTasks>
<task id="task1" modelReference="model1" simulationReference="sim1"/>
</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="task1" modelReference="model1"/>
</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="task1" modelReference="model1"/>
</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="task1" modelReference="model1"/>
</listOfVariables>
</dataGenerator>
</listOfDataGenerators>
<listOfOutputs>
<plot2D id="plot_0" name="Figure 1">
<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"/>
</listOfCurves>
</plot2D>
</listOfOutputs>
</sedML>
4) Simulating the model¶
In [14]:
te.setDefaultPlottingEngine('matplotlib')
# Load a SBML model we want to simulate
sbml_loc = str(RESULTS_DIR) + "/hello_world_sbml.xml"
sbml_str = open(sbml_loc).read()
# Load SED-ML string created in section 2
sedml_str = open(sedml_loc).read()
# Create a temporary directory to store SED-ML and SBML files
workingDir = tempfile.mkdtemp(suffix="_sedml")
# Get paths for a temporary file, the filename here should
# match what is in our SED-ML document
sbml_file = os.path.join(workingDir, 'sbml.xml')
# Save SBML to the temporary file
with open(sbml_file, 'wb') as f:
f.write(sbml_str.encode('utf-8'))
f.flush()
print('SBML written to temporary file')
SBML written to temporary file
In [15]:
# Validate our SED-ML
n_errors = doc.getErrorLog().getNumFailsWithSeverity(libsedml.LIBSEDML_SEV_ERROR)
print('Read SED-ML file, number of errors: {}'.format(n_errors))
if n_errors > 0:
print(doc.getErrorLog().toString())
# execute SED-ML using Tellurium
te.executeSEDML(sedml_str, workingDir=workingDir)
Read SED-ML file, number of errors: 0
