SimpleSEDML

A simple API for using the Simulation Experiment Description Markup Language (SED-ML), a community standard for describing simulation experiments.

The project exposes an programmatic interface to the textual capabilities provided by phraSED-ML to describe simulation experiments. SimpleSEDML uses the abstractions provided by phraSED-ML to generate and validate SED-ML. PhraSEDML is a human-readable language designed for creating simulation experiment descriptions that can be converted to SED-ML (Simulation Experiment Description Markup Language). It serves as an easier way to author SED-ML files, which are XML-based documents used to describe computational experiments in systems biology. Key features of PhraSEDML include:

• Human-Readable Syntax: Much easier to write and understand than raw XML-based SED-ML Core Components:

• Model definitions (references to SBML models) Simulation settings (time courses, steady state analyses) Task definitions (running simulations on models) Output specifications (plots, reports)

• Integration with Tellurium: In the Tellurium Python package, PhraSEDML scripts can be written and then converted to SED-ML using the phrasedml.convertString() function Model References: Uses phrasedml.setReferencedSBML() to link models mentioned in PhraSEDML to actual SBML model content Execution: After conversion to SED-ML, simulations can be executed using functions like te.executeSEDML()

• PhraSEDML simplifies the process of creating reproducible computational experiments in systems biology by providing a more intuitive syntax that gets converted to standard SED-ML files for broader compatibility with simulation tools.

For example, consider the model below in the Antimony language.

model myModel J1: S1 -> S2; k1*S1; k1 = 0.5; end

We want to simulate this model and plot the species S1, S2. To do this, we use the following phraSEDML directives:

// Define the model being usede
model1 = model "myModel"

// Define simulation settings 
sim1 = simulate uniform(0, 20, 100) // time 0 to 20, 100 points

// Define task
task1 = run sim1 on model1

// Define output
plot "Conversion of S1 to S2" time vs S1, S2
report time vs S1, S2

And let sbml_str be this model in the SBML representation. The following Python code executes the phraSEDML directives:

import tellurium as te # get the Tellurium simulator

sedml_str = phrasedml.convertString(phrasedml_str)
# Set the reference to the SBML model
phrasedml.setReferencedSBML("myModel", sbml_str)

# Convert PhraSEDML to SED-ML
sedml_str = phrasedml.convertString(phrasedml_str)

# Execute SED-ML
if sedml_str:
    te.executeSEDML(sedml_str)
else:
    # Error in the directives
    print(phrasedml.getLastPhrasedError())

The output is

Futures

SimpleSEDML new features are described below. (Items in italics parentheses require modifications to phraSEDML).

• Model: describes the model to simulate with simplifications such as avoiding the conversion of Antimony into SBML.
• Simulate: reports errors detectable from the model representation
• Task: uses an optional callback object to return results for each iteration in repeated_tasks as well as indicate when repeated tasks should stop
• Plot, Report: Indicate the data structure to populate (e.g., dictionary, dataframe)