SimpleSEDML 0.0.2

Python Subnet Discovery for Systems Biology

SimpleSEDML

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

The project provides a python interface to generating 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.
• Convenience methods are provided to simplify the API.

Example

See this Jupyter notebook for a detailed example.

Consider the model below in the Antimony language.

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

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

from simple_sedml import SimpleSEDML

sedml_str = SimpleSEDML.makeTimeCourse(mymodel)

We can print, save, or execute sedml_str. To execute it,

SimpleSEDML.executeSEDML(sedml_str)

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.

Plans

1. First implementation of SimpleSEDML with methods for addModel, addSimulation, addTask, addReport, execute, and to_sedml.