sbmlsim.examples.experiments.glucose.experiments.dose_response

Module Contents

Classes

DoseResponseExperiment

Hormone dose-response curves.

class sbmlsim.examples.experiments.glucose.experiments.dose_response.DoseResponseExperiment(sid=None, base_path=None, data_path=None, ureg=None, **kwargs)

Bases: sbmlsim.experiment.SimulationExperiment

Hormone dose-response curves.

Parameters:

• sid (str) –

• base_path (pathlib.Path) –

• data_path (pathlib.Path) –

• ureg (sbmlsim.units.UnitRegistry) –

models()

Define model definitions.

The child classes fill out the information.

Return type:

Dict[str, Union[sbmlsim.model.AbstractModel, pathlib.Path]]

datasets()

Define dataset definitions (experimental data).

The child classes fill out the information.

Return type:

Dict[str, sbmlsim.data.DataSet]

tasks()

Tasks

Return type:

Dict[str, sbmlsim.task.Task]

simulations()

Scanning dose-response curves of hormones and gamma function.

Vary external glucose concentrations (boundary condition).

Return type:

Dict[str, sbmlsim.simulation.ScanSim]

data()

Define DataGenerators including functions.

This determines the selection in the model.

All data which is accessed in a simulation result must be defined in a data generator. The data generators are important for defining the selections of a simulation experiment.

Return type:

Dict[str, sbmlsim.data.Data]

figures_mpl()

Matplotlib figure definition.

Selections accessed in figures and analyses must be registered beforehand via datagenerators.

Most figures do not require access to concrete data, but only abstract data concepts.

Return type:

Dict[str, matplotlib.pyplot.Figure]