2.1.2.10. enkie.parameter_space

A class representing parameter values and their uncertainty.

2.1.2.10.1. Module Contents

class enkie.parameter_space.ParameterSpace(reactions: List[enkie.reaction.Reaction], rate_laws: List[enkie.modular_rate_law.ModularRateLaw], enzymes: List[enkie.enzyme.Enzyme], metabolites: List[enkie.metabolite.Metabolite], parameters: enkie.compartment_parameters.CompartmentParameters = None, estimator: enkie.estimators.parameter_balancer.ParameterBalancer = None)[source]

A class representing parameter values and their uncertainty.

Parameters:

  • reactions (List[Reaction]) – The reactions included in the parameter space.

  • rate_laws (List[ModularRateLaw]) – The rate laws describing the reaction kinetics.

  • enzymes (List[Enzyme]) – The enzymes associated with the rate laws.

  • metabolites (List[Metabolite]) – The metabolites included in the parameter space.

  • parameters (CompartmentParameters, optional) – The physiological parameters of the reaction compartments, by default None

  • estimator (ParameterBalancer, optional) – The object used for estimating kinetic and thermodynamic parameters, by default None

property core_mean: pandas.Series[source]

The mean of the core variables (standard formation energies, log velocities and log affinities).

property core_cov: pandas.DataFrame[source]

The covariance of the core variables (standard formation energies, log velocities and log affinities).

property core_to_all[source]

The linear transform from the core parameters to all parameters.

property mean: pandas.Series[source]

The mean of all parameter values (standard formation and reaction energies, log velocities, log catalytic rates and log affinities).

property cov: pandas.DataFrame[source]

The covariance of all parameter values (standard formation and reaction energies, log velocities, log catalytic rates and log affinities).

property metadata: pandas.DataFrame[source]

The association between rate law identifiers and genes.

sample(num_samples: int, parameters: Union[List[str], List[Tuple[str, str]]] = None) → pandas.DataFrame[source]

Draw parameter samples from their estimated distribution.

Parameters:

  • num_samples (int) – Number of parameter samples to draw.

  • parameters (Union[List[str], List[Tuple[str, str]]], optional) – The parameters to draw samples for, by default None (all parameters)

Returns:

The parameter samples.

Return type:

pd.DataFrame