petab.sbml

Functions for direct access of SBML models

Functions

add_global_parameter(sbml_model, …) Add new global parameter to SBML model
add_model_output(sbml_model, observable_id, …) Add PEtab-style output to model
add_model_output_sigma(sbml_model, …) Add PEtab-style sigma for the given observable id
add_model_output_with_sigma(sbml_model, …) Add PEtab-style output and corresponding sigma with single (newly created) parameter
assignment_rules_to_dict(sbml_model[, …]) Turn assignment rules into dictionary.
constant_species_to_parameters(sbml_model) Convert constant species in the SBML model to constant parameters.
create_assigment_rule(sbml_model, …) Create SBML AssignmentRule
globalize_parameters(sbml_model, …) Turn all local parameters into global parameters with the same properties
is_sbml_consistent(sbml_document, check_units) Check for SBML validity / consistency
log_sbml_errors(sbml_document[, …]) Log libsbml errors
petab.sbml.add_global_parameter(sbml_model: libsbml.Model, parameter_id: str, parameter_name: str = None, constant: bool = False, units: str = 'dimensionless', value: float = 0.0) → libsbml.Parameter

Add new global parameter to SBML model

petab.sbml.add_model_output(sbml_model: libsbml.Model, observable_id: str, formula: str, observable_name: str = None) → None

Add PEtab-style output to model

We expect that all formula parameters are added to the model elsewhere.

Parameters:
  • sbml_model – Model to add output to
  • formula – Formula string for model output
  • observable_id – ID without “observable_” prefix
  • observable_name – Any observable name
petab.sbml.add_model_output_sigma(sbml_model: libsbml.Model, observable_id: str, formula: str)

Add PEtab-style sigma for the given observable id

We expect that all formula parameters are added to the model elsewhere.

Parameters:
  • sbml_model – Model to add to
  • observable_id – Observable id for which to add sigma
  • formula – Formula for sigma
petab.sbml.add_model_output_with_sigma(sbml_model: libsbml.Model, observable_id: str, observable_formula: str, observable_name: str = None)

Add PEtab-style output and corresponding sigma with single (newly created) parameter

We expect that all formula parameters are added to the model elsewhere.

Parameters:
  • sbml_model – Model to add output to
  • observable_formula – Formula string for model output
  • observable_id – ID without “observable_” prefix
  • observable_name – Any name
petab.sbml.assignment_rules_to_dict(sbml_model: libsbml.Model, filter_function=<function <lambda>>, remove: bool = False) → Dict[str, Dict[str, Any]]

Turn assignment rules into dictionary.

Parameters:
  • sbml_model – a sbml model instance.
  • filter_function – callback function taking assignment variable as input and returning True/False to indicate if the respective rule should be turned into an observable.
  • remove – Remove the all matching assignment rules from the model
Returns:

{
    assigneeId:
    {
        'name': assigneeName,
        'formula': formulaString
    }
}

petab.sbml.constant_species_to_parameters(sbml_model: libsbml.Model) → list

Convert constant species in the SBML model to constant parameters.

This can be used e.g. for setting up models with condition-specific constant species for PEtab, since there it is not possible to specify constant species in the condition table.

Parameters:sbml_model – libsbml model instance
Returns:species IDs that have been turned into constants
petab.sbml.create_assigment_rule(sbml_model: libsbml.Model, assignee_id: str, formula: str, rule_id: str = None, rule_name: str = None) → libsbml.AssignmentRule

Create SBML AssignmentRule

Parameters:
  • sbml_model – Model to add output to
  • assignee_id – Target of assignment
  • formula – Formula string for model output
  • rule_id – SBML id for created rule
  • rule_name – SBML name for created rule
petab.sbml.globalize_parameters(sbml_model: libsbml.Model, prepend_reaction_id: bool = False) → None

Turn all local parameters into global parameters with the same properties

Local parameters are currently ignored by other PEtab functions. Use this function to convert them to global parameters. There may exist local parameters with identical IDs within different kinetic laws. This is not checked here. If in doubt that local parameter IDs are unique, enable prepend_reaction_id to create global parameters named ${reaction_id}_${local_parameter_id}.

Parameters:
  • sbml_model – The SBML model to operate on
  • prepend_reaction_id – Prepend reaction id of local parameter when creating global parameters
petab.sbml.is_sbml_consistent(sbml_document: libsbml.SBMLDocument, check_units: bool = False)

Check for SBML validity / consistency

Parameters:
  • sbml_document – SBML document to check
  • check_units – Also check for unit-related issues
Returns:

False if problems were detected, otherwise True
petab.sbml.log_sbml_errors(sbml_document: libsbml.SBMLDocument, minimum_severity=1) → None

Log libsbml errors

Parameters:
  • sbml_document – SBML document to check
  • minimum_severity – Minimum severity level to report (see libsbml)