geckopy.integration

Package Contents

Functions

relax_thermo_concentrations_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE, metabolites_to_ignore: Optional[Set[str]] = None) → Set

Get one IIS of protein concentrations and the log concentration constraints.

relax_thermo_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE) → Set

Get one IIS of protein concentrations and the \(\Delta G_r\) constraints.
geckopy.integration.relax_thermo_concentrations_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE, metabolites_to_ignore: Optional[Set[str]] = None) → Set[source]

Get one IIS of protein concentrations and the log concentration constraints.

__Inplace__ operation. The log concentrations refer to metabolomics constraints in the MILP as formulated by pytfa. This combines both pytfa.optim.relax_lc and geckopy.experimental.relaxation.get_upper_relaxation() and avoids copying the model since that fails for a pytfa.ThermoModel with proteins.

Parameters

  • original_model (cobra.Model) –

  • elastic_candidates (list[str]) –

  • objective_rule (Objective_rule) – The IIS is selected by minimizing an objective as defined in Objective_rule.

  • metabolites_to_ignore (Optional[Set[str]]) – Metabolites whose LogConcentration will not be used in the relaxation of the problem. Proteins will be automatically added to this set.

Returns

  • iis (Set) – variables that relax the model to make it feashible.

  • status (str)

geckopy.integration.relax_thermo_proteins(model: M, prot_candidates: Set[str], objective_rule: geckopy.experimental.relaxation.Objective_rule = Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE) → Set[source]

Get one IIS of protein concentrations and the \(\Delta G_r\) constraints.

__Inplace__ operation. This combines both pytfa.optim.relax_dgo and geckopy.experimental.relaxation.get_upper_relaxation() and avoids copying the model since that fails for a pytfa.ThermoModel with proteins.

Parameters

  • original_model (cobra.Model) –

  • elastic_candidates (list[str]) –

  • objective_rule (Objective_rule) – The IIS is selected by minimizing an objective as defined in Objective_rule.

Returns

  • iis (Set) – variables that relax the model to make it feashible.

  • status (str)