msgapfill

MSGapfill

A class that gapfills a given model:

msgap = MSGapfill(model, default_gapfill_templates=[], default_gapfill_models=[], test_conditions=[], reaction_scores={}, blacklist=[])

  • model cobra.core.model.Model: The model whose ATP Hydrolysis reaction will be corrected.

  • default_gapfill_templates & default_gapfill_models list: The collection of modelseedpy.core.mstemplate.MSTemplate templates and cobra.core.model.Model models, respectively, whose gapfilling penalties will be determined and updated during gapfilling.

  • test_conditions list: The collection of conditions in which the model is examined for feasibility.

  • reaction_scores dict: The collection of reaction scores (values) for all genes of each core reaction ID (keys).

  • blacklist list: The collection of reaction IDs that will not be included while gapfilling.

run_gapfilling()

Executes gapfilling for the specified model:

check_solution = msgap.run_gapfilling(media=None, target=None, minimum_obj=0.01, binary_check=True, solver = 'optland-cplex')
solution = msgap.run_gapfilling(media=None, target=None, minimum_obj=0.01, binary_check=False, solver = 'optland-cplex')

  • media modelseedpy.core.msmedia.MSMedia: The media in which gapfilling will occur.

  • target str: The ID of the reaction that will be set as the gapfilling objective.

  • minimum_obj float: The minimum tolerable objective value, which is constrained as the lower bound of the objective function.

  • binary_check bool: specifies whether the reaction directions for all gapfilled reactions will be returned.

  • solver str: The ID specification of the linear programming solver that is used during optimization.

Returns check_solution dict: The collection of “<” or “>” directions for all reversed reactions in the model that are described with gapfilling penalties.

Returns solution cobra.core.solution.Solution: The COBRA optimization solution.

integrate_gapfill_solution()

Embeds a gapfilling solution into a model:

new_model = msgap.run_gapfilling(solution)

  • solution cobra.core.solution.Solution: The optimization solution that will be embedded in the model within the MSGapfill object.

Returns new_model cobra.core.model.Model: The COBRA model that is updated with the gapfilling optimization solution.

gapfill()

staticMethod Executes gapfilling of the specified :

new_model = MSGapfill.gapfill(model, media=None, target_reaction="bio1", default_gapfill_templates=[], default_gapfill_models=[], test_conditions=[], reaction_scores={}, blacklist=[])

  • model cobra.core.model.Model: The model whose ATP Hydrolysis reaction will be corrected.

  • media modelseedpy.core.msmedia.MSMedia: The media in which gapfilling will occur.

  • target_reaction str: The ID of the reaction that will be set as the gapfilling objective.

  • default_gapfill_templates & default_gapfill_models list: The collection of modelseedpy.core.mstemplate.MSTemplate templates and cobra.core.model.Model models, respectively, whose gapfilling penalties will be determined and updated during gapfilling.

  • test_conditions list: The collection of conditions in which the model is examined for feasibility.

  • reaction_scores dict: The collection of reaction scores (values) for all genes of each core reaction ID (keys).

  • blacklist list: The collection of reaction IDs that will not be included while gapfilling.

Returns new_model cobra.core.model.Model: The COBRA model that is updated with the gapfilling optimization solution.