msgrowthphenotypes

MSGrowthPhenotype

A class that defines a growth phenotype and constructs media for the phenotype:

mspheno = MSGrowthPhenotype(obj_id, media=None, growth=None, gene_ko=[], additional_compounds=[], parent=None, name=None)

  • obj_id & name str: The ID and name of the growth phenotype.

  • media modelseedpy.core.msmedia.MSMedia: The media in which the phenotype will be simulated.

  • growth float: The objective value of the growth phenotype.

  • gene_ko list: The collection of genes that are knocked-out in association with this phenotype.

  • additional_compounds list: A collection of compounds that will construct a media through the build_media function.

  • parent list: The source of base media information, through the base_media, base_uptake, and base_excretion attributes.

build_media()

Returns a media that is constructed as the amalgamation of extant media and parent media and the additional_compounds:

media = mspheno.build_media()

Returns media cobra.core.msmedia.MSMedia: The media that is constituted from the additional_compounds and existing media in the media and parent attributes of the MSGrowthPhenotype object.

simulate()

Simulates the growht phenotype of a model that is defined within MSModelUtils object:

results = mspheno.simulate(modelutl, growth_threshold=0.001, add_missing_exchanges=False, save_fluxes=False, pfba=False)

  • modelutl modelseedpy.core.msmodelutl.MSModelUtil: A MSModelUtils object which possesses the model that will be manipulated and simulated.

  • growth_threshold float: The objective value threshold for the growth phenotype that is examined by this class.

  • add_missing_exchanges bool: specifies whether the missing exchange reactions will be added to the model.

  • save_fluxes bool: specifies whether the solution fluxes will be stored in the results dictionary.

Returns results dict: The organization of simulation results and intermediate values in key-value pairs, including whether the growth predictions were correct or false.

gapfill_model_for_phenotype()

Formats COBRA reactions and metabolites for ModelSEED operations, respectively:

gpf_model = gfhelper.convert_modelreaction(modelutl, default_gapfill_templates, test_conditions, default_gapfill_models=[], blacklist=[], growth_threshold=0.001, add_missing_exchanges=False)

  • modelutl modelseedpy.core.msmodelutl.MSModelUtil: A MSModelUtils object which possesses the model that will be gapfilled.

  • default_gapfill_templates & test_conditions list: A collection of gapfilling templates and test conditions that will be used to gapfill the model.

  • default_gapfill_models list: The collection of models that will extend modelutl.model for gapfilling.

  • blacklist list: The collection of reactions that will not be included during gapfilling.

  • growth_threshold float: The objective value threshold for the growth phenotype that is examined by this class.

  • add_missing_exchanges bool: specifies whether the missing exchange reactions will be added to the model.

Returns gpf_model cobra.core.model.Model: The gapfilled model.

MSGrowthPhenotypes

A class that defines a growth phenotype and combines phenotypes that are defined through MSGrowthPhenotype:

mspheno = MSGrowthPhenotype(base_media=None, base_uptake=0, base_excretion=1000)

  • base_media modelseedpy.core.msmedia.MSMedia: The media that is associated with the growth phenotype.

  • base_uptake & base_excretion int: The uptake and excretion fluxes for the examined phenotype.

from_compound_hash()

staticMethod Returns a MSGrowthPhenotypes object that is constructed from a dictionary that describes the compounds of the phenotype:

growthpheno = MSGrowthPhenotypes.from_compound_hash(compounds, base_media, base_uptake=0, base_excretion=1000)

  • compounds list: The collection of compounds that will comprise the growth phenotype.

  • base_media str: The media that is associated with the growth phenotype.

  • base_uptake & base_excretion int: The uptake and excretion fluxes for the examined phenotype.

Returns growthpheno modelseedpy.core.msgrowthphenotypes.MSGrowthPhenotypes: A growth phenotype that is constructed from a dictionary that describes a compound.

from_kbase_object()

staticMethod Returns a MSGrowthPhenotypes object that is constructed from KBase through the kbase API object:

growthpheno = MSGrowthPhenotypes.from_compound_hash(data, kbase_api)

  • data dict: The collection of phenotypes that will be defined and examined (values), under the phenotypes key.

  • kbase_api KBase API: The KBase API object that can acquire media information from a KBase reference from each phenotype.

Returns growthpheno modelseedpy.core.msgrowthphenotypes.MSGrowthPhenotypes: The collective of growth phenotypes that are defined from the data dictionary.

from_kbase_file()

staticMethod Returns a MSGrowthPhenotypes object that is constructed from a KBase TSV file:

growthpheno = MSGrowthPhenotypes.from_kbase_file(filename, base_media, kbase_api)

  • filename str: The name of the TSV file – with a header of “media mediaws growth geneko addtlCpd” – that will be parsed into a MSGrowthPhenotypes object.

  • base_media str: The media that is associated with the growth phenotype.

  • kbase_api KBase API: The KBase API object that can acquire media information from a KBase reference from each phenotype.

Returns growthpheno modelseedpy.core.msgrowthphenotypes.MSGrowthPhenotypes: The collective of growth phenotypes that are defined from the data dictionary.

from_ms_file()

staticMethod Returns a MSGrowthPhenotypes object that is constructed from a ModelSEED CSV file:

growthpheno = MSGrowthPhenotypes.from_ms_file(filename, base_media, base_uptake=0, base_excretion=100)

  • filename str: The name of the CSV file – with a header of “media mediaws growth geneko addtlCpd” – that will be parsed into a MSGrowthPhenotypes object.

  • base_media str: The media that is associated with the growth phenotype.

  • base_uptake & base_excretion int: The uptake and excretion fluxes for the examined phenotype.

Returns growthpheno modelseedpy.core.msgrowthphenotypes.MSGrowthPhenotypes: The collective of growth phenotypes that are defined from the data dictionary.

add_phenotypes()

Constructs a metadata dictionary of a COBRA Reaction object that is returned and can be added to a KBase model:

MSGrowthPhenotypes.add_phenotypes(new_phenotypes)

  • new_phenotypes list: The collection of phenotypes that will be added to the MSGrowthPhenotypes object list of phenotypes.

simulate_phenotypes()

Coverts an old objective function into a variable and constructs a new constraint that the new objective must equate the old object. The variable and constraint are added to the cobramodel in the extant object:

gfhelper.convert_objective_to_constraint(model, biomass, add_missing_exchanges=False, correct_false_negatives=False, template=None, growth_threshold=0.001)

  • model cobra.core.model.Model: The model wqhose phenotypes will be simulated.

  • biomass cobra.core.reaction.Reaction: The biomass reaction which is set as the model objective.

  • add_missing_exchanges bool: specifies whether the missing exchange reactions will be added to the model.

  • correct_false_negatives bool: specifies whether false negatives from each phenotype simulation will be corrected.

  • template modelseedpy.core.mstemplate.MSTemplate: The model template that is used to gapfill the model.

  • growth_threshold float: The objective value threshold for the growth phenotypes.