def predict_pathways(self, product, hosts, database, aerobic=True):
"""Predict production routes for a desired product and host spectrum.
Parameters
----------
product : str or Metabolite
The desired product.
hosts : list
A list of hosts (e.g. cameo.api.hosts), models, mixture thereof, or a single model or host.
database: Model
A model to use as database. See also: cameo.models.universal
aerobic: bool
If True, it will set `model.reactions.EX_o2_e.lower_bound` to 0.
Returns
-------
dict
([Host, Model] -> PredictedPathways)
"""
pathways = dict()
product = self.__translate_product_to_universal_reactions_model_metabolite(
product, database)
for host in hosts:
logging.debug('Processing host {}'.format(host.name))
if isinstance(host, Model):
host = Host(name='UNKNOWN_HOST', models=[host])
for model in list(host.models):
with model:
if not aerobic and "EX_o2_e" in model.reactions:
model.reactions.EX_o2_e.lower_bound = 0
identifier = searching()
logging.debug('Processing model {} for host {}'.format(
model.id, host.name))
notice(
'Predicting pathways for product %s in %s (using model %s).'
% (product.name, host, model.id))
logging.debug('Predicting pathways for model {}'.format(
model.id))
pathway_predictor = pathway_prediction.PathwayPredictor(
model, universal_model=database)
predicted_pathways = pathway_predictor.run(
product,
max_predictions=self.options.max_pathway_predictions,
timeout=self.options.pathway_prediction_timeout,
silent=not self.options.verbose)
stop_loader(identifier)
pathways[(host, model)] = predicted_pathways
self.__display_pathways_information(
predicted_pathways, host, model)
return pathways
def predict_pathways(
self,
product,
hosts=None,
database=None): # TODO: make this work with a single host or model
"""Predict production routes for a desired product and host spectrum.
Parameters
----------
product : str or Metabolite
The desired product.
hosts : list or Model or Host
A list of hosts (e.g. cameo.api.hosts), models, mixture thereof, or a single model or host.
Returns
-------
dict
...
"""
pathways = dict()
product = self.__translate_product_to_universal_reactions_model_metabolite(
product, database)
for host in hosts:
logging.debug('Processing host {}'.format(host.name))
if isinstance(host, Model):
host = Host(name='UNKNOWN_HOST', models=[host])
for model in list(host.models):
identifier = searching()
logging.debug('Processing model {} for host {}'.format(
model.id, host.name))
notice(
'Predicting pathways for product %s in %s (using model %s).'
% (product.name, host, model.id))
logging.debug('Predicting pathways for model {}'.format(
model.id))
pathway_predictor = pathway_prediction.PathwayPredictor(
model,
universal_model=database,
compartment_regexp=re.compile(".*_c$"))
# TODO adjust these numbers to something reasonable
predicted_pathways = pathway_predictor.run(product,
max_predictions=4,
timeout=3 * 60,
silent=True)
stop_loader(identifier)
pathways[(host, model)] = predicted_pathways
self.__display_pathways_information(predicted_pathways, host,
model)
return pathways
def predict_pathways(self,
product,
hosts=None,
database=None,
aerobic=True):
"""Predict production routes for a desired product and host spectrum.
Parameters
----------
product : str or Metabolite
The desired product.
hosts : list or Model or Host
A list of hosts (e.g. cameo.api.hosts), models, mixture thereof, or a single model or host.
database: SolverBasedModel
A model to use as database. See also: cameo.models.universal
aerobic: bool
If True, it will set `model.reactions.EX_o2_e.lower_bound` to 0.
Returns
-------
dict
([Host, Model] -> PredictedPathways)
"""
max_predictions = 8
timeout = 3 * 60
pathways = dict()
product = self.__translate_product_to_universal_reactions_model_metabolite(
product, database)
for host in hosts:
logging.debug('Processing host {}'.format(host.name))
if isinstance(host, Model):
host = Host(name='UNKNOWN_HOST', models=[host])
for model in list(host.models):
with TimeMachine() as tm:
if not aerobic and "EX_o2_e" in model.reactions:
model.reactions.EX_o2_e.change_bounds(lb=0,
time_machin=tm)
identifier = searching()
logging.debug('Processing model {} for host {}'.format(
model.id, host.name))
notice(
'Predicting pathways for product %s in %s (using model %s).'
% (product.name, host, model.id))
logging.debug('Predicting pathways for model {}'.format(
model.id))
pathway_predictor = pathway_prediction.PathwayPredictor(
model,
universal_model=database,
compartment_regexp=re.compile(".*_c$"))
if self.debug:
max_predictions = 2
timeout = 60
predicted_pathways = pathway_predictor.run(
product,
max_predictions=max_predictions,
timeout=timeout,
silent=True)
stop_loader(identifier)
pathways[(host, model)] = predicted_pathways
self.__display_pathways_information(
predicted_pathways, host, model)
return pathways