def test_reconstruct(self, b_theta_genome_id, b_theta_id, b_theta_name):
stats = mackinac.reconstruct_modelseed_model(b_theta_genome_id, model_id=b_theta_id)
assert stats['id'] == b_theta_id
assert stats['name'] == b_theta_name
assert stats['num_compartments'] == 2
assert stats['num_genes'] == 739
assert stats['num_biomass_compounds'] == 85
assert stats['source'] == 'PATRIC'
f.close()
return templist
#Script
path = '/home/jan/sbml_models/'
input_list = read_list('/home/jan/Downloads/PATRIC_genome.csv',
skip_first_line=True)
modelled_strain_ids = []
model_dict = {}
gapfilled_model_dict = {}
mackinac.get_token('JBaijens')
for patric_id in input_list[43:]: #CHANGE THIS IF RESTARTING
try:
mackinac.reconstruct_modelseed_model(patric_id)
except:
pass
model_dict[patric_id] = mackinac.create_cobra_model_from_modelseed_model(
patric_id)
tempmodel = model_dict[patric_id]
if len(tempmodel.genes) > 299:
tempname = path + patric_id + '.sbml'
cobra.io.write_sbml_model(tempmodel, tempname)
modelled_strain_ids.append(patric_id)
print 'Added model %s to model_dict.' % patric_id
try:
mackinac.gapfill_modelseed_model(patric_id)
except:
pass
gapfilled_model_dict[
def create_species_models(genome_ids,
output_folder,
replace=False,
optimize=False):
""" Create ModelSEED models from a list of PATRIC genome IDs.
The ModelSEED server can be overwhelmed by too many requests so reconstructing
models is single-threaded (and got weird errors trying to use multiprocessing).
Parameters
----------
genome_ids : list of str
List of PATRIC genome IDs
output_folder: str
Path to folder where single species models are stored
replace : bool, optional
When True, always reconstruct model using ModelSEED service and replace local model
optimize : bool, optional
When True, optimize the model and check for growth
Returns
-------
list of str
List of paths to single species model files
"""
# Get a model for each genome in the list.
output_models = list()
for genome_id in genome_ids:
# If possible, use the model that was previously obtained from ModelSEED.
model_filename = join(output_folder, '{0}.json'.format(genome_id))
if exists(model_filename) and not replace:
output_models.append(model_filename)
else:
reconstruct = False
try:
get_modelseed_model_stats(genome_id)
if replace:
delete_modelseed_model(genome_id)
reconstruct = True
except ObjectNotFoundError:
reconstruct = True
# If needed, reconstruct and gap fill a model from the genome.
if reconstruct:
reconstruct_modelseed_model(genome_id)
gapfill_modelseed_model(genome_id)
# Create a COBRA model and save in JSON format.
model = create_cobra_model_from_modelseed_model(genome_id)
save_json_model(model, model_filename)
output_models.append(model_filename)
# If requested, verify ModelSEED models produce growth.
if optimize:
for model_filename in output_models:
model = load_json_model(model_filename)
solution = model.optimize()
if solution.f <= 0.0001:
warn('Model {0} does not produce growth'.format(model.id))
return output_models
mackinac.genome.patric_url = 'https://www.patricbrc.org/api/'
mackinac.get_token('asuccurro')
orgs = {'Root491': '1736548.3', 'Root9': '1736604.3', 'Root66D1': '1736582.3'}
mymodels = mackinac.list_modelseed_models()
mymodelsnames = ';'.join(map(lambda y: y['ref'], mymodels))
gprs = {'Root491': {}, 'Root9': {}, 'Root66D1': {}}
for o in ['Root491', 'Root9', 'Root66D1']:
#for o in ['Root491']:
if orgs[o] not in mymodelsnames:
print('Reconstructing modelseed model for', o, '(', orgs[o], ')')
mackinac.get_genome_summary(orgs[o])
mackinac.reconstruct_modelseed_model(orgs[o])
mackinac.get_modelseed_model_stats(orgs[o])
model = mackinac.get_modelseed_model_data(orgs[o])
for r in model['modelreactions']:
tmpgprs = []
for p in r['modelReactionProteins']:
for s in p['modelReactionProteinSubunits']:
if s['feature_refs'] == []:
print(s)
gpr = 'Unknown'
else:
gpr = ' OR '.join(
map(lambda y: y.split('/')[-1], s['feature_refs']))
tmpgprs.append('(%s)' % gpr)
gprs[o][r['id']] = ' AND '.join(tmpgprs)
def test_model(b_theta_genome_id, b_theta_id):
# Reconstruct a model so there is a folder in the workspace.
stats = mackinac.reconstruct_modelseed_model(b_theta_genome_id,
model_id=b_theta_id)
yield stats
mackinac.delete_modelseed_model(b_theta_id)