def __init__(self,
model_code='ecoli:iJO1366',
solver='gurobi',
min_biomass=0.55):
self.logger = get_bistream_logger(
(model_code + ':' + self.__class__.__name__).replace(':', '_'))
self.logger.setLevel(logging.INFO)
self.species = model_code.split(':')[0]
self.model_name = model_code.split(':')[-1]
if self.species.lower() == 'ecoli':
if self.model_name.lower() == 'iml1515':
self.model = load_json_model(
os.path.dirname(os.path.abspath(__file__)) +
'/data/ecoli/iML1515.json')
self.biomass_reaction = 'BIOMASS_Ec_iML1515_core_75p37M'
else:
self.model = load_json_model(
os.path.dirname(os.path.abspath(__file__)) +
'/data/ecoli/iJO1366.json')
self.biomass_reaction = 'BIOMASS_Ec_iJO1366_core_53p95M'
self.model.objective = self.biomass_reaction
self.model.reactions.get_by_id(
self.biomass_reaction).lower_bound = min_biomass
self.model.solver = solver
self._init_model()
self._print_summary()
self.reserved_bounds = {}
self.dummy_reactions = set()
def _load_model(self, file_storage):
try:
filename, content = self._decompress(file_storage.filename.lower(),
file_storage)
except IOError as err:
msg = "Failed to decompress file."
LOGGER.exception(msg)
api.abort(400, msg, error=str(err))
try:
if file_storage.mimetype in self.JSON_TYPES or \
filename.endswith("json"):
LOGGER.debug("Loading model from JSON.")
model = load_json_model(content)
elif file_storage.mimetype in self.XML_TYPES or \
filename.endswith("xml") or filename.endswith("sbml"):
LOGGER.debug("Loading model from SBML.")
model = read_sbml_model(content)
else:
msg = f"'{file_storage.mimetype}' is an unhandled MIME type."
LOGGER.error(msg)
api.abort(415, msg, recognizedMIMETypes=list(chain(
self.JSON_TYPES, self.XML_TYPES)))
except (CobraSBMLError, ValueError) as err:
msg = "Failed to parse model."
LOGGER.exception(msg)
api.abort(400, msg, error=str(err))
finally:
content.close()
file_storage.close()
return model
def load_model_from_file(filename):
""" Load a model from a file based on the extension of the file name.
Parameters
----------
filename : str
Path to model file
Returns
-------
cobra.core.Model
Model object loaded from file
Raises
------
IOError
If model file extension is not supported.
"""
(root, ext) = splitext(filename)
if ext == '.mat':
model = load_matlab_model(filename)
elif ext == '.xml' or ext == '.sbml':
model = read_sbml_model(filename)
elif ext == '.json':
model = load_json_model(filename)
else:
raise IOError(
'Model file extension not supported for {0}'.format(filename))
return model
def load_universal_modelseed():
seed_rxn_table = pd.read_csv(
'./medusa/test/data/reactions_seed_20180809.tsv', sep='\t')
seed_rxn_table['id'] = seed_rxn_table['id'] + '_c'
universal = load_json_model('./medusa/test/data/universal_mundy.json')
# remove any reactions from the universal that don't have "OK" status
# in modelSEED (guards against mass and charge-imbalanced reactions)
ok_ids = list(seed_rxn_table.loc[(seed_rxn_table['status'] == 'OK') |
(seed_rxn_table['status'] == 'HB')]['id'])
remove_rxns = []
for reaction in universal.reactions:
if reaction.id not in ok_ids:
remove_rxns.append(reaction)
universal.remove_reactions(remove_rxns)
# remove metabolites from the universal that are no longer present in any
# reactions.
mets_in_reactions = []
for reaction in universal.reactions:
mets = [met.id for met in reaction.metabolites]
mets_in_reactions.extend(mets)
mets_in_reactions = set(mets_in_reactions)
mets_missing_reactions = []
for metabolite in universal.metabolites:
if metabolite.id not in mets_in_reactions:
mets_missing_reactions.append(metabolite)
universal.remove_metabolites(mets_missing_reactions)
universal.repair()
return universal
def _create_visualisation(model_filename, svg_filename, output_filename, analysis_type='FBA',
analysis_results=None, intermediate_filename=None):
# Check arguments
supported_analysis_types = {"FBA", "FVA"}
if analysis_type not in supported_analysis_types:
message = "Analysis type is wrong. It has to be one of the values: {}"
raise ValueError(message.format(supported_analysis_types))
try:
model = cio.load_json_model(model_filename)
except Exception as exc:
raise CobraModelFileError("Failed to load model from given JSON : {}".format(exc.args))
# Set default arguments if none provided
if analysis_results is None:
fba_results = model.optimize()
if analysis_type == 'FBA':
fba_results.fluxes = fba_results.fluxes.round(5)
analysis_results = fba_results
elif analysis_type == 'FVA':
fva_results = flux_variability_analysis(model, fraction_of_optimum=0.5)
fva_results = fva_results.round(3)
analysis_results = fva_results
vizan_kwargs = {
'model': model,
'file_source_path': svg_filename,
'analysis_results': analysis_results,
'analysis_type': analysis_type,
'output_filename': output_filename,
}
if intermediate_filename is None:
with NamedTemporaryFile(mode="w") as intermediate_file:
vizan_kwargs['intermediate_filename'] = intermediate_file.name
produce_output_file(**vizan_kwargs)
else:
vizan_kwargs['intermediate_filename'] = intermediate_filename
produce_output_file(**vizan_kwargs)
def _load_model_from_file(path, handle):
"""Try to parse a model from a file handle using different encodings."""
logger.debug('Reading file from %s assuming pickled model.' % path)
try:
model = pickle.load(handle)
except (TypeError, pickle.UnpicklingError):
logger.debug('Cannot unpickle %s. Assuming json model next.' % path)
try:
model = load_json_model(path)
except ValueError:
logger.debug(
"Cannot import %s as json model. Assuming sbml model next." %
path)
try:
model = read_sbml_model(path)
except AttributeError as e:
logger.error(
"cobrapy doesn't raise a proper exception if a file does not contain an SBML model"
)
raise e
except Exception as e:
logger.error(
"Looks like something blow up while trying to import {} as a SBML model."
"Try validating the model at http://sbml.org/Facilities/Validator/ to get more information."
.format(path))
raise e
return model
def create_cobra_model_from_bigg_model(bigg_id, validate=False):
""" Create a COBRA model from a BiGG model.
Parameters
----------
bigg_id : str
ID of BiGG model
validate : bool, optional
When True, perform validity checks on COBRA model
Returns
-------
cobra.core.Model
COBRA model created from JSON representation of BiGG model
"""
# Download the JSON representation and details of the model from BiGG.
LOGGER.info('Started download of %s model', bigg_id)
response = requests.get('{0}models/{1}'.format(bigg_url, bigg_id))
if response.status_code != requests.codes.OK:
response.raise_for_status()
details = response.json()
response = requests.get('{0}models/{1}/download'.format(bigg_url, bigg_id))
if response.status_code != requests.codes.OK:
response.raise_for_status()
LOGGER.info('Finished download of %s model', bigg_id)
# Convert to a cobra.Model object.
with io.StringIO(response.text) as f:
model = load_json_model(f)
# Add some details to the Model object.
model.name = details['organism']
model.notes['genome_name'] = details['genome_name']
model.notes['reference_type'] = details['reference_type']
model.notes['reference_id'] = details['reference_id']
model.notes['source'] = 'BiGG'
# Confirm a few basics.
if len(model.reactions) != details['reaction_count']:
warn('{0} reactions in model does not equal {1} in model details'.
format(len(model.reactions), details['reaction_count']))
if len(model.metabolites) != details['metabolite_count']:
warn('{0} metabolites in model does not equal {1} in model details'.
format(len(model.metabolites), details['metabolite_count']))
if len(model.genes) != details['gene_count']:
warn('{0} genes in model does not equal {1} in model details'.format(
len(model.genes), details['gene_count']))
# If requested, validate the COBRA model.
if validate:
warn('Coming soon')
return model
def get_model_from_bigg(id):
try:
response = requests.get('http://bigg.ucsd.edu/api/v2/models/{}/download'.format(id))
except requests.ConnectionError as e:
logger.error("Cannot reach http://bigg.ucsd.edu. Are you sure that you are connected to the internet?")
raise e
if response.ok:
with io.StringIO(response.text) as f:
return to_solver_based_model(load_json_model(f))
else:
raise Exception(
"Could not download model {}. bigg.ucsd.edu returned status code {}".format(id, response.status_code))
def test_reaction_bounds_json(data_directory, tmp_path):
"""Test reading and writing of model with inf bounds in json"""
"""Path to XML file with INF bounds"""
path_to_xml_inf_file = join(data_directory, "fbc_ex1.xml")
model_xml_inf = cio.read_sbml_model(path_to_xml_inf_file)
path_to_output = join(str(tmp_path), "fbc_ex1_json.json")
"""Saving model with inf bounds in json form without error"""
cio.save_json_model(model_xml_inf, path_to_output)
"""Path to JSON file with INF bounds"""
path_to_JSON_inf_file = join(data_directory, "JSON_with_inf_bounds.json")
model_json_inf = cio.load_json_model(path_to_JSON_inf_file)
assert model_json_inf.reactions[0].upper_bound == float("inf")
def test_load_json_model_valid(data_directory, tmp_path):
"""Test loading a valid annotation from JSON."""
path_to_file = join(data_directory, "valid_annotation_format.json")
model = load_json_model(path_to_file)
expected = {
"bigg.reaction": [["is", "PFK26"]],
"kegg.reaction": [["is", "R02732"]],
"rhea": [["is", "15656"]],
}
for metabolite in model.metabolites:
assert metabolite.annotation == expected
path_to_output = join(str(tmp_path), "valid_annotation_output.xml")
write_sbml_model(model, path_to_output)
def get_form_data(test_analysis_type, with_analysis=False):
data = {
'model': open(test_model_filename, 'rb'),
'svg': open(test_svg_filename, 'rb'),
'analysis_type': test_analysis_type,
}
if with_analysis:
return data
else:
model = load_json_model(test_model_filename)
analysis_json = analysis_in_json(model, test_analysis_type)
data['analysis_results'] = StringIO(analysis_json)
return data
def test_load_json_model_valid(data_directory, tmp_path):
"""Test loading a valid annotation from JSON."""
path_to_file = join(data_directory, "valid_annotation_format.json")
model = load_json_model(path_to_file)
expected = {
'bigg.reaction': [['is', 'PFK26']],
'kegg.reaction': [['is', 'R02732']],
'rhea': [['is', '15656']]
}
for metabolite in model.metabolites:
assert metabolite.annotation == expected
path_to_output = join(str(tmp_path), 'valid_annotation_output.xml')
write_sbml_model(model, path_to_output)
def perform_visualisation(model_filename, svg_filename, analysis_type='FBA',
analysis_results=None, output_filename=None, intermediate_filename=None):
model = cio.load_json_model(model_filename)
if analysis_results is None:
model, analysis_results = perform_analysis(model, analysis_type)
else:
model.optimize()
if output_filename is None:
output_file = tempfile.NamedTemporaryFile(mode="w")
output_filename = output_file.name
if intermediate_filename is None:
intermediate_file = tempfile.NamedTemporaryFile(mode="w")
intermediate_filename = intermediate_file.name
vizan.call_vizan_cli(model, svg_filename, analysis_results, analysis_type, output_filename, intermediate_filename)
def load_cbm(model_name) -> Model:
"""
Load a JSON cobra model stored in the static folder.
Parameters
----------
model_name : str
The name of the model.
Returns
-------
cobra.Model
The loaded cobra model.
"""
return load_json_model(static_path(model_name + ".json"))
def get_model_from_bigg(id, solver_interface=optlang, sanitize=True):
try:
response = requests.get('http://bigg.ucsd.edu/api/v2/models/{}/download'.format(id))
except requests.ConnectionError as e:
logger.error("Cannot reach http://bigg.ucsd.edu. Are you sure that you are connected to the internet?")
raise e
if response.ok:
with io.StringIO(response.text) as f:
model = load_json_model(f)
model.solver = solver_interface
if sanitize:
sanitize_ids(model)
return model
else:
raise Exception(
"Could not download model {}. bigg.ucsd.edu returned status code {}".format(id, response.status_code))
def _load_model(self, file_storage):
try:
filename, content = self._decompress(file_storage.filename.lower(),
file_storage)
except IOError as err:
msg = f"Failed to decompress file: {str(err)}"
LOGGER.exception(msg)
abort(400, msg)
try:
if file_storage.mimetype in self.JSON_TYPES or filename.endswith(
"json"):
LOGGER.debug("Loading model from JSON using cobrapy.")
model = load_json_model(content)
elif (file_storage.mimetype in self.XML_TYPES
or filename.endswith("xml") or filename.endswith("sbml")):
LOGGER.debug("Loading model from SBML using memote.")
# Memote accepts only a file path, so write to a temporary file.
with tempfile.NamedTemporaryFile() as file_:
file_.write(content.getvalue())
file_.seek(0)
model, sbml_ver, notifications = memote.validate_model(
file_.name, )
if model is None:
LOGGER.info("SBML validation failure")
raise SBMLValidationError(
code=400,
warnings=notifications["warnings"],
errors=notifications["errors"],
)
else:
mime_types = ", ".join((chain(self.JSON_TYPES,
self.XML_TYPES)))
msg = (f"'{file_storage.mimetype}' is an unhandled MIME type. "
f"Recognized MIME types are: {mime_types}")
LOGGER.warning(msg)
abort(415, msg)
except (CobraSBMLError, ValueError) as err:
msg = f"Failed to parse model: {str(err)}"
LOGGER.exception(msg)
abort(400, msg)
finally:
content.close()
file_storage.close()
return model
def get_model_from_bigg(id, solver_interface=optlang, sanitize=True):
try:
response = requests.get(
'http://bigg.ucsd.edu/api/v2/models/{}/download'.format(id))
except requests.ConnectionError as e:
logger.error(
"Cannot reach http://bigg.ucsd.edu. Are you sure that you are connected to the internet?"
)
raise e
if response.ok:
with io.StringIO(response.text) as f:
model = load_json_model(f)
model.solver = solver_interface
if sanitize:
sanitize_ids(model)
return model
else:
raise Exception(
"Could not download model {}. bigg.ucsd.edu returned status code {}"
.format(id, response.status_code))
def fva(json):
model = load_json_model(json)
bio = {
reaction: reaction.objective_coefficient
for reaction in model.reactions if search('biomass', reaction.name)
}
biom = bio.keys()[0]
# set the objective
model.change_objective(biom)
# add constraints
model.optimize()
f0 = model.solution.f
# get a dictionary with all non-zero fluxes
fluxes = {
reaction.id: reaction.x
for reaction in model.reactions if reaction.x != 0
}
# first time store for the wild type
v = variability.flux_variability_analysis(model)
return v
def analyze(name='ptfa_mlp', model='iML1515', threshold=0):
coefs = pd.read_csv(f'output/{name}_coefs.csv', index_col=0)
# coefs = (coefs - coefs.min()) / (coefs.max() - coefs.min()) * 100
coefs = coefs[(coefs >= threshold).all(axis=1)]
model = load_json_model(
os.path.dirname(os.path.abspath(__file__)) +
f'/../simulation/data/ecoli/{model}.json')
reactions = []
for react_id in coefs.index:
score = coefs.loc[react_id, 'All_IC50']
if react_id.endswith('_b'):
react_id = react_id[:-2]
if model.reactions.has_id(react_id):
reaction = model.reactions.get_by_id(react_id)
subsystem = reaction.subsystem
subsystem = subsystem.replace(',', ':')
kegg_react_id = ''
biocyc_react_id = ''
if 'kegg.reaction' in reaction.annotation:
kegg_react_id = reaction.annotation['kegg.reaction'][0]
if 'biocyc' in reaction.annotation:
biocyc_react_id = reaction.annotation['biocyc'][0][5:]
reactions.append(
[react_id, subsystem, kegg_react_id, biocyc_react_id, score])
results = pd.DataFrame(
reactions,
columns=['id', 'subsystem', 'kegg.reaction', 'biocyc', 'score'])
results = results[~((results['kegg.reaction'] == '') |
(results['biocyc'] == ''))].set_index('id')
results = results[~results.index.duplicated()]
results.to_csv(f'output/reactions/{name}_scores.csv')
results['kegg.reaction'].to_csv(f'output/reactions/{name}_kegg.txt',
sep=' ',
index=False,
header=False)
results['biocyc'].to_csv(f'output/reactions/{name}_biocyc.txt',
sep=' ',
index=False,
header=False)
def model_loader(gem_file_path, gem_file_type):
"""Consolidated function to load a GEM using COBRApy. Specify the file type being loaded.
Args:
gem_file_path (str): Path to model file
gem_file_type (str): GEM model type - ``sbml`` (or ``xml``), ``mat``, or ``json`` format
Returns:
COBRApy Model object.
"""
if gem_file_type.lower() == 'xml' or gem_file_type.lower() == 'sbml':
model = read_sbml_model(gem_file_path)
elif gem_file_type.lower() == 'mat':
model = load_matlab_model(gem_file_path)
elif gem_file_type.lower() == 'json':
model = load_json_model(gem_file_path)
else:
raise ValueError('File type must be "sbml", "xml", "mat", or "json".')
return model
def _load_model_from_file(path, handle):
"""Try to parse a model from a file handle using different encodings."""
logger.debug('Reading file from %s assuming pickled model.' % path)
try:
model = pickle.load(handle)
except (TypeError, pickle.UnpicklingError):
logger.debug('Cannot unpickle %s. Assuming json model next.' % path)
try:
model = load_json_model(path)
except ValueError:
logger.debug("Cannot import %s as json model. Assuming sbml model next." % path)
try:
model = read_sbml_model(path)
except AttributeError as e:
logger.error("cobrapy doesn't raise a proper exception if a file does not contain an SBML model")
raise e
except Exception as e:
logger.error(
"Looks like something blow up while trying to import {} as a SBML model."
"Try validating the model at http://sbml.org/Facilities/Validator/ to get more information.".format(
path))
raise e
return model
def pub_model(request):
# get a specific model. This fixture and all the tests that use it will run
# for every model in the model_files list.
model_file = request.param
model_path = join(settings.model_directory, model_file)
# load the file
start = time()
try:
if model_path.endswith('.xml'):
# LibSBML does not like unicode filepaths in Python 2.7
pub_model = read_sbml_model(str(model_path))
elif model_path.endswith('.mat'):
pub_model = load_matlab_model(model_path)
elif model_path.endswith('.json'):
pub_model = load_json_model(model_path)
else:
raise Exception('Unrecongnized extension for model %s' % model_file)
except IOError:
raise Exception('Could not find model %s' % model_path)
print("Loaded %s in %.2f sec" % (model_file, time() - start))
return pub_model
def _load_model_from_file(path, handle):
"""Try to parse a model from a file handle using different encodings.
Adapted from cameo.
"""
try:
model = pickle.load(handle)
except (TypeError, pickle.UnpicklingError):
try:
model = load_json_model(path)
except ValueError:
try:
model = load_matlab_model(path)
except ValueError:
try:
model = read_sbml_model(path)
except AttributeError:
click.ClickException(
"cobrapy doesn't raise a proper exception"
" if a file does not contain an SBML model"
).show()
except Exception as e:
click.ClickException(e).show()
return model
def test_load_json_model(data_directory, mini_model):
"""Test the reading of JSON model."""
json_model = cio.load_json_model(join(data_directory, "mini.json"))
assert compare_models(mini_model, json_model) is None
def test_load_json_model(data_directory, mini_model):
"""Test the reading of JSON model."""
json_model = cio.load_json_model(join(data_directory, "mini.json"))
assert compare_models(mini_model, json_model) is None
def load_model(path_or_handle, solver_interface=optlang.glpk_interface, sanitize=True):
"""Read a metabolic model .
Parameters
----------
path_or_handle : path, fhandle or name.
One of:
* file path of a model file;
* file handle to a SBML or pickled model; or
* the identifier of a model in a web database (optflux.org/models)
solver_interface : solver_interface, optional
E.g. optlang.glpk_interface or any other optlang interface.
sanitize : boolean, optional
If reaction and metabolite IDs should be sanitized (works only for SBML models).
"""
if isinstance(path_or_handle, str):
path = path_or_handle
try:
handle = open(path_or_handle, 'rb')
except IOError:
logger.debug('%s not a file path. Querying webmodels ... trying http://bigg.ucsd.edu first' % path)
try:
return cameo.models.webmodels.get_model_from_bigg(path)
except:
logger.debug('%s not a file path. Querying webmodels ... trying minho next' % path)
try:
df = cameo.models.webmodels.index_models_minho()
except requests.ConnectionError as e:
logger.error("You need to be connected to the internet to load an online model.")
raise e
except Exception as e:
logger.error("Something went wrong while looking up available webmodels.")
raise e
try:
index = df.query('name == "%s"' % path_or_handle).id.values[0]
handle = cameo.models.webmodels.get_sbml_file(index)
path = handle.name
except IndexError:
raise ValueError("%s is neither a file nor a model ID." % path)
elif hasattr(path_or_handle, 'read'):
path = path_or_handle.name
handle = path_or_handle
else:
raise ValueError('Provided argument %s has to be either a file path or handle' % path_or_handle)
logger.debug('Reading file from %s assuming pickled model.' % path)
try:
model = pickle.load(handle)
except Exception:
logger.debug('Cannot unpickle %s. Assuming json model next.' % path)
try:
model = load_json_model(path)
except Exception:
logger.debug("Cannot import %s as json model. Assuming sbml model next." % path)
try:
model = read_sbml_model(path)
except AttributeError as e:
logger.error("cobrapy doesn't raise a proper exception if a file does not contain an SBML model")
raise e
except Exception as e:
logger.error(
"Looks like something blow up while trying to import {} as a SBML model. Try validating the model at http://sbml.org/Facilities/Validator/ to get more information.".format(
path))
raise e
if sanitize:
sanitize_ids(model)
if not isinstance(model, SolverBasedModel):
if solver_interface is not None:
logger.debug("Changing solver interface to %s" % solver_interface)
model = to_solver_based_model(model, solver_interface=solver_interface)
else:
if model.interface is not solver_interface and solver_interface is not None:
logger.debug("Changing solver interface to %s" % solver_interface)
model.solver = solver_interface
return model
'''
Created on 5 Oct 2017
@author: mate
This script reads in the iJR904 E. Coli model from a json format,
and adds the BDO synthesis pathway to it
'''
# from cobra import Reaction, Metabolite
#this is a very detailed E. Coli model with about 1000 enzymes:
from cobra.io import load_json_model
bigg = load_json_model("/home/mate/code/met_model/src/models/iJR904.json")
newReaction = bigg.reactions.get_by_id("BIOMASS_Ecoli")
newThing = bigg.reactions.get_by_id("EX_glc__D_e")
print(newThing.reaction)
print(newThing.metabolites)
bigg.objective = newReaction
bigg.optimize()
print(bigg.objective.value)
print(newThing.flux)
print(len(bigg.reactions))
print(len(bigg.metabolites))
print(len(bigg.genes))
GLPK = 'optlang-glpk'
solver = GUROBI
case = 'full' # 'reduced' or full'
# Load reaction DB
print("Loading thermo data...")
thermo_data = load_thermoDB(thermo_database)
print("Done !")
#biomass_rxn = 'BIOMASS_Ec_iJO1366_WT_53p95M'
biomass_rxn = 'Ec_biomass_iJO1366_WT_53p95M'
# We import pre-compiled data as it is faster for bigger models
model_path = '/projectnb2/bioinfor/SEGRE/goldford/CoenzymeSpecificity/pytfa/models'
cobra_model = load_json_model(model_path +
'/iJO1366_WT_semi-unconstrained.11Oct2021.json')
lexicon = read_lexicon(model_path + '/iJO1366/lexicon.csv')
compartment_data = read_compartment_data(model_path +
'/iJO1366/compartment_data.json')
# Initialize the cobra_model
mytfa = pytfa.ThermoModel(thermo_data, cobra_model)
# Annotate the cobra_model
annotate_from_lexicon(mytfa, lexicon)
apply_compartment_data(mytfa, compartment_data)
mytfa.name = 'iJO1366[WT]'
mytfa.solver = solver
mytfa.objective = biomass_rxn
def test_load_json_model_invalid(data_directory):
"""Test that loading an invalid annotation from JSON raises TypeError"""
path = join(data_directory, "invalid_annotation_format.json")
with pytest.raises(TypeError):
model = load_json_model(path)
import cobra.test
from cobra import Model, Reaction, Metabolite
from cobra.io import read_sbml_model, load_json_model
from cobra.flux_analysis import moma, add_moma
import json, sys
try:
assert (len(sys.argv) == 2)
method = sys.argv[1].lower()
assert (method in ['fba', 'moma'])
except:
print(('Usage: %s <fba, moma>') % sys.argv[0])
exit()
model = load_json_model('data/iML1428-iso_Glucose.json')
# Set constrants for aerobic growth in glucose minimal media
model.reactions.get_by_id('EX_glc__D_e').lower_bound = -10
model.reactions.get_by_id('EX_o2_e').lower_bound = -15
# Load experiments and reaction data
experiments = [
'Isozyme overexpression',
('glyA', 'ltaE'),
('ilvA', 'tdcB'),
('ilvE', 'avtA'),
('metC', 'malY'),
'Substrate ambiguity',
('glnA', 'asnB'),
('pdxB', 'tdh'),
GLPK = 'optlang-glpk'
solver = GUROBI
case = 'full' # 'reduced' or full'
# Load reaction DB
print("Loading thermo data...")
thermo_data = load_thermoDB(thermo_database)
print("Done !")
#biomass_rxn = 'BIOMASS_Ec_iJO1366_WT_53p95M'
biomass_rxn = 'Ec_biomass_iJO1366_WT_53p95M'
# We import pre-compiled data as it is faster for bigger models
model_path = '/projectnb2/bioinfor/SEGRE/goldford/CoenzymeSpecificity/pytfa/models'
cobra_model = load_json_model(model_path + '/iJO1366_NAD_ratio_1.fromTFA.json')
lexicon = read_lexicon(model_path + '/iJO1366/lexicon.csv')
compartment_data = read_compartment_data(model_path +
'/iJO1366/compartment_data.json')
# Initialize the cobra_model
mytfa = pytfa.ThermoModel(thermo_data, cobra_model)
# Annotate the cobra_model
annotate_from_lexicon(mytfa, lexicon)
apply_compartment_data(mytfa, compartment_data)
mytfa.name = 'iJO1366[NAD]'
mytfa.solver = solver
mytfa.objective = biomass_rxn
def test_load_json(db_model):
model = load_json_model(join(static_model_dir, db_model.id + '.json'))
assert model.id == db_model.id
def load_modelseed_model(model_name):
if model_name == 'Staphylococcus aureus':
model = load_json_model('./medusa/test/data/' + model_name + '.json')
else:
raise ValueError('Unsupported model_name provided')
return model
UB_DICT = {'c': 1e99, 'p': 1e99, 'e': 1e99} # in M
sys.path.append(os.path.expanduser('~/git/equilibrator-api/'))
from equilibrator_api import ComponentContribution, Reaction
equilibrator = ComponentContribution(pH=CYTOPLASMIC_PH,
ionic_strength=CYTOPLASMIC_IONIC_STRENGTH)
USE_CORE = False
if USE_CORE:
from cobra.io import read_sbml_model
cobra_model = read_sbml_model(settings.CORE_SBML_FNAME)
BM_RXN = 'BIOMASS_Ecoli_core_w_GAM'
else:
from cobra.io import load_json_model
cobra_model = load_json_model(settings.IJO1366_JSON_FNAME)
BM_RXN = 'BIOMASS_Ec_iJO1366_core_53p95M'
###############################################################################
def get_metabolite_df():
bigg2kegg = []
# manually add nh4_c and nh4_e (they appear as nh3 in the text
# file BIGG_METABOLITE_FNAME, but in some models nh4 is used)
bigg2kegg += [('nh4_c', 'nh4', 'C00014')]
bigg2kegg += [('nh4_p', 'nh4', 'C00014')]
bigg2kegg += [('nh4_e', 'nh4', 'C00014')]
# manually add q8_c q8h2_c (ubiquinone and ubiquinol)