def test_model_history(tmp_path):
"""Testing reading and writing of ModelHistory."""
model = Model("test")
model._sbml = {
"creators": [{
"familyName": "Mustermann",
"givenName": "Max",
"organisation": "Muster University",
"email": "*****@*****.**",
}]
}
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
assert "creators" in model2._sbml
assert len(model2._sbml["creators"]) is 1
c = model2._sbml["creators"][0]
assert c["familyName"] == "Mustermann"
assert c["givenName"] == "Max"
assert c["organisation"] == "Muster University"
assert c["email"] == "*****@*****.**"
def create_model(model,
constraining_dict,
lethal_df,
save_fn=None,
model_id=None,
skip=[]):
# Disregard all reactions in the first column of lethal_df
lethal_reactions = list(
set(
list(lethal_df.loc[:, "rxn1"]) + list(lethal_df.loc[:, "rxn2"]) +
list(lethal_df.loc[:, "rxn3"])))
lethal_reactions += skip
i = 0
for r_id, bounds in constraining_dict.items():
if r_id in lethal_reactions:
continue
i += 1
r = model.reactions.get_by_id(r_id)
logging.info("Change direction of reaction {0} from {1} to {2}".format(
r_id, r.bounds, bounds))
r.bounds = bounds
logging.debug(model.optimize())
logging.info(
"Changed the reversibility of {0} reactions in total".format(i))
if model_id:
model.id = model_id
if save_fn:
write_sbml_model(model, save_fn)
return model
def test_model_history(tmp_path):
"""Testing reading and writing of ModelHistory."""
model = Model("test")
model._sbml = {
"creators": [{
"familyName": "Mustermann",
"givenName": "Max",
"organisation": "Muster University",
"email": "*****@*****.**",
}]
}
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
assert "creators" in model2._sbml
assert len(model2._sbml["creators"]) is 1
c = model2._sbml["creators"][0]
assert c["familyName"] == "Mustermann"
assert c["givenName"] == "Max"
assert c["organisation"] == "Muster University"
assert c["email"] == "*****@*****.**"
def check_feasibility(metabolic_model,
time_limit=60,
tolerance_feasibility=1e-6,
pool=None,
debug=True):
#Some solvers may crash python with unfeasible parameters, by creating a new process we prevent that error from crashing the main process
#model_copy=copy.deepcopy(metabolic_model)
if pool == None:
pool = Pool(processes=1)
task_start = time.time() # start time
task = pool.map_async(get_status,
[{
"model": metabolic_model,
"tolerance_feasibility": tolerance_feasibility
}])
while not (task.ready()):
#print task._number_left
if (
time.time() - task_start
) > time_limit: # check maximum time (user def.) print "timeout"
pool.terminate() # kill old pool
timeout = True # redo computation
pool = Pool(processes=1)
if debug:
print "infeasible"
write_sbml_model(metabolic_model, "feasibility.sbml")
return "infeasible", pool
status = task.get()[0]
#pool.close()
if debug:
print status
return status, pool
def test_notes(tmp_path):
"""Testing if model notes are written in SBML"""
path_to_file = join(str(tmp_path), "model_notes.xml")
# making a minimal cobra model to test notes
model = cobra.Model("e_coli_core")
model.notes["Remark"] = "...Model Notes..."
met = cobra.Metabolite("pyr_c", compartment="c")
model.add_metabolites([met])
met.notes["Remark"] = "Note with \n newline"
rxn = cobra.Reaction("R_ATPM")
model.add_reactions([rxn])
rxn.notes["Remark"] = "What about me?"
model.objective_direction = "max"
model.objective = rxn
write_sbml_model(model, path_to_file)
# reading the model back
model_after_reading = read_sbml_model(path_to_file)
met_after_reading = model_after_reading.metabolites.get_by_id("pyr_c")
reaction_after_reading = model_after_reading.reactions.get_by_id("R_ATPM")
# checking if notes are written to model
assert model_after_reading.notes["Remark"] == "...Model Notes..."
# checking notes for metabolite and reaction
assert met_after_reading.notes["Remark"] == "Note with \n newline"
assert reaction_after_reading.notes["Remark"] == "What about me?"
def save_model_to_file(model, filename):
""" Save a model to a file based on the extension of the file name.
Parameters
----------
model : cobra.core.Model
Model object loaded from file
filename : str
Path to model file
Raises
------
IOError
If model file extension is not supported.
"""
(root, ext) = splitext(filename)
if ext == '.mat':
save_matlab_model(model, filename)
elif ext == '.xml' or ext == '.sbml':
write_sbml_model(model, filename)
elif ext == '.json':
save_json_model(model, filename)
else:
raise IOError(
'Model file extension not supported for {0}'.format(filename))
return
def _removeDeadEnd(sbml_path):
cobraModel = cobra_io.read_sbml_model(sbml_path, use_fbc_package=True)
cobraModel = _reduce_model(cobraModel)
with TemporaryDirectory() as tmpOutputFolder:
cobra_io.write_sbml_model(cobraModel,
os_path.join(tmpOutputFolder, 'tmp.xml'))
rpsbml = rpSBML(os_path.join(tmpOutputFolder, 'tmp.xml'))
return rpsbml
def test_stable_gprs(data_directory, tmp_path):
mini = read_sbml_model(join(data_directory, "mini_fbc2.xml"))
mini.reactions.GLCpts.gene_reaction_rule = "((b2415 and b2417)or (b2416))"
fixed = join(str(tmp_path), "fixed_gpr.xml")
write_sbml_model(mini, fixed)
fixed_model = read_sbml_model(fixed)
assert (fixed_model.reactions.GLCpts.gene_reaction_rule ==
"(b2415 and b2417) or b2416")
def write_yeast_model(model):
"""Writes the SBML file of the yeast model using COBRA.
Parameters
----------
model : cobra.core.Model
Yeast model to be written
"""
write_sbml_model(model, MODEL_PATH)
def raise_libsbml_errors():
with pytest.raises(ImportError):
io.read_sbml_model('test')
with pytest.raises(ImportError):
io.write_sbml_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
with pytest.raises(ImportError):
io.write_legacy_sbml(None, 'test')
with pytest.raises(ImportError):
io.read_legacy_sbml(None, 'test')
def raise_libsbml_errors():
with pytest.raises(ImportError):
io.read_sbml_model('test')
with pytest.raises(ImportError):
io.write_sbml_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
with pytest.raises(ImportError):
io.write_legacy_sbml(None, 'test')
with pytest.raises(ImportError):
io.read_legacy_sbml(None, 'test')
def test_read_write_sbml_annotations(data_directory, tmp_path):
"""Test reading and writing annotations."""
with open(join(data_directory, "annotation.xml"), "r") as f_in:
model1 = read_sbml_model(f_in)
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model1, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
_check_sbml_annotations(model2)
def test_read_write_sbml_annotations(data_directory, tmp_path):
"""Test reading and writing annotations."""
with open(join(data_directory, "annotation.xml"), "r") as f_in:
model1 = read_sbml_model(f_in)
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model1, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
_check_sbml_annotations(model2)
def update_local_models(model_id, model_store=None):
"""Update locally stored models.
Annotate model metabolites with CHEBI identifiers and store them locally for easy access.
:param model_id: string, model identifier
:param model_store: path to directory where to store the processed models.
"""
model_store = model_store or 'data/models'
if model_id in LOCAL_MODELS:
sbml_file = os.path.join(model_store, 'original',
model_id + '.sbml.gz')
model = read_sbml_model(sbml_file)
else:
model = load_model(model_id)
# annotate metabolites
namespace = storage.get(model_id).namespace
metabolite_namespace = MODEL_METABOLITE_NAMESPACE[model_id]
db_name = 'CHEBI'
metabolites_missing_annotation = [
m.id for m in model.metabolites
if len(m.annotation.get(db_name, [])) < 1
]
model_xref = sync_query_identifiers([
strip_compartment[namespace](mid)
for mid in metabolites_missing_annotation
], namespace, db_name)
for metabolite_id in metabolites_missing_annotation:
compound_id = strip_compartment[namespace](metabolite_id)
if compound_id in model_xref:
metabolite = model.metabolites.get_by_id(metabolite_id)
if db_name not in metabolite.annotation:
metabolite.annotation[db_name] = []
metabolite.annotation[db_name].extend([
f'{db_name}:{i}' if not i.startswith(f"{db_name}:") else i
for i in model_xref[compound_id]
])
# TODO: For some reason, id-mapper doesn't make this link, add manually for now
if compound_id in GLUCOSE and db_name == 'CHEBI':
metabolite.annotation[db_name].append('CHEBI:42758')
if metabolite_namespace not in metabolite.annotation:
metabolite.annotation[metabolite_namespace] = []
metabolite.annotation[metabolite_namespace].append(compound_id)
# gecko protein exchanges
db_name = 'uniprot'
protein_exchanges = model.reactions.query(
lambda rxn: re.match(r'^prot_.*_exchange$', rxn.id))
for rxn in protein_exchanges:
rxn.annotation[db_name] = [
re.findall('^prot_(.*)_exchange$', rxn.id)[0]
]
write_sbml_model(model, os.path.join(model_store, model_id + '.sbml.gz'))
def main(args):
'''main method.'''
model = read_sbml_model(args[0])
# Create updated model:
build(model)
add_creator(model, *args[3:7])
# Write updated model:
out_filename = os.path.join(args[1], '%s.xml' % args[2])
makedirs(out_filename)
write_sbml_model(model, out_filename)
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 test_create_universal(self, test_folder):
universal = cobrababel.create_metanetx_universal_model()
assert universal.id == 'metanetx_universal'
assert len(universal.reactions) >= 42952
assert len(universal.metabolites) >= 31130
file_name = join(test_folder, 'metanetx.xml')
write_sbml_model(universal, file_name)
model, errors = validate_sbml_model(file_name)
assert len(errors['other']) == 0
assert len(errors['SBML errors']) == 0
assert len(errors['warnings']) == 0
assert len(errors['validator']) >= 325
unlink(file_name)
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 excel_sbml(modelname,outputname):
import datetime
import cobra
from cobra import Metabolite, Reaction, Model
from cobra.io import write_sbml_model
#from __future__ import print_function
import pandas as pd
#myfolder='/media/jupyter/zhang_x/study/'
#mname="seed_1"
#若需包括更多代谢物信息，则可先在模型中创建代谢物对象并添加到模型
#A = Metabolite('A')
#model.add_metabolites([A, B, C, D, E, P])
starttime = datetime.datetime.now()
model = Model('model')
colnames=['id','reaction_eq','name','lower_bound','upper_bound','Object']
#将上述值改为excel表格中的相应列名称数据以便正确读出值，表格中不一定包括所有列,不需要改变列的顺序，通过列头识别
#objr='Objective' #目标反应的名字
#rin=[['PGI',-10,10]] #可设置多个输入反应及其上下限
#若表格中不包括目标反应数据需人为给出，同样对输入反应需人为添加约束,若已有相应数据则这两个值不用改
#data = pd.read_csv(mname+'.csv', delimiter=",", na_values=['(none)']).fillna('')
data = pd.read_excel(modelname, sheet_name='reactions', header=0, na_values=['(none)']).fillna('') #'(none)'变NaN，NaN数据用''填充
#print(data)
#直接从excel文件读取不用再转换txt,header行为数据起始行和表头（因前面行可能有模型说明文件） 表中空值替换为空字符以便处理，不能用dropna，否则整行数据都会丢掉
keys=data.keys()
for index, row in data.iterrows():
r = Reaction(row[colnames[0]].strip()) #r即每个反应对应的反应名字（name）
model.add_reaction(r)
r.build_reaction_from_string(row[colnames[1]],fwd_arrow='-->', rev_arrow='<--', reversible_arrow='<=>', term_split='+') #excel中方程式colnames[1],转成SBML格式
if colnames[2] in keys:
r.subsystem=row[colnames[2]]
if colnames[3]in keys:
r.lower_bound=row[colnames[3]]
r.upper_bound=row[colnames[4]]
if colnames[4] in keys: #目标反应
if row[colnames[5]]:
r.objective_coefficient=row[colnames[5]]
#if colnames[5] in keys:
# if row[colnames[5]]:
# r.objective_coefficient=1
#s=row[colnames[6]] #对基因关系处理要看表格中是如何存储and/or关系的
#if s:
# genes=s.split(", ")
# r.gene_reaction_rule='('+" or ".join(genes)+' )'
#if colnames[3] not in keys: #需人为设定输入反应边界，注意根据反应方向确定是改下限还是上限及值的正负
# for r in rin:
# rea=model.reactions.get_by_id(r[0])
# rea.lower_bound=r[1]
# rea.upper_bound=r[2]
write_sbml_model(model, outputname)
def test_filehandle(data_directory, tmp_path):
"""Test reading and writing to file handle."""
with open(join(data_directory, "mini_fbc2.xml"), "r") as f_in:
model1 = read_sbml_model(f_in)
assert model1 is not None
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model1, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
TestCobraIO.compare_models(name="filehandle", model1=model1, model2=model2)
def test_filehandle(data_directory, tmp_path):
"""Test reading and writing to file handle."""
with open(join(data_directory, "mini_fbc2.xml"), "r") as f_in:
model1 = read_sbml_model(f_in)
assert model1 is not None
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model1, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
TestCobraIO.compare_models(name="filehandle",
model1=model1, model2=model2)
def mvgem(input_model, output_model):
"""Convert GEM format INPUT_MODEL to OUTPUT_MODEL.
The format will be infered based on the extension of OUTPUT_MODEL.
Supported formats: .sbml, .xml, .json, .mat
"""
model = load_model(input_model)
out_format = get_destination_format(output_model)
if out_format == "SBML":
write_sbml_model(model, output_model)
elif out_format == "MAT":
save_matlab_model(model, output_model)
elif out_format == "JSON":
save_json_model(model, output_model)
else:
click.BadParameter(
"Output format %s could not be recognised" % out_format
).show()
def test_gprs(data_directory, tmp_path):
"""Test that GPRs are written and read correctly"""
model1 = read_sbml_model(join(data_directory, "iJO1366.xml.gz"))
sbml_path = join(str(tmp_path), "test.xml")
with open(sbml_path, "w") as f_out:
write_sbml_model(model1, f_out)
with open(sbml_path, "r") as f_in:
model2 = read_sbml_model(f_in)
for r1 in model1.reactions:
rid = r1.id
r2 = model2.reactions.get_by_id(rid)
gpr1 = r1.gene_reaction_rule
gpr2 = r2.gene_reaction_rule
assert gpr1 == gpr2
def test_groups(data_directory, tmp_path):
"""Testing reading and writing of groups"""
sbml_path = join(data_directory, "e_coli_core.xml")
model = read_sbml_model(sbml_path)
assert model.groups is not None
assert len(model.groups) == 10
g1 = model.groups[0]
assert len(g1.members) == 6
temp_path = join(str(tmp_path), "test.xml")
with open(temp_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(temp_path, "r") as f_in:
model2 = read_sbml_model(f_in)
assert model2.groups is not None
assert len(model2.groups) == 10
g1 = model2.groups[0]
assert len(g1.members) == 6
def test_groups(data_directory, tmp_path):
"""Testing reading and writing of groups"""
sbml_path = join(data_directory, "e_coli_core.xml")
model = read_sbml_model(sbml_path)
assert model.groups is not None
assert len(model.groups) == 10
g1 = model.groups[0]
assert len(g1.members) == 6
temp_path = join(str(tmp_path), "test.xml")
with open(temp_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(temp_path, "r") as f_in:
model2 = read_sbml_model(f_in)
assert model2.groups is not None
assert len(model2.groups) == 10
g1 = model2.groups[0]
assert len(g1.members) == 6
def create_sbml(reaction_genes, reactions_metabolites, output_file):
model = Model()
metabolites_created = []
for reaction_id in reaction_genes:
reaction = Reaction(reaction_id)
reaction.name = reaction_id
reaction_metabolites = {}
for reactant_id in reactions_metabolites[reaction_id][0]:
if reactant_id not in metabolites_created:
reactant_metabolite = Metabolite(reactant_id, compartment='c')
reaction_metabolites[reactant_metabolite] = -1.0
for product_id in reactions_metabolites[reaction_id][1]:
if product_id not in metabolites_created:
product_metabolite = Metabolite(product_id, compartment='c')
reaction_metabolites[product_metabolite] = 1.0
reaction.add_metabolites(reaction_metabolites)
reaction.notes['GENE_ASSOCIATION'] = '(' + ' or ' .join(reaction_genes[reaction_id]) + ')'
model.add_reactions([reaction])
write_sbml_model(model, output_file)
def test_infinity_bounds(data_directory, tmp_path):
"""Test infinity bound example. """
sbml_path = join(data_directory, "fbc_ex1.xml")
model = read_sbml_model(sbml_path)
# check that simulation works
solution = model.optimize()
# check that values are set
r = model.reactions.get_by_id("EX_X")
assert r.lower_bound == -float("Inf")
assert r.upper_bound == float("Inf")
temp_path = join(str(tmp_path), "test.xml")
with open(temp_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(temp_path, "r") as f_in:
model2 = read_sbml_model(f_in)
r = model2.reactions.get_by_id("EX_X")
assert r.lower_bound == -float("Inf")
assert r.upper_bound == float("Inf")
def check_feasibility(metabolic_model,time_limit=60,tolerance_feasibility=1e-6,pool=None,debug=True):
#Some solvers may crash python with unfeasible parameters, by creating a new process we prevent that error from crashing the main process
#model_copy=copy.deepcopy(metabolic_model)
if pool==None:
pool = Pool(processes=1)
task_start = time.time() # start time
task = pool.map_async(get_status,[{"model":metabolic_model,"tolerance_feasibility":tolerance_feasibility}])
while not(task.ready()):
#print task._number_left
if (time.time() - task_start) > time_limit: # check maximum time (user def.) print "timeout"
pool.terminate() # kill old pool
timeout = True # redo computation
pool = Pool(processes=1)
if debug:
print "infeasible"
write_sbml_model(metabolic_model, "feasibility.sbml")
return "infeasible",pool
status=task.get()[0]
#pool.close()
if debug:
print status
return status,pool
def test_infinity_bounds(data_directory, tmp_path):
"""Test infinity bound example. """
sbml_path = join(data_directory, "fbc_ex1.xml")
model = read_sbml_model(sbml_path)
# check that simulation works
solution = model.optimize()
# check that values are set
r = model.reactions.get_by_id("EX_X")
assert r.lower_bound == -float("Inf")
assert r.upper_bound == float("Inf")
temp_path = join(str(tmp_path), "test.xml")
with open(temp_path, "w") as f_out:
write_sbml_model(model, f_out)
with open(temp_path, "r") as f_in:
model2 = read_sbml_model(f_in)
r = model2.reactions.get_by_id("EX_X")
assert r.lower_bound == -float("Inf")
assert r.upper_bound == float("Inf")
def main(args):
'''main method.'''
model = read_sbml_model(args[0])
# Get biomass MW:
print(get_mw(model, 's_0450__91__c__93__'))
# Create updated model:
build(model)
add_creator(model, *args[3:7])
# Write updated model:
makedirs(args[1])
write_sbml_model(model, os.path.join(args[1], '%s.xml' % args[2]))
to_df(model).to_csv(os.path.join(args[1], '%s.csv' % args[2]))
# Simulate updated model:
react_flux_df = simulate(model, args[1])
# Save and plot:
react_flux_df.to_csv(os.path.join(args[1], '%s.csv' % react_flux_df.name))
plot(react_flux_df, 'flux / mmol h-1',
os.path.join(args[1], '%s.png' % react_flux_df.name))
print "\n check reaction mass balance"
fixed_mass_balance = open("fixed_mass_balance.txt", "w")
for r in model.reactions:
if r.check_mass_balance() != [] and r.id[:3] != "EX_":
if not cyc.fix_mass_balance(r, model, fixed_mass_balance):
print "\t", r, "is not balanced!"
print >> mass_balance, r.id, r.name, r.check_mass_balance()
for pwy in p_ignored_set:
print >> p_ignored, pwy, org.get_name_string(pwy), p_ignored_set[pwy]
p_generic_set[org.get_name_string(pwy)] = len(p_ignored_set[pwy])
for s in sorted(m_generic_set.iteritems(), key=operator.itemgetter(1)):
print >> m_generic, s[0], s[1]
for s in sorted(p_generic_set.iteritems(), key=operator.itemgetter(1)):
print >> m_generic, s[0], s[1]
print "\n---\n%i reaction in model" % len(model.reactions)
print "%i metabolites in model" % len(model.metabolites)
print "%i genes in model\n---\n" % len(model.genes)
print "reactions in database:", r_total
print "generic reactions:", r_generic
print "generic metabolites:", len(m_generic_set.keys())
print "ignored reactions:", len(r_ignored_set)
print "thus incomplete pathways:", len(p_ignored_set), "\n"
sbml_out_file = answer_org + ".xml"
write_sbml_model(model, sbml_out_file, use_fbc_package=False)
from cobra.test import create_test_model
model_names = ['salmonella', 'iJO1366', 'Yersinia_pestis_CO92_iPC815']
for model_name in model_names:
# read in old pickle and model from sbml
model_pickle = model_name + '.pickle'
if model_name == "iJO1366":
new_model = read_legacy_sbml(model_name + '.xml')
else:
new_model = read_sbml_model(model_name + '.xml')
# update other attributes
if isfile(model_name + ".genes"):
with open(model_name + ".genes", "rb") as infile:
gene_names = load(infile)
for gene in new_model.genes:
gene.name = gene_names[gene.id]
if isfile(model_name + ".media"):
with open(model_name + ".media", "rb") as infile:
new_model.media_compositions = load(infile)
new_model._cobra_version = get_version()
# write out new pickle
with open(model_pickle, 'wb') as outfile:
dump(new_model, outfile, protocol=2)
# write out other formats for iJO1366
if model_name == "iJO1366":
save_matlab_model(new_model, model_name + ".mat")
save_json_model(new_model, model_name + ".json")
if model_name == "salmonella":
write_sbml_model(new_model, model_name + "_fbc.xml")
for g in mini.genes:
try:
tg = textbook.genes.get_by_id(g.id)
except KeyError:
continue
g.name = tg.name
g.annotation = tg.annotation
mini.reactions.sort()
mini.genes.sort()
mini.metabolites.sort()
# output to various formats
with open("mini.pickle", "wb") as outfile:
dump(mini, outfile, protocol=2)
save_matlab_model(mini, "mini.mat")
save_json_model(mini, "mini.json", pretty=True)
write_sbml_model(mini, "mini_fbc2.xml")
write_sbml_model(mini, "mini_fbc2.xml.bz2")
write_sbml_model(mini, "mini_fbc2.xml.gz")
write_sbml2(mini, "mini_fbc1.xml", use_fbc_package=True)
write_sbml_model(mini, "mini_cobra.xml", use_fbc_package=False)
raven = load_matlab_model("raven.mat")
with open("raven.pickle", "wb") as outfile:
dump(raven, outfile, protocol=2)
# fva results
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
def excel_to_sbml(file_excel_path, file_sbml_path,model_id="default_model",**kwargs):
write_sbml_model(import_excel_model(file_excel_path,model_id),file_sbml_path,**kwargs)
# ASSERTION 2: Adding some dummy carbon source (glc,fum,pyr) => there should be growth
sol = set_medium(tmpmod,
minmed + ["cpd00027", "cpd00020", "cpd00106"],
verbose=False).optimize()
print "Positive growth control", sol.status, sol.objective_value
if sol.status == "optimal" and round(sol.objective_value, 3) < 0:
print "No growth possible, check minimal medium?"
sys.exit()
# 5) Try Gapfilling for each exchange reaction and carbon source
print "\nStarting gapfilling"
tmpmod = set_medium(tmpmod, minmed, verbose=False)
debugmod = tmpmod.copy()
debugmod.add_reactions(
[r for r in refmod.reactions if r not in debugmod.reactions])
write_sbml_model(debugmod, filename=fileID + "_debug.xml")
Nrea = len(tmpmod.reactions)
s1 = [ex for ex in substances["exid_seed"].dropna().values]
s2 = [ex.id for ex in tmpmod.exchanges]
csources = set(s1 + s2)
tmpmod = add_Exchanges(tmpmod,
set(s1).difference(set(s2))) # add exchange reactions
newmod = add_Exchanges(newmod,
set(s1).difference(set(s2))) # add exchange reactions
Nfix = 0
for cs in csources:
csname = tmpmod.reactions.get_by_id(cs).metabolites.keys()[0].name
med = minmed + [cs]
tmpmod = set_medium(tmpmod, med, verbose=False)
sol = tmpmod.slim_optimize()
if round(sol, 6) > 0:
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import sys, re, os, glob
from cobra.core import Model
from cobra.io import read_sbml_model, write_sbml_model
folder = sys.argv[1]
metamodel_id = sys.argv[2]
assert os.path.isdir(folder)
metamodel = Model(metamodel_id)
metamodel.description = metamodel_id
reactions = set()
models = []
for fname in glob.glob(os.path.join(folder, "*.xml")):
model = read_sbml_model(fname)
models.append(model)
print "%s loaded" % model.id
for r in model.reactions:
r.id = re.sub('_[ec][0-9]', '', r.id)
if r.id in reactions:
continue
metamodel.add_reaction(r.copy())
reactions.add(r.id)
write_sbml_model(metamodel_id, metamodel)
def test_sbml_write(self):
test_output_filename = join(gettempdir(), 'test_sbml_write.xml')
io.write_sbml_model(self.model, test_output_filename)
#cleanup the test file
unlink(test_output_filename)
mini.reactions.get_by_id(i).lower_bound = mini.reactions.PGI.lower_bound
# set names and annotation
for g in mini.genes:
try:
tg = textbook.genes.get_by_id(g.id)
except KeyError:
continue
g.name = tg.name
g.annotation = tg.annotation
mini.reactions.sort()
mini.genes.sort()
mini.metabolites.sort()
# output to various formats
with open("mini.pickle", "wb") as outfile:
dump(mini, outfile, protocol=2)
save_matlab_model(mini, "mini.mat")
save_json_model(mini, "mini.json", pretty=True)
write_sbml_model(mini, "mini_fbc2.xml")
write_sbml_model(mini, "mini_fbc2.xml.bz2")
write_sbml_model(mini, "mini_fbc2.xml.gz")
write_sbml2(mini, "mini_fbc1.xml", use_fbc_package=True)
write_sbml_model(mini, "mini_cobra.xml", use_fbc_package=False)
# fva results
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
def test_sbml_write(self):
test_output_filename = join(gettempdir(), 'test_sbml_write.xml')
io.write_sbml_model(self.model, test_output_filename)
#cleanup the test file
unlink(test_output_filename)
if len(gapsol[0]) > 0:
Cfix += 1
print "\t => could be fixed:", ",".join(
[r.id for r in gapsol[0]])
modnew.add_reactions(
[r for r in gapsol[0] if r not in modnew.reactions])
print "\nTotal compounds:", Call, "\t can be produced:", Cwork, "\t could be fixed", Cfix, "\t altogether:", round(
100 * float(Cwork + Cfix) / Call,
1), "%", " (before:", round(100 * float(Cwork) / Call, 1), "% )"
if fill_gaps:
return (modnew)
mod.add_reactions([ex for ex in refmod.exchanges if ex not in mod.reactions])
checkProduction(mod, "atp[c]")
sol = mod.optimize()
if sol.status != "optimal" or round(sol.objective_value, 6) == 0:
print "no biomass production possible try to fix"
tmpmod = mod.copy()
for ex in tmpmod.exchanges:
ex.lower_bound = -1000
gapsol = GapFiller(tmpmod,
refmod,
demand_reactions=False,
integer_threshold=1e-16).fill()
print gapsol
modnew = checkBiomass(mod, fill_gaps=True)
fileID = os.path.splitext(os.path.basename(sys.argv[1]))[0]
write_sbml_model(modnew, filename=fileID + "_gapfilled.xml")
### Change the MNXM IDs to Bigg IDs
for met in model.metabolites:
if "bigg.metabolite" not in met.annotation.keys():
continue
if met.id in [
"cpd00261_c0", "cpd06227_c0", "cpd03572_c0", "cpd02446_c0",
"cpd02572_c0", "cpd01466_c0"
]:
pass
else:
#if met.id.startswith('cpd'):
split, compartment = met.id.split("_")
met.id
met.id = met.annotation['bigg.metabolite'] + '_' + compartment
model.repair()
print(met.id)
#Number of metabolites without Bigg ID
not_in_bigg = []
for met in model.metabolites:
if "bigg.metabolite" not in met.annotation:
not_in_bigg.append(met.id)
len(not_in_bigg)
### Save draft with mapped Bigg IDs
write_sbml_model(model, "/Users/lizrad/Dev/iVnat/iVnat.xml")
def test_sbml_write(self):
test_output_filename = "test_sbml_write.xml"
io.write_sbml_model(self.model, test_output_filename)
# cleanup the test file
unlink(test_output_filename)
clean = True
if clean:
aux = consistent_model.copy()
biomass = aux.reactions.get_by_id(biomass.id)
aux.remove_reactions([r for r in aux.reactions if r.startswith('EFF') and r._metabolites.keys()[0] in biomass.metabolites])
new_blocked_reactions = find_blocked_reactions(aux)
if len(new_blocked_reactions) == 0:
print "Cleaning model"
consistent_model = aux
gapfilling_reactions = [r for r in gapfilling_reactions if r in consistent_model.reactions]
final_model_reactions = {r.id for r in consistent_model.reactions}
sbml_out = '.'.join([model.id,settings.OUTPUT_SUFIX,"xml"])
sbml_out = os.path.join(settings.OUTPUT_FOLDER,sbml_out)
write_sbml_model(consistent_model,sbml_out,use_fbc_package=False)
print "Curated model %s saved as %s" % (model.id,sbml_out)
print "====================================================="
print "MODEL_ID\tRXN_initial\tBLK_curated\tBLK_Removed\tGF\tRXN_final"
print "%s\t%i\t%i\t%i\t%i\t%i" % ( consistent_model.id,
len(initial_reactions),
len(blocked_curated),
len(initial_reactions - final_model_reactions),
len(gapfilling_reactions),
len(final_model_reactions)
)
print "====================================================="
import sys, re, os, glob
from cobra.core import Model
from cobra.io import read_sbml_model,write_sbml_model
folder = sys.argv[1]
metamodel_id = sys.argv[2]
assert os.path.isdir(folder)
metamodel = Model(metamodel_id)
metamodel.description = metamodel_id
reactions = set()
models = []
for fname in glob.glob(os.path.join(folder,"*.xml")):
model = read_sbml_model(fname)
models.append(model)
print "%s loaded" % model.id
for r in model.reactions:
r.id = re.sub('_[ec][0-9]','',r.id)
if r.id in reactions:
continue
metamodel.add_reaction(r.copy())
reactions.add(r.id)
write_sbml_model(metamodel_id,metamodel)
