def test_gpr():
model = Model()
reaction = Reaction("test")
# Set GPR to a reaction not in a model
reaction.gene_reaction_rule = "(g1 or g2) and g3"
assert reaction.gene_reaction_rule == "(g1 or g2) and g3"
assert len(reaction.genes) == 3
# Adding reaction with a GPR propagates to the model
model.add_reactions([reaction])
assert len(model.genes) == 3
# Ensure the gene objects are the same in the model and reaction
reaction_gene = list(reaction.genes)[0]
model_gene = model.genes.get_by_id(reaction_gene.id)
assert reaction_gene is model_gene
# Test ability to handle uppercase AND/OR
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reaction.gene_reaction_rule = "(b1 AND b2) OR (b3 and b4)"
assert reaction.gene_reaction_rule == "(b1 and b2) or (b3 and b4)"
assert len(reaction.genes) == 4
# Ensure regular expressions correctly extract genes from malformed
# GPR string
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reaction.gene_reaction_rule = "(a1 or a2"
assert len(reaction.genes) == 2
reaction.gene_reaction_rule = "(forT or "
assert len(reaction.genes) == 1
def test__has_gene_reaction_rule():
reaction = Reaction('rxn')
assert _has_gene_reaction_rule(reaction) is False
reaction.gene_reaction_rule = 'b1779'
assert _has_gene_reaction_rule(reaction) is True
reaction.gene_reaction_rule = ' '
assert _has_gene_reaction_rule(reaction) is False
def test_gpr(self):
model = Model()
reaction = Reaction("test")
# set a gpr to reaction not in a model
reaction.gene_reaction_rule = "(g1 or g2) and g3"
assert reaction.gene_reaction_rule == "(g1 or g2) and g3"
assert len(reaction.genes) == 3
# adding reaction with a GPR propagates to the model
model.add_reaction(reaction)
assert len(model.genes) == 3
# ensure the gene objects are the same in the model and reaction
reaction_gene = list(reaction.genes)[0]
model_gene = model.genes.get_by_id(reaction_gene.id)
assert reaction_gene is model_gene
# test ability to handle uppercase AND/OR
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reaction.gene_reaction_rule = "(b1 AND b2) OR (b3 and b4)"
assert reaction.gene_reaction_rule == "(b1 and b2) or (b3 and b4)"
assert len(reaction.genes) == 4
# ensure regular expressions correctly extract genes from malformed
# GPR string
with warnings.catch_warnings():
warnings.simplefilter("ignore")
reaction.gene_reaction_rule = "(a1 or a2"
assert len(reaction.genes) == 2
reaction.gene_reaction_rule = "(forT or "
assert len(reaction.genes) == 1
def test__has_gene_reaction_rule():
reaction = Reaction('rxn')
assert _has_gene_reaction_rule(reaction) is False
reaction.gene_reaction_rule = 'b1779'
assert _has_gene_reaction_rule(reaction) is True
reaction.gene_reaction_rule = ' '
assert _has_gene_reaction_rule(reaction) is False
def test_gene_knockout(salmonella: Model) -> None:
"""Test gene knockout."""
gene_list = ["STM1067", "STM0227"]
dependent_reactions = {
"3HAD121",
"3HAD160",
"3HAD80",
"3HAD140",
"3HAD180",
"3HAD100",
"3HAD181",
"3HAD120",
"3HAD60",
"3HAD141",
"3HAD161",
"T2DECAI",
"3HAD40",
}
_gene_knockout_computation(salmonella, gene_list, dependent_reactions)
_gene_knockout_computation(salmonella, ["STM4221"], {"PGI"})
_gene_knockout_computation(salmonella, ["STM1746.S"], {"4PEPTabcpp"})
# test cumulative behavior
delete_model_genes(salmonella, gene_list[:1])
delete_model_genes(salmonella, gene_list[1:], cumulative_deletions=True)
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=True)
dependent_reactions.add("PGI")
assert _get_removed(salmonella) == dependent_reactions
# non-cumulative following cumulative
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=False)
assert _get_removed(salmonella) == {"PGI"}
# make sure on reset that the bounds are correct
reset_bound = salmonella.reactions.get_by_id("T2DECAI").upper_bound
assert reset_bound == 1000.0
# test computation when gene name is a subset of another
test_model = Model()
test_reaction_1 = Reaction("test1")
test_reaction_1.gene_reaction_rule = "eggs or (spam and eggspam)"
test_model.add_reactions([test_reaction_1])
_gene_knockout_computation(test_model, ["eggs"], set())
_gene_knockout_computation(test_model, ["eggs", "spam"], {"test1"})
# test computation with nested boolean expression
test_reaction_1.gene_reaction_rule = "g1 and g2 and (g3 or g4 or (g5 and g6))"
_gene_knockout_computation(test_model, ["g3"], set())
_gene_knockout_computation(test_model, ["g1"], {"test1"})
_gene_knockout_computation(test_model, ["g5"], set())
_gene_knockout_computation(test_model, ["g3", "g4", "g5"], {"test1"})
# test computation when gene names are python expressions
test_reaction_1.gene_reaction_rule = "g1 and (for or in)"
_gene_knockout_computation(test_model, ["for", "in"], {"test1"})
_gene_knockout_computation(test_model, ["for"], set())
test_reaction_1.gene_reaction_rule = "g1 and g2 and g2.conjugate"
_gene_knockout_computation(test_model, ["g2"], {"test1"})
_gene_knockout_computation(test_model, ["g2.conjugate"], {"test1"})
test_reaction_1.gene_reaction_rule = "g1 and (try:' or 'except:1)"
_gene_knockout_computation(test_model, ["try:'"], set())
_gene_knockout_computation(test_model, ["try:'", "'except:1"], {"test1"})
def test__normalize_pseudoreaction_biomass_has_gpr():
reaction = Reaction('my_biomass_2')
reaction.gene_reaction_rule = 'b1779'
with pytest.raises(ConflictingPseudoreaction) as excinfo:
_ = _normalize_pseudoreaction(reaction.id, reaction)
assert 'has a gene_reaction_rule' in str(excinfo.value)
assert reaction.id == 'my_biomass_2'
def test_add_reaction_context(model):
old_reaction_count = len(model.reactions)
old_metabolite_count = len(model.metabolites)
dummy_metabolite_1 = Metabolite("test_foo_1")
dummy_metabolite_2 = Metabolite("test_foo_2")
actual_metabolite = model.metabolites[0]
copy_metabolite = model.metabolites[1].copy()
dummy_reaction = Reaction("test_foo_reaction")
dummy_reaction.add_metabolites({dummy_metabolite_1: -1,
dummy_metabolite_2: 1,
copy_metabolite: -2,
actual_metabolite: 1})
dummy_reaction.gene_reaction_rule = 'dummy_gene'
with model:
model.add_reaction(dummy_reaction)
assert model.reactions.get_by_id(
dummy_reaction.id) == dummy_reaction
assert len(model.reactions) == old_reaction_count + 1
assert len(model.metabolites) == old_metabolite_count + 2
assert dummy_metabolite_1._model == model
assert 'dummy_gene' in model.genes
assert len(model.reactions) == old_reaction_count
assert len(model.metabolites) == old_metabolite_count
with pytest.raises(KeyError):
model.reactions.get_by_id(dummy_reaction.id)
assert dummy_metabolite_1._model is None
assert 'dummy_gene' not in model.genes
def test__normalize_pseudoreaction_biomass_has_gpr():
reaction = Reaction('my_biomass_2')
reaction.gene_reaction_rule = 'b1779'
with pytest.raises(ConflictingPseudoreaction) as excinfo:
_ = _normalize_pseudoreaction(reaction.id, reaction)
assert 'has a gene_reaction_rule' in str(excinfo.value)
assert reaction.id == 'my_biomass_2'
def test_add_reaction_context(self, model):
old_reaction_count = len(model.reactions)
old_metabolite_count = len(model.metabolites)
dummy_metabolite_1 = Metabolite("test_foo_1")
dummy_metabolite_2 = Metabolite("test_foo_2")
actual_metabolite = model.metabolites[0]
copy_metabolite = model.metabolites[1].copy()
dummy_reaction = Reaction("test_foo_reaction")
dummy_reaction.add_metabolites({
dummy_metabolite_1: -1,
dummy_metabolite_2: 1,
copy_metabolite: -2,
actual_metabolite: 1
})
dummy_reaction.gene_reaction_rule = 'dummy_gene'
with model:
model.add_reaction(dummy_reaction)
assert model.reactions.get_by_id(dummy_reaction.id) == \
dummy_reaction
assert len(model.reactions) == old_reaction_count + 1
assert len(model.metabolites) == old_metabolite_count + 2
assert dummy_metabolite_1._model == model
assert 'dummy_gene' in model.genes
assert len(model.reactions) == old_reaction_count
assert len(model.metabolites) == old_metabolite_count
with pytest.raises(KeyError):
model.reactions.get_by_id(dummy_reaction.id)
assert dummy_metabolite_1._model is None
assert 'dummy_gene' not in model.genes
def convert_modelreaction(self, reaction, bigg=False):
mr_id = reaction.id
name = reaction.name
annotation = reaction.annotation
lower_bound, upper_bound = reaction.get_reaction_constraints()
id = build_rxn_id(mr_id)
if bigg and "bigg.reaction" in annotation:
id = annotation["bigg.reaction"]
gpr = reaction.get_gpr()
cobra_reaction = Reaction(id,
name=name,
lower_bound=lower_bound,
upper_bound=upper_bound)
cobra_reaction.annotation[self.SBO_ANNOTATION] = "SBO:0000176" #biochemical reaction
cobra_reaction.annotation.update(annotation)
if id.startswith('rxn'):
cobra_reaction.annotation["seed.reaction"] = id.split("_")[0]
cobra_reaction.add_metabolites(self.convert_modelreaction_stoichiometry(reaction))
cobra_reaction.gene_reaction_rule = reaction.gene_reaction_rule
for genes in gpr:
for gene in genes:
if not gene in self.genes:
self.genes[gene] = gene
return cobra_reaction
def test__normalize_pseudoreaction_exchange_error_has_gpr():
reaction = Reaction('EX_gone')
reaction.add_metabolites({Metabolite('glu__L_e'): -1})
reaction.gene_reaction_rule = 'b1779'
with pytest.raises(ConflictingPseudoreaction) as excinfo:
_ = _normalize_pseudoreaction(reaction.id, reaction)
assert 'has a gene_reaction_rule' in str(excinfo.value)
assert reaction.id == 'EX_gone'
def test__normalize_pseudoreaction_exchange_error_has_gpr():
reaction = Reaction('EX_gone')
reaction.add_metabolites({Metabolite('glu__L_e'): -1})
reaction.gene_reaction_rule = 'b1779'
with pytest.raises(ConflictingPseudoreaction) as excinfo:
_ = _normalize_pseudoreaction(reaction.id, reaction)
assert 'has a gene_reaction_rule' in str(excinfo.value)
assert reaction.id == 'EX_gone'
def test__normalize_pseudoreaction_atpm_has_gpr():
reaction = Reaction('NPT1')
reaction.add_metabolites({Metabolite('atp_c'): -1,
Metabolite('h2o_c'): -1,
Metabolite('pi_c'): 1,
Metabolite('h_c'): 1,
Metabolite('adp_c'): 1})
reaction.gene_reaction_rule = 'b1779'
_normalize_pseudoreaction(reaction)
# should not change
assert reaction.id == 'NPT1'
def test_gene_knock_out(self, model):
rxn = Reaction('rxn')
rxn.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
rxn.gene_reaction_rule = 'A2B1 or A2B2 and A2B3'
assert hasattr(list(rxn.genes)[0], 'knock_out')
model.add_reaction(rxn)
with model:
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
model.genes.A2B3.knock_out()
assert not rxn.functional
assert model.genes.A2B3.functional
assert rxn.functional
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
assert model.reactions.rxn.functional
model.genes.A2B3.knock_out()
assert not model.reactions.rxn.functional
def test_gene_knock_out(model):
rxn = Reaction('rxn')
rxn.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
rxn.gene_reaction_rule = 'A2B1 or A2B2 and A2B3'
assert hasattr(list(rxn.genes)[0], 'knock_out')
model.add_reaction(rxn)
with model:
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
model.genes.A2B3.knock_out()
assert not rxn.functional
assert model.genes.A2B3.functional
assert rxn.functional
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
assert model.reactions.rxn.functional
model.genes.A2B3.knock_out()
assert not model.reactions.rxn.functional
def test_gene_knock_out(model: Model) -> None:
"""Test gene knockout effect on reaction."""
rxn = Reaction("rxn")
rxn.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1})
rxn.gene_reaction_rule = "A2B1 or A2B2 and A2B3"
assert hasattr(list(rxn.genes)[0], "knock_out")
model.add_reaction(rxn)
with model:
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
model.genes.A2B3.knock_out()
assert not rxn.functional
assert model.genes.A2B3.functional
assert rxn.functional
model.genes.A2B1.knock_out()
assert not model.genes.A2B1.functional
assert model.reactions.rxn.functional
model.genes.A2B3.knock_out()
assert not model.reactions.rxn.functional
def convert_kmodel(kmodel, media=None, exchanges=True, model_id="kbase"):
model_test = cobra.Model(model_id)
comps = {}
mets = {}
sink = set()
reactions = []
extra = set()
for mcomp in kmodel['modelcompartments']:
mcomp_id = mcomp['id']
name = mcomp['label']
comps[mcomp_id] = name
for mc in kmodel["modelcompounds"]:
#print(mc.keys())
formula = None
if not mc['formula'] == 'null':
formula = mc['formula']
name = mc['name']
charge = get_int('charge', 0, mc)
mc_id = mc['id']
annotation = {}
if 'dblinks' in mc:
annotation = get_cpd_annotation(mc['dblinks'])
compartment = get_compartment_id(mc, simple=True)
id = build_cpd_id(mc_id)
if bigg:
if "bigg.metabolite" in annotation:
id = annotation["bigg.metabolite"] + "_" + compartment
#print(id)
if mc_id in SINK:
logger.info('Add Sink: [%s]', mc_id)
extra.add(mc_id)
sink.add(mc_id)
if compartment.startswith("e"):
extra.add(mc_id)
met = Metabolite(id=id,
formula=formula,
name=name,
charge=charge,
compartment=compartment)
met.annotation[
SBO_ANNOTATION] = "SBO:0000247" #simple chemical - Simple, non-repetitive chemical entity.
if id.startswith('cpd'):
met.annotation["seed.compound"] = id.split("_")[0]
#met.annotation[""] = "!!!"
met.annotation.update(annotation)
mets[mc_id] = met
genes = set()
#print(mc)
for mr in kmodel["modelreactions"]:
mr_id = mr['id']
name = mr['name']
lower_bound, upper_bound = get_reaction_constraints(mr)
annotation = {}
if 'dblinks' in mr:
annotation = get_rxn_annotation(mr['dblinks'])
id = build_rxn_id(mr_id)
if bigg:
if "bigg.reaction" in annotation:
id = annotation["bigg.reaction"]
#print(id)
reaction = Reaction(id=id,
name=name,
lower_bound=lower_bound,
upper_bound=upper_bound)
#print(mr['maxrevflux'], mr['maxforflux'], reaction.lower_bound)
reaction.annotation[SBO_ANNOTATION] = "!!!"
if id.startswith('rxn'):
reaction.annotation["seed.reaction"] = id.split("_")[0]
reaction.annotation.update(annotation)
object_stoichiometry = {}
for mrr in mr['modelReactionReagents']:
modelcompound_ref = mrr['modelcompound_ref']
coefficient = mrr['coefficient']
mc_id = get_id_from_ref(modelcompound_ref)
met_id = build_cpd_id(mc_id)
met = mets[mc_id] #model_test.metabolites.get_by_id(met_id)
#print(met, met_id, coefficient)
object_stoichiometry[met] = coefficient
reaction.annotation[
SBO_ANNOTATION] = "SBO:0000176" #biochemical reaction
reaction.add_metabolites(object_stoichiometry)
gpr = get_gpr(mr)
gpr_string = get_gpr_string(gpr)
#print(gpr_string)
reaction.gene_reaction_rule = gpr_string
genes |= get_genes(gpr)
#print(reaction)
reactions.append(reaction)
#print(mr.keys())
objective_id = None
for biomass in kmodel['biomasses']:
reaction = convert_biomass_to_reaction(biomass, mets)
reactions.append(reaction)
objective_id = reaction.id
#print(biomass)
#print(media)
if exchanges:
logger.info('Setup Drains. EX: %d SK: %d', len(extra), len(sink))
for e in extra:
met = mets[e]
prefix = "EX_"
if e in sink:
prefix = "DM_"
id = prefix + met.id
lower_bound = COBRA_DEFAULT_LB
upper_bound = COBRA_DEFAULT_UB
if not media == None:
lower_bound = 0
if not media == None and e.split("_")[0] in media:
ct = media[e.split("_")[0]]
lower_bound = ct[0]
upper_bound = ct[1]
#print(e, met, id, lower_bound, upper_bound)
object_stoichiometry = {met: -1}
reaction = Reaction(id=id,
name="Exchange for " + met.name,
lower_bound=lower_bound,
upper_bound=upper_bound)
reaction.add_metabolites(object_stoichiometry)
reaction.annotation[
SBO_ANNOTATION] = "SBO:0000627" #exchange reaction - ... provide matter influx or efflux to a model, for example to replenish a metabolic network with raw materials ...
reactions.append(reaction)
#print(reaction.name)
#print("Genes:", genes)
for g in genes:
gene = Gene(id=build_gene_id(g), name=g)
gene.annotation[SBO_ANNOTATION] = "SBO:0000243"
model_test.genes.append(gene)
model_test.compartments = comps
#model_test.add_metabolites(mets.values)
try:
model_test.add_reactions(reactions)
except ValueError as e:
warn(str(e))
if not objective_id == None:
model_test.objective = model_test.reactions.get_by_id(id=objective_id)
linear_reaction_coefficients(model_test)
return model_test
def from_mat_struct(mat_struct, model_id=None, inf=inf):
"""create a model from the COBRA toolbox struct
The struct will be a dict read in by scipy.io.loadmat
"""
m = mat_struct
if m.dtype.names is None:
raise ValueError("not a valid mat struct")
if not {"rxns", "mets", "S", "lb", "ub"} <= set(m.dtype.names):
raise ValueError("not a valid mat struct")
if "c" in m.dtype.names:
c_vec = m["c"][0, 0]
else:
c_vec = None
warn("objective vector 'c' not found")
model = Model()
if model_id is not None:
model.id = model_id
elif "description" in m.dtype.names:
description = m["description"][0, 0][0]
if not isinstance(description, string_types) and len(description) > 1:
model.id = description[0]
warn("Several IDs detected, only using the first.")
else:
model.id = description
else:
model.id = "imported_model"
for i, name in enumerate(m["mets"][0, 0]):
new_metabolite = Metabolite()
new_metabolite.id = str(name[0][0])
if all(var in m.dtype.names for var in
['metComps', 'comps', 'compNames']):
comp_index = m["metComps"][0, 0][i][0] - 1
new_metabolite.compartment = m['comps'][0, 0][comp_index][0][0]
if new_metabolite.compartment not in model.compartments:
comp_name = m['compNames'][0, 0][comp_index][0][0]
model.compartments[new_metabolite.compartment] = comp_name
else:
new_metabolite.compartment = _get_id_compartment(new_metabolite.id)
if new_metabolite.compartment not in model.compartments:
model.compartments[
new_metabolite.compartment] = new_metabolite.compartment
try:
new_metabolite.name = str(m["metNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.formula = str(m["metFormulas"][0][0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.charge = float(m["metCharge"][0, 0][i][0])
int_charge = int(new_metabolite.charge)
if new_metabolite.charge == int_charge:
new_metabolite.charge = int_charge
except (IndexError, ValueError):
pass
model.add_metabolites([new_metabolite])
new_reactions = []
coefficients = {}
for i, name in enumerate(m["rxns"][0, 0]):
new_reaction = Reaction()
new_reaction.id = str(name[0][0])
new_reaction.lower_bound = float(m["lb"][0, 0][i][0])
new_reaction.upper_bound = float(m["ub"][0, 0][i][0])
if isinf(new_reaction.lower_bound) and new_reaction.lower_bound < 0:
new_reaction.lower_bound = -inf
if isinf(new_reaction.upper_bound) and new_reaction.upper_bound > 0:
new_reaction.upper_bound = inf
if c_vec is not None:
coefficients[new_reaction] = float(c_vec[i][0])
try:
new_reaction.gene_reaction_rule = str(m['grRules'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.name = str(m["rxnNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.subsystem = str(m['subSystems'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
new_reactions.append(new_reaction)
model.add_reactions(new_reactions)
set_objective(model, coefficients)
coo = scipy_sparse.coo_matrix(m["S"][0, 0])
for i, j, v in zip(coo.row, coo.col, coo.data):
model.reactions[j].add_metabolites({model.metabolites[i]: v})
return model
def test_gene_knockout_computation(self, salmonella):
def find_gene_knockout_reactions_fast(cobra_model, gene_list):
compiled_rules = get_compiled_gene_reaction_rules(
cobra_model)
return find_gene_knockout_reactions(
cobra_model, gene_list,
compiled_gene_reaction_rules=compiled_rules)
def get_removed(m):
return {x.id for x in m._trimmed_reactions}
def test_computation(m, gene_ids, expected_reaction_ids):
genes = [m.genes.get_by_id(i) for i in gene_ids]
expected_reactions = {m.reactions.get_by_id(i)
for i in expected_reaction_ids}
removed1 = set(find_gene_knockout_reactions(m, genes))
removed2 = set(find_gene_knockout_reactions_fast(m, genes))
assert removed1 == expected_reactions
assert removed2 == expected_reactions
delete_model_genes(m, gene_ids, cumulative_deletions=False)
assert get_removed(m) == expected_reaction_ids
undelete_model_genes(m)
gene_list = ['STM1067', 'STM0227']
dependent_reactions = {'3HAD121', '3HAD160', '3HAD80', '3HAD140',
'3HAD180', '3HAD100', '3HAD181', '3HAD120',
'3HAD60', '3HAD141', '3HAD161', 'T2DECAI',
'3HAD40'}
test_computation(salmonella, gene_list, dependent_reactions)
test_computation(salmonella, ['STM4221'], {'PGI'})
test_computation(salmonella, ['STM1746.S'], {'4PEPTabcpp'})
# test cumulative behavior
delete_model_genes(salmonella, gene_list[:1])
delete_model_genes(salmonella, gene_list[1:],
cumulative_deletions=True)
delete_model_genes(salmonella, ["STM4221"],
cumulative_deletions=True)
dependent_reactions.add('PGI')
assert get_removed(salmonella) == dependent_reactions
# non-cumulative following cumulative
delete_model_genes(salmonella, ["STM4221"],
cumulative_deletions=False)
assert get_removed(salmonella) == {'PGI'}
# make sure on reset that the bounds are correct
reset_bound = salmonella.reactions.get_by_id("T2DECAI").upper_bound
assert reset_bound == 1000.
# test computation when gene name is a subset of another
test_model = Model()
test_reaction_1 = Reaction("test1")
test_reaction_1.gene_reaction_rule = "eggs or (spam and eggspam)"
test_model.add_reaction(test_reaction_1)
test_computation(test_model, ["eggs"], set())
test_computation(test_model, ["eggs", "spam"], {'test1'})
# test computation with nested boolean expression
test_reaction_1.gene_reaction_rule = \
"g1 and g2 and (g3 or g4 or (g5 and g6))"
test_computation(test_model, ["g3"], set())
test_computation(test_model, ["g1"], {'test1'})
test_computation(test_model, ["g5"], set())
test_computation(test_model, ["g3", "g4", "g5"], {'test1'})
# test computation when gene names are python expressions
test_reaction_1.gene_reaction_rule = "g1 and (for or in)"
test_computation(test_model, ["for", "in"], {'test1'})
test_computation(test_model, ["for"], set())
test_reaction_1.gene_reaction_rule = "g1 and g2 and g2.conjugate"
test_computation(test_model, ["g2"], {"test1"})
test_computation(test_model, ["g2.conjugate"], {"test1"})
test_reaction_1.gene_reaction_rule = "g1 and (try:' or 'except:1)"
test_computation(test_model, ["try:'"], set())
test_computation(test_model, ["try:'", "'except:1"], {"test1"})
def create_cobra_model_from_sbml_file(sbml_filename, old_sbml=False,
legacy_metabolite=False,
print_time=False, use_hyphens=False):
"""convert an SBML XML file into a cobra.Model object.
Supports SBML Level 2 Versions 1 and 4. The function will detect if the
SBML fbc package is used in the file and run the converter if the fbc
package is used.
Parameters
----------
sbml_filename: string
old_sbml: bool
Set to True if the XML file has metabolite formula appended to
metabolite names. This was a poorly designed artifact that persists in
some models.
legacy_metabolite: bool
If True then assume that the metabolite id has the compartment id
appended after an underscore (e.g. _c for cytosol). This has not been
implemented but will be soon.
print_time: bool
deprecated
use_hyphens: bool
If True, double underscores (__) in an SBML ID will be converted to
hyphens
Returns
-------
Model : The parsed cobra model
"""
if not libsbml:
raise ImportError('create_cobra_model_from_sbml_file '
'requires python-libsbml')
__default_lower_bound = -1000
__default_upper_bound = 1000
__default_objective_coefficient = 0
# Ensure that the file exists
if not isfile(sbml_filename):
raise IOError('Your SBML file is not found: %s' % sbml_filename)
# Expressions to change SBML Ids to Palsson Lab Ids
metabolite_re = re.compile('^M_')
reaction_re = re.compile('^R_')
compartment_re = re.compile('^C_')
if print_time:
warn("print_time is deprecated", DeprecationWarning)
model_doc = libsbml.readSBML(sbml_filename)
if model_doc.getPlugin("fbc") is not None:
from libsbml import ConversionProperties, LIBSBML_OPERATION_SUCCESS
conversion_properties = ConversionProperties()
conversion_properties.addOption(
"convert fbc to cobra", True, "Convert FBC model to Cobra model")
result = model_doc.convert(conversion_properties)
if result != LIBSBML_OPERATION_SUCCESS:
raise Exception("Conversion of SBML+fbc to COBRA failed")
sbml_model = model_doc.getModel()
sbml_model_id = sbml_model.getId()
sbml_species = sbml_model.getListOfSpecies()
sbml_reactions = sbml_model.getListOfReactions()
sbml_compartments = sbml_model.getListOfCompartments()
compartment_dict = dict([(compartment_re.split(x.getId())[-1], x.getName())
for x in sbml_compartments])
if legacy_metabolite:
# Deal with the palsson lab appending the compartment id to the
# metabolite id
new_dict = {}
for the_id, the_name in compartment_dict.items():
if the_name == '':
new_dict[the_id[0].lower()] = the_id
else:
new_dict[the_id] = the_name
compartment_dict = new_dict
legacy_compartment_converter = dict(
[(v, k) for k, v in iteritems(compartment_dict)])
cobra_model = Model(sbml_model_id)
metabolites = []
metabolite_dict = {}
# Convert sbml_metabolites to cobra.Metabolites
for sbml_metabolite in sbml_species:
# Skip sbml boundary species
if sbml_metabolite.getBoundaryCondition():
continue
if (old_sbml or legacy_metabolite) and \
sbml_metabolite.getId().endswith('_b'):
# Deal with incorrect sbml from bigg.ucsd.edu
continue
tmp_metabolite = Metabolite()
metabolite_id = tmp_metabolite.id = sbml_metabolite.getId()
tmp_metabolite.compartment = compartment_re.split(
sbml_metabolite.getCompartment())[-1]
if legacy_metabolite:
if tmp_metabolite.compartment not in compartment_dict:
tmp_metabolite.compartment = legacy_compartment_converter[
tmp_metabolite.compartment]
tmp_metabolite.id = parse_legacy_id(
tmp_metabolite.id, tmp_metabolite.compartment,
use_hyphens=use_hyphens)
if use_hyphens:
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('__', '-')
else:
# Just in case the SBML ids are ill-formed and use -
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('-', '__')
tmp_metabolite.name = sbml_metabolite.getName()
tmp_formula = ''
tmp_metabolite.notes = parse_legacy_sbml_notes(
sbml_metabolite.getNotesString())
if sbml_metabolite.isSetCharge():
tmp_metabolite.charge = sbml_metabolite.getCharge()
if "CHARGE" in tmp_metabolite.notes:
note_charge = tmp_metabolite.notes["CHARGE"][0]
try:
note_charge = float(note_charge)
if note_charge == int(note_charge):
note_charge = int(note_charge)
except:
warn("charge of %s is not a number (%s)" %
(tmp_metabolite.id, str(note_charge)))
else:
if ((tmp_metabolite.charge is None) or
(tmp_metabolite.charge == note_charge)):
tmp_metabolite.notes.pop("CHARGE")
# set charge to the one from notes if not assigend before
# the same
tmp_metabolite.charge = note_charge
else: # tmp_metabolite.charge != note_charge
msg = "different charges specified for %s (%d and %d)"
msg = msg % (tmp_metabolite.id,
tmp_metabolite.charge, note_charge)
warn(msg)
# Chances are a 0 note charge was written by mistake. We
# will default to the note_charge in this case.
if tmp_metabolite.charge == 0:
tmp_metabolite.charge = note_charge
for the_key in tmp_metabolite.notes.keys():
if the_key.lower() == 'formula':
tmp_formula = tmp_metabolite.notes.pop(the_key)[0]
break
if tmp_formula == '' and old_sbml:
tmp_formula = tmp_metabolite.name.split('_')[-1]
tmp_metabolite.name = tmp_metabolite.name[:-len(tmp_formula) - 1]
tmp_metabolite.formula = tmp_formula
metabolite_dict.update({metabolite_id: tmp_metabolite})
metabolites.append(tmp_metabolite)
cobra_model.add_metabolites(metabolites)
# Construct the vectors and matrices for holding connectivity and numerical
# info to feed to the cobra toolbox.
# Always assume steady state simulations so b is set to 0
cobra_reaction_list = []
coefficients = {}
for sbml_reaction in sbml_reactions:
if use_hyphens:
# Change the ids to match conventions used by the Palsson lab.
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('__', '-'))
else:
# Just in case the SBML ids are ill-formed and use -
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('-', '__'))
cobra_reaction_list.append(reaction)
# reaction.exchange_reaction = 0
reaction.name = sbml_reaction.getName()
cobra_metabolites = {}
# Use the cobra.Metabolite class here
for sbml_metabolite in sbml_reaction.getListOfReactants():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
cobra_metabolites[tmp_metabolite] = - \
sbml_metabolite.getStoichiometry()
for sbml_metabolite in sbml_reaction.getListOfProducts():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
# Handle the case where the metabolite was specified both
# as a reactant and as a product.
if tmp_metabolite in cobra_metabolites:
warn("%s appears as a reactant and product %s" %
(tmp_metabolite_id, reaction.id))
cobra_metabolites[
tmp_metabolite] += sbml_metabolite.getStoichiometry()
# if the combined stoichiometry is 0, remove the metabolite
if cobra_metabolites[tmp_metabolite] == 0:
cobra_metabolites.pop(tmp_metabolite)
else:
cobra_metabolites[
tmp_metabolite] = sbml_metabolite.getStoichiometry()
# check for nan
for met, v in iteritems(cobra_metabolites):
if isnan(v) or isinf(v):
warn("invalid value %s for metabolite '%s' in reaction '%s'" %
(str(v), met.id, reaction.id))
reaction.add_metabolites(cobra_metabolites)
# Parse the kinetic law info here.
parameter_dict = {}
# If lower and upper bounds are specified in the Kinetic Law then
# they override the sbml reversible attribute. If they are not
# specified then the bounds are determined by getReversible.
if not sbml_reaction.getKineticLaw():
if sbml_reaction.getReversible():
parameter_dict['lower_bound'] = __default_lower_bound
parameter_dict['upper_bound'] = __default_upper_bound
else:
# Assume that irreversible reactions only proceed from left to
# right.
parameter_dict['lower_bound'] = 0
parameter_dict['upper_bound'] = __default_upper_bound
parameter_dict[
'objective_coefficient'] = __default_objective_coefficient
else:
for sbml_parameter in \
sbml_reaction.getKineticLaw().getListOfParameters():
parameter_dict[
sbml_parameter.getId().lower()] = sbml_parameter.getValue()
if 'lower_bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower_bound']
elif 'lower bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower bound']
elif sbml_reaction.getReversible():
reaction.lower_bound = __default_lower_bound
else:
reaction.lower_bound = 0
if 'upper_bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper_bound']
elif 'upper bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper bound']
else:
reaction.upper_bound = __default_upper_bound
objective_coefficient = parameter_dict.get(
'objective_coefficient', parameter_dict.get(
'objective_coefficient', __default_objective_coefficient))
if objective_coefficient != 0:
coefficients[reaction] = objective_coefficient
# ensure values are not set to nan or inf
if isnan(reaction.lower_bound) or isinf(reaction.lower_bound):
reaction.lower_bound = __default_lower_bound
if isnan(reaction.upper_bound) or isinf(reaction.upper_bound):
reaction.upper_bound = __default_upper_bound
reaction_note_dict = parse_legacy_sbml_notes(
sbml_reaction.getNotesString())
# Parse the reaction notes.
# POTENTIAL BUG: DEALING WITH LEGACY 'SBML' THAT IS NOT IN A
# STANDARD FORMAT
# TODO: READ IN OTHER NOTES AND GIVE THEM A reaction_ prefix.
# TODO: Make sure genes get added as objects
if 'GENE ASSOCIATION' in reaction_note_dict:
rule = reaction_note_dict['GENE ASSOCIATION'][0]
try:
rule.encode('ascii')
except (UnicodeEncodeError, UnicodeDecodeError):
warn("gene_reaction_rule '%s' is not ascii compliant" % rule)
if rule.startswith(""") and rule.endswith("""):
rule = rule[6:-6]
reaction.gene_reaction_rule = rule
if 'GENE LIST' in reaction_note_dict:
reaction.systematic_names = reaction_note_dict['GENE LIST'][0]
elif ('GENES' in reaction_note_dict and
reaction_note_dict['GENES'] != ['']):
reaction.systematic_names = reaction_note_dict['GENES'][0]
elif 'LOCUS' in reaction_note_dict:
gene_id_to_object = dict([(x.id, x) for x in reaction._genes])
for the_row in reaction_note_dict['LOCUS']:
tmp_row_dict = {}
the_row = 'LOCUS:' + the_row.lstrip('_').rstrip('#')
for the_item in the_row.split('#'):
k, v = the_item.split(':')
tmp_row_dict[k] = v
tmp_locus_id = tmp_row_dict['LOCUS']
if 'TRANSCRIPT' in tmp_row_dict:
tmp_locus_id = tmp_locus_id + \
'.' + tmp_row_dict['TRANSCRIPT']
if 'ABBREVIATION' in tmp_row_dict:
gene_id_to_object[tmp_locus_id].name = tmp_row_dict[
'ABBREVIATION']
if 'SUBSYSTEM' in reaction_note_dict:
reaction.subsystem = reaction_note_dict.pop('SUBSYSTEM')[0]
reaction.notes = reaction_note_dict
# Now, add all of the reactions to the model.
cobra_model.id = sbml_model.getId()
# Populate the compartment list - This will be done based on
# cobra.Metabolites in cobra.Reactions in the future.
cobra_model.compartments = compartment_dict
cobra_model.add_reactions(cobra_reaction_list)
set_objective(cobra_model, coefficients)
return cobra_model
def parse_xml_into_model(xml, number=float):
xml_model = xml.find(ns("sbml:model"))
if get_attrib(xml_model, "fbc:strict") != "true":
warn('loading SBML model without fbc:strict="true"')
model_id = get_attrib(xml_model, "id")
model = Model(model_id)
model.name = xml_model.get("name")
model.compartments = {c.get("id"): c.get("name") for c in
xml_model.findall(COMPARTMENT_XPATH)}
# add metabolites
for species in xml_model.findall(SPECIES_XPATH % 'false'):
met = get_attrib(species, "id", require=True)
met = Metabolite(clip(met, "M_"))
met.name = species.get("name")
annotate_cobra_from_sbml(met, species)
met.compartment = species.get("compartment")
met.charge = get_attrib(species, "fbc:charge", int)
met.formula = get_attrib(species, "fbc:chemicalFormula")
model.add_metabolites([met])
# Detect boundary metabolites - In case they have been mistakenly
# added. They should not actually appear in a model
boundary_metabolites = {clip(i.get("id"), "M_")
for i in xml_model.findall(SPECIES_XPATH % 'true')}
# add genes
for sbml_gene in xml_model.iterfind(GENES_XPATH):
gene_id = get_attrib(sbml_gene, "fbc:id").replace(SBML_DOT, ".")
gene = Gene(clip(gene_id, "G_"))
gene.name = get_attrib(sbml_gene, "fbc:name")
if gene.name is None:
gene.name = get_attrib(sbml_gene, "fbc:label")
annotate_cobra_from_sbml(gene, sbml_gene)
model.genes.append(gene)
def process_gpr(sub_xml):
"""recursively convert gpr xml to a gpr string"""
if sub_xml.tag == OR_TAG:
return "( " + ' or '.join(process_gpr(i) for i in sub_xml) + " )"
elif sub_xml.tag == AND_TAG:
return "( " + ' and '.join(process_gpr(i) for i in sub_xml) + " )"
elif sub_xml.tag == GENEREF_TAG:
gene_id = get_attrib(sub_xml, "fbc:geneProduct", require=True)
return clip(gene_id, "G_")
else:
raise Exception("unsupported tag " + sub_xml.tag)
bounds = {bound.get("id"): get_attrib(bound, "value", type=number)
for bound in xml_model.iterfind(BOUND_XPATH)}
# add reactions
reactions = []
for sbml_reaction in xml_model.iterfind(
ns("sbml:listOfReactions/sbml:reaction")):
reaction = get_attrib(sbml_reaction, "id", require=True)
reaction = Reaction(clip(reaction, "R_"))
reaction.name = sbml_reaction.get("name")
annotate_cobra_from_sbml(reaction, sbml_reaction)
lb_id = get_attrib(sbml_reaction, "fbc:lowerFluxBound", require=True)
ub_id = get_attrib(sbml_reaction, "fbc:upperFluxBound", require=True)
try:
reaction.upper_bound = bounds[ub_id]
reaction.lower_bound = bounds[lb_id]
except KeyError as e:
raise CobraSBMLError("No constant bound with id '%s'" % str(e))
reactions.append(reaction)
stoichiometry = defaultdict(lambda: 0)
for species_reference in sbml_reaction.findall(
ns("sbml:listOfReactants/sbml:speciesReference")):
met_name = clip(species_reference.get("species"), "M_")
stoichiometry[met_name] -= \
number(species_reference.get("stoichiometry"))
for species_reference in sbml_reaction.findall(
ns("sbml:listOfProducts/sbml:speciesReference")):
met_name = clip(species_reference.get("species"), "M_")
stoichiometry[met_name] += \
get_attrib(species_reference, "stoichiometry",
type=number, require=True)
# needs to have keys of metabolite objects, not ids
object_stoichiometry = {}
for met_id in stoichiometry:
if met_id in boundary_metabolites:
warn("Boundary metabolite '%s' used in reaction '%s'" %
(met_id, reaction.id))
continue
try:
metabolite = model.metabolites.get_by_id(met_id)
except KeyError:
warn("ignoring unknown metabolite '%s' in reaction %s" %
(met_id, reaction.id))
continue
object_stoichiometry[metabolite] = stoichiometry[met_id]
reaction.add_metabolites(object_stoichiometry)
# set gene reaction rule
gpr_xml = sbml_reaction.find(GPR_TAG)
if gpr_xml is not None and len(gpr_xml) != 1:
warn("ignoring invalid geneAssociation for " + repr(reaction))
gpr_xml = None
gpr = process_gpr(gpr_xml[0]) if gpr_xml is not None else ''
# remove outside parenthesis, if any
if gpr.startswith("(") and gpr.endswith(")"):
gpr = gpr[1:-1].strip()
gpr = gpr.replace(SBML_DOT, ".")
reaction.gene_reaction_rule = gpr
try:
model.add_reactions(reactions)
except ValueError as e:
warn(str(e))
# objective coefficients are handled after all reactions are added
obj_list = xml_model.find(ns("fbc:listOfObjectives"))
if obj_list is None:
warn("listOfObjectives element not found")
return model
target_objective_id = get_attrib(obj_list, "fbc:activeObjective")
target_objective = obj_list.find(
ns("fbc:objective[@fbc:id='{}']".format(target_objective_id)))
obj_direction_long = get_attrib(target_objective, "fbc:type")
obj_direction = LONG_SHORT_DIRECTION[obj_direction_long]
obj_query = OBJECTIVES_XPATH % target_objective_id
coefficients = {}
for sbml_objective in obj_list.findall(obj_query):
rxn_id = clip(get_attrib(sbml_objective, "fbc:reaction"), "R_")
try:
objective_reaction = model.reactions.get_by_id(rxn_id)
except KeyError:
raise CobraSBMLError("Objective reaction '%s' not found" % rxn_id)
try:
coefficients[objective_reaction] = get_attrib(
sbml_objective, "fbc:coefficient", type=number)
except ValueError as e:
warn(str(e))
set_objective(model, coefficients)
model.solver.objective.direction = obj_direction
return model
def create_cobra_model_from_sbml_file(sbml_filename, old_sbml=False,
legacy_metabolite=False,
print_time=False, use_hyphens=False):
"""convert an SBML XML file into a cobra.Model object.
Supports SBML Level 2 Versions 1 and 4. The function will detect if the
SBML fbc package is used in the file and run the converter if the fbc
package is used.
Parameters
----------
sbml_filename: string
old_sbml: bool
Set to True if the XML file has metabolite formula appended to
metabolite names. This was a poorly designed artifact that persists in
some models.
legacy_metabolite: bool
If True then assume that the metabolite id has the compartment id
appended after an underscore (e.g. _c for cytosol). This has not been
implemented but will be soon.
print_time: bool
deprecated
use_hyphens: bool
If True, double underscores (__) in an SBML ID will be converted to
hyphens
Returns
-------
Model : The parsed cobra model
"""
if not libsbml:
raise ImportError('create_cobra_model_from_sbml_file '
'requires python-libsbml')
__default_lower_bound = -1000
__default_upper_bound = 1000
__default_objective_coefficient = 0
# Ensure that the file exists
if not isfile(sbml_filename):
raise IOError('Your SBML file is not found: %s' % sbml_filename)
# Expressions to change SBML Ids to Palsson Lab Ids
metabolite_re = re.compile('^M_')
reaction_re = re.compile('^R_')
compartment_re = re.compile('^C_')
if print_time:
warn("print_time is deprecated", DeprecationWarning)
model_doc = libsbml.readSBML(sbml_filename)
if model_doc.getPlugin("fbc") is not None:
from libsbml import ConversionProperties, LIBSBML_OPERATION_SUCCESS
conversion_properties = ConversionProperties()
conversion_properties.addOption(
"convert fbc to cobra", True, "Convert FBC model to Cobra model")
result = model_doc.convert(conversion_properties)
if result != LIBSBML_OPERATION_SUCCESS:
raise Exception("Conversion of SBML+fbc to COBRA failed")
sbml_model = model_doc.getModel()
sbml_model_id = sbml_model.getId()
sbml_species = sbml_model.getListOfSpecies()
sbml_reactions = sbml_model.getListOfReactions()
sbml_compartments = sbml_model.getListOfCompartments()
compartment_dict = dict([(compartment_re.split(x.getId())[-1], x.getName())
for x in sbml_compartments])
if legacy_metabolite:
# Deal with the palsson lab appending the compartment id to the
# metabolite id
new_dict = {}
for the_id, the_name in compartment_dict.items():
if the_name == '':
new_dict[the_id[0].lower()] = the_id
else:
new_dict[the_id] = the_name
compartment_dict = new_dict
legacy_compartment_converter = dict(
[(v, k) for k, v in iteritems(compartment_dict)])
cobra_model = Model(sbml_model_id)
metabolites = []
metabolite_dict = {}
# Convert sbml_metabolites to cobra.Metabolites
for sbml_metabolite in sbml_species:
# Skip sbml boundary species
if sbml_metabolite.getBoundaryCondition():
continue
if (old_sbml or legacy_metabolite) and \
sbml_metabolite.getId().endswith('_b'):
# Deal with incorrect sbml from bigg.ucsd.edu
continue
tmp_metabolite = Metabolite()
metabolite_id = tmp_metabolite.id = sbml_metabolite.getId()
tmp_metabolite.compartment = compartment_re.split(
sbml_metabolite.getCompartment())[-1]
if legacy_metabolite:
if tmp_metabolite.compartment not in compartment_dict:
tmp_metabolite.compartment = legacy_compartment_converter[
tmp_metabolite.compartment]
tmp_metabolite.id = parse_legacy_id(
tmp_metabolite.id, tmp_metabolite.compartment,
use_hyphens=use_hyphens)
if use_hyphens:
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('__', '-')
else:
# Just in case the SBML ids are ill-formed and use -
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('-', '__')
tmp_metabolite.name = sbml_metabolite.getName()
tmp_formula = ''
tmp_metabolite.notes = parse_legacy_sbml_notes(
sbml_metabolite.getNotesString())
if sbml_metabolite.isSetCharge():
tmp_metabolite.charge = sbml_metabolite.getCharge()
if "CHARGE" in tmp_metabolite.notes:
note_charge = tmp_metabolite.notes["CHARGE"][0]
try:
note_charge = float(note_charge)
if note_charge == int(note_charge):
note_charge = int(note_charge)
except:
warn("charge of %s is not a number (%s)" %
(tmp_metabolite.id, str(note_charge)))
else:
if ((tmp_metabolite.charge is None) or
(tmp_metabolite.charge == note_charge)):
tmp_metabolite.notes.pop("CHARGE")
# set charge to the one from notes if not assigend before
# the same
tmp_metabolite.charge = note_charge
else: # tmp_metabolite.charge != note_charge
msg = "different charges specified for %s (%d and %d)"
msg = msg % (tmp_metabolite.id,
tmp_metabolite.charge, note_charge)
warn(msg)
# Chances are a 0 note charge was written by mistake. We
# will default to the note_charge in this case.
if tmp_metabolite.charge == 0:
tmp_metabolite.charge = note_charge
for the_key in tmp_metabolite.notes.keys():
if the_key.lower() == 'formula':
tmp_formula = tmp_metabolite.notes.pop(the_key)[0]
break
if tmp_formula == '' and old_sbml:
tmp_formula = tmp_metabolite.name.split('_')[-1]
tmp_metabolite.name = tmp_metabolite.name[:-len(tmp_formula) - 1]
tmp_metabolite.formula = tmp_formula
metabolite_dict.update({metabolite_id: tmp_metabolite})
metabolites.append(tmp_metabolite)
cobra_model.add_metabolites(metabolites)
# Construct the vectors and matrices for holding connectivity and numerical
# info to feed to the cobra toolbox.
# Always assume steady state simulations so b is set to 0
cobra_reaction_list = []
coefficients = {}
for sbml_reaction in sbml_reactions:
if use_hyphens:
# Change the ids to match conventions used by the Palsson lab.
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('__', '-'))
else:
# Just in case the SBML ids are ill-formed and use -
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('-', '__'))
cobra_reaction_list.append(reaction)
# reaction.exchange_reaction = 0
reaction.name = sbml_reaction.getName()
cobra_metabolites = {}
# Use the cobra.Metabolite class here
for sbml_metabolite in sbml_reaction.getListOfReactants():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
cobra_metabolites[tmp_metabolite] = - \
sbml_metabolite.getStoichiometry()
for sbml_metabolite in sbml_reaction.getListOfProducts():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
# Handle the case where the metabolite was specified both
# as a reactant and as a product.
if tmp_metabolite in cobra_metabolites:
warn("%s appears as a reactant and product %s" %
(tmp_metabolite_id, reaction.id))
cobra_metabolites[
tmp_metabolite] += sbml_metabolite.getStoichiometry()
# if the combined stoichiometry is 0, remove the metabolite
if cobra_metabolites[tmp_metabolite] == 0:
cobra_metabolites.pop(tmp_metabolite)
else:
cobra_metabolites[
tmp_metabolite] = sbml_metabolite.getStoichiometry()
# check for nan
for met, v in iteritems(cobra_metabolites):
if isnan(v) or isinf(v):
warn("invalid value %s for metabolite '%s' in reaction '%s'" %
(str(v), met.id, reaction.id))
reaction.add_metabolites(cobra_metabolites)
# Parse the kinetic law info here.
parameter_dict = {}
# If lower and upper bounds are specified in the Kinetic Law then
# they override the sbml reversible attribute. If they are not
# specified then the bounds are determined by getReversible.
if not sbml_reaction.getKineticLaw():
if sbml_reaction.getReversible():
parameter_dict['lower_bound'] = __default_lower_bound
parameter_dict['upper_bound'] = __default_upper_bound
else:
# Assume that irreversible reactions only proceed from left to
# right.
parameter_dict['lower_bound'] = 0
parameter_dict['upper_bound'] = __default_upper_bound
parameter_dict[
'objective_coefficient'] = __default_objective_coefficient
else:
for sbml_parameter in \
sbml_reaction.getKineticLaw().getListOfParameters():
parameter_dict[
sbml_parameter.getId().lower()] = sbml_parameter.getValue()
if 'lower_bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower_bound']
elif 'lower bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower bound']
elif sbml_reaction.getReversible():
reaction.lower_bound = __default_lower_bound
else:
reaction.lower_bound = 0
if 'upper_bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper_bound']
elif 'upper bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper bound']
else:
reaction.upper_bound = __default_upper_bound
objective_coefficient = parameter_dict.get(
'objective_coefficient', parameter_dict.get(
'objective_coefficient', __default_objective_coefficient))
if objective_coefficient != 0:
coefficients[reaction] = objective_coefficient
# ensure values are not set to nan or inf
if isnan(reaction.lower_bound) or isinf(reaction.lower_bound):
reaction.lower_bound = __default_lower_bound
if isnan(reaction.upper_bound) or isinf(reaction.upper_bound):
reaction.upper_bound = __default_upper_bound
reaction_note_dict = parse_legacy_sbml_notes(
sbml_reaction.getNotesString())
# Parse the reaction notes.
# POTENTIAL BUG: DEALING WITH LEGACY 'SBML' THAT IS NOT IN A
# STANDARD FORMAT
# TODO: READ IN OTHER NOTES AND GIVE THEM A reaction_ prefix.
# TODO: Make sure genes get added as objects
if 'GENE ASSOCIATION' in reaction_note_dict:
rule = reaction_note_dict['GENE ASSOCIATION'][0]
try:
rule.encode('ascii')
except (UnicodeEncodeError, UnicodeDecodeError):
warn("gene_reaction_rule '%s' is not ascii compliant" % rule)
if rule.startswith(""") and rule.endswith("""):
rule = rule[6:-6]
reaction.gene_reaction_rule = rule
if 'GENE LIST' in reaction_note_dict:
reaction.systematic_names = reaction_note_dict['GENE LIST'][0]
elif ('GENES' in reaction_note_dict and
reaction_note_dict['GENES'] != ['']):
reaction.systematic_names = reaction_note_dict['GENES'][0]
elif 'LOCUS' in reaction_note_dict:
gene_id_to_object = dict([(x.id, x) for x in reaction._genes])
for the_row in reaction_note_dict['LOCUS']:
tmp_row_dict = {}
the_row = 'LOCUS:' + the_row.lstrip('_').rstrip('#')
for the_item in the_row.split('#'):
k, v = the_item.split(':')
tmp_row_dict[k] = v
tmp_locus_id = tmp_row_dict['LOCUS']
if 'TRANSCRIPT' in tmp_row_dict:
tmp_locus_id = tmp_locus_id + \
'.' + tmp_row_dict['TRANSCRIPT']
if 'ABBREVIATION' in tmp_row_dict:
gene_id_to_object[tmp_locus_id].name = tmp_row_dict[
'ABBREVIATION']
if 'SUBSYSTEM' in reaction_note_dict:
reaction.subsystem = reaction_note_dict.pop('SUBSYSTEM')[0]
reaction.notes = reaction_note_dict
# Now, add all of the reactions to the model.
cobra_model.id = sbml_model.getId()
# Populate the compartment list - This will be done based on
# cobra.Metabolites in cobra.Reactions in the future.
cobra_model.compartments = compartment_dict
cobra_model.add_reactions(cobra_reaction_list)
set_objective(cobra_model, coefficients)
return cobra_model
def test_gene_knockout_computation(self, salmonella):
def find_gene_knockout_reactions_fast(cobra_model, gene_list):
compiled_rules = get_compiled_gene_reaction_rules(cobra_model)
return find_gene_knockout_reactions(
cobra_model,
gene_list,
compiled_gene_reaction_rules=compiled_rules)
def get_removed(m):
return {x.id for x in m._trimmed_reactions}
def test_computation(m, gene_ids, expected_reaction_ids):
genes = [m.genes.get_by_id(i) for i in gene_ids]
expected_reactions = {
m.reactions.get_by_id(i)
for i in expected_reaction_ids
}
removed1 = set(find_gene_knockout_reactions(m, genes))
removed2 = set(find_gene_knockout_reactions_fast(m, genes))
assert removed1 == expected_reactions
assert removed2 == expected_reactions
delete_model_genes(m, gene_ids, cumulative_deletions=False)
assert get_removed(m) == expected_reaction_ids
undelete_model_genes(m)
gene_list = ['STM1067', 'STM0227']
dependent_reactions = {
'3HAD121', '3HAD160', '3HAD80', '3HAD140', '3HAD180', '3HAD100',
'3HAD181', '3HAD120', '3HAD60', '3HAD141', '3HAD161', 'T2DECAI',
'3HAD40'
}
test_computation(salmonella, gene_list, dependent_reactions)
test_computation(salmonella, ['STM4221'], {'PGI'})
test_computation(salmonella, ['STM1746.S'], {'4PEPTabcpp'})
# test cumulative behavior
delete_model_genes(salmonella, gene_list[:1])
delete_model_genes(salmonella,
gene_list[1:],
cumulative_deletions=True)
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=True)
dependent_reactions.add('PGI')
assert get_removed(salmonella) == dependent_reactions
# non-cumulative following cumulative
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=False)
assert get_removed(salmonella) == {'PGI'}
# make sure on reset that the bounds are correct
reset_bound = salmonella.reactions.get_by_id("T2DECAI").upper_bound
assert reset_bound == 1000.
# test computation when gene name is a subset of another
test_model = Model()
test_reaction_1 = Reaction("test1")
test_reaction_1.gene_reaction_rule = "eggs or (spam and eggspam)"
test_model.add_reaction(test_reaction_1)
test_computation(test_model, ["eggs"], set())
test_computation(test_model, ["eggs", "spam"], {'test1'})
# test computation with nested boolean expression
test_reaction_1.gene_reaction_rule = \
"g1 and g2 and (g3 or g4 or (g5 and g6))"
test_computation(test_model, ["g3"], set())
test_computation(test_model, ["g1"], {'test1'})
test_computation(test_model, ["g5"], set())
test_computation(test_model, ["g3", "g4", "g5"], {'test1'})
# test computation when gene names are python expressions
test_reaction_1.gene_reaction_rule = "g1 and (for or in)"
test_computation(test_model, ["for", "in"], {'test1'})
test_computation(test_model, ["for"], set())
test_reaction_1.gene_reaction_rule = "g1 and g2 and g2.conjugate"
test_computation(test_model, ["g2"], {"test1"})
test_computation(test_model, ["g2.conjugate"], {"test1"})
test_reaction_1.gene_reaction_rule = "g1 and (try:' or 'except:1)"
test_computation(test_model, ["try:'"], set())
test_computation(test_model, ["try:'", "'except:1"], {"test1"})
def from_mat_struct(mat_struct, model_id=None, inf=inf):
"""create a model from the COBRA toolbox struct
The struct will be a dict read in by scipy.io.loadmat
"""
m = mat_struct
if m.dtype.names is None:
raise ValueError("not a valid mat struct")
if not {"rxns", "mets", "S", "lb", "ub"} <= set(m.dtype.names):
raise ValueError("not a valid mat struct")
if "c" in m.dtype.names:
c_vec = m["c"][0, 0]
else:
c_vec = None
warn("objective vector 'c' not found")
model = Model()
if model_id is not None:
model.id = model_id
elif "description" in m.dtype.names:
description = m["description"][0, 0][0]
if not isinstance(description, string_types) and len(description) > 1:
model.id = description[0]
warn("Several IDs detected, only using the first.")
else:
model.id = description
else:
model.id = "imported_model"
for i, name in enumerate(m["mets"][0, 0]):
new_metabolite = Metabolite()
new_metabolite.id = str(name[0][0])
if all(var in m.dtype.names
for var in ['metComps', 'comps', 'compNames']):
comp_index = m["metComps"][0, 0][i][0] - 1
new_metabolite.compartment = m['comps'][0, 0][comp_index][0][0]
if new_metabolite.compartment not in model.compartments:
comp_name = m['compNames'][0, 0][comp_index][0][0]
model.compartments[new_metabolite.compartment] = comp_name
else:
new_metabolite.compartment = _get_id_compartment(new_metabolite.id)
if new_metabolite.compartment not in model.compartments:
model.compartments[
new_metabolite.compartment] = new_metabolite.compartment
try:
new_metabolite.name = str(m["metNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.formula = str(m["metFormulas"][0][0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.charge = float(m["metCharge"][0, 0][i][0])
int_charge = int(new_metabolite.charge)
if new_metabolite.charge == int_charge:
new_metabolite.charge = int_charge
except (IndexError, ValueError):
pass
model.add_metabolites([new_metabolite])
new_reactions = []
coefficients = {}
for i, name in enumerate(m["rxns"][0, 0]):
new_reaction = Reaction()
new_reaction.id = str(name[0][0])
new_reaction.lower_bound = float(m["lb"][0, 0][i][0])
new_reaction.upper_bound = float(m["ub"][0, 0][i][0])
if isinf(new_reaction.lower_bound) and new_reaction.lower_bound < 0:
new_reaction.lower_bound = -inf
if isinf(new_reaction.upper_bound) and new_reaction.upper_bound > 0:
new_reaction.upper_bound = inf
if c_vec is not None:
coefficients[new_reaction] = float(c_vec[i][0])
try:
new_reaction.gene_reaction_rule = str(m['grRules'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.name = str(m["rxnNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.subsystem = str(m['subSystems'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
new_reactions.append(new_reaction)
model.add_reactions(new_reactions)
set_objective(model, coefficients)
coo = scipy_sparse.coo_matrix(m["S"][0, 0])
for i, j, v in zip(coo.row, coo.col, coo.data):
model.reactions[j].add_metabolites({model.metabolites[i]: v})
return model
