def energy_transfer_annotations(base):
"""Provide a model with a membrane-spanning electron transfer reaction."""
cytaox = cobra.Metabolite("cytaox_c", compartment="c")
cytared = cobra.Metabolite("cytared_c", compartment="c")
cytbox = cobra.Metabolite("cytbox_m", compartment="m")
cytbred = cobra.Metabolite("cytbred_m", compartment="m")
cytaox.annotation["kegg.compound"] = "NAD"
cytared.annotation["kegg.compound"] = "NADH"
cytbox.annotation["kegg.compound"] = "UBIQUINONE-ox"
cytbred.annotation["kegg.compound"] = "UBIQUINONE-red"
et = cobra.Reaction("ET")
et.add_metabolites({cytaox: -1, cytbred: -1, cytared: 1, cytbox: 1})
base.add_reactions([et])
return base
def bare_mini():
"""Define a minimal metabolism with two metabolites."""
model = cobra.Model()
met_a = cobra.Metabolite("a_c", name="A", formula="C2")
met_b = cobra.Metabolite("b_c", name="B", formula="C2")
model.add_metabolites([met_a, met_b])
rxn = cobra.Reaction("FORMB")
rxn.add_metabolites({met_a: -1, met_b: 1})
model.add_reactions([rxn])
model.add_boundary(met_a)
demand = model.add_boundary(met_b, type="demand")
model.objective = demand
model.objective_direction = "max"
return model
def straight_pathway_with_cofactors_and_adapters():
"""Return a simple one-step heterologous pathway with co-factors."""
met_b = cobra.Metabolite("b_c", name="B")
met_c = cobra.Metabolite("c_c", name="C")
met_d = cobra.Metabolite("d_c", name="D")
met_b_prime = cobra.Metabolite("b_p_c", name="B'", formula="C2")
met_c_prime = cobra.Metabolite("c_p_c", name="C'", formula="O2")
met_d_prime = cobra.Metabolite("d_p_c", name="D'", formula="CO")
met_p_prime = cobra.Metabolite("p_p_c", name="P'", formula="C2O")
rxn = cobra.Reaction("FORMP")
rxn.add_metabolites({
met_b_prime: -1,
met_c_prime: -1,
met_d_prime: 1,
met_p_prime: 1
})
# Single production step heterologous pathway.
reactions = [rxn]
# Define drains for all heterologous metabolites.
exchanges = []
demand = cobra.Reaction(f"DM_{met_b_prime.id}")
demand.add_metabolites({met_b_prime: -1})
exchanges.append(demand)
# Provide a source for C'.
exchange = cobra.Reaction(f"EX_{met_c_prime.id}")
exchange.add_metabolites({met_c_prime: -1})
exchange.bounds = -10, 10
exchanges.append(exchange)
# Provide an exchange for D'.
exchange = cobra.Reaction(f"EX_{met_d_prime.id}")
exchange.add_metabolites({met_d_prime: -1})
exchange.bounds = -10, 10
exchanges.append(exchange)
# Define adapter reactions between native and heterologous metabolites.
adapters = []
adapter = cobra.Reaction(f"adapter_b_c_to_b_p_c")
adapter.add_metabolites({met_b: -1, met_b_prime: 1})
adapters.append(adapter)
adapter = cobra.Reaction(f"adapter_c_c_to_c_p_c")
adapter.add_metabolites({met_c: -1, met_c_prime: 1})
adapters.append(adapter)
adapter = cobra.Reaction(f"adapter_d_c_to_d_p_c")
adapter.add_metabolites({met_d: -1, met_d_prime: 1})
adapters.append(adapter)
product = cobra.Reaction(f"DM_{met_p_prime}")
product.add_metabolites({met_p_prime: -1})
return PathwayResult(reactions, exchanges, adapters, product)
def proton_pump(base):
# Proton pump
rxn = cobra.Reaction('Ah')
rxn.add_metabolites({
cobra.Metabolite(id="h_c", formula='H', compartment='c'):
-2,
cobra.Metabolite(id="o2_c", formula='O2', compartment='c'):
-0.5,
cobra.Metabolite(id="h_p", formula='H', compartment='p'):
2,
cobra.Metabolite(id="h2o_c", formula='HO2', compartment='c'):
1
})
base.add_reaction(rxn)
return base
def gene_broken_id(base):
rxn = cobra.Reaction(id='RXN', name="Rxn")
rxn.gene_reaction_rule = 'gene1'
base.add_reactions([rxn])
base.genes[0].annotation = {'refseq': "YPA_002410268.1",
'uniprot': "PGG31663",
'ecogene': "EG:10173",
'kegg.genes': "syn_ssr3451",
'ncbigi': "GIX_9082283",
'ncbigene': "0000xx1",
'ncbiprotein': "CAA+xz71AB118.1",
'ccds': "CCDS:13573.1",
'hprd': "A:00001",
'asap': "ABE_9634"}
return base
def constrained_toy_model(base):
base.add_metabolites([cobra.Metabolite(i) for i in "ABC"])
base.add_reactions(
[cobra.Reaction(i) for i in ["VA", "VB", "v1", "v2", "v3"]])
base.reactions.VA.add_metabolites({"A": 1})
base.reactions.VB.add_metabolites({"C": -1})
base.reactions.v1.add_metabolites({"A": -1, "B": 1})
base.reactions.v2.add_metabolites({"B": -1, "C": 1})
base.reactions.v3.add_metabolites({"A": -1, "C": 1})
base.reactions.v1.bounds = -1000, 1000
base.reactions.v2.bounds = -1000, 1000
base.reactions.v3.bounds = 1, 1
base.objective = 'VB'
base.reactions.VB.bounds = 0, 1
return base
def gene_each_present(base):
rxn = cobra.Reaction(id='RXN', name="Rxn")
rxn.gene_reaction_rule = 'gene1'
base.add_reactions([rxn])
base.genes[0].annotation = {'refseq': "YP_002410268.1",
'uniprot': "P31663",
'ecogene': "EG10173",
'kegg.genes': "syn:ssr3451",
'ncbigi': "GI:9082283",
'ncbigene': "00001",
'ncbiprotein': "CAA71118.1",
'ccds': "CCDS13573.1",
'hprd': "00001",
'asap': "ABE-0009634"}
return base
def problematic_metabolites():
"""Provide a reaction where some met cause problems in calculation."""
a = cobra.Metabolite("frdp_c", compartment="c")
a.annotation["kegg.compound"] = "C00448"
b = cobra.Metabolite("h20_c", compartment="c")
b.annotation["kegg.compound"] = "C00001"
c = cobra.Metabolite("pheme_c", compartment="c")
c.annotation["kegg.compound"] = "C00032"
d = cobra.Metabolite("hemeO_c", compartment="c")
d.annotation["kegg.compound"] = "C15672"
e = cobra.Metabolite("ppi_c", compartment="c")
e.annotation["kegg.compound"] = "C00013"
r = cobra.Reaction("Heme O synthase")
r.add_metabolites({a: -1, b: -1, c: -1, d: 1, e: 1})
return r
def no_gam_in_biomass(base):
met_a = cobra.Metabolite("lipid_c", "H744", compartment="c")
met_b = cobra.Metabolite("protein_c", "H119", compartment="c")
met_c = cobra.Metabolite("rna_c", "H496", compartment="c")
met_d = cobra.Metabolite("dna_c", "H483", compartment="c")
met_e = cobra.Metabolite("ash_c", "H80", compartment="c")
met_f = cobra.Metabolite("cellwall_c", "H177", compartment="c")
met_g = cobra.Metabolite("atp_c", "C10H12N5O13P3", compartment="c")
# Reactions
rxn_1 = cobra.Reaction("BIOMASS_TEST")
rxn_1.add_metabolites({met_a: -0.133, met_b: -5.834, met_c: -0.1,
met_d: -0.0625, met_e: -0.875, met_f: -0.2778,
met_g: -0.032})
base.add_reactions([rxn_1])
return base
def make_cobra_reaction(reaction_dict, cobra_metabs, rxn):
'''
Make a cobra reaction object from a rxn, setting the bounds, and adding metabolites.
Return the cobra reaction object.
'''
reaction = cobra.Reaction(rxn)
reaction.name = rxn
reaction.lower_bound = reaction_dict[rxn]['lower_bound']
reaction.upper_bound = reaction_dict[rxn]['upper_bound']
metabolites = {
cobra_metabs[k]: v
for k, v in reaction_dict[rxn]['metabolites'].items()
}
reaction.add_metabolites(metabolites)
return reaction
def add_m_model_content(me_model, m_model, complex_metabolite_ids=[]):
"""
Add metabolite and reaction attributes to me_model from m_model. Also
creates StoichiometricData objects for each reaction in m_model, and adds
reactions directly to me_model if they are exchanges or demands.
Args:
me_model: cobra.model.MEModel
The MEModel object to which the content will be added
m_model: cobra.model
The m_model which will act as the source of metabolic content for
MEModel
complex_metabolite_ids: list
List of complexes which are 'metabolites' in the m-model reaction
matrix, but should be treated as complexes
"""
for met in m_model.metabolites:
if met.id in complex_metabolite_ids:
new_met = Complex(met.id)
elif met.id.startswith("RNA"):
new_met = TranscribedGene(met.id)
else:
new_met = MEComponent(met.id)
new_met.name = met.name
new_met.formula = met.formula
new_met.compartment = met.compartment
new_met.charge = met.charge
new_met.annotation = met.annotation
new_met.notes = met.notes
me_model.add_metabolites(new_met)
for reaction in m_model.reactions:
if reaction.id.startswith("EX_") or reaction.id.startswith("DM_"):
new_reaction = cobra.Reaction(reaction.id)
me_model.add_reaction(new_reaction)
new_reaction.lower_bound = reaction.lower_bound
new_reaction.upper_bound = reaction.upper_bound
for met, stoichiometry in iteritems(reaction.metabolites):
new_reaction.add_metabolites(
{me_model.metabolites.get_by_id(met.id): stoichiometry})
else:
reaction_data = StoichiometricData(reaction.id, me_model)
reaction_data.lower_bound = reaction.lower_bound
reaction_data.upper_bound = reaction.upper_bound
reaction_data._stoichiometry = {
k.id: v
for k, v in iteritems(reaction.metabolites)
}
def _reaction_from_dict(reaction, model):
new_reaction = cobra.Reaction()
for k, v in iteritems(reaction):
if k in {'objective_coefficient', 'reversibility', 'reaction'}:
continue
elif k == 'metabolites':
new_reaction.add_metabolites(
OrderedDict((model.metabolites.get_by_id(str(met)),
get_numeric_from_string(coeff))
for met, coeff in iteritems(v)))
elif k in {'upper_bound', 'lower_bound'}:
v = get_numeric_from_string(v)
setattr(new_reaction, k, v)
else:
setattr(new_reaction, k, v)
return new_reaction
def phosphotransferase_system_annotations(base):
"""Provide a model with a PTS transport reaction."""
pep = cobra.Metabolite("pep_c", compartment="c")
pyr = cobra.Metabolite("pyr_c", compartment="c")
malt = cobra.Metabolite(",malt_e", compartment="e")
malt6p = cobra.Metabolite("malt6p_c", compartment="c")
pep.annotation["biocyc"] = "META:PHOSPHO-ENOL-PYRUVATE"
pyr.annotation["biocyc"] = "META:PYRUVATE"
malt.annotation["biocyc"] = ["META:ALPHA-MALTOSE", "META:MALTOSE"]
malt6p.annotation["biocyc"] = ["META:CPD-1244", "META:CPD-15982"]
pst = cobra.Reaction("PST")
pst.add_metabolites({pep: -1, malt: -1, pyr: 1, malt6p: 1})
base.add_reactions([pst])
return base
def gap_model_2(base):
a_c = cobra.Metabolite("a_c", compartment="c")
b_c = cobra.Metabolite("b_c", compartment="c")
c_c = cobra.Metabolite("c_c", compartment="c")
d_c = cobra.Metabolite("d_c", compartment="c")
base.add_reactions([cobra.Reaction(i)
for i in ["EX_A", "A2B", "C2D", "EX_D"]])
base.reactions.EX_A.add_metabolites({a_c: -1})
base.reactions.EX_D.add_metabolites({d_c: -1})
base.reactions.A2B.add_metabolites({a_c: -1, b_c: 1})
base.reactions.C2D.add_metabolites({c_c: -1, d_c: 1})
base.reactions.EX_A.bounds = -1000, 1000
base.reactions.A2B.bounds = 0, 1000
base.reactions.C2D.bounds = 0, 1000
base.reactions.EX_D.bounds = -1000, 1000
return base
def construct_model_from_mat(N, rxn_names, met_names):
model = cobra.Model('test_model')
model.add_metabolites(
[cobra.Metabolite(id=met_name) for met_name in met_names])
for row, rxn_name in zip(N.T, rxn_names):
reaction = cobra.Reaction(id=rxn_name)
model.add_reaction(reaction)
reaction.add_metabolites({
met_id: float(stoich)
for met_id, stoich in zip(met_names, row) if abs(stoich) > 1E-6
})
return model
def infeasible_toy_model(base):
base.add_metabolites([cobra.Metabolite(i) for i in "ABC"])
base.add_reactions(
[cobra.Reaction(i) for i in ["VA", "VB", "v1", "v2", "v3"]])
base.reactions.VA.add_metabolites({"A": 1})
base.reactions.VB.add_metabolites({"C": -1})
base.reactions.v1.add_metabolites({"A": -1, "B": 1})
base.reactions.v2.add_metabolites({"B": -1, "C": 1})
base.reactions.v3.add_metabolites({"A": -1, "C": 1})
# Forcing a lower bound on a 'metabolic' reaction that is higher than the
# uptake rate will make a model infeasible.
base.reactions.v1.bounds = 2, 1000
base.reactions.v2.bounds = -1000, 1000
base.reactions.v3.bounds = 1, 1
base.objective = 'VB'
base.reactions.VB.bounds = 0, 1
return base
def save(self, model):
if self.errors:
raise ValueError()
cobra_model: cobra.Model = model.build()
add_reaction_from_string_to_model(cobra_model,
self.cleaned_data['reaction_str'])
cobra_reaction = cobra.Reaction(
id=self.cleaned_data['cobra_id'],
name=self.cleaned_data['name'],
subsystem=self.cleaned_data['subsystem'],
lower_bound=float(self.cleaned_data['lower_bound']),
upper_bound=float(self.cleaned_data['upper_bound']))
cobra_model.add_reactions([cobra_reaction])
cobra_reaction.build_reaction_from_string(
self.cleaned_data['reaction_str'])
cobra_reaction.reaction = self.cleaned_data['reaction_str']
cobra_reaction.gene_reaction_rule = self.cleaned_data[
'gene_reaction_rule']
models.CobraModelChange.objects.create(
change_type=self.cleaned_data['change_type'],
model=model,
reaction_info=json.dumps({
'reactions': [get_reaction_json(cobra_reaction)],
}))
model.sbml_content = dump_sbml(cobra_model)
model.save()
model.cache(cobra_model)
keywords = set()
reaction_dict_list = [
json.loads(change.reaction_info)
for change in models.CobraModelChange.objects.filter(
model=model, change_type='add_reaction')[:10]
]
for reaction_dict in reaction_dict_list:
for reaction in reaction_dict['reactions']:
keywords.add(reaction['name'])
keywords.update(reaction['metabolites'])
keywords.update(reaction['genes'])
biobricks = search_biobricks(*keywords)
for bb in biobricks:
biobrick = models.CobraBiobrick()
biobrick.part_name = bb.partname
biobrick.cobra_model = model
biobrick.save()
return model
def mini_with_cofactors():
"""Define a minimal metabolism with two metabolites and co-factors."""
model = cobra.Model()
met_a = cobra.Metabolite("a_c", name="A", formula="C2")
met_b = cobra.Metabolite("b_c", name="B", formula="C2")
met_c = cobra.Metabolite("c_c", name="C", formula="O2")
met_d = cobra.Metabolite("d_c", name="D", formula="CO")
model.add_metabolites([met_a, met_b])
rxn = cobra.Reaction("FORMB")
rxn.add_metabolites({met_a: -1, met_b: 1})
model.add_reactions([rxn])
model.add_boundary(met_a)
model.add_boundary(met_c)
model.add_boundary(met_d)
demand = model.add_boundary(met_b, type="demand")
model.objective = demand
model.objective_direction = "max"
return model
def sum_missing_formula(base):
"""Define a biomass reaction whose components lack a formula."""
met_a = cobra.Metabolite("lipid_c", "H744")
met_b = cobra.Metabolite("protein_c", "H119")
met_c = cobra.Metabolite("rna_c")
met_d = cobra.Metabolite("dna_c")
met_e = cobra.Metabolite("ash_c", None)
# Reactions
rxn_1 = cobra.Reaction("BIOMASS_TEST")
rxn_1.add_metabolites({
met_a: -0.2,
met_b: -0.2,
met_c: -0.2,
met_d: -0.2,
met_e: -0.2
})
base.add_reactions([rxn_1])
return base
def add_R02301(model):
reaction = cobra.Reaction("FTHFCL_1")
reaction.name = "5-Formyltetrahydrofolate cyclo-ligase"
metabolite_dict = {
model.metabolites.atp_c: -1,
model.metabolites.get_by_id("5fthf_c"): -1,
model.metabolites.adp_c: 1,
model.metabolites.pi_c: 1,
model.metabolites.methf_c: 1
}
reaction.add_metabolites(metabolite_dict)
reaction.annotation["ec-code"] = "6.3.3.2"
reaction.annotation["kegg.reaction"] = "R02301"
reaction.annotation["origin"] = "KEGG"
reaction.gene_reaction_rule = "SCO3183"
reaction.bounds = (0, 1000)
model.add_reaction(reaction)
def new_transport_reactions_to_existing_metabolites(
model, new_transport_reactions_fn):
df = pd.read_csv(new_transport_reactions_fn,
header=0,
sep=",",
index_col=False,
quotechar='"')
for i, row in df.iterrows():
r_id = row["RxnID"].strip()
new_reaction = cobra.Reaction(r_id)
new_reaction.gene_reaction_rule = row["Gene association"]
model.add_reaction(new_reaction)
reaction_string = row["Rxn_Formula"].replace("[", "_").replace("]", "")
new_reaction.build_reaction_from_string(reaction_string)
new_reaction.annotation["sbo"] = "SBO:0000655"
new_reaction.annotation["subsystem"] = row["Subsystem"]
new_reaction.name = row["Reaction name"].strip()
_check_and_fix_new_extracellular_metabolites(model, new_reaction)
def consuming_toy_model(base):
base.add_metabolites([cobra.Metabolite(i) for i in "ABCD"])
base.add_reactions([
cobra.Reaction(i) for i in ["VA", "VB", "VD", "v1", "v2", "v3", "v4"]
])
base.reactions.VA.add_metabolites({"A": 1})
base.reactions.VB.add_metabolites({"C": -1})
base.reactions.VD.add_metabolites({"D": -1})
base.reactions.v1.add_metabolites({"A": -1, "B": 1})
base.reactions.v2.add_metabolites({"B": -1, "C": 1})
base.reactions.v3.add_metabolites({"A": -1, "C": 1})
base.reactions.v4.add_metabolites({"A": -1, "C": 1, "D": -1})
base.reactions.v1.bounds = -1000, 1000
base.reactions.v2.bounds = -1000, 1000
base.reactions.v3.bounds = -1000, 1000
base.reactions.v4.bounds = -1, 0
base.objective = 'VB'
base.reactions.VB.bounds = 0, 1
return base
def add_iron_sulfur_modifications(me_model):
for name, complexes in generic_fes_transfer_complexes.items():
generic_fes_transfer = GenericData(name, me_model, complexes)
generic_fes_transfer.create_reactions()
for fes in ['2fe2s_c', '4fe4s_c']:
me_model.add_metabolites([cobra.Metabolite(fes)])
for name in fes_transfer.values():
rxn = cobra.Reaction('_'.join([name, fes, 'unloading']))
me_model.add_reactions([rxn])
rxn.add_metabolites({
name + '_mod_1:' + fes.replace('_c', ''): -1,
fes: 1,
name: 1
})
# add fes transfer enzymes to proper modification data
mod_2fe2s = me_model.modification_data.mod_2fe2s_c
mod_2fe2s.enzyme = 'generic_2fe2s_transfer_complex'
mod_2fe2s.stoichiometry = {'2fe2s_c': -1.}
mod_4fe4s = me_model.modification_data.mod_4fe4s_c
mod_4fe4s.enzyme = 'generic_4fe4s_transfer_complex'
mod_4fe4s.stoichiometry = {'4fe4s_c': -1.}
mod_3fe4s = me_model.modification_data.mod_3fe4s_c
mod_3fe4s.enzyme = 'generic_4fe4s_transfer_complex'
mod_3fe4s.stoichiometry = {'4fe4s_c': -1., 'fe2_c': 1}
for chaperone in set(fes_chaperones.values()):
new_mod = ModificationData('mod_2fe2s_c_' + chaperone, me_model)
new_mod.enzyme = [chaperone, 'generic_2fe2s_transfer_complex']
new_mod.stoichiometry = {'2fe2s_c': -1.}
for cplx_data in me_model.modification_data.get_by_id(
'mod_2fe2s_c').get_complex_data():
cplx_id = cplx_data.id.split('_mod')[0]
if cplx_id in fes_chaperones:
cplx_data.modifications['mod_2fe2s_c_' + fes_chaperones[
cplx_id]] = \
cplx_data.modifications.pop('mod_2fe2s_c')
def sum_within_deviation(base):
"""Metabolites for a superficial, simple toy biomass reaction. The
composition will follow the distribution depicted here:
Lipid = 10% = 0.1 g/g
Protein = 70% = 0.7 g/g
RNA = 5% = 0.05 g/g
DNA = 3% = 0.03 g/g
Ash = 7% = 0.07 g/g
Carbohydrates = 5% = 0.05 g/g
The arbitrary molar masses for the metabolites used in this toy
reaction will be approximated using hydrogen atoms in the formula.
"""
met_a = cobra.Metabolite("lipid_c", "H744")
met_b = cobra.Metabolite("protein_c", "H119")
met_c = cobra.Metabolite("rna_c", "H496")
met_d = cobra.Metabolite("dna_c", "H483")
met_e = cobra.Metabolite("ash_c", "H80")
met_f = cobra.Metabolite("cellwall_c", "H177")
met_g = cobra.Metabolite("atp_c", "C10H12N5O13P3")
met_h = cobra.Metabolite("adp_c", "C10H12N5O10P2")
met_i = cobra.Metabolite("h_c", "H")
met_j = cobra.Metabolite("h2o_c", "H2O")
met_k = cobra.Metabolite("pi_c", "HO4P")
# Reactions
rxn_1 = cobra.Reaction("BIOMASS_TEST")
rxn_1.add_metabolites({
met_a: -0.133,
met_b: -5.834,
met_c: -0.1,
met_d: -0.0625,
met_e: -0.875,
met_f: -0.2778,
met_g: -30.0,
met_h: 30.0,
met_i: 30.0,
met_j: -30.0,
met_k: 30.0
})
base.add_reactions([rxn_1])
return base
def rxns_with_two_substrates(base):
"""Provide a model with two substrates that can be taken up"""
met_a = cobra.Metabolite("a_c", compartment="c")
met_b = cobra.Metabolite("b_c", compartment="c")
met_c = cobra.Metabolite("c_e", compartment="e")
met_d = cobra.Metabolite("d_e", compartment="e")
rxn_1 = cobra.Reaction("rxn1")
rxn_1.add_metabolites({met_a: -1, met_b: 1})
base.add_reactions([rxn_1])
base.add_boundary(met_c,
type="custom",
reaction_id="EX_c",
lb=-1000,
ub=1000)
base.add_boundary(met_d,
type="custom",
reaction_id="EX_d",
lb=-1000,
ub=1000)
return base
def met_broken_id(base):
met = cobra.Metabolite(id='met_c', name="Met")
met.annotation = {
'pubchem.compound': "x",
'metanetx.chemical': "MNXR23",
'kegg.compound': "K00022",
'seed.compound': "cdp00020",
'inchi': "InBhI=1/C2H6O/z1-2-3/h3H,2Z2,1Y3",
'inchikey': "LCT-ONWCANYUPML-UHFFFAOYSA-M",
'chebi': ["CHEBI:487", "CHEBI:15361", "CHEBI:26462", "CHEBI:26466",
"CHEBI:32816", "CEBI:O", "CHEBI:86354", "CHEBI:8685"],
'hmdb': "HMBD00243",
'biocyc': "/:PYRUVATE",
'reactome': ["113557", "29398", "389680"],
'bigg.metabolite': ""
}
rxn = cobra.Reaction(id='RXN', name="Rxn")
rxn.add_metabolites({met: -1})
base.add_reactions([rxn])
return base
def proton_pump_annotations(base):
"""Provide a model with an ABC proton pump reaction."""
atp = cobra.Metabolite("atp_c", formula='C10H12N5O13P3', compartment="c")
adp = cobra.Metabolite("adp_c", formula='C10H12N5O10P2', compartment="c")
h_c = cobra.Metabolite("h_c", formula='H', compartment="c")
pi = cobra.Metabolite("pi_c", formula='HO4P', compartment="c")
h2o = cobra.Metabolite("h2o_c", formula='H2O', compartment="c")
h_p = cobra.Metabolite("h_p", formula='H', compartment="p")
atp.annotation["biocyc"] = ["META:ATP", "META:CPD0-1634"]
adp.annotation["biocyc"] = ["META:ADP", "META:CPD0-1651"]
h_c.annotation["biocyc"] = "META:PROTON"
pi.annotation["biocyc"] = ["META:CPD-16459", "META:CPD-9010"]
h2o.annotation["biocyc"] = ["META:CPD-15815", "META:HYDROXYL-GROUP"]
h_p.annotation["biocyc"] = "META:PROTON"
pump = cobra.Reaction("PUMP")
pump.add_metabolites({h_c: -4, adp: -1, pi: -1, atp: 1, h2o: 1, h_p: 3})
base.add_reactions([pump])
return base
def direct_met_no_compartments(base):
base.add_metabolites([cobra.Metabolite(i) for i in "ABCDEFG"])
base.add_reactions([
cobra.Reaction(i) for i in
["EX_A", "EX_C", "EX_E", "EX_G", "A2B", "C2D", "E2F", "biomass"]
])
base.reactions.EX_A.add_metabolites({"A": 1})
base.reactions.EX_C.add_metabolites({"C": -1})
base.reactions.EX_E.add_metabolites({"E": -1})
base.reactions.EX_G.add_metabolites({"G": -1})
base.reactions.A2B.add_metabolites({"A": -1, "B": 1})
base.reactions.C2D.add_metabolites({"C": -1, "D": 1})
base.reactions.E2F.add_metabolites({"E": -1, "F": 1})
base.reactions.biomass.add_metabolites({
"B": -1,
"D": -1,
"F": -1,
"G": -1
})
return base
def post(self, request):
if not request.user.is_authenticated:
return redirect("/accounts/login")
user = request.user
try:
reaction_pk = request.POST["reaction_pk"]
except KeyError:
return HttpResponse("reaction pk required", status=400)
try:
model_pk = request.POST["model_pk"]
except KeyError:
return HttpResponse("model pk required", status=400)
try:
cobra_model_object = CobraModel.objects.get(pk=model_pk)
except ObjectDoesNotExist:
return HttpResponse("No model found", status=404)
if cobra_model_object.owner != user:
return HttpResponse("Not your model", status=403)
try:
data_reaction_object = DataReaction.objects.get(pk=reaction_pk)
except ObjectDoesNotExist:
return HttpResponse("No reaction found", status=404)
reaction = cobra.Reaction(data_reaction_object.bigg_id)
reaction.name = data_reaction_object.name
metabolites_dict = reaction_string_to_metabolites(
data_reaction_object.reaction_string)
if metabolites_dict is None:
return HttpResponse("Internal Error", status=500)
reaction.add_metabolites(metabolites_dict)
cobra_object = load_sbml(cobra_model_object.sbml_content)
reaction.gene_reaction_rule = [
gene.gene_reaction_rule
for gene in data_reaction_object.reactiongene_set.all()
][0]
cobra_model_object.sbml_content = dump_sbml(cobra_object)
cobra_model_object.save()
cobra_model_object.cache(load_sbml(cobra_model_object.sbml_content))
# return JsonResponse({"messages": "OK"}, status=200)
return redirect("/cobra/models/" + str(cobra_model_object.pk))
def abc_pump_formulae(base):
"""Provide a model with an ABC transport reaction."""
atp = cobra.Metabolite("atp_c", formula='C10H12N5O13P3', compartment="c")
adp = cobra.Metabolite("adp_c", formula='C10H12N5O10P2', compartment="c")
h = cobra.Metabolite("h_c", formula='H', compartment="c")
pi = cobra.Metabolite("pi_c", formula='HO4P', compartment="c")
h2o = cobra.Metabolite("h2o_c", formula='H2O', compartment="c")
aso_c = cobra.Metabolite("aso3_c", formula='AsO3', compartment="c")
aso_e = cobra.Metabolite("aso3_e", formula='AsO3', compartment="e")
pump = cobra.Reaction("PUMP")
pump.add_metabolites({
aso_c: -1,
atp: -1,
h2o: -1,
adp: 1,
h: 1,
pi: 1,
aso_e: 1
})
base.add_reactions([pump])
return base
