def _add_target_constraints(self, c):
"""Add the target constraints to the dual model"""
targets = self._target_constraints
w_reactions = []
for i, target in enumerate(targets):
assert isinstance(target, optlang.interface.Constraint)
if not target.is_Linear:
raise ValueError("Target constraints must be linear.")
if (target.lb is None and target.ub is None) or (target.lb is not None and target.ub is not None):
raise ValueError("Target constraints must be one-sided inequalities.")
coefficients_dict = target.expression.as_coefficients_dict()
w_reac = Reaction("w_" + str(i))
w_reac.lower_bound = c
if target.ub is not None:
coefficients = {
self._dual_model.metabolites.get_by_id(var.name): coef for var, coef in coefficients_dict.items()
if var.name in self._dual_model.metabolites
}
elif target.lb is not None:
coefficients = {
self._dual_model.metabolites.get_by_id(var.name): -coef for var, coef in coefficients_dict.items()
if var.name in self._dual_model.metabolites
}
w_reac.add_metabolites(coefficients)
w_reactions.append(w_reac)
self._dual_model.add_reactions(w_reactions)
return None
def test_weird_left_to_right_reaction_issue(self):
model = Model("Toy Model")
m1 = Metabolite("M1")
d1 = Reaction("ex1")
d1.add_metabolites({m1: -1})
d1.upper_bound = 0
d1.lower_bound = -1000
# print d1.reaction, d1.lower_bound, d1.upper_bound
model.add_reactions([d1])
self.assertFalse(d1.reversibility)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
with TimeMachine() as tm:
d1.knock_out(time_machine=tm)
self.assertEqual(d1.lower_bound, 0)
self.assertEqual(d1._lower_bound, 0)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
def test_weird_left_to_right_reaction_issue(self):
model = Model("Toy Model")
m1 = Metabolite("M1")
d1 = Reaction("ex1")
d1.add_metabolites({m1: -1})
d1.upper_bound = 0
d1.lower_bound = -1000
# print d1.reaction, d1.lower_bound, d1.upper_bound
model.add_reactions([d1])
self.assertFalse(d1.reversibility)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
with TimeMachine() as tm:
d1.knock_out(time_machine=tm)
self.assertEqual(d1.lower_bound, 0)
self.assertEqual(d1._lower_bound, 0)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1._upper_bound, 0)
def cad_reaction(core_model):
reaction = Reaction(id="CAD", name="Cis-Aconitate Decarboxylase")
acon = core_model.metabolites.acon_DASH_C_c
co2_c = core_model.metabolites.co2_c
ita_c = Metabolite(id="ita_c", name="Itaconate", compartment="c")
reaction.add_metabolites({acon: -1, co2_c: 1, ita_c: 1})
return reaction
def test_one_left_to_right_reaction_set_positive_ub(self):
model = Model("Toy Model")
m1 = Metabolite("M1")
d1 = Reaction("ex1")
d1.add_metabolites({m1: -1})
d1.upper_bound = 0
d1.lower_bound = -1000
model.add_reactions([d1])
self.assertEqual(d1.reverse_variable.lb, 0)
self.assertEqual(d1.reverse_variable.ub, 1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._upper_bound, 0)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1.forward_variable.lb, 0)
self.assertEqual(d1.forward_variable.ub, 0)
d1.upper_bound = .1
self.assertEqual(d1.forward_variable.lb, 0)
self.assertEqual(d1.forward_variable.ub, .1)
self.assertEqual(d1.reverse_variable.lb, 0)
self.assertEqual(d1.reverse_variable.ub, 1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, .1)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, .1)
def create_adapter_reactions(original_metabolites, database, mapping,
compartment_regexp):
adapter_reactions = []
for metabolite in original_metabolites: # model is the original host model
if not compartment_regexp.match(metabolite.id):
continue
name = metabolite.id[0:-2]
try:
mapped_name = mapping[name]
except KeyError:
continue
# print name, 'N/A'
adapter_reaction = Reaction('adapter_' + metabolite.id + '_' +
mapped_name)
adapter_reaction.lower_bound = -1000
try:
adapter_reaction.add_metabolites({
metabolite:
-1,
database.metabolites.get_by_id(mapped_name):
1
})
except KeyError:
pass
else:
adapter_reactions.append(adapter_reaction)
return adapter_reactions
def test_add_reactions(self):
r1 = Reaction('r1')
r1.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
r1.lower_bound, r1.upper_bound = -999999., 999999.
r2 = Reaction('r2')
r2.add_metabolites({
Metabolite('A'): -1,
Metabolite('C'): 1,
Metabolite('D'): 1
})
r2.lower_bound, r2.upper_bound = 0., 999999.
r2.objective_coefficient = 3.
self.model.add_reactions([r1, r2])
self.assertEqual(self.model.reactions[-2], r1)
self.assertEqual(self.model.reactions[-1], r2)
self.assertTrue(
isinstance(self.model.reactions[-2].reverse_variable,
self.model.solver.interface.Variable))
self.assertEqual(
self.model.objective.expression.coeff(
self.model.reactions.
Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2.
forward_variable), 1.)
self.assertEqual(
self.model.objective.expression.coeff(
self.model.reactions.
Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2.
reverse_variable), -1.)
self.assertEqual(
self.model.objective.expression.coeff(
self.model.reactions.r2.forward_variable), 3.)
self.assertEqual(
self.model.objective.expression.coeff(
self.model.reactions.r2.reverse_variable), -3.)
def test_one_left_to_right_reaction_set_positive_ub(self):
model = Model("Toy Model")
m1 = Metabolite("M1")
d1 = Reaction("ex1")
d1.add_metabolites({m1: -1})
d1.upper_bound = 0
d1.lower_bound = -1000
model.add_reactions([d1])
self.assertEqual(d1.reverse_variable.lb, 0)
self.assertEqual(d1.reverse_variable.ub, 1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.lower_bound, -1000)
self.assertEqual(d1._upper_bound, 0)
self.assertEqual(d1.upper_bound, 0)
self.assertEqual(d1.forward_variable.lb, 0)
self.assertEqual(d1.forward_variable.ub, 0)
d1.upper_bound = .1
self.assertEqual(d1.forward_variable.lb, 0)
self.assertEqual(d1.forward_variable.ub, .1)
self.assertEqual(d1.reverse_variable.lb, 0)
self.assertEqual(d1.reverse_variable.ub, 1000)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, .1)
self.assertEqual(d1._lower_bound, -1000)
self.assertEqual(d1.upper_bound, .1)
def _add_target_constraints(self, c):
"""Add the target constraints to the dual model"""
targets = self._target_constraints
w_reactions = []
for i, target in enumerate(targets):
assert isinstance(target, optlang.interface.Constraint)
if not target.is_Linear:
raise ValueError("Target constraints must be linear.")
if (target.lb is None
and target.ub is None) or (target.lb is not None
and target.ub is not None):
raise ValueError(
"Target constraints must be one-sided inequalities.")
coefficients_dict = target.expression.as_coefficients_dict()
w_reac = Reaction("w_" + str(i))
w_reac.lower_bound = c
if target.ub is not None:
coefficients = {
self._dual_model.metabolites.get_by_id(var.name): coef
for var, coef in coefficients_dict.items()
if var.name in self._dual_model.metabolites
}
elif target.lb is not None:
coefficients = {
self._dual_model.metabolites.get_by_id(var.name): -coef
for var, coef in coefficients_dict.items()
if var.name in self._dual_model.metabolites
}
w_reac.add_metabolites(coefficients)
w_reactions.append(w_reac)
self._dual_model.add_reactions(w_reactions)
return None
def construct_universal_model(list_of_db_prefixes):
# Select which reactions to include in universal reaction database
reaction_selection = reac_prop[[
any([source.startswith(db_prefix) for db_prefix in list_of_db_prefixes]) and re.match('.*biomass.*', source,
re.I) is None for source
in reac_prop.Source]]
reactions = list()
for index, row in reaction_selection.iterrows():
try:
stoichiometry = parse_reaction(row.Equation, rev_arrow='=')
except ValueError:
continue
else:
for met, coeff in stoichiometry.items():
met.name = chem_prop.loc[met.id]['name']
try:
met.formula = Formula(chem_prop.loc[met.id].formula)
except:
logger.debug('Cannot parse formula %s. Skipping formula' % chem_prop.loc[met.id].formula)
continue
# if met.formula.weight is None:
# logger.debug('Cannot calculate weight for formula %s. Skipping reaction %s' % (met.formula, row.Equation))
# # print('Cannot calculate weight for formula %s. Skipping reaction %s' % (met.formula, row.Equation))
# continue
try:
met.charge = int(chem_prop.loc[met.id].charge)
except (ValueError, TypeError):
logger.debug('Cannot parse charge %s. Skipping charge' % chem_prop.loc[met.id].charge)
pass
rest = chem_prop.loc[met.id].to_dict()
met.annotation = dict((key, rest[key]) for key in rest if key in ('mass', 'InChI', 'source'))
mets = [met.id for met in stoichiometry.keys()]
if len(mets) != len(set(mets)):
continue
reaction = Reaction(index)
reaction.add_metabolites(stoichiometry)
try:
if len(reaction.check_mass_balance()) != 0:
continue
except AttributeError as e:
logger.debug(str(e))
continue
if row.Balance:
reaction.lower_bound = -1 * reaction.upper_bound
reaction.name = row['Source']
row = row.fillna("")
rest = row.to_dict()
reaction.annotation = dict((key, rest[key]) for key in rest if key in ('EC', 'Description'))
reactions.append(reaction)
model = Model('metanetx_universal_model_' + '_'.join(list_of_db_prefixes), solver_interface=optlang.interface)
model.add_reactions(reactions)
# Add sinks for all metabolites
for metabolite in model.metabolites:
model.add_demand(metabolite)
return model
def construct_universal_model(list_of_db_prefixes):
# Select which reactions to include in universal reaction database
reaction_selection = reac_prop[[
any([source.startswith(db_prefix) for db_prefix in list_of_db_prefixes]) and re.match('.*biomass.*', source,
re.I) is None for source
in reac_prop.Source]]
reactions = list()
for index, row in reaction_selection.iterrows():
try:
stoichiometry = parse_reaction(row.Equation, rev_arrow='=')
except ValueError:
continue
else:
for met, coeff in stoichiometry.iteritems():
met.name = chem_prop.loc[met.id]['name']
try:
met.formula = Formula(chem_prop.loc[met.id].formula)
except:
logger.debug('Cannot parse formula %s. Skipping formula' % chem_prop.loc[met.id].formula)
continue
# if met.formula.weight is None:
# logger.debug('Cannot calculate weight for formula %s. Skipping reaction %s' % (met.formula, row.Equation))
# # print('Cannot calculate weight for formula %s. Skipping reaction %s' % (met.formula, row.Equation))
# continue
try:
met.charge = int(chem_prop.loc[met.id].charge)
except (ValueError, TypeError):
logger.debug('Cannot parse charge %s. Skipping charge' % chem_prop.loc[met.id].charge)
pass
rest = chem_prop.loc[met.id].to_dict()
met.annotation = dict((key, rest[key]) for key in rest if key in ('mass', 'InChI', 'source'))
mets = [met.id for met in stoichiometry.keys()]
if len(mets) != len(set(mets)):
continue
reaction = Reaction(index)
reaction.add_metabolites(stoichiometry)
if reaction.check_mass_balance() != []:
continue
if row.Balance:
reaction.lower_bound = -1 * reaction.upper_bound
reaction.name = row['Source']
rest = row.to_dict()
reaction.annotation = dict((key, rest[key]) for key in rest if key in ('EC', 'Description'))
reactions.append(reaction)
model = Model('metanetx_universal_model_' + '_'.join(list_of_db_prefixes), solver_interface=optlang.interface)
model.add_reactions(reactions)
# Add sinks for all metabolites
for metabolite in model.metabolites:
model.add_demand(metabolite)
return model
def test_reaction_knock_in_target(self):
reaction = Reaction(id="atpzase", name="Cosmic ATP generator")
atp_z = Metabolite(id="atp_z", name="Cosmic ATP", compartment="c")
reaction.add_metabolites({self.model.metabolites.atp_c: 1, atp_z: -1})
knockin_target = ReactionKnockinTarget("atpzase", reaction)
with TimeMachine() as tm:
knockin_target.apply(self.model, time_machine=tm)
self.assertIn(atp_z, self.model.metabolites)
self.assertIn(reaction, self.model.reactions)
self.assertNotIn(atp_z, self.model.metabolites)
self.assertNotIn(reaction, self.model.reactions)
def test_reaction_knock_in_target(self, model):
reaction = Reaction(id="atpzase", name="Cosmic ATP generator")
atp_z = Metabolite(id="atp_z", name="Cosmic ATP", compartment="c")
reaction.add_metabolites({model.metabolites.atp_c: 1, atp_z: -1})
knockin_target = ReactionKnockinTarget("atpzase", reaction)
with TimeMachine() as tm:
knockin_target.apply(model, time_machine=tm)
assert atp_z in model.metabolites
assert reaction in model.reactions
assert atp_z not in model.metabolites
assert reaction not in model.reactions
def test_add_reactions(self):
r1 = Reaction('r1')
r1.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
r1.lower_bound, r1.upper_bound = -999999., 999999.
r2 = Reaction('r2')
r2.add_metabolites({Metabolite('A'): -1, Metabolite('C'): 1, Metabolite('D'): 1})
r2.lower_bound, r2.upper_bound = 0., 999999.
r2.objective_coefficient = 3.
self.model.add_reactions([r1, r2])
self.assertEqual(self.model.reactions[-2], r1)
self.assertEqual(self.model.reactions[-1], r2)
self.assertTrue(isinstance(self.model.reactions[-2].reverse_variable, self.model.solver.interface.Variable))
self.assertEqual(self.model.objective.expression.coeff(
self.model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2.forward_variable), 1.)
self.assertEqual(self.model.objective.expression.coeff(
self.model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2.reverse_variable), -1.)
self.assertEqual(self.model.objective.expression.coeff(self.model.reactions.r2.forward_variable), 3.)
self.assertEqual(self.model.objective.expression.coeff(self.model.reactions.r2.reverse_variable), -3.)
def test_make_lhs_irreversible_reversible(self):
model = self.model
rxn = Reaction('test')
rxn.add_metabolites({model.metabolites[0]: -1., model.metabolites[1]: 1.})
rxn.lower_bound = -999999.
rxn.upper_bound = -100
model.add_reaction(rxn)
self.assertEqual(rxn.lower_bound, -999999.)
self.assertEqual(rxn.upper_bound, -100.)
self.assertEqual(rxn.forward_variable.lb, 0.)
self.assertEqual(rxn.forward_variable.ub, 0.)
self.assertEqual(rxn.reverse_variable.lb, 100.)
self.assertEqual(rxn.reverse_variable.ub, 999999.)
rxn.upper_bound = 666.
self.assertEqual(rxn.lower_bound, -999999.)
self.assertEqual(rxn.upper_bound, 666.)
self.assertEqual(rxn.forward_variable.lb, 0.)
self.assertEqual(rxn.forward_variable.ub, 666)
self.assertEqual(rxn.reverse_variable.lb, 0.)
self.assertEqual(rxn.reverse_variable.ub, 999999.)
def create_adapter_reactions(original_metabolites, database, mapping, compartment_regexp):
adapter_reactions = []
for metabolite in original_metabolites: # model is the original host model
if not compartment_regexp.match(metabolite.id):
continue
name = metabolite.id[0:-2]
try:
mapped_name = mapping[name]
except KeyError:
continue
# print name, 'N/A'
adapter_reaction = Reaction('adapter_' + metabolite.id + '_' + mapped_name)
adapter_reaction.lower_bound = -1000
try:
adapter_reaction.add_metabolites({metabolite: -1,
database.metabolites.get_by_id(mapped_name): 1})
except KeyError:
pass
else:
adapter_reactions.append(adapter_reaction)
return adapter_reactions
def test_make_lhs_irreversible_reversible(self):
model = self.model
rxn = Reaction('test')
rxn.add_metabolites({
model.metabolites[0]: -1.,
model.metabolites[1]: 1.
})
rxn.lower_bound = -999999.
rxn.upper_bound = -100
model.add_reaction(rxn)
self.assertEqual(rxn.lower_bound, -999999.)
self.assertEqual(rxn.upper_bound, -100.)
self.assertEqual(rxn.forward_variable.lb, 0.)
self.assertEqual(rxn.forward_variable.ub, 0.)
self.assertEqual(rxn.reverse_variable.lb, 100.)
self.assertEqual(rxn.reverse_variable.ub, 999999.)
rxn.upper_bound = 666.
self.assertEqual(rxn.lower_bound, -999999.)
self.assertEqual(rxn.upper_bound, 666.)
self.assertEqual(rxn.forward_variable.lb, 0.)
self.assertEqual(rxn.forward_variable.ub, 666)
self.assertEqual(rxn.reverse_variable.lb, 0.)
self.assertEqual(rxn.reverse_variable.ub, 999999.)
def test_gnomic_integration(self):
from gnomic.models import Accession, Feature, Mutation, FeatureTree
abstract_target = Target("test")
abstract_target_gnomic = abstract_target.to_gnomic()
self.assertIsInstance(abstract_target_gnomic, Accession)
self.assertEqual(abstract_target_gnomic.identifier, abstract_target.id)
flux_modulation_target = FluxModulationTarget("test", 1, 0)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
self.assertIsInstance(flux_modulation_target_gnomic, Mutation)
self.assertIsInstance(flux_modulation_target_gnomic.old, FeatureTree)
self.assertIsInstance(flux_modulation_target_gnomic.old[0], Feature)
self.assertEqual(
flux_modulation_target_gnomic.old[0].accession.identifier,
flux_modulation_target.id)
self.assertEqual(flux_modulation_target_gnomic.old[0].variant, None)
self.assertEqual(flux_modulation_target_gnomic.old[0].type, 'flux')
self.assertIsInstance(flux_modulation_target_gnomic.new, FeatureTree)
self.assertIsInstance(flux_modulation_target_gnomic.new[0], Feature)
self.assertEqual(
flux_modulation_target_gnomic.new[0].accession.identifier,
flux_modulation_target.id)
self.assertEqual(flux_modulation_target_gnomic.new[0].type, 'flux')
self.assertEqual(
flux_modulation_target_gnomic.new[0].variant,
"over-expression(%f)" % flux_modulation_target.fold_change)
flux_modulation_target = FluxModulationTarget("test", 0.5, 1)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
self.assertIsInstance(flux_modulation_target_gnomic, Mutation)
self.assertIsInstance(flux_modulation_target_gnomic.old, FeatureTree)
self.assertIsInstance(flux_modulation_target_gnomic.old[0], Feature)
self.assertEqual(
flux_modulation_target_gnomic.old[0].accession.identifier,
flux_modulation_target.id)
self.assertEqual(flux_modulation_target_gnomic.old[0].variant, None)
self.assertEqual(flux_modulation_target_gnomic.old[0].type, 'flux')
self.assertIsInstance(flux_modulation_target_gnomic.new, FeatureTree)
self.assertIsInstance(flux_modulation_target_gnomic.new[0], Feature)
self.assertEqual(
flux_modulation_target_gnomic.new[0].accession.identifier,
flux_modulation_target.id)
self.assertEqual(flux_modulation_target_gnomic.new[0].type, 'flux')
self.assertEqual(
flux_modulation_target_gnomic.new[0].variant,
"down-regulation(%f)" % flux_modulation_target.fold_change)
flux_modulation_target = FluxModulationTarget("test", 0, 1)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
self.assertIsInstance(flux_modulation_target_gnomic, Mutation)
self.assertIsInstance(flux_modulation_target_gnomic.old, FeatureTree)
self.assertIsInstance(flux_modulation_target_gnomic.old[0], Feature)
self.assertEqual(
flux_modulation_target_gnomic.old[0].accession.identifier,
flux_modulation_target.id)
self.assertEqual(flux_modulation_target_gnomic.old[0].variant, None)
self.assertEqual(flux_modulation_target_gnomic.old[0].type, 'flux')
self.assertIs(flux_modulation_target_gnomic.new, None)
reaction = Reaction(id="atpzase", name="Cosmic ATP generator")
atp_z = Metabolite(id="atp_z", name="Cosmic ATP", compartment="c")
reaction.add_metabolites({self.model.metabolites.atp_c: 1, atp_z: -1})
knockin_target = ReactionKnockinTarget("atpzase", reaction)
knockin_target_gnomic = knockin_target.to_gnomic()
self.assertIsInstance(knockin_target_gnomic, Mutation)
self.assertIsInstance(knockin_target_gnomic.new, FeatureTree)
self.assertIsInstance(knockin_target_gnomic.new[0], Feature)
self.assertEqual(knockin_target_gnomic.new[0].accession.identifier,
knockin_target.id)
self.assertEqual(knockin_target_gnomic.new[0].variant, None)
self.assertEqual(knockin_target_gnomic.new[0].type, 'reaction')
self.assertIs(knockin_target_gnomic.old, None)
cofactor_id_swaps = [("nad_c", "nadh_c"), ("nadp_c", "nadph_c")]
swap_pairs = ([
self.model.metabolites.get_by_id(m) for m in cofactor_id_swaps[0]
], [self.model.metabolites.get_by_id(m) for m in cofactor_id_swaps[1]])
swap_target = ReactionCofactorSwapTarget("GAPD", swap_pairs)
swap_target_gnomic = swap_target.to_gnomic()
self.assertIsInstance(swap_target_gnomic, Mutation)
self.assertIsInstance(swap_target_gnomic.old, FeatureTree)
self.assertIsInstance(swap_target_gnomic.old[0], Feature)
self.assertEqual(swap_target_gnomic.old[0].accession.identifier,
swap_target.id)
self.assertEqual(swap_target_gnomic.old[0].variant, None)
self.assertEqual(swap_target_gnomic.old[0].type, 'reaction')
self.assertIsInstance(swap_target_gnomic.new, FeatureTree)
self.assertIsInstance(swap_target_gnomic.new[0], Feature)
self.assertEqual(swap_target_gnomic.new[0].accession.identifier,
swap_target.id + swap_target.swap_str)
self.assertEqual(swap_target_gnomic.new[0].variant, None)
self.assertEqual(swap_target_gnomic.new[0].type, 'reaction')
def test_gnomic_integration(self, model):
from gnomic.models import Accession, Feature, Mutation, FeatureTree
abstract_target = Target("test")
abstract_target_gnomic = abstract_target.to_gnomic()
assert isinstance(abstract_target_gnomic, Accession)
assert abstract_target_gnomic.identifier == abstract_target.id
flux_modulation_target = FluxModulationTarget("test", 1, 0)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
flux_mod_new = flux_modulation_target_gnomic.new
flux_mod_old = flux_modulation_target_gnomic.old
assert isinstance(flux_modulation_target_gnomic, Mutation)
assert isinstance(flux_mod_old, FeatureTree)
assert isinstance(flux_mod_old[0], Feature)
assert flux_mod_old[
0].accession.identifier == flux_modulation_target.id
assert flux_mod_old[0].variant is None
assert flux_mod_old[0].type == 'flux'
assert isinstance(flux_mod_new, FeatureTree)
assert isinstance(flux_mod_new[0], Feature)
assert flux_mod_new[
0].accession.identifier == flux_modulation_target.id
assert flux_mod_new[0].type == 'flux'
assert flux_mod_new[
0].variant == "over-expression(%.3f)" % flux_modulation_target.fold_change
flux_modulation_target = FluxModulationTarget("test", 0.5, 1)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
flux_mod_new = flux_modulation_target_gnomic.new
flux_mod_old = flux_modulation_target_gnomic.old
assert isinstance(flux_modulation_target_gnomic, Mutation)
assert isinstance(flux_mod_old, FeatureTree)
assert isinstance(flux_mod_old[0], Feature)
assert flux_mod_old[
0].accession.identifier == flux_modulation_target.id
assert flux_mod_old[0].variant is None
assert flux_mod_old[0].type == 'flux'
assert isinstance(flux_mod_new, FeatureTree)
assert isinstance(flux_mod_new[0], Feature)
assert flux_mod_new[
0].accession.identifier == flux_modulation_target.id
assert flux_mod_new[0].type == 'flux'
assert flux_mod_new[
0].variant == "down-regulation(%.3f)" % flux_modulation_target.fold_change
flux_modulation_target = FluxModulationTarget("test", 0, 1)
flux_modulation_target_gnomic = flux_modulation_target.to_gnomic()
assert isinstance(flux_modulation_target_gnomic, Mutation)
assert isinstance(flux_modulation_target_gnomic.old, FeatureTree)
assert isinstance(flux_modulation_target_gnomic.old[0], Feature)
assert flux_modulation_target_gnomic.old[
0].accession.identifier == flux_modulation_target.id
assert flux_modulation_target_gnomic.old[0].variant is None
assert flux_modulation_target_gnomic.old[0].type == 'flux'
assert flux_modulation_target_gnomic.new is None
reaction = Reaction(id="atpzase", name="Cosmic ATP generator")
atp_z = Metabolite(id="atp_z", name="Cosmic ATP", compartment="c")
reaction.add_metabolites({model.metabolites.atp_c: 1, atp_z: -1})
knockin_target = ReactionKnockinTarget("atpzase", reaction)
knockin_target_gnomic = knockin_target.to_gnomic()
assert isinstance(knockin_target_gnomic, Mutation)
assert isinstance(knockin_target_gnomic.new, FeatureTree)
assert isinstance(knockin_target_gnomic.new[0], Feature)
assert knockin_target_gnomic.new[
0].accession.identifier == knockin_target.id
assert knockin_target_gnomic.new[0].variant is None
assert knockin_target_gnomic.new[0].type == 'reaction'
assert knockin_target_gnomic.old is None
cofactor_id_swaps = [("nad_c", "nadh_c"), ("nadp_c", "nadph_c")]
swap_pairs = ([
model.metabolites.get_by_id(m) for m in cofactor_id_swaps[0]
], [model.metabolites.get_by_id(m) for m in cofactor_id_swaps[1]])
swap_target = ReactionCofactorSwapTarget("GAPD", swap_pairs)
swap_target_gnomic = swap_target.to_gnomic()
assert isinstance(swap_target_gnomic, Mutation)
assert isinstance(swap_target_gnomic.old, FeatureTree)
assert isinstance(swap_target_gnomic.old[0], Feature)
assert swap_target_gnomic.old[0].accession.identifier == swap_target.id
assert swap_target_gnomic.old[0].variant is None
assert swap_target_gnomic.old[0].type == 'reaction'
assert isinstance(swap_target_gnomic.new, FeatureTree)
assert isinstance(swap_target_gnomic.new[0], Feature)
assert swap_target_gnomic.new[
0].accession.identifier == swap_target.id + swap_target.swap_str
assert swap_target_gnomic.new[0].variant is None
assert swap_target_gnomic.new[0].type == 'reaction'
def _make_dual_model(self, model):
dual_model = SolverBasedModel(solver_interface=model.solver.interface)
# Add dual metabolites
dual_metabolite_names = []
irreversibles = [] # Metabolites for which a z-reaction must be added
self._dual_to_primal_mapping = {}
for re in model.reactions:
forward_id = re._get_forward_id()
reverse_id = re._get_reverse_id()
if forward_id in self._split_vars or reverse_id in self._split_vars:
if re.upper_bound > 0 or forward_id in self._split_vars:
dual_metabolite_names.append(forward_id)
irreversibles.append(forward_id)
self._dual_to_primal_mapping[forward_id] = re.id
if re.lower_bound < 0 or reverse_id in self._split_vars:
dual_metabolite_names.append(reverse_id)
irreversibles.append(reverse_id)
self._dual_to_primal_mapping[reverse_id] = re.id
else:
dual_metabolite_names.append(re.id)
if re.lower_bound >= 0:
irreversibles.append(re.id)
self._dual_to_primal_mapping[re.id] = re.id
dual_model.add_metabolites([Metabolite(name) for name in dual_metabolite_names])
# Add dual "u-reactions"
transposed_stoichiometry = {}
for reaction in model.reactions:
for met, coef in reaction.metabolites.items():
if reaction._get_reverse_id() in dual_metabolite_names:
transposed_stoichiometry.setdefault(met, {})[
dual_model.metabolites.get_by_id(reaction._get_reverse_id())] = -coef
if reaction._get_forward_id() in dual_metabolite_names:
transposed_stoichiometry.setdefault(met, {})[
dual_model.metabolites.get_by_id(reaction._get_forward_id())] = coef
elif reaction.id in dual_metabolite_names: # This should be the same as forward_var.name but in general it might not be
transposed_stoichiometry.setdefault(met, {})[
dual_model.metabolites.get_by_id(reaction.id)] = coef
u_reactions = []
for met, stoichiometry in transposed_stoichiometry.items():
met_id = met.id
reac = Reaction("u_" + met_id)
reac.lower_bound = -reac.upper_bound # Make reversible
reac.add_metabolites(stoichiometry)
u_reactions.append(reac)
dual_model.add_reactions(u_reactions)
# Add dual "v-reactions"
v_reactions = []
for dual_met in dual_model.metabolites:
reac = Reaction("v_" + dual_met.id)
reac.lower_bound = -reac.upper_bound # Make reversible
reac.add_metabolites({dual_met: 1})
v_reactions.append(reac)
dual_model.add_reactions(v_reactions)
self._v_reactions = v_reactions
# Add dual "z-reactions"
z_reactions = []
for dual_met in dual_model.metabolites:
if dual_met.id in irreversibles:
reac = Reaction("z_" + dual_met.id)
reac.lower_bound = 0
reac.add_metabolites({dual_met: -1})
z_reactions.append(reac)
dual_model.add_reactions(z_reactions)
return dual_model
def _make_dual_model(self, model):
dual_model = SolverBasedModel(solver_interface=model.solver.interface)
# Add dual metabolites
dual_metabolite_names = []
irreversibles = [] # Metabolites for which a z-reaction must be added
self._dual_to_primal_mapping = {}
for re in model.reactions:
forward_id = re._get_forward_id()
reverse_id = re._get_reverse_id()
if forward_id in self._split_vars or reverse_id in self._split_vars:
if re.upper_bound > 0 or forward_id in self._split_vars:
dual_metabolite_names.append(forward_id)
irreversibles.append(forward_id)
self._dual_to_primal_mapping[forward_id] = re.id
if re.lower_bound < 0 or reverse_id in self._split_vars:
dual_metabolite_names.append(reverse_id)
irreversibles.append(reverse_id)
self._dual_to_primal_mapping[reverse_id] = re.id
else:
dual_metabolite_names.append(re.id)
if re.lower_bound >= 0:
irreversibles.append(re.id)
self._dual_to_primal_mapping[re.id] = re.id
dual_model.add_metabolites(
[Metabolite(name) for name in dual_metabolite_names])
# Add dual "u-reactions"
transposed_stoichiometry = {}
for reaction in model.reactions:
for met, coef in reaction.metabolites.items():
if reaction._get_reverse_id() in dual_metabolite_names:
transposed_stoichiometry.setdefault(
met, {})[dual_model.metabolites.get_by_id(
reaction._get_reverse_id())] = -coef
if reaction._get_forward_id() in dual_metabolite_names:
transposed_stoichiometry.setdefault(
met, {})[dual_model.metabolites.get_by_id(
reaction._get_forward_id())] = coef
elif reaction.id in dual_metabolite_names: # This should be the same as forward_var.name but in general it might not be
transposed_stoichiometry.setdefault(
met, {})[dual_model.metabolites.get_by_id(
reaction.id)] = coef
u_reactions = []
for met, stoichiometry in transposed_stoichiometry.items():
met_id = met.id
reac = Reaction("u_" + met_id)
reac.lower_bound = -reac.upper_bound # Make reversible
reac.add_metabolites(stoichiometry)
u_reactions.append(reac)
dual_model.add_reactions(u_reactions)
# Add dual "v-reactions"
v_reactions = []
for dual_met in dual_model.metabolites:
reac = Reaction("v_" + dual_met.id)
reac.lower_bound = -reac.upper_bound # Make reversible
reac.add_metabolites({dual_met: 1})
v_reactions.append(reac)
dual_model.add_reactions(v_reactions)
self._v_reactions = v_reactions
# Add dual "z-reactions"
z_reactions = []
for dual_met in dual_model.metabolites:
if dual_met.id in irreversibles:
reac = Reaction("z_" + dual_met.id)
reac.lower_bound = 0
reac.add_metabolites({dual_met: -1})
z_reactions.append(reac)
dual_model.add_reactions(z_reactions)
return dual_model