def __init__(self, genes=None, essential_genes=None, use_nullspace_simplification=True, *args, **kwargs):
super(GeneKnockoutOptimization, self).__init__(*args, **kwargs)
if genes is None:
self.genes = set([g.id for g in self.model.genes])
else:
self.genes = genes
if essential_genes is None:
self.essential_genes = {g.id for g in find_essential_genes(self.model, processes=1)}
else:
self.essential_genes = set([g.id for g in find_essential_genes(self.model, processes=1)] + essential_genes)
# TODO: use genes from groups
if use_nullspace_simplification:
ns = nullspace(create_stoichiometric_array(self.model))
dead_end_reactions = find_blocked_reactions_nullspace(self.model, ns=ns)
dead_end_genes = {g.id for g in self.model.genes if all(r in dead_end_reactions for r in g.reactions)}
exclude_genes = self.essential_genes.union(dead_end_genes)
genes = [g for g in self.model.genes if g.id not in exclude_genes]
self.representation = [g.id for g in genes]
else:
self.representation = list(self.genes.difference(self.essential_genes))
self._target_type = GENE_KNOCKOUT_TYPE
self._decoder = decoders.GeneSetDecoder(self.representation, self.model)
self._evaluator = evaluators.KnockoutEvaluator(model=self.model,
decoder=self._decoder,
objective_function=self.objective_function,
simulation_method=self._simulation_method,
simulation_kwargs=self._simulation_kwargs)
def __init__(self, genes=None, essential_genes=None, use_nullspace_simplification=True, *args, **kwargs):
super(GeneKnockoutOptimization, self).__init__(*args, **kwargs)
if genes is None:
self.genes = set([g.id for g in self.model.genes])
else:
self.genes = genes
if essential_genes is None:
self.essential_genes = {g.id for g in find_essential_genes(self.model)}
else:
self.essential_genes = set([g.id for g in find_essential_genes(self.model)] + essential_genes)
# TODO: use genes from groups
if use_nullspace_simplification:
ns = nullspace(create_stoichiometric_array(self.model))
dead_end_reactions = find_blocked_reactions_nullspace(self.model, ns=ns)
dead_end_genes = {g.id for g in self.model.genes if all(r in dead_end_reactions for r in g.reactions)}
exclude_genes = self.essential_genes.union(dead_end_genes)
genes = [g for g in self.model.genes if g.id not in exclude_genes]
self.representation = [g.id for g in genes]
else:
self.representation = list(self.genes.difference(self.essential_genes))
self._target_type = GENE_KNOCKOUT_TYPE
self._decoder = decoders.GeneSetDecoder(self.representation, self.model)
self._evaluator = evaluators.KnockoutEvaluator(model=self.model,
decoder=self._decoder,
objective_function=self.objective_function,
simulation_method=self._simulation_method,
simulation_kwargs=self._simulation_kwargs)
def __init__(self,
reactions=None,
essential_reactions=None,
use_nullspace_simplification=True,
*args,
**kwargs):
super(ReactionKnockoutOptimization, self).__init__(*args, **kwargs)
if reactions is None:
self.reactions = set([r.id for r in self.model.reactions])
else:
self.reactions = reactions
logger.debug("Computing essential reactions...")
if essential_reactions is None:
self.essential_reactions = set(
r.id
for r in find_essential_reactions(self.model, processes=1))
else:
self.essential_reactions = set([
r.id for r in find_essential_reactions(self.model, processes=1)
])
self.essential_reactions.update(essential_reactions)
if use_nullspace_simplification:
ns = nullspace(create_stoichiometric_array(self.model))
dead_ends = set(find_blocked_reactions_nullspace(self.model,
ns=ns))
exchanges = set(self.model.boundary)
reactions = [
r for r in self.model.reactions
if (r not in exchanges) and (r not in dead_ends) and (
r.id not in self.essential_reactions)
]
groups = find_coupled_reactions_nullspace(self.model, ns=ns)
groups_keys = [
set(group) for group in groups
if any(r.id in reactions for r in group)
]
reduced_set = reduce_reaction_set(reactions, groups_keys)
to_keep = [r.id for r in reduced_set]
else:
groups = None
to_keep = set(r.id for r in self.model.reactions)
to_keep.difference_update(r.id for r in self.model.boundary)
to_keep.difference_update(self.essential_reactions)
to_keep = list(to_keep)
self.representation = to_keep
self._target_type = REACTION_KNOCKOUT_TYPE
self._decoder = decoders.ReactionSetDecoder(self.representation,
self.model,
groups=groups)
self._evaluator = evaluators.KnockoutEvaluator(
model=self.model,
decoder=self._decoder,
objective_function=self.objective_function,
simulation_method=self._simulation_method,
simulation_kwargs=self._simulation_kwargs)
def __init__(self, reactions=None, essential_reactions=None, use_nullspace_simplification=True, *args, **kwargs):
super(ReactionKnockoutOptimization, self).__init__(*args, **kwargs)
if reactions is None:
self.reactions = set([r.id for r in self.model.reactions])
else:
self.reactions = reactions
logger.debug("Computing essential reactions...")
if essential_reactions is None:
self.essential_reactions = set(r.id for r in find_essential_reactions(self.model, processes=1))
else:
self.essential_reactions = set([r.id for r in find_essential_reactions(self.model, processes=1)])
self.essential_reactions.update(essential_reactions)
if use_nullspace_simplification:
ns = nullspace(create_stoichiometric_array(self.model))
dead_ends = set(find_blocked_reactions_nullspace(self.model, ns=ns))
exchanges = set(self.model.boundary)
reactions = [
r for r in self.model.reactions
if (r not in exchanges) and (
r not in dead_ends) and (
r.id not in self.essential_reactions)
]
groups = find_coupled_reactions_nullspace(self.model, ns=ns)
groups_keys = [set(group) for group in groups if any(r.id in reactions for r in group)]
reduced_set = reduce_reaction_set(reactions, groups_keys)
to_keep = [r.id for r in reduced_set]
else:
groups = None
to_keep = set(r.id for r in self.model.reactions)
to_keep.difference_update(r.id for r in self.model.boundary)
to_keep.difference_update(self.essential_reactions)
to_keep = list(to_keep)
self.representation = to_keep
self._target_type = REACTION_KNOCKOUT_TYPE
self._decoder = decoders.ReactionSetDecoder(self.representation, self.model, groups=groups)
self._evaluator = evaluators.KnockoutEvaluator(model=self.model,
decoder=self._decoder,
objective_function=self.objective_function,
simulation_method=self._simulation_method,
simulation_kwargs=self._simulation_kwargs)
def __init__(self, design_space_model, objective, variables=None, reference_model=None,
exclude=(), normalize_ranges_by=None, points=10):
super(DifferentialFVA, self).__init__()
self.design_space_model = design_space_model
self.design_space_nullspace = nullspace(create_stoichiometric_array(self.design_space_model))
if reference_model is None:
self.reference_model = self.design_space_model.copy()
fix_objective_as_constraint(self.reference_model)
self.reference_nullspace = self.design_space_nullspace
else:
self.reference_model = reference_model
self.reference_nullspace = nullspace(create_stoichiometric_array(self.reference_model))
if isinstance(objective, Reaction):
self.objective = objective.id
elif isinstance(objective, Metabolite):
try:
self.reference_model.add_boundary(objective, type='demand')
except ValueError:
pass
try:
self.objective = self.design_space_model.add_boundary(objective, type='demand').id
except ValueError:
self.objective = self.design_space_model.reactions.get_by_id("DM_" + objective.id).id
elif isinstance(objective, six.string_types):
self.objective = objective
else:
raise ValueError('You need to provide an objective as a Reaction, Metabolite or a reaction id')
if variables is None:
# try to establish the current objective reaction
obj_var_ids = [variable.name for variable in self.design_space_model.objective.expression.free_symbols]
obj_var_ids = [re.sub('_reverse.*', '', id) for id in obj_var_ids]
if len(set(obj_var_ids)) != 1:
raise ValueError(
"The current objective in design_space_model is not a single reaction objective. "
"DifferentialFVA does not support composite objectives.")
else:
self.variables = [self.design_space_model.reactions.get_by_id(obj_var_ids[0]).id]
else:
self.variables = list()
for variable in variables:
if isinstance(variable, Reaction):
self.variables.append(variable.id)
else:
self.variables.append(variable)
self.exclude = list()
for elem in exclude:
if isinstance(elem, Reaction):
self.exclude.append(elem.id)
else:
self.exclude.append(elem)
design_space_blocked_reactions = find_blocked_reactions_nullspace(self.design_space_model,
self.design_space_nullspace)
self.exclude += [reaction.id for reaction in design_space_blocked_reactions]
reference_blocked_reactions = find_blocked_reactions_nullspace(self.reference_model, self.reference_nullspace)
self.exclude += [reaction.id for reaction in reference_blocked_reactions]
self.exclude += [reaction.id for reaction in self.design_space_model.boundary]
self.exclude += [reaction.id for reaction in self.reference_model.boundary]
self.exclude += [reaction.id for reaction in self.design_space_model.reactions
if _BIOMASS_RE_.match(reaction.id)]
self.exclude = set(self.exclude)
self.points = points
self.envelope = None
self.grid = None
self.reference_flux_ranges = None
self.reference_flux_dist = None
if isinstance(normalize_ranges_by, Reaction):
self.normalize_ranges_by = normalize_ranges_by.id
else:
self.normalize_ranges_by = normalize_ranges_by
def test_with_core_model(self, core_model):
s = create_stoichiometric_matrix(core_model)
ns = nullspace(s)
assert round(abs(np.abs(s.dot(ns)).max() - 0), 10) == 0
def test_wikipedia_toy(self):
a = np.array([[2, 3, 5], [-4, 2, 3]])
ns = nullspace(a)
assert round(abs(np.dot(ns.T, a[0])[0] - 0), 10) == 0
assert round(abs(np.dot(ns.T, a[1])[0] - 0), 10) == 0
def __init__(self,
design_space_model,
objective,
variables=None,
reference_model=None,
exclude=(),
normalize_ranges_by=None,
points=10):
super(DifferentialFVA, self).__init__()
self.design_space_model = design_space_model
self.design_space_nullspace = nullspace(
create_stoichiometric_array(self.design_space_model))
if reference_model is None:
self.reference_model = self.design_space_model.copy()
fix_objective_as_constraint(self.reference_model)
self.reference_nullspace = self.design_space_nullspace
else:
self.reference_model = reference_model
self.reference_nullspace = nullspace(
create_stoichiometric_array(self.reference_model))
if isinstance(objective, Reaction):
self.objective = objective.id
elif isinstance(objective, Metabolite):
try:
self.reference_model.add_boundary(objective, type='demand')
except ValueError:
pass
try:
self.objective = self.design_space_model.add_boundary(
objective, type='demand').id
except ValueError:
self.objective = self.design_space_model.reactions.get_by_id(
"DM_" + objective.id).id
elif isinstance(objective, six.string_types):
self.objective = objective
else:
raise ValueError(
'You need to provide an objective as a Reaction, Metabolite or a reaction id'
)
if variables is None:
# try to establish the current objective reaction
obj_var_ids = [
variable.name for variable in
self.design_space_model.objective.expression.free_symbols
]
obj_var_ids = [re.sub('_reverse.*', '', id) for id in obj_var_ids]
if len(set(obj_var_ids)) != 1:
raise ValueError(
"The current objective in design_space_model is not a single reaction objective. "
"DifferentialFVA does not support composite objectives.")
else:
self.variables = [
self.design_space_model.reactions.get_by_id(
obj_var_ids[0]).id
]
else:
self.variables = list()
for variable in variables:
if isinstance(variable, Reaction):
self.variables.append(variable.id)
else:
self.variables.append(variable)
self.exclude = list()
for elem in exclude:
if isinstance(elem, Reaction):
self.exclude.append(elem.id)
else:
self.exclude.append(elem)
design_space_blocked_reactions = find_blocked_reactions_nullspace(
self.design_space_model, self.design_space_nullspace)
self.exclude += [
reaction.id for reaction in design_space_blocked_reactions
]
reference_blocked_reactions = find_blocked_reactions_nullspace(
self.reference_model, self.reference_nullspace)
self.exclude += [
reaction.id for reaction in reference_blocked_reactions
]
self.exclude += [
reaction.id for reaction in self.design_space_model.exchanges
]
self.exclude += [
reaction.id for reaction in self.reference_model.exchanges
]
self.exclude += [
reaction.id for reaction in self.design_space_model.reactions
if _BIOMASS_RE_.match(reaction.id)
]
self.exclude = set(self.exclude)
self.points = points
self.envelope = None
self.grid = None
self.reference_flux_ranges = None
self.reference_flux_dist = None
if isinstance(normalize_ranges_by, Reaction):
self.normalize_ranges_by = normalize_ranges_by.id
else:
self.normalize_ranges_by = normalize_ranges_by
def __init__(self,
design_space_model,
objective,
variables=None,
reference_model=None,
exclude=(),
normalize_ranges_by=None,
points=10):
super(DifferentialFVA, self).__init__()
self.design_space_model = design_space_model
self.design_space_nullspace = nullspace(
create_stoichiometric_array(self.design_space_model))
if reference_model is None:
self.reference_model = self.design_space_model.copy()
self.reference_nullspace = self.design_space_nullspace
else:
self.reference_model = reference_model
self.reference_nullspace = nullspace(
create_stoichiometric_array(self.reference_model))
if isinstance(objective, Reaction):
self.objective = objective.id
elif isinstance(objective, Metabolite):
try:
self.reference_model.add_boundary(objective, type='demand')
except ValueError:
pass
try:
self.objective = self.design_space_model.add_boundary(
objective, type='demand').id
except ValueError:
self.objective = self.design_space_model.reactions.get_by_id(
"DM_" + objective.id).id
elif isinstance(objective, str):
self.objective = objective
else:
raise ValueError(
'You need to provide an objective as a Reaction, Metabolite or a reaction id'
)
if variables is None:
# try to establish the current objective reaction
obj_var_ids = [
variable.name for variable in
self.design_space_model.objective.expression.free_symbols
]
obj_var_ids = [re.sub('_reverse.*', '', id) for id in obj_var_ids]
if len(set(obj_var_ids)) != 1:
raise ValueError(
"The current objective in design_space_model is not a single reaction objective. "
"DifferentialFVA does not support composite objectives.")
else:
self.variables = [
self.design_space_model.reactions.get_by_id(
obj_var_ids[0]).id
]
else:
self.variables = list()
for variable in variables:
if isinstance(variable, Reaction):
self.variables.append(variable.id)
else:
self.variables.append(variable)
if len(self.variables) > 1:
raise NotImplementedError(
"We also think that searching the production envelope over "
"more than one variable would be a neat feature. However, "
"at the moment there are some assumptions in the code that "
"prevent this and we don't have the resources to change it. "
"Pull request welcome ;-)")
self.exclude = list()
for elem in exclude:
if isinstance(elem, Reaction):
self.exclude.append(elem.id)
else:
self.exclude.append(elem)
design_space_blocked_reactions = find_blocked_reactions_nullspace(
self.design_space_model, self.design_space_nullspace)
self.exclude += [
reaction.id for reaction in design_space_blocked_reactions
]
reference_blocked_reactions = find_blocked_reactions_nullspace(
self.reference_model, self.reference_nullspace)
self.exclude += [
reaction.id for reaction in reference_blocked_reactions
]
self.exclude += [
reaction.id for reaction in self.design_space_model.boundary
]
self.exclude += [
reaction.id for reaction in self.reference_model.boundary
]
self.exclude += [
reaction.id for reaction in self.design_space_model.reactions
if _BIOMASS_RE_.match(reaction.id)
]
self.exclude = set(self.exclude)
self.points = points
self.envelope = None
self.grid = None
self.reference_flux_ranges = None
self.reference_flux_dist = None
if isinstance(normalize_ranges_by, Reaction):
self.normalize_ranges_by = normalize_ranges_by.id
else:
self.normalize_ranges_by = normalize_ranges_by
self.included_reactions = {
r.id
for r in self.design_space_model.reactions
if r.id not in self.exclude
}
# Re-introduce key reactions in case they were excluded.
self.included_reactions.update(self.variables)
self.included_reactions.add(self.objective)
if self.normalize_ranges_by is not None:
self.included_reactions.add(self.normalize_ranges_by)
self.included_reactions = sorted(self.included_reactions)
def test_with_core_model(self, core_model):
s = create_stoichiometric_matrix(core_model)
ns = nullspace(s)
assert round(abs(np.abs(s.dot(ns)).max() - 0), 10) == 0
def test_wikipedia_toy(self):
a = np.array([[2, 3, 5], [-4, 2, 3]])
ns = nullspace(a)
assert round(abs(np.dot(ns.T, a[0])[0] - 0), 10) == 0
assert round(abs(np.dot(ns.T, a[1])[0] - 0), 10) == 0
def test_with_core_model(self, core_model):
s = core_model.S
ns = nullspace(s)
assert round(abs(np.abs(s.dot(ns)).max() - 0), 10) == 0