def add_reaction(model, id, name, sparse,
lower_bound=0, upper_bound=1000):
"""
Adds a reaction to the model
"""
# convert the sparse representation using the metabolites in the model
for key in sparse.keys():
if key not in model.metabolites:
raise Exception("cannot find the cytoplasmic metabolite %s in the model" % key)
r = dict([(model.metabolites[model.metabolites.index(key)], val)
for key, val in sparse.iteritems()])
reaction = Reaction(name)
reaction.id = id
reaction.add_metabolites(r)
reaction.lower_bound = lower_bound
reaction.upper_bound = upper_bound
model.add_reactions([reaction])
return reaction
def add_reaction(model, id, name, sparse,
lower_bound=0, upper_bound=1000):
"""
Adds a reaction to the model
"""
# convert the sparse representation using the metabolites in the model
for key in sparse.keys():
if key not in model.metabolites:
raise Exception("cannot find the cytoplasmic metabolite %s in the model" % key)
r = dict([(model.metabolites[model.metabolites.index(key)], val)
for key, val in sparse.iteritems()])
reaction = Reaction(name)
reaction.id = id
reaction.add_metabolites(r)
reaction.lower_bound = lower_bound
reaction.upper_bound = upper_bound
model.add_reactions([reaction])
return reaction
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 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 assess_precursors(model,
reaction,
flux_coefficient_cutoff=0.001,
solver=None):
"""Assesses the ability of the model to provide sufficient precursors for
a reaction operating at, or beyond, the specified cutoff.
model: A :class:`~cobra.core.Model` object
reaction: A :class:`~cobra.core.Reaction` object
flux_coefficient_cutoff: Float. The minimum flux that reaction must carry
to be considered active.
solver : String or solver name. If None, the default solver will be used.
returns: True if the precursors can be simultaneously produced at the
specified cutoff. False, if the model has the capacity to produce each
individual precursor at the specified threshold but not all precursors at
the required level simultaneously. Otherwise a dictionary of the required
and the produced fluxes for each reactant that is not produced in
sufficient quantities.
"""
model = model.copy()
reaction = model.reactions.get_by_id(reaction.id)
original_objective = model.objective
model.objective = reaction
model.optimize(solver=solver)
model.objective = original_objective
if model.solution.f >= flux_coefficient_cutoff:
return True
#
simulation_results = {}
# build the sink reactions and add all at once
sink_reactions = {}
for the_component in reaction.reactants:
# add in a sink reaction for each component
sink_reaction = Reaction('test_sink_%s' % the_component.id)
# then simulate production ability
# then check it can exceed objective cutoff * component stoichiometric
# coefficient.
coefficient = reaction.get_coefficient(the_component)
sink_reaction.add_metabolites({the_component: coefficient})
sink_reaction.upper_bound = 1000
sink_reactions[sink_reaction] = (the_component, coefficient)
# First assess whether all precursors can pbe produced simultaneously
super_sink = Reaction("super_sink")
for reaction in sink_reactions:
super_sink += reaction
super_sink.id = 'super_sink'
model.add_reactions(sink_reactions.keys() + [super_sink])
model.objective = super_sink
model.optimize(solver=solver)
model.objective = original_objective
if flux_coefficient_cutoff <= model.solution.f:
return True
# Otherwise assess the ability of the model to produce each precursor
# individually. Now assess the ability of the model to produce each
# reactant for a reaction
for sink_reaction, (component, coefficient) in iteritems(sink_reactions):
# Calculate the maximum amount of the
model.objective = sink_reaction
model.optimize(solver=solver)
model.objective = original_objective
# metabolite that can be produced.
if flux_coefficient_cutoff > model.solution.f:
# Scale the results to a single unit
simulation_results.update({
component: {
'required': flux_coefficient_cutoff / abs(coefficient),
'produced': model.solution.f / abs(coefficient)
}
})
if len(simulation_results) == 0:
simulation_results = False
return simulation_results
def assess_products(model,
reaction,
flux_coefficient_cutoff=0.001,
solver=None):
"""Assesses whether the model has the capacity to absorb the products of
a reaction at a given flux rate. Useful for identifying which components
might be blocking a reaction from achieving a specific flux rate.
model: A :class:`~cobra.core.Model` object
reaction: A :class:`~cobra.core.Reaction` object
flux_coefficient_cutoff: Float. The minimum flux that reaction must carry
to be considered active.
solver : String or solver name. If None, the default solver will be used.
returns: True if the model has the capacity to absorb all the reaction
products being simultaneously given the specified cutoff. False, if the
model has the capacity to absorb each individual product but not all
products at the required level simultaneously. Otherwise a dictionary of
the required and the capacity fluxes for each product that is not absorbed
in sufficient quantities.
"""
model = model.copy()
reaction = model.reactions.get_by_id(reaction.id)
original_objective = model.objective
model.objective = reaction
model.optimize(solver=solver)
model.objective = original_objective
if model.solution.f >= flux_coefficient_cutoff:
return True
#
simulation_results = {}
# build the sink reactions and add all at once
source_reactions = {}
for the_component in reaction.products:
# add in a sink reaction for each component
source_reaction = Reaction('test_source_%s' % the_component.id)
# then simulate production ability
# then check it can exceed objective cutoff * component stoichiometric
# coefficient.
coefficient = reaction.get_coefficient(the_component)
source_reaction.add_metabolites({the_component: coefficient})
source_reaction.upper_bound = 1000
source_reactions[source_reaction] = (the_component, coefficient)
#
super_source = Reaction('super_source')
for reaction in source_reactions:
super_source += reaction
super_source.id = 'super_source'
model.add_reactions(source_reactions.keys() + [super_source])
model.objective = super_source
model.optimize(solver=solver)
model.objective = original_objective
if flux_coefficient_cutoff <= model.solution.f:
return True
# Now assess the ability of the model to produce each reactant for a
# reaction
for source_reaction, (component, coefficient) in \
iteritems(source_reactions):
# Calculate the maximum amount of the
model.objective = source_reaction
model.optimize(solver=solver)
model.objective = original_objective
# metabolite that can be produced.
if flux_coefficient_cutoff > model.solution.f:
# Scale the results to a single unit
simulation_results.update({
component: {
'required': flux_coefficient_cutoff / abs(coefficient),
'capacity': model.solution.f / abs(coefficient)
}
})
if len(simulation_results) == 0:
simulation_results = False
return simulation_results
