def test_fva_infeasible(model):
"""Test FVA infeasibility."""
infeasible_model = model.copy()
infeasible_model.reactions.get_by_id("EX_glc__D_e").lower_bound = 0
# ensure that an infeasible model does not run FVA
with pytest.raises(Infeasible):
flux_variability_analysis(infeasible_model)
def test_fva_infeasible(model):
"""Test FVA infeasibility."""
infeasible_model = model.copy()
infeasible_model.reactions.get_by_id("EX_glc__D_e").lower_bound = 0
# ensure that an infeasible model does not run FVA
with pytest.raises(Infeasible):
flux_variability_analysis(infeasible_model)
def test_pfba_flux_variability(
model: Model,
pfba_fva_results: pd.DataFrame,
fva_results: pd.DataFrame,
all_solvers: List[str],
) -> None:
"""Test FVA using pFBA."""
model.solver = all_solvers
with pytest.warns(UserWarning):
flux_variability_analysis(
model,
pfba_factor=0.1,
reaction_list=model.reactions[1::3],
processes=1,
)
fva_out = flux_variability_analysis(model,
pfba_factor=1.1,
reaction_list=model.reactions,
processes=1)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, pfba_fva_results)
abs_fva_out = fva_out.dropna().abs()
abs_fva_results = fva_results.dropna().abs()
comparison = np.isclose(abs_fva_out,
abs_fva_results) | (abs_fva_out < abs_fva_results)
assert comparison["minimum"].all()
assert comparison["maximum"].all()
def run_FVA(self, r):
print("Running FVA for reaction", r)
if self.perturbed_model:
b = fva.flux_variability_analysis(self.perturbed_model, [r])[r]
else:
b = fva.flux_variability_analysis(self.model, [r])[r]
return [b['minimum'], b['maximum']]
def test_flux_variability_loopless(model, all_solvers):
"""Test loopless FVA."""
model.solver = all_solvers
loop_reactions = [model.reactions.get_by_id(rid) for rid in ("FRD7", "SUCDi")]
fva_normal = flux_variability_analysis(model, reaction_list=loop_reactions)
fva_loopless = flux_variability_analysis(
model, reaction_list=loop_reactions, loopless=True
)
assert not np.allclose(fva_normal["maximum"], fva_normal["minimum"])
assert np.allclose(fva_loopless["maximum"], fva_loopless["minimum"])
def test_flux_variability_loopless(model, all_solvers):
"""Test loopless FVA."""
model.solver = all_solvers
loop_reactions = [model.reactions.get_by_id(rid)
for rid in ("FRD7", "SUCDi")]
fva_normal = flux_variability_analysis(
model, reaction_list=loop_reactions)
fva_loopless = flux_variability_analysis(
model, reaction_list=loop_reactions, loopless=True)
assert not np.allclose(fva_normal["maximum"],
fva_normal["minimum"])
assert np.allclose(fva_loopless["maximum"],
fva_loopless["minimum"])
def write_fva(model,fn="reaction_fluxes.xlsx",fraction=1,remove0=True,change_threshold=0.001,mode="full",lp_tolerance_feasibility=1e-6,flux_precision=1e-3):
precision=max(int(-1*(math.log10(flux_precision))),6)
fva=flux_variability_analysis(model,fraction_of_optimum=fraction,tolerance_feasibility=lp_tolerance_feasibility)
if mode=="full":
row=["ID","Name","Stoichiometry","Minimum","Maximum"]
else:
row=["ID","Name","Flux"]
sheet_row_data_dict={"fva":[row]}
for x in sorted(fva,key=lambda v: v.upper()):
if "_RATIO" in x:
continue
if abs(fva[x]["maximum"])>0.000001 or abs(fva[x]["minimum"])>0.000001 or remove0==False:
row=[]
reaction=model.reactions.get_by_id(x)
row.append(reaction.id)
row.append(reaction.name)
minimum=fva[x]["minimum"]
maximum=fva[x]["maximum"]
if mode=="full":
row.append(reaction.reaction)
row.append(round(minimum,precision))
row.append(round(maximum,precision))
else:
if (maximum-minimum)>change_threshold:
string=("%s < "+reaction.id+" <%s ")%( round(minimum,precision),round(maximum,precision))
else:
string=str(round(maximum,precision))
row.append(string)
sheet_row_data_dict["fva"].append(row)
#f.write(reaction.name+";"+reaction.subsystem+" ;"+reaction.reaction+";"+str(fva[x]["minimum"])+";"+str(fva[x]["maximum"])+"\n")
print 1
write_spreadsheet(file_name=fn,sheet_row_data_dict=sheet_row_data_dict,force_extension=True)
def test_flux_variability(model, fva_results, all_solvers):
"""Test FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model, reaction_list=model.reactions,
processes=1)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, fva_results)
def test_fva_minimization(model):
"""Test minimization using FVA."""
model.objective = model.reactions.EX_glc__D_e
model.objective_direction = 'min'
solution = flux_variability_analysis(model, fraction_of_optimum=.95)
assert solution.at['EX_glc__D_e', 'minimum'] == -10.0
assert solution.at['EX_glc__D_e', 'maximum'] == -9.5
def test_flux_variability(model, fva_results, all_solvers):
"""Test FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model, reaction_list=model.reactions)
for name, result in iteritems(fva_out.T):
for k, v in iteritems(result):
assert abs(fva_results[k][name] - v) < 0.00001
def find_blocked_reactions(cobra_model, the_reactions=None, allow_loops=True,
solver='glpk', the_problem='return',
tolerance_optimality=1e-9,
open_exchanges=False, **kwargs):
"""Finds reactions that cannot carry a flux with the current
exchange reaction settings for cobra_model, using flux variability
analysis.
"""
print 'This needs to be updated to deal with external boundaries'
cobra_model = cobra_model.copy()
blocked_reactions = []
if not the_reactions:
the_reactions = cobra_model.reactions
if open_exchanges:
print 'DEPRECATED: Move to using the Reaction.boundary attribute'
exchange_reactions = [x for x in cobra_model.reactions
if x.startswith('EX')]
for the_reaction in exchange_reactions:
if the_reaction.lower_bound >= 0:
the_reaction.lower_bound = -1000
if the_reaction.upper_bound >= 0:
the_reaction.upper_bound = 1000
flux_span_dict = flux_variability_analysis(cobra_model,
fraction_of_optimum = 0.,
the_reactions = the_reactions,
allow_loops = allow_loops,
solver = solver,
the_problem = the_problem,
tolerance_optimality = tolerance_optimality,
**kwargs)
blocked_reactions = [k for k, v in flux_span_dict.items()\
if max(map(abs,v.values())) < tolerance_optimality]
return(blocked_reactions)
def flux_variability_analysis_wrapper(keywords):
"""Provides an interface to call flux_variability_analysis from ppmap
"""
try:
from cPickle import dump
except:
from pickle import dump
import sys
#Need to do a top level import because of how the parallel processes are called
sys.path.insert(0, "../..")
from cobra.flux_analysis.variability import flux_variability_analysis
sys.path.pop(0)
results_dict = {}
new_objective = keywords.pop('new_objective')
output_directory = None
if 'output_directory' in keywords:
output_directory = keywords.pop('output_directory')
if not hasattr(new_objective, '__iter__'):
new_objective = [new_objective]
for the_objective in new_objective:
the_result = results_dict[the_objective] = flux_variability_analysis(**keywords)
if output_directory:
with open('%s%s.pickle'%(output_directory,
the_objective), 'w') as out_file:
dump(the_result, out_file)
if len(new_objective) == 1:
return the_result
else:
return results_dict
def test_parallel_flux_variability(model: Model, fva_results: pd.DataFrame,
all_solvers: List[str]) -> None:
"""Test parallel FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model, processes=2)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, fva_results)
def test_fva_minimization(model):
"""Test minimization using FVA."""
model.objective = model.reactions.EX_glc__D_e
model.objective_direction = 'min'
solution = flux_variability_analysis(model, fraction_of_optimum=.95)
assert solution.at['EX_glc__D_e', 'minimum'] == -10.0
assert solution.at['EX_glc__D_e', 'maximum'] == -9.5
def test_fva_minimization(model: Model) -> None:
"""Test minimization using FVA."""
model.objective = model.reactions.EX_glc__D_e
model.objective_direction = "min"
solution = flux_variability_analysis(model, fraction_of_optimum=0.95)
assert solution.at["EX_glc__D_e", "minimum"] == -10.0
assert solution.at["EX_glc__D_e", "maximum"] == -9.5
def test_flux_variability(model, fva_results, all_solvers):
"""Test FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model,
reaction_list=model.reactions,
processes=1)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, fva_results)
def test_loopless_pfba_fva(model):
loop_reactions = [model.reactions.get_by_id(rid)
for rid in ("FRD7", "SUCDi")]
fva_loopless = flux_variability_analysis(
model, pfba_factor=1.1, reaction_list=loop_reactions,
loopless=True, processes=1)
assert np.allclose(fva_loopless["maximum"],
fva_loopless["minimum"])
def run_FVA(self, r_id):
print("Running FVA for reaction", r_id)
if cobra.__version__ >= '0.9.1':
r = self.model.reactions[self.reaction_position[r_id]]
else:
r = r_id
if self.perturbed_model:
b = fva.flux_variability_analysis(self.perturbed_model, [r])
else:
b = fva.flux_variability_analysis(self.model, [r])
if cobra.__version__ >= '0.9.1':
b = b.to_dict()
return [b['minimum'][r_id], b['maximum'][r_id]]
else:
return [b[r_id]['minimum'], b[r_id]['maximum']]
def test_flux_variability(self):
fva_results = {
'5DGLCNtex': {'minimum': 0.0, 'maximum': 0.0},
'ABTA': {'minimum': 0.0, 'maximum': 0.0},
'5DOAN': {'minimum': 0.0, 'maximum': 0.0},
'A5PISO': {'minimum': 0.00692, 'maximum': 0.00692},
'AACPS1': {'minimum': 0.0, 'maximum': 0.0},
'AACPS2': {'minimum': 0.0, 'maximum': 0.0},
'ACALDtex': {'minimum': 0.0, 'maximum': 0.0},
'AACPS3': {'minimum': 0.0, 'maximum': 0.0},
'AACPS4': {'minimum': 0.0, 'maximum': 0.0},
'ABUTD': {'minimum': 0.0, 'maximum': 0.0},
'AACPS5': {'minimum': 0.0, 'maximum': 0.0},
'AACPS6': {'minimum': 0.0, 'maximum': 0.0},
'AACPS7': {'minimum': 0.0, 'maximum': 0.0},
'AACPS8': {'minimum': 0.0, 'maximum': 0.0},
'AACPS9': {'minimum': 0.0, 'maximum': 0.0},
'AACTOOR': {'minimum': 0.0, 'maximum': 0.0},
'ABUTt2pp': {'minimum': 0.0, 'maximum': 0.0},
'3OAS140': {'minimum': 0.50419, 'maximum': 0.50419},
'3OAS141': {'minimum': 0.03748, 'maximum': 0.03748},
'3OAS160': {'minimum': 0.41769, 'maximum': 0.41769},
'3OAS161': {'minimum': 0.03748, 'maximum': 0.03748},
'3OAS180': {'minimum': 0.01071, 'maximum': 0.01071},
'3OAS181': {'minimum': 0.01606, 'maximum': 0.01606},
'ABUTtex': {'minimum': 0.0, 'maximum': 0.0},
'3OAS60': {'minimum': 0.54399, 'maximum': 0.54399},
'3OAS80': {'minimum': 0.54399, 'maximum': 0.54399},
'AAMYL': {'minimum': 0.0, 'maximum': 0.0},
'3PEPTabcpp': {'minimum': 0.0, 'maximum': 0.0},
'3PEPTtex': {'minimum': 0.0, 'maximum': 0.0},
'3UMPtex': {'minimum': 0.0, 'maximum': 0.0},
'4HOXPACDtex': {'minimum': 0.0, 'maximum': 0.0},
'ACACtex': {'minimum': 0.0, 'maximum': 0.0},
'4PCP': {'minimum': 0.0, 'maximum': 0.0},
'4PCPpp': {'minimum': 0.0, 'maximum': 0.0},
'AAMYLpp': {'minimum': 0.0, 'maximum': 0.0},
'4PEPTabcpp': {'minimum': 0.0, 'maximum': 0.0},
'4PEPTtex': {'minimum': 0.0, 'maximum': 0.0},
'5DGLCNR': {'minimum': 0.0, 'maximum': 0.0},
'5DGLCNt2rpp': {'minimum': 0.0, 'maximum': 0.0},
'ACALD': {'minimum': 3.35702, 'maximum': 7.49572}}
infeasible_model = create_test_model()
infeasible_model.reactions.get_by_id("EX_glyc_e").lower_bound = 0
for solver in solver_dict:
cobra_model = create_test_model()
initialize_growth_medium(cobra_model, 'LB')
fva_out = flux_variability_analysis(
cobra_model, solver=solver,
reaction_list=cobra_model.reactions[100:140:2])
for the_reaction, the_range in iteritems(fva_out):
for k, v in iteritems(the_range):
self.assertAlmostEqual(fva_results[the_reaction][k], v,
places=5)
# ensure that an infeasible model does not run FVA
self.assertRaises(ValueError, flux_variability_analysis,
infeasible_model, solver=solver)
def test_loopless_pfba_fva(model):
loop_reactions = [
model.reactions.get_by_id(rid) for rid in ("FRD7", "SUCDi")
]
fva_loopless = flux_variability_analysis(model,
pfba_factor=1.1,
reaction_list=loop_reactions,
loopless=True,
processes=1)
assert np.allclose(fva_loopless["maximum"], fva_loopless["minimum"])
def test_pfba_flux_variability(model, pfba_fva_results,
fva_results, all_solvers):
"""Test FVA using pFBA."""
model.solver = all_solvers
with pytest.warns(UserWarning):
flux_variability_analysis(
model, pfba_factor=0.1, reaction_list=model.reactions[1::3],
processes=1)
fva_out = flux_variability_analysis(
model, pfba_factor=1.1, reaction_list=model.reactions,
processes=1)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, pfba_fva_results)
abs_fva_out = fva_out.dropna().abs()
abs_fva_results = fva_results.dropna().abs()
comparison = np.isclose(abs_fva_out, abs_fva_results) | (
abs_fva_out < abs_fva_results)
assert comparison["minimum"].all()
assert comparison["maximum"].all()
def removeBlockedReactionsLoopless(GEM):
fva = flux_variability_analysis(GEM,
reaction_list=None,
fraction_of_optimum=0,
processes=2,
loopless=True).round(decimals=8)
for rxn_id in fva.index:
v_min, v_max = fva.loc[rxn_id].minimum, fva.loc[rxn_id].maximum
if v_min == 0 and v_max == 0:
GEM.reactions.get_by_id(rxn_id).remove_from_model(
remove_orphans=True)
def create_minimal_fux_model(metabolic_model,fraction_of_optimum_objective=0.8, fraction_of_flux_minimum=2,boundaries_precision=0.001,label_model=None,metabolite_list_file_name=None):
model=copy.deepcopy(metabolic_model)
if len(model.objective)>1:
raise ValueError('Error:Only one objective supported')
original_objective=copy.deepcopy(model.objective)
for reaction in model.objective:
fva=flux_variability_analysis(model,reaction_list=[reaction.id], fraction_of_optimum=fraction_of_optimum_objective)
if reaction.objective_coefficient>0:
reaction.lower_bound=min(fva[reaction.id]["maximum"],reaction.upper_bound)
reaction.upper_bound=min(fva[reaction.id]["maximum"],reaction.upper_bound)
elif reaction.objective_coefficient<0:
reaction.lower_bound=max(fva[reaction.id]["minimum"],reaction.lower_bound)
reaction.upper_bound=max(fva[reaction.id]["minimum"],reaction.lower_bound)
reaction.lower_bound-=boundaries_precision/2.0
reaction.upper_bound+=boundaries_precision/2.0
reaction.objective_coefficient=0
reversible_reactions=[]
#convert_to_irreversible(model)
for reaction in model.reactions:
if reaction.lower_bound<0:
reversible_reactions.append(reaction.id)
convert_to_irreversible_with_indicators(model,reversible_reactions,metabolite_list=[], mutually_exclusive_directionality_constraint = True)
if metabolite_list_file_name!=None and metabolite_list_file_name!="":
metabolite_id_list=read_metabolomics_data(model,metabolite_list_fname=metabolite_list_file_name)
else:
metabolite_id_list=[]
add_turnover_metabolites(model, metabolite_id_list=metabolite_id_list, epsilon=1e-6,label_model=label_model)
flux_reporter = Metabolite('flux_reporter_0',formula='',name='',compartment='0')
reporter_coef=1
for reaction in model.reactions:
if "IRRMILP_" in reaction.id or "RATIO_" in reaction.id or "RGROUP_" in reaction.id or "TMS_" in reaction.id:
continue
reaction.add_metabolites({flux_reporter:reporter_coef})
total_flux_reaction = Reaction('total_flux')
total_flux_reaction.name = 'total_flux'
total_flux_reaction.subsystem = 'misc'
total_flux_reaction.lower_bound = 0
total_flux_reaction.upper_bound = 1000*reporter_coef*len(model.reactions)
total_flux_reaction.objective_coefficient=-1
total_flux_reaction.add_metabolites({flux_reporter:-1})
model.add_reaction(total_flux_reaction)
minimal_flux=-1*model.optimize().f
total_flux_reaction.upper_bound = minimal_flux*fraction_of_flux_minimum
total_flux_reaction.objective_coefficient=0.0
for objective in original_objective:
reaction=model.reactions.get_by_id(objective.id)
reaction.lower_bound=(fva[reaction.id]["minimum"]-boundaries_precision/2.0)
reaction.upper_bound=(fva[reaction.id]["maximum"]+boundaries_precision/2.0)
milp_reactions=model.reactions.query("IRRMILP_")
non_milp_reactions=[]
for reaction in model.reactions:
if reaction not in milp_reactions:
non_milp_reactions.append(reaction)
return model,minimal_flux,non_milp_reactions
def FVA(self, obj_frac=0.9, reactions=None, constraints=None, loopless=False, internal=None, solver=None,
format='dict'):
""" Flux Variability Analysis (FVA).
:param model: An instance of a constraint-based model.
:param float obj_frac: The minimum fraction of the maximum growth rate (default 0.9). Requires that the \
objective value is at least the fraction times maximum objective value. A value of 0.85 for instance \
means that the objective has to be at least at 85% percent of its maximum.
:param list reactions: List of reactions to analyze (default: all).
:param dic constraints: Additional constraints (optional).
:param boolean loopless: Run looplessFBA internally (very slow) (default: false).
:param list internal: List of internal reactions for looplessFBA (optional).
:param solver: A pre-instantiated solver instance (optional).
:param format: The return format: 'dict', returns a dictionary,'df' returns a data frame.
:returns: A dictionary of flux variation ranges.
"""
from cobra.flux_analysis.variability import flux_variability_analysis
simul_constraints = {}
if self.environmental_conditions:
simul_constraints.update(self.environmental_conditions)
if constraints:
simul_constraints.update(constraints)
with self.model as model:
if simul_constraints:
for rxn in list(simul_constraints.keys()):
reac = model.reactions.get_by_id(rxn)
if isinstance(simul_constraints.get(rxn), tuple):
reac.bounds = (simul_constraints.get(
rxn)[0], simul_constraints.get(rxn)[1])
else:
reac.bounds = (simul_constraints.get(
rxn), simul_constraints.get(rxn))
df = flux_variability_analysis(
model, reaction_list=reactions, loopless=loopless, fraction_of_optimum=obj_frac)
variability = {}
for r_id in reactions:
variability[r_id] = [
float(df.loc[r_id][0]), float(df.loc[r_id][1])]
if format == 'df':
import pandas as pd
e = variability.items()
f = [[a, b, c] for a, [b, c] in e]
df = pd.DataFrame(f, columns=['Reaction ID', 'Minimum', 'Maximum'])
return df
else:
return variability
def FVA(model, reaclist=None, subopt=1.0, IncZeroes=True, VaryOnly=False,
AsMtx=False, tol=1e-10, PrintStatus=False, cobra=False, processes=None):
state = model.GetState()
model.Solve(PrintStatus=PrintStatus)
if model.solution.status != 'optimal' or math.isnan(model.solution.f):
statusmsg = model.solution.status
model.SetState(state)
print "no optimal solution, problem "+statusmsg
else:
if reaclist == None:
reaclist = model.Reactions()
elif (type(reaclist) in types.StringTypes) or isinstance(
reaclist, Reaction):
reaclist = [reaclist]
if not cobra:
model.DelSumReacsConstraint("FVA_objective")
model.SetObjAsConstraint(name="FVA_objective", subopt=subopt)
rv = fva(bounds=model.bounds)
pool = multiprocessing.Pool(processes=processes)
results = [pool.apply_async(FluxRange, args=(model, reac, tol,
False, True)) for reac in reaclist]
pool.close()
pool.join()
for x in results:
rv[x.get().keys()[0]] = x.get().values()[0]
# for reac in reaclist:
# rv[str(reac)] = pool.apply_async(FluxRange, args=(model, reac, tol,
# False))
# for reac in reaclist:
# lo,hi = model.FluxRange(reac,tol=tol,resetstate=False)
# if IncZeroes or abs(lo) > tol or abs(hi) > 0.0:
# rv[str(reac)] = (lo,hi)
model.DelSumReacsConstraint("FVA_objective")
else:
fvadict = variability.flux_variability_analysis(
cobra_model=model, reaction_list=reaclist,
fraction_of_optimum=subopt, solver=model.solver,
objective_sense=model.objective_direction)
rv = fva(bounds=model.bounds)
for reac in fvadict:
lo = fvadict[reac]["minimum"] if abs(
fvadict[reac]["minimum"]) > tol else 0.0
hi = fvadict[reac]["maximum"] if abs(
fvadict[reac]["maximum"]) > tol else 0.0
rv[reac] = (lo,hi)
if VaryOnly:
rv = rv.Variable()
if AsMtx:
rv = rv.AsMtx()
model.SetState(state)
return rv
def test_flux_variability(self):
fva_results = {
'5DGLCNtex': {'minimum': -1.9748300208638403e-05, 'maximum': 0.0},
'ABTA': {'minimum': 0.0, 'maximum': 0.00014811225541408996},
'5DOAN': {'minimum': 0.0, 'maximum': 3.2227507421302166e-06},
'A5PISO': {'minimum': 0.006920856282000001, 'maximum': 0.006922717378372606},
'AACPS1': {'minimum': 0.0, 'maximum': 3.7028063376249126e-05},
'AACPS2': {'minimum': 0.0, 'maximum': 3.7028063733878864e-05},
'ACALDtex': {'minimum': -0.00011848980305159615, 'maximum': 0.0},
'AACPS3': {'minimum': 0.0, 'maximum': 3.702806337623859e-05},
'AACPS4': {'minimum': 0.0, 'maximum': 3.702806373387888e-05},
'ABUTD': {'minimum': 0.0, 'maximum': 0.00014811225541406058},
'AACPS5': {'minimum': 0.0, 'maximum': 2.8211857518389774e-05},
'AACPS6': {'minimum': 0.0, 'maximum': 2.821185753295664e-05},
'AACPS7': {'minimum': 0.0, 'maximum': 3.702806368868028e-05},
'AACPS8': {'minimum': 0.0, 'maximum': 3.702806338788376e-05},
'AACPS9': {'minimum': 0.0, 'maximum': 3.702806309933293e-05},
'AACTOOR': {'minimum': 0.0, 'maximum': 1.5388286124597477e-05},
'ABUTt2pp': {'minimum': 0.0, 'maximum': 0.0},
'3OAS140': {'minimum': 0.5041754136687804, 'maximum': 0.5042009621703677},
'3OAS141': {'minimum': 0.037484893950000084, 'maximum': 0.03750284695065363},
'3OAS160': {'minimum': 0.41767086529953557, 'maximum': 0.41769641380045963},
'3OAS161': {'minimum': 0.03748489395, 'maximum': 0.03750284695060761},
'3OAS180': {'minimum': 0.01069201669939239, 'maximum': 0.010717565200387778},
'3OAS181': {'minimum': 0.01606495455, 'maximum': 0.01608290755044158},
'ABUTtex': {'minimum': 0.0, 'maximum': 0.0},
'3OAS60': {'minimum': 0.5439852127139995, 'maximum': 0.5439896193596934},
'3OAS80': {'minimum': 0.5439852127140001, 'maximum': 0.5439896193596934},
'AAMYL': {'minimum': 0.0, 'maximum': 0.0},
'3PEPTabcpp': {'minimum': 0.0, 'maximum': 5.808323730923103e-06},
'3PEPTtex': {'minimum': -3.4245609402880297e-06, 'maximum': 0.0},
'3UMPtex': {'minimum': 0.0, 'maximum': 0.0},
'4HOXPACDtex': {'minimum': 0.0, 'maximum': 0.0},
'ACACtex': {'minimum': 0.0, 'maximum': 0.0},
'4PCP': {'minimum': 0.0, 'maximum': 6.171343917391756e-06},
'4PCPpp': {'minimum': 0.0, 'maximum': 5.58914186256664e-06},
'AAMYLpp': {'minimum': 0.0, 'maximum': 0.0},
'4PEPTabcpp': {'minimum': 0.0, 'maximum': 5.696625084349692e-06},
'4PEPTtex': {'minimum': -3.2198316806921494e-06, 'maximum': 0.0},
'5DGLCNR': {'minimum': -2.1942555793285538e-05, 'maximum': 0.0},
'5DGLCNt2rpp': {'minimum': -1.9748300208638403e-05, 'maximum': 0.0},
'ACALD': {'minimum': 3.356574143593833, 'maximum': 7.4957163478682105}}
for solver in solver_dict:
cobra_model = create_test_model()
initialize_growth_medium(cobra_model, 'LB')
fva_out = flux_variability_analysis(cobra_model, solver=solver,
reaction_list=cobra_model.reactions[100:140])
for the_reaction, the_range in fva_out.iteritems():
for k, v in the_range.iteritems():
self.assertAlmostEqual(fva_results[the_reaction][k], v, places=3)
def reduce_model(cobra_model, cobra_model_outFileName=None):
'''reduce model'''
# Input: cobra_model
# Output: cobra_model
# the lower and upper bounds have been set to 0.0
# for all reactions that cannot carry a flux
cobra_model.optimize()
sol_f = cobra_model.solution.f
fva_data = flux_variability_analysis(cobra_model,
fraction_of_optimum=0.9,
objective_sense='maximize',
the_reactions=None,
allow_loops=True,
solver='gurobi',
the_problem='return',
tolerance_optimality=1e-6,
tolerance_feasibility=1e-6,
tolerance_barrier=1e-8,
lp_method=1,
lp_parallel=0,
new_objective=None,
relax_b=None,
error_reporting=None,
number_of_processes=1,
copy_model=False)
#with open("data\\ijo1366_irrev_fva.json", 'w') as outfile:
# json.dump(data, outfile, indent=4);
#fva_data = json.load(open("data\\ijo1366_irrev_fva.json"));
# Reduce model
rxns_noflux = []
for k, v in fva_data.items():
if v['minimum'] == 0.0 and v['maximum'] == 0.0:
cobra_model.reactions.get_by_id(k).lower_bound = 0.0
cobra_model.reactions.get_by_id(k).upper_bound = 0.0
rxns_noflux.append(k)
if cobra_model_outFileName:
write_cobra_model_to_sbml_file(cobra_model, cobra_model_outFileName)
cobra_model.optimize()
sol_reduced_f = cobra_model.solution.f
# Check that the reduced model is consistent with the original model
if not sol_f == sol_reduced_f:
print('reduced model is inconsistent with the original model')
print('original model solution: ' + str(sol_f))
print('reduced model solution: ' + str(sol_reduced_f))
def build_flux_confidence_interval_dict(label_model,flux_confidence_interval_dict,parameter_list=[]):
fva=flux_variability_analysis(label_model.constrained_model, fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility)
for flux in label_model.flux_dict:
forward_reaction=False
reverse_reaction=False
flux_value=label_model.flux_dict[flux]
if "RATIO" in flux:
continue
if flux in fva:
fva_max=fva[flux]["maximum"]
fva_min=fva[flux]["minimum"]
if (flux+"_reverse") in label_model.flux_dict:
forward_reaction=True
#net_flux=flux_value-flux_dict[flux+"_reverse"]
elif ("_reverse") in flux:
reverse_reaction=True
#net_flux=flux_value-flux_dict[flux+"_reverse"]
if flux not in flux_confidence_interval_dict:
if forward_reaction:
flux_confidence_interval_dict[flux]={"lb":fva_min,"ub":fva_max}
flux_confidence_interval_dict[flux+"_forward"]={"lb":flux_value,"ub":flux_value}
elif reverse_reaction:
flux_confidence_interval_dict[flux]={"lb":flux_value,"ub":flux_value}
else:
flux_confidence_interval_dict[flux]={"lb":fva_min,"ub":fva_max}
else:
if forward_reaction:
flux_confidence_interval_dict[flux]["lb"]=min(flux_confidence_interval_dict[flux]["lb"],fva_min)
flux_confidence_interval_dict[flux]["ub"]=max(flux_confidence_interval_dict[flux]["ub"],fva_max)
if flux+"_forward" not in flux_confidence_interval_dict:
flux_confidence_interval_dict[flux+"_forward"]={"lb":flux_value,"ub":flux_value}
flux_confidence_interval_dict[flux+"_forward"]["lb"]=min(flux_confidence_interval_dict[flux+"_forward"]["lb"],flux_value)
flux_confidence_interval_dict[flux+"_forward"]["ub"]=max(flux_confidence_interval_dict[flux+"_forward"]["ub"],flux_value)
elif reverse_reaction:
flux_confidence_interval_dict[flux]["lb"]=min(flux_confidence_interval_dict[flux]["lb"],flux_value)
flux_confidence_interval_dict[flux]["ub"]=max(flux_confidence_interval_dict[flux]["ub"],flux_value)
else:
flux_confidence_interval_dict[flux]["lb"]=min(flux_confidence_interval_dict[flux]["lb"],fva_min)
flux_confidence_interval_dict[flux]["ub"]=max(flux_confidence_interval_dict[flux]["ub"],fva_max)
for parameter in parameter_list:
if "/" in parameter:
try:
value,lb,ub=get_ratios_bounds(label_model,parameter,0.1,label_model.lp_tolerance_feasibility)
if parameter not in flux_confidence_interval_dict:
flux_confidence_interval_dict[parameter]={"lb":lb,"ub":ub}
else:
flux_confidence_interval_dict[parameter]["lb"]=min(flux_confidence_interval_dict[parameter]["lb"],lb)
flux_confidence_interval_dict[parameter]["ub"]=max(flux_confidence_interval_dict[parameter]["ub"],ub)
except:
print parameter+" not recognized as ratio"
def test_pfba_flux_variability(model, pfba_fva_results, fva_results,
all_solvers):
"""Test FVA using pFBA."""
model.solver = all_solvers
with pytest.warns(UserWarning):
flux_variability_analysis(model,
pfba_factor=0.1,
reaction_list=model.reactions[1::3])
fva_out = flux_variability_analysis(model,
pfba_factor=1.1,
reaction_list=model.reactions)
for name, result in iteritems(fva_out.T):
for k, v in iteritems(result):
assert abs(pfba_fva_results[k][name] - v) < 0.00001
assert abs(pfba_fva_results[k][name]) <= abs(fva_results[k][name])
loop_reactions = [
model.reactions.get_by_id(rid) for rid in ("FRD7", "SUCDi")
]
fva_loopless = flux_variability_analysis(model,
pfba_factor=1.1,
reaction_list=loop_reactions,
loopless=True)
assert np.allclose(fva_loopless["maximum"], fva_loopless["minimum"])
def findReactionsWithPositiveFluxAtOptimum(GEM, fraction_of_optimum=1):
"""from cobra.flux_analysis.variability import flux_variability_analysis
GEM must be irreversible, i.e., split reversible reactions
"""
positive_rxns = []
fva = flux_variability_analysis(GEM,
reaction_list=None,
fraction_of_optimum=fraction_of_optimum,
processes=2,
loopless=False).round(decimals=8)
for rxn_id in fva.index:
v_min, v_max = fva.loc[rxn_id].minimum, fva.loc[rxn_id].maximum
if v_min > 0:
positive_rxns.append(rxn_id)
return positive_rxns
def run_fva(solver='cplex'):
"""
For debugging purposes only
"""
from test import salmonella_pickle
try:
from cPickle import load
except:
from pickle import load
with open(salmonella_pickle) as in_file:
cobra_model = load(in_file)
fva_out = flux_variability_analysis(cobra_model,
the_reactions=cobra_model.reactions,#[100:140],
solver=solver,number_of_processes=1)
def fva(json):
model = load_json_model(json)
bio = {
reaction: reaction.objective_coefficient
for reaction in model.reactions if search('biomass', reaction.name)
}
biom = bio.keys()[0]
# set the objective
model.change_objective(biom)
# add constraints
model.optimize()
f0 = model.solution.f
# get a dictionary with all non-zero fluxes
fluxes = {
reaction.id: reaction.x
for reaction in model.reactions if reaction.x != 0
}
# first time store for the wild type
v = variability.flux_variability_analysis(model)
return v
def reduce_model(self,cobra_model,cobra_model_outFileName=None):
'''reduce model'''
# Input: cobra_model
# Output: cobra_model
# the lower and upper bounds have been set to 0.0
# for all reactions that cannot carry a flux
cobra_model.optimize()
sol_f = cobra_model.solution.f
fva_data = flux_variability_analysis(cobra_model, fraction_of_optimum=0.9,
objective_sense='maximize', the_reactions=None,
allow_loops=True, solver='gurobi',
the_problem='return', tolerance_optimality=1e-6,
tolerance_feasibility=1e-6, tolerance_barrier=1e-8,
lp_method=1, lp_parallel=0, new_objective=None,
relax_b=None, error_reporting=None,
number_of_processes=1, copy_model=False);
#with open("data/ijo1366_irrev_fva.json", 'w') as outfile:
# json.dump(data, outfile, indent=4);
#fva_data = json.load(open("data/ijo1366_irrev_fva.json"));
# Reduce model
rxns_noflux = [];
for k,v in fva_data.items():
if v['minimum'] == 0.0 and v['maximum'] == 0.0:
cobra_model.reactions.get_by_id(k).lower_bound = 0.0;
cobra_model.reactions.get_by_id(k).upper_bound = 0.0;
rxns_noflux.append(k);
if cobra_model_outFileName:
write_cobra_model_to_sbml_file(cobra_model,cobra_model_outFileName)
cobra_model.optimize()
sol_reduced_f = cobra_model.solution.f
# Check that the reduced model is consistent with the original model
if not sol_f == sol_reduced_f:
print('reduced model is inconsistent with the original model')
print('original model solution: ' + str(sol_f))
print('reduced model solution: ' + str(sol_reduced_f))
def get_bounds(label_model,best_parameter_dict,parameter,force_flux_value_bounds,flux_parameter_list):
lb_list=[]#[best_parameter_dict[parameter]["lb"]]
ub_list=[]#[best_parameter_dict[parameter]["ub"]]
if best_parameter_dict[parameter]["type"]=="flux value":
clear_parameters(label_model,parameter_dict=best_parameter_dict,parameter_list=flux_parameter_list, clear_ratios=False,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #Clear all parameters
#Get real upper and lower bound for the parameters
for reaction_id in best_parameter_dict[parameter]["reactions"]:
fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,reaction_list=[reaction_id],tolerance_feasibility=label_model.lp_tolerance_feasibility)
lb_list.append(fva[reaction_id]["minimum"])
ub_list.append(fva[reaction_id]["maximum"])
if best_parameter_dict[parameter]["type"]!="flux value" or force_flux_value_bounds:
lb_list.append(best_parameter_dict[parameter]["lb"])
ub_list.append(best_parameter_dict[parameter]["ub"])
"""if best_parameter_dict[parameter]["type"]=="turnover" or force_flux_value_bounds:
lb_list.append(best_parameter_dict[parameter]["lb"])
ub_list.append(best_parameter_dict[parameter]["ub"])
if "ratio" in best_parameter_dict[parameter]["type"]:
lb_list,ub_list=get_ratios_bounds(label_model.constrained_model,parameter,0.1,lp_tolerance_feasibility=label_model.lp_tolerance_feasibility)"""
parameter_lb=max(lb_list)
parameter_ub=min(ub_list)
return parameter_lb, parameter_ub
def add_flux_limit_constraints(metabolic_model,fraction_of_optimum_objective=0.8, fraction_of_flux_minimum=2,solver=None,boundaries_precision=0.001,label_model=None,metabolite_list_file_name=None):
precision=int(-1*(math.log10(boundaries_precision)))
irreversible_model,minimal_flux,non_milp_reactions=create_minimal_fux_model(metabolic_model,fraction_of_optimum_objective, fraction_of_flux_minimum,boundaries_precision=boundaries_precision,label_model=label_model,metabolite_list_file_name=metabolite_list_file_name)
irreversible_fva=flux_variability_analysis(irreversible_model,reaction_list=non_milp_reactions,fraction_of_optimum=0,solver=solver)
reversible_fva={}
reverese_reactions=irreversible_model.reactions.query("_reverse")
for reaction in irreversible_model.reactions:
if reaction in reverese_reactions or "IRRMILP_" in reaction.id:
continue
reversible_fva[reaction.id]=copy.deepcopy(irreversible_fva[reaction.id])
if "reflection" in reaction.notes:
reverse_reaction= reaction.notes["reflection"]
reversible_fva[reaction.id]["minimum"]=-irreversible_fva[reverse_reaction]["maximum"]
if irreversible_fva[reverse_reaction]["minimum"]!=0:
reversible_fva[reaction.id]["maximum"]=-irreversible_fva[reverse_reaction]["minimum"]
if reaction.id in metabolic_model.reactions:
original_model_reaction=metabolic_model.reactions.get_by_id(reaction.id)
if original_model_reaction.lower_bound!=0:
original_model_reaction.lower_bound=max(round_down(reversible_fva[reaction.id]["minimum"],precision),original_model_reaction.lower_bound)
if original_model_reaction.upper_bound!=0:
original_model_reaction.upper_bound=min(round_up(reversible_fva[reaction.id]["maximum"],precision),original_model_reaction.upper_bound)
original_model_reaction.objective_coefficient=0.0
return reversible_fva
def FVA(model,
reaclist=None,
subopt=1.0,
IncZeroes=True,
VaryOnly=False,
AsMtx=False,
tol=1e-10,
PrintStatus=False,
cobra=True,
processes=None,
loopless=False,
pfba_factor=None,
reset_state=True):
if reset_state:
state = model.GetState()
# import time
# print 'before fva solve ' + time.asctime(time.localtime(time.time()))
model.Solve(PrintStatus=PrintStatus)
# print 'after fva solve ' + time.asctime(time.localtime(time.time()))
if model.solution.status != 'optimal' or math.isnan(
model.solution.objective_value):
statusmsg = model.solution.status
model.SetState(state)
raise ValueError("no optimal solution, problem " + statusmsg)
else:
if reaclist == None:
reaclist = model.reactions
elif isinstance(reaclist, str) or isinstance(reaclist, Reaction):
reaclist = [reaclist]
if cobra:
# print 'before cobra fva ' + time.asctime(time.localtime(time.time()))
reaclist = model.GetReactions(reaclist)
fvadict = variability.flux_variability_analysis(
model=model,
reaction_list=reaclist,
fraction_of_optimum=subopt,
loopless=loopless,
pfba_factor=pfba_factor)
#solver=model.solver, objective_sense=model.objective_direction)
# print 'after cobra fva ' + time.asctime(time.localtime(time.time()))
rv = fva({}, bounds=model.bounds)
# for reac in fvadict:
# lo = fvadict[reac]["minimum"] if abs(
# fvadict[reac]["minimum"]) > tol else 0.0
# hi = fvadict[reac]["maximum"] if abs(
# fvadict[reac]["maximum"]) > tol else 0.0
# rv[reac] = (lo,hi)
for row in fvadict.iterrows():
lo = row[1][0] if abs(row[1][0]) > tol else 0.0
hi = row[1][1] if abs(row[1][1]) > tol else 0.0
rv[model.GetReactionName(row[0])] = (lo, hi)
# rv[model.GetReaction(row[0])] = (lo,hi)
else:
# print 'not cobra fva'
model.DelSumReacsConstraint("FVA_objective")
model.SetObjAsConstraint(name="FVA_objective", subopt=subopt)
rv = fva({}, bounds=model.bounds)
pool = multiprocessing.Pool(processes=processes)
results = [
pool.apply_async(FluxRange,
args=(model, reac, tol, False, True))
for reac in reaclist
]
pool.close()
pool.join()
for x in results:
rv[model.GetReactionName(
x.get().keys()[0])] = x.get().values()[0]
if "FVA_objective_sum_reaction" in rv:
rv.pop("FVA_objective_sum_reaction")
# for reac in reaclist:
# rv[str(reac)] = pool.apply_async(FluxRange, args=(model, reac, tol,
# False))
# for reac in reaclist:
# lo,hi = model.FluxRange(reac,tol=tol,reset_state=False)
# if IncZeroes or abs(lo) > tol or abs(hi) > 0.0:
# rv[str(reac)] = (lo,hi)
model.DelSumReacsConstraint("FVA_objective")
if VaryOnly:
rv = rv.Variable()
if AsMtx:
rv = rv.AsMtx()
# print 'before fva set state ' + time.asctime(time.localtime(time.time()))
if reset_state:
model.SetState(state)
# print 'after fva set state ' + time.asctime(time.localtime(time.time()))
return rv
def test_parallel_flux_variability(model, fva_results, all_solvers):
"""Test parallel FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model, processes=2)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, fva_results)
#!/bin/sh
gurobi-env python2 << '!'
import cobra
from cobra.flux_analysis import variability as fva
m = cobra.io.load_json_model("small.json")
o = m.optimize()
s = fva.flux_variability_analysis(m, fraction_of_optimum=0.99, solver='gurobi') # this is a dict of fluxes that are 99% of optimum
for (rx, flux) in o.x_dict.items():
print "{} {}".format(rx, flux/10)
!
def evaluate_parameter(label_model,parameter_dict,flux_parameter_list,parameter,parameter_lb,parameter_ub,parameter_new_value,signficance_threshold,feasability_process,parameter_precision,max_perturbation,force_flux_value_bounds,annealing_n,annealing_m,annealing_p0,annealing_pf,max_random_sample,min_random_sample,annealing_n_processes,annealing_cycle_time_limit, annealing_cycle_max_attempts,annealing_iterations,annealing_restore_parameters=True):
"Function used internally to evaluate the ChiSquare with a given parameter to locked to a single value"
parameter_dict=copy.deepcopy(parameter_dict)
#is_flux_value=parameter in flux_parameter_list
if parameter_dict[parameter]["type"] in ("ratio","flux value"):
clear_parameters(label_model,parameter_dict=parameter_dict,parameter_list=flux_parameter_list, clear_ratios=False,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #Clear all parameters
"""#Get real upper and lower bound for the parameters
for reaction_id in parameter_dict[parameter]["reactions"]:
status,feasability_process=check_feasibility(label_model.constrained_model,tolerance_feasibility=label_model.lp_tolerance_feasibility,time_limit=60,pool=feasability_process)
fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,reaction_list=[reaction_id],tolerance_feasibility=label_model.lp_tolerance_feasibility)
lb_list.append(fva[reaction_id]["minimum"])
ub_list.append(fva[reaction_id]["maximum"])
parameter_lb=max(lb_list)
parameter_ub=min(ub_list)"""
print parameter_dict
parameter_dict[parameter]["v"]=parameter_new_value
print [parameter,parameter_dict[parameter]]
apply_parameters(label_model,parameter_dict,apply_flux_values=True,parameter_precision=parameter_precision,parameter_list=[parameter])
apply_ratios(label_model.constrained_model,label_model.ratio_dict)
if parameter_dict[parameter]["type"] in ("ratio","flux value"):
parameter_backup=copy.deepcopy(parameter_dict)
for attempt in range(0,10):
retry_flag=False
random_parameter_sample=random.sample(flux_parameter_list, len(flux_parameter_list))
for flux_value in random_parameter_sample:
if flux_value==parameter:
continue
lb=-999999
ub=999999
for reaction_id in parameter_dict[flux_value]["reactions"]:
status,feasability_process=check_feasibility(label_model.constrained_model,tolerance_feasibility=label_model.lp_tolerance_feasibility,time_limit=60,pool=feasability_process)
if status =="infeasible":
retry_flag=True
clear_parameters(label_model,parameter_dict=parameter_dict,parameter_list=flux_parameter_list, clear_ratios=False ,clear_turnover=False ,clear_fluxes=True, restore_objectives=False)
parameter_dict=copy.deepcopy(parameter_backup)
apply_parameters(label_model,parameter_dict,apply_flux_values=True,parameter_precision=parameter_precision,parameter_list=[parameter])
break
fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,reaction_list=[reaction_id],tolerance_feasibility=label_model.lp_tolerance_feasibility)
ub=min(fva[reaction_id]["maximum"],ub)
lb=max(fva[reaction_id]["minimum"],lb)
if retry_flag==True:
break
value=parameter_dict[flux_value]["v"] #previous value
parameter_dict[flux_value]["v"]=min(max(lb,value),ub)
apply_parameters(label_model,parameter_dict,apply_flux_values=True,parameter_precision=parameter_precision,parameter_list=[flux_value])
#print model.optimize()
if retry_flag==False:
break #If no errors where encountered no need for further attempts
apply_parameters(label_model,parameter_dict,apply_flux_values=True,parameter_precision=parameter_precision)
print "Delta1"
a,b=solver(label_model)
print "Delta2"
f_best,b,c=get_objective_function(label_model,output=False)
best_flux_dict=copy.deepcopy(label_model.flux_dict)
print ["FBEST",f_best]
if f_best>=signficance_threshold:
backup_parameter_dict=copy.deepcopy(parameter_dict)
print "coordinated descent"
parameters_to_fit=copy.copy(parameter_dict)
del parameters_to_fit[parameter]
f_best, new_parameters,best_flux_dict=coordinate_descent(label_model,mode="fsolve",parameter_precision=parameter_precision,parameter_to_be_fitted=parameters_to_fit,max_perturbation=max_perturbation,perturbation=1.2,fba_mode="fba",parameter_dict=parameter_dict,force_flux_value_bounds=force_flux_value_bounds)
best_flux_dict=label_model.flux_dict
print [parameter_new_value,f_best]
if f_best<=signficance_threshold:
parameter_dict=new_parameters
else:
for x in range(0,annealing_iterations): #Try several times to make sure the result is above the signficance threeshol
parameter_dict,best_flux_dict,f_best=annealing(label_model,n=annealing_n,m=annealing_m,p0=annealing_p0,pf=annealing_pf,max_random_sample=max_random_sample,min_random_sample=min_random_sample,mode="fsolve",fraction_of_optimum=0,parameter_precision=parameter_precision,parameter_to_be_fitted=parameters_to_fit,max_perturbation=max_perturbation,gui=None,fba_mode="fba", break_threshold=signficance_threshold,parameter_dict=parameter_dict,n_processes=annealing_n_processes,cycle_time_limit=annealing_cycle_time_limit, cycle_max_attempts=annealing_cycle_max_attempts,output=False,force_flux_value_bounds=force_flux_value_bounds)
if f_best<signficance_threshold:
break
elif annealing_restore_parameters==True:
parameter_dict=copy.deepcopy(backup_parameter_dict)
return parameter_dict, f_best
def metabolite_summary(met, solution=None, threshold=0.01, fva=False,
floatfmt='.3g'):
"""Print a summary of the reactions which produce and consume this
metabolite
solution : cobra.core.Solution
A previously solved model solution to use for generating the
summary. If none provided (default), the summary method will resolve
the model. Note that the solution object must match the model, i.e.,
changes to the model such as changed bounds, added or removed
reactions are not taken into account by this method.
threshold : float
a percentage value of the maximum flux below which to ignore reaction
fluxes
fva : float (0->1), or None
Whether or not to include flux variability analysis in the output.
If given, fva should be a float between 0 and 1, representing the
fraction of the optimum objective to be searched.
floatfmt : string
format method for floats, passed to tabulate. Default is '.3g'.
"""
if solution is None:
met.model.slim_optimize(error_value=None)
solution = get_solution(met.model, reactions=met.reactions)
rxn_id = list()
flux = list()
reaction = list()
for rxn in met.reactions:
rxn_id.append(format_long_string(rxn.id, 10))
flux.append(solution.fluxes[rxn.id] * rxn.metabolites[met])
reaction.append(format_long_string(rxn.reaction, 40 if fva else 50))
flux_summary = pd.DataFrame(data={
"id": rxn_id, "flux": flux, "reaction": reaction})
if fva:
fva_results = flux_variability_analysis(
met.model, met.reactions, fraction_of_optimum=fva)
flux_summary.index = flux_summary["id"]
flux_summary["maximum"] = zeros(len(rxn_id))
flux_summary["minimum"] = zeros(len(rxn_id))
for rid, rxn in zip(rxn_id, met.reactions):
imax = rxn.metabolites[met] * fva_results.loc[rxn.id, "maximum"]
imin = rxn.metabolites[met] * fva_results.loc[rxn.id, "minimum"]
flux_summary.loc[rid, "fmax"] = (imax if abs(imin) <= abs(imax)
else imin)
flux_summary.loc[rid, "fmin"] = (imin if abs(imin) <= abs(imax)
else imax)
assert flux_summary.flux.sum() < 1E-6, "Error in flux balance"
flux_summary = _process_flux_dataframe(flux_summary, fva, threshold,
floatfmt)
flux_summary['percent'] = 0
total_flux = flux_summary[flux_summary.is_input].flux.sum()
flux_summary.loc[flux_summary.is_input, 'percent'] = \
flux_summary.loc[flux_summary.is_input, 'flux'] / total_flux
flux_summary.loc[~flux_summary.is_input, 'percent'] = \
flux_summary.loc[~flux_summary.is_input, 'flux'] / total_flux
flux_summary['percent'] = flux_summary.percent.apply(
lambda x: '{:.0%}'.format(x))
if fva:
flux_table = tabulate(
flux_summary.loc[:, ['percent', 'flux', 'fva_fmt', 'id',
'reaction']].values, floatfmt=floatfmt,
headers=['%', 'FLUX', 'RANGE', 'RXN ID', 'REACTION']).split('\n')
else:
flux_table = tabulate(
flux_summary.loc[:, ['percent', 'flux', 'id', 'reaction']].values,
floatfmt=floatfmt, headers=['%', 'FLUX', 'RXN ID', 'REACTION']
).split('\n')
flux_table_head = flux_table[:2]
met_tag = "{0} ({1})".format(format_long_string(met.name, 45),
format_long_string(met.id, 10))
head = "PRODUCING REACTIONS -- " + met_tag
print_(head)
print_("-" * len(head))
print_('\n'.join(flux_table_head))
print_('\n'.join(
pd.np.array(flux_table[2:])[flux_summary.is_input.values]))
print_()
print_("CONSUMING REACTIONS -- " + met_tag)
print_("-" * len(head))
print_('\n'.join(flux_table_head))
print_('\n'.join(
pd.np.array(flux_table[2:])[~flux_summary.is_input.values]))
def estimate_confidence_intervals(label_model,significance=0.95,perturbation=0.1,min_absolute_perturbation=0.1,max_absolute_perturbation=25,parameter_precision=None,best_parameter_dict=None,evaluate_turnovers=False,parameter_list=None,fraction_of_optimum=0.9,force_flux_value_bounds=False,relative_max_random_sample=0.5, relative_min_random_sample= 0.25,annealing_n=50,annealing_m=100,annealing_p0=0.4,annealing_pf=0.001,annealing_n_processes=1,annealing_cycle_time_limit=1800, annealing_cycle_max_attempts=5,annealing_iterations=2,annealing_restore_parameters=True,fname="confidence.json",sbml_name=None,output=True):
"""
Computes the confidence intervals for fluxes
label_model: label_model object
signficance: float
Signficance level for the confidence intervals. Default is 0.95 (95%)
perturbation: float
Relative perturbation for each parameter at each step. Default is 0.1 (10%). Regardless of this value the absolute perturbation will will never be lower than the value defined by the min_absolute_perturbation and will never be larger than the maximum value defined by the max_absolute_perturbation
min_absolute_perturbation: float
See above
max_absolute_perturbation: float
See above
parameter_precision: float,
Defines the precision of the flux value parameters. If none is defined the precision defined in the label_model object will be used
best_parameter_dict: dict
Dict with the best parameters that have been obtained after fitting the parameters to experimental data
evaluate_turnovers: bool,
If set to False (default) it will not calculate the confidence intervals for turnovers.
parameter_list: list
List of the parameters that should be evaluated. Unless all flux value parameters are selected, the confidence intervals for fluxes (other than those directly analyzed) won't be meaningful
fraction_of_optimum: float
Fraction of the objective flux that should be mantained. If the parameters have been added automatically this will have no effect as the objective is alsways added as parameters
force_flux_value_bounds: bool,
If False it will ignore the bounds defined in the parameter dict and use the FVA limits for flux value parameters. If set to True it migh in some instances result on unfeasible solutions
relative_max_random_sample: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
relative_min_random_sample: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_n: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_m: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_p0: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_pf: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_n_processes: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_cycle_time_limit: float
Defines the parameter of the same name in the annealing function used to reoptimize the parameters
annealing_cycle_max_attempts
annealing_iterations: int
Number of times annealing should be run once the signficance threeshold has been surpassed by a parameter to ensure this values is the real upper/lower limit for the paramater
annealing_restore_parameters: bool:
If True after each annealing iterations it will return the paramaters to each original value to reduce the risk of being trapper into local minimums
fname: string:
Name of the file where the results will be saved. It must have ither a xlsx, CSV or json extension.
sbml_name;: string
Name of of the SBML that will generated containng the constrained_model restricted by the confidence interval results. If none (default) no SBML will be generated.
output: bool
If True it will indicate progress of the analysis on a text file named estimate estimate_confidence_interval_output.txt
"""
if parameter_precision==None:
parameter_precision=label_model.parameter_precision
if best_parameter_dict==None:
best_parameter_dict=label_model.parameter_dict
print parameter_list
if parameter_list==None or parameter_list==[]:
full_mode=True
if evaluate_turnovers:
parameter_list=best_parameter_dict.keys()
else:
parameter_list=[]
for x in best_parameter_dict:
if best_parameter_dict[x]["type"] != "turnover":
parameter_list.append(x)
else:
full_mode=False
precision=int(-1*(math.log10(parameter_precision)))
max_random_sample=int(relative_max_random_sample*len(best_parameter_dict))
min_random_sample=int(relative_min_random_sample*len(best_parameter_dict))
#chi_parameters_sets_dict={}
parameter_confidence_interval_dict={}
flux_confidence_interval_dict={}
parameter_value_parameters_sets_dict={}
build_flux_confidence_interval_dict(label_model,flux_confidence_interval_dict,parameter_list)
build_confidence_dicts(parameter_confidence_interval_dict,parameter_value_parameters_sets_dict,best_parameter_dict)
"""for flux in label_model.flux_dict:
flux_confidence_interval_dict[flux]={"lb":label_model.flux_dict[flux],"ub":label_model.flux_dict[flux]}
if (flux+"_reverse") in label_model.flux_dict:
net_flux=label_model.flux_dict[flux]-label_model.flux_dict[flux+"_reverse"]
flux_confidence_interval_dict["net_"+flux]={"lb":net_flux,"ub":net_flux}"""
print flux_confidence_interval_dict
apply_parameters(label_model,best_parameter_dict,parameter_precision=parameter_precision)
a,b=solver(label_model)
best_objective,b,c=get_objective_function(label_model,output=False)
delta_chi = chi2.isf(q=1-significance, df=1)
signficance_threshold=delta_chi+best_objective
print signficance_threshold
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write("signficance_threshold "+str(signficance_threshold)+"\n")
original_objectives_bounds={}
for reaction in label_model.constrained_model.objective:
original_objectives_bounds[reaction.id]={}
original_objectives_bounds[reaction.id]["lb"]=reaction.lower_bound
original_objectives_bounds[reaction.id]["ub"]=reaction.upper_bound
original_objectives_bounds[reaction.id]["obj_coef"]=reaction.objective_coefficient
fva=flux_variability_analysis(label_model.constrained_model,reaction_list=[reaction], fraction_of_optimum=fraction_of_optimum,tolerance_feasibility=label_model.lp_tolerance_feasibility)
reaction.lower_bound=max(round_down(fva[reaction.id]["minimum"],precision),reaction.lower_bound)
reaction.upper_bound=min(round_up(fva[reaction.id]["maximum"],precision),reaction.upper_bound)
reaction.objective_coefficient=0
flux_parameter_list=[]
for parameter in best_parameter_dict:
if best_parameter_dict[parameter]["type"]=="flux value":
flux_parameter_list.append(parameter)
feasability_process = Pool(processes=1)
for parameter in parameter_list:
apply_parameters(label_model,best_parameter_dict,parameter_precision=parameter_precision)
a,b=solver(label_model)
#chi_parameters_sets_dict[parameter]={}
#variation_range= best_parameter_dict[parameter]["ub"]-best_parameter_dict[parameter]["lb"]
#Find the highest/lowest value found on previous simulations
n=1
sign=1
#is_flux_value=parameter in flux_parameter_list
if parameter not in best_parameter_dict:
additional_parameter=True
parameter_dict=max_parameter_dict=min_parameter_dict=copy.deepcopy(best_parameter_dict)
if parameter in label_model.constrained_model.reactions:
print "is not ratio"
value=label_model.constrained_model.solution.x_dict[parameter]
reaction=label_model.constrained_model.reactions.get_by_id(parameter)
lb=reaction.lower_bound
ub=reaction.upper_bound
parameter_dict[parameter]={"v":value,"lb":lb,"ub":ub ,"type":"flux value","reactions":[parameter],"max_d":0.1,"original_lb":lb,"original_ub":ub,"original_objective_coefficient":0.0}
elif "/" in parameter:
print "is ratio"
reaction1=parameter.split("/")[0]
reaction2=parameter.split("/")[1]
value,lb,ub=get_ratios_bounds(label_model,parameter,0.1,lp_tolerance_feasibility=label_model.lp_tolerance_feasibility,parameter_dict=parameter_dict)
parameter_dict[parameter]={"v":value,"lb":lb,"ub":ub ,"type":"ratio","ratio":{reaction1:"v",reaction2:1},"max_d":0.1}
print parameter
else:
additional_parameter=False
#TODO Make it so it can start from the highuest and lowest value of the parameter found in previous simulations
min_parameter_dict=parameter_value_parameters_sets_dict[parameter]["lb_parameter_dict"]
max_parameter_dict=parameter_value_parameters_sets_dict[parameter]["ub_parameter_dict"]
parameter_lb,parameter_ub=get_bounds(label_model,min_parameter_dict,parameter,force_flux_value_bounds,flux_parameter_list)
"""lb_list=[]#[best_parameter_dict[parameter]["lb"]]
ub_list=[]#[best_parameter_dict[parameter]["ub"]]
if is_flux_value==True:
clear_parameters(label_model,parameter_dict=best_parameter_dict,parameter_list=flux_parameter_list, clear_ratios=False,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #Clear all parameters
#Get real upper and lower bound for the parameters
for reaction_id in best_parameter_dict[parameter]["reactions"]:
fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,reaction_list=[reaction_id],tolerance_feasibility=label_model.lp_tolerance_feasibility)
lb_list.append(fva[reaction_id]["minimum"])
ub_list.append(fva[reaction_id]["maximum"])
if is_flux_value==False or force_flux_value_bounds:
lb_list.append(best_parameter_dict[parameter]["lb"])
ub_list.append(best_parameter_dict[parameter]["ub"])
parameter_lb=max(lb_list)
parameter_ub=min(ub_list)"""
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write("///////"+parameter+"(lb="+str(parameter_lb)+" ub="+str(parameter_ub)+ ")\n")
while(n<=100000):
stop_flag=False
if n==1:
parameter_dict=copy.deepcopy(max_parameter_dict)
#Run a quick evaluation of the upper bound to see if it is not necessary to "walk there"
parameter_dict,f_best=evaluate_parameter(label_model,parameter_dict,flux_parameter_list,parameter,parameter_lb,parameter_ub,parameter_ub,signficance_threshold,feasability_process,parameter_precision,max_absolute_perturbation/10.0,force_flux_value_bounds,max(int(annealing_n*0.5),2),annealing_m,annealing_p0,annealing_pf,max_random_sample,min_random_sample,annealing_n_processes,annealing_cycle_time_limit, annealing_cycle_max_attempts,annealing_iterations=1,annealing_restore_parameters=annealing_restore_parameters)
if f_best<=signficance_threshold:
build_flux_confidence_interval_dict(label_model,flux_confidence_interval_dict,parameter_list)
parameter_dict_to_store=copy.deepcopy(parameter_dict)
if additional_parameter:
del parameter_dict_to_store[parameter]
build_confidence_dicts(parameter_confidence_interval_dict,parameter_value_parameters_sets_dict,parameter_dict_to_store)
else:
parameter_dict=copy.deepcopy(max_parameter_dict)
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write(parameter+" "+"v="+str(parameter_ub)+" chi="+str(f_best)+"\n")
delta_parameter=min(max(perturbation*abs(parameter_dict[parameter]["v"]),min_absolute_perturbation),max_absolute_perturbation)
print delta_parameter
parameter_new_value=max(min(parameter_dict[parameter]["v"]+delta_parameter*sign,parameter_ub),parameter_lb)
parameter_dict,f_best=evaluate_parameter(label_model,parameter_dict,flux_parameter_list,parameter,parameter_lb,parameter_ub,parameter_new_value,signficance_threshold,feasability_process,parameter_precision,max_absolute_perturbation/10.0,force_flux_value_bounds,annealing_n,annealing_m,annealing_p0,annealing_pf,max_random_sample,min_random_sample,annealing_n_processes,annealing_cycle_time_limit, annealing_cycle_max_attempts,annealing_iterations,annealing_restore_parameters=annealing_restore_parameters)
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write(parameter+" "+"v="+str(parameter_new_value)+" chi="+str(f_best)+"\n")
if f_best>signficance_threshold:
stop_flag=True
else:
if f_best<best_objective: #If a solution is found that is better than the optimal solution restart the confidence interval simulation with the new parameter set
parameter_dict_to_store=copy.deepcopy(parameter_dict)
if additional_parameter:
clear_parameters(label_model,parameter_dict=parameter_dict,parameter_list=[parameter], clear_ratios=True,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #
del parameter_dict_to_store[parameter]
parameter_confidence_interval_dict={}
flux_confidence_interval_dict={}
parameter_value_parameters_sets_dict={}
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write("Restarting analysis with new bestfit\n")
best_parameter_dict,best_flux_dict,f_best=annealing(label_model,n=annealing_n,m=annealing_m,p0=annealing_p0,pf=annealing_pf,max_random_sample=max_random_sample,min_random_sample=min_random_sample,mode="fsolve",fraction_of_optimum=0,parameter_precision=parameter_precision,parameter_to_be_fitted=[],max_perturbation=max_absolute_perturbation,gui=None,fba_mode="fba", break_threshold=signficance_threshold,parameter_dict=parameter_dict_to_store,n_processes=annealing_n_processes,cycle_time_limit=annealing_cycle_time_limit, cycle_max_attempts=annealing_cycle_max_attempts,output=False,force_flux_value_bounds=force_flux_value_bounds)
if full_mode:
parameter_list=None
parameter_confidence_interval_dict,flux_confidence_interval_dict,parameter_value_parameters_sets_dict,constrained_model=estimate_confidence_intervals(label_model,significance=significance,perturbation=perturbation,min_absolute_perturbation=min_absolute_perturbation, max_absolute_perturbation=max_absolute_perturbation ,parameter_precision=parameter_precision, best_parameter_dict=best_parameter_dict ,parameter_list=parameter_list ,fraction_of_optimum=fraction_of_optimum ,force_flux_value_bounds=force_flux_value_bounds ,relative_max_random_sample=relative_max_random_sample, relative_min_random_sample= relative_min_random_sample,annealing_n=annealing_n,annealing_m=annealing_m,annealing_p0=annealing_p0,annealing_pf=annealing_pf,annealing_n_processes=annealing_n_processes,annealing_cycle_time_limit=annealing_cycle_time_limit, annealing_cycle_max_attempts= annealing_cycle_max_attempts, annealing_iterations=annealing_iterations ,annealing_restore_parameters=annealing_restore_parameters ,fname=fname,output=output,sbml_name=sbml_name,evaluate_turnovers=evaluate_turnovers)
#parameter_confidence_interval_dict,flux_confidence_interval_dict,parameter_value_parameters_sets_dict =estimate_confidence_intervals(label_model,significance=significance,perturbation=perturbation,min_absolute_perturbation=min_absolute_perturbation,max_absolute_perturbation=max_absolute_perturbation,parameter_precision=parameter_precision,best_parameter_dict=parameter_dict,parameter_list=parameter_list,fraction_of_optimum=fraction_of_optimum,force_flux_value_bounds=force_flux_value_bounds,relative_max_random_sample=relative_max_random_sample, relative_min_random_sample= relative_min_random_sample,annealing_n=annealing_n,annealing_m=annealing_m,annealing_p0=annealing_p0,annealing_pf=annealing_pf,output=output,annealing_n_processes=annealing_n_processes,annealing_cycle_time_limit=annealing_cycle_time_limit, annealing_cycle_max_attempts=annealing_cycle_max_attempts,annealing_iterations=annealing_iterations,annealing_restore_parameters=annealing_restore_parameters,fname=fname)
return parameter_confidence_interval_dict,flux_confidence_interval_dict,parameter_value_parameters_sets_dict,constrained_model
if parameter_dict[parameter]["v"]<=parameter_lb or parameter_dict[parameter]["v"]>=parameter_ub:
stop_flag=True
"""if sign==1:
parameter_confidence_interval_dict[parameter]["ub"]=new_value
else:
parameter_confidence_interval_dict[parameter]["lb"]=new_value"""
build_flux_confidence_interval_dict(label_model,flux_confidence_interval_dict,parameter_list)
parameter_dict_to_store=copy.deepcopy(parameter_dict)
if additional_parameter:
del parameter_dict_to_store[parameter]
build_confidence_dicts(parameter_confidence_interval_dict,parameter_value_parameters_sets_dict,parameter_dict_to_store)
if stop_flag==True:
print "stop"
if sign==1:
sign=-1
parameter_dict=copy.deepcopy(min_parameter_dict)
parameter_dict,f_best=evaluate_parameter(label_model,parameter_dict,flux_parameter_list,parameter,parameter_lb,parameter_ub,parameter_lb,signficance_threshold,feasability_process,parameter_precision,max_absolute_perturbation/10.0,force_flux_value_bounds,annealing_n,annealing_m,annealing_p0,annealing_pf,max_random_sample,min_random_sample,annealing_n_processes,annealing_cycle_time_limit, annealing_cycle_max_attempts,annealing_iterations=1,annealing_restore_parameters=annealing_restore_parameters)
if f_best<=signficance_threshold:
build_flux_confidence_interval_dict(label_model,flux_confidence_interval_dict,parameter_list)
parameter_dict_to_store=copy.deepcopy(parameter_dict)
if additional_parameter:
del parameter_dict_to_store[parameter]
build_confidence_dicts(parameter_confidence_interval_dict,parameter_value_parameters_sets_dict,parameter_dict_to_store)
else:
parameter_dict=copy.deepcopy(min_parameter_dict)
if output:
with open("estimate_confidence_interval_output.txt", "a") as myfile:
myfile.write(parameter+" "+"v="+str(parameter_lb)+" chi="+str(f_best)+"\n")
else:
clear_parameters(label_model,parameter_dict=parameter_dict,parameter_list=[parameter], clear_ratios=True,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #Clear all parameters
break
n+=1
print ["n",n]
for reaction_id in original_objectives_bounds:
reaction=label_model.constrained_model.reactions.get_by_id(reaction_id)
reaction.lower_bound=original_objectives_bounds[reaction_id]["lb"]
reaction.upper_bound=original_objectives_bounds[reaction_id]["ub"]
reaction.objective_coefficient=original_objectives_bounds[reaction_id]["obj_coef"]
#apply_parameters(label_model,best_parameter_dict,parameter_precision=parameter_precision)
feasability_process.close()
if "xlsx" in fname or "csv" in fname:
print [full_mode]
if not full_mode:
save_flux_confidence_interval(label_model,flux_confidence_interval_dict,significance=significance,fn=fname,omit_turnovers=not evaluate_turnovers,parameter_list=parameter_list)
else:
save_flux_confidence_interval(label_model,flux_confidence_interval_dict,significance=significance,fn=fname,omit_turnovers=not evaluate_turnovers,parameter_list=None)
elif "json" in fname:
save_confidence_interval_json(flux_confidence_interval_dict,parameter_confidence_interval_dict,fn=fname)
constrained_model=save_sbml_with_confidence_results(label_model,flux_confidence_interval_dict,fname=sbml_name,parameter_dict=best_parameter_dict,full_mode=full_mode,parameter_list=parameter_list,precision=precision)
apply_parameters(label_model,best_parameter_dict,parameter_precision=parameter_precision)
return parameter_confidence_interval_dict,flux_confidence_interval_dict,parameter_value_parameters_sets_dict,constrained_model
def test_parallel_flux_variability(model, fva_results, all_solvers):
"""Test parallel FVA."""
model.solver = all_solvers
fva_out = flux_variability_analysis(model, processes=2)
fva_out.sort_index(inplace=True)
assert np.allclose(fva_out, fva_results)
def get_ratios_bounds(label_model,ratio,perturbation,lp_tolerance_feasibility=1e-9,parameter_dict=None):
reactions=ratio.split("/")
reaction1=reactions[0]
reaction2=reactions[1]
model=label_model.constrained_model
model.optimize(tolerance_feasibility=lp_tolerance_feasibility)
try:
v=original_v=ub=lb=teoric_lb=teoric_ub=model.solution.x_dict[reaction1]/(model.solution.x_dict[reaction2])
except:
v=original_v=ub=lb=teoric_lb=teoric_ub=v=model.solution.x_dict[reaction1]/(model.solution.x_dict[reaction2]+lp_tolerance_feasibility)
if parameter_dict!=None:
clear_parameters(label_model,parameter_dict=parameter_dict,parameter_list=parameter_dict.keys(), clear_ratios=True,clear_turnover=False,clear_fluxes=True,restore_objectives=False) #Clear all parameters
fva=flux_variability_analysis(model,reaction_list=[reaction1,reaction2], fraction_of_optimum=0,tolerance_feasibility=lp_tolerance_feasibility)
try:
value=fva[reaction1]["minimum"]/(fva[reaction2]["maximum"])
print [fva[reaction1]["minimum"],fva[reaction2]["maximum"]]
except:
value=fva[reaction1]["minimum"]/(fva[reaction2]["maximum"]+lp_tolerance_feasibility)
print value
teoric_lb=min(teoric_lb,value)
teoric_ub=max(teoric_ub,value)
try:
value=fva[reaction1]["maximum"]/(fva[reaction2]["minimum"])
print [fva[reaction1]["maximum"],fva[reaction2]["minimum"]]
except:
value=fva[reaction1]["maximum"]/(fva[reaction2]["minimum"]+lp_tolerance_feasibility)
print value
teoric_lb=min(teoric_lb,value)
teoric_ub=max(teoric_ub,value)
try:
value=fva[reaction1]["minimum"]/(fva[reaction2]["minimum"])
print [fva[reaction1]["minimum"],fva[reaction2]["minimum"]]
except:
value=fva[reaction1]["minimum"]/(fva[reaction2]["minimum"]+lp_tolerance_feasibility)
print value
teoric_lb=min(teoric_lb,value)
teoric_ub=max(teoric_ub,value)
try:
value=fva[reaction1]["maximum"]/(fva[reaction2]["maximum"])
print [fva[reaction1]["maximum"],fva[reaction2]["maximum"]]
except:
value=fva[reaction1]["maximum"]/(fva[reaction2]["maximum"]+lp_tolerance_feasibility)
print value
teoric_lb=min(teoric_lb,value)
teoric_ub=max(teoric_ub,value)
print [teoric_lb,teoric_ub]
v=lb=ub=original_v
print v
#Increase until not feasible
ratio_dict={ratio:{reaction1:v,reaction2:1}}
while model.optimize().status=="optimal":
ub=v
delta_parameter=min(max(perturbation*abs(v),0.001),100000000)
print delta_parameter
v=max(min(v+delta_parameter*1,teoric_ub),teoric_lb)
ratio_dict[ratio]={reaction1:v,reaction2:1}
print ratio_dict
apply_ratios(model,ratio_dict)
print model.optimize()
if v>=teoric_ub:
print "breaking 1"
break
ub=v
ratio_dict={ratio:{reaction1:original_v,reaction2:1}}
v=original_v
while model.optimize().status=="optimal":
lb=v
delta_parameter=min(max(perturbation*abs(v),0.001),100000000)
print delta_parameter
v=max(min(v+delta_parameter*-1,teoric_ub),teoric_lb)
ratio_dict[ratio]={reaction1:v,reaction2:1}
apply_ratios(model,ratio_dict)
ratio_dict[ratio]={reaction1:v,reaction2:1}
print ratio_dict
apply_ratios(model,ratio_dict)
print model.optimize()
if v<=teoric_lb:
print "breaking -1"
break
lb=v
remove_ratio(model,ratio,ratio_dict) #Remove the Ratio
if parameter_dict!=None:
apply_parameters(label_model,parameter_dict)
apply_ratios(label_model.constrained_model,label_model.ratio_dict) #Re add all ratios that migh have been disabled
return original_v,lb,ub
def identify_free_fluxes(label_model,parameter_dict={},fraction_of_optimum=0,change_threshold=0.1,parameter_precision=0.01,max_d=0.1,key_reactions=[],original_fva=None,restore_model=False,debug=False):
precision=int(-1*(math.log10(parameter_precision)))
#print label_model.constrained_model.reactions.get_by_id("biomass").lower_bound
reaction_list=[]
original_model=copy.deepcopy(label_model.constrained_model)
#apply_parameters(label_model,parameter_dict=parameter_dict) #Paremeters will be applied with the default precision im label_model
#original_objective_list=[]
#model=label_model.constrained_model
for reaction in label_model.reactions_propagating_label:#label_model.reaction_n_dict: #Find reactions that propagate label
if reaction in label_model.merged_reactions_reactions_dict:
for merged_reaction in label_model.merged_reactions_reactions_dict[reaction]:
if "_reverse" not in merged_reaction:
reaction_list.append(merged_reaction)
elif "_reverse" not in reaction and "RATIO_" not in reaction:
reaction_list.append(reaction)
for reaction in key_reactions:
if reaction in label_model.constrained_model.reactions and reaction not in reaction_list:
reaction_list.append(reaction)
#Classify reactions
if original_fva==None:
original_fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility, reaction_list=reaction_list)
#original_mfva=flux_variability_analysis(label_model.constrained_model,reaction_list=reaction_list, fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility)
additional_parameters_list=[]
first_time=True
done_reactions=[]
for i in xrange(0,10000):
#print model.optimize()
free_ex_reactions_list=[]
free_reactions_list=[]
priortizided_reactions_list=[]
mfva=flux_variability_analysis(label_model.constrained_model,reaction_list=reaction_list, fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility)
for reaction in mfva:
"""if reaction not in label_model.reaction_emu_dict and reaction not in label_model.reaction_merged_reactions_dict:
print "Error"
continue"""
maximum=mfva[reaction]["maximum"]
minimum=mfva[reaction]["minimum"]
if abs(maximum-minimum)>change_threshold:
if reaction in key_reactions:
priortizided_reactions_list.append(reaction)
"""elif "EX_" in reaction:
free_ex_reactions_list.append(reaction)
#print free_ex_reactions_list"""
else:
free_reactions_list.append(reaction)
#print free_reactions_list
#print free_ex_reactions_list
if free_reactions_list==[] and priortizided_reactions_list==[]:
break
elif priortizided_reactions_list!=[]:
#Find the reaction with more variation allowed
max_variation=0
for reaction in priortizided_reactions_list:
variation=mfva[reaction]["maximum"]-mfva[reaction]["minimum"]
if variation>=max_variation:
max_variation=variation
reaction_id=reaction
maximum=mfva[reaction_id]["maximum"]
minimum=mfva[reaction_id]["minimum"]
original_maximum=round_down(original_fva[reaction_id]["maximum"],precision)
original_minimum=round_up(original_fva[reaction_id]["minimum"],precision)
"""elif free_ex_reactions_list!=[]:
#Find the reaction with more variation allowed
max_variation=0
for reaction in free_ex_reactions_list:
variation=mfva[reaction]["maximum"]-mfva[reaction]["minimum"]
if variation>=max_variation:
max_variation=variation
reaction_id=reaction
maximum=mfva[reaction_id]["maximum"]
minimum=mfva[reaction_id]["minimum"]
original_maximum=round(original_mfva[reaction_id]["maximum"],precision)
original_minimum=round(original_mfva[reaction_id]["minimum"],precision)"""
elif free_reactions_list!=[]:
max_variation=0
for reaction in free_reactions_list:
variation=mfva[reaction]["maximum"]-mfva[reaction]["minimum"]
if variation>=max_variation:
max_variation=variation
reaction_id=reaction
maximum=mfva[reaction_id]["maximum"]
minimum=mfva[reaction_id]["minimum"]
original_maximum=original_fva[reaction_id]["maximum"]#round_down(original_fva[reaction_id]["maximum"],precision)
original_minimum=original_fva[reaction_id]["minimum"]#round_up(original_fva[reaction_id]["minimum"],precision)
print ["max variation",max_variation]
reaction=label_model.constrained_model.reactions.get_by_id(reaction_id)
value=random.uniform(minimum,maximum)
#value=round((4*minimum+1*maximum)/5,precision) #Weighted average
parameter_dict[reaction_id]={"v":value,"lb":original_minimum,"ub":original_maximum ,"type":"flux value","reactions":[reaction_id],"max_d":max_d,"original_lb":original_model.reactions.get_by_id(reaction_id).lower_bound,"original_ub":original_model.reactions.get_by_id(reaction_id).upper_bound,"original_objective_coefficient":0.0}
apply_parameters(label_model,parameter_dict=parameter_dict,parameter_precision=parameter_precision,parameter_list=[reaction_id])
done_reactions.append(reaction_id)
if debug:
print [reaction_id,mfva[reaction_id],[reaction.lower_bound,reaction.upper_bound]]
"""reaction.lower_bound=value
reaction.upper_bound=value"""
#print([reaction.id,mfva[reaction_id],value,model.optimize()])
#print(reaction_id+" "+str(parameter_dict[reaction_id]))
print ("%s free fluxes found"%(len(done_reactions)))
"""if add_turnover==True:
for turnover in label_model.turnover_flux_dict:
if turnover not in excluded_turnovers or (turnover+"_turnover") in label_model.parameter_dict:
parameter_dict[turnover+"_turnover"]={"v":label_model.turnover_flux_dict[turnover],"lb":0,"ub":turnover_upper_bound,"max_d":max_d,"type":"turnover","reactions":[turnover]}"""
if restore_model==True:
label_model.constrained_model=original_model
return parameter_dict
flux_counter.add_metabolites(metabolites={fake:-1})
m.add_reaction(flux_counter)
m.change_objective(flux_counter)
m.reactions.get_by_id('EX_glc_e_reverse').upper_bound = 0
rxns = {r.id:r for r in m.reactions}
index = pd.MultiIndex.from_product([gc.index, ['maximum', 'minimum']])
fluxes = pd.DataFrame(index=index, columns=rxns.keys())
for i,c in enumerate(gc.index):
rxns['EX_'+gc['media_key'][c]+'_e'].lower_bound = -1000
rxns['Ec_biomass_iJO1366_WT_53p95M'].upper_bound = gc['growth rate [h-1]'][c]
rxns['Ec_biomass_iJO1366_WT_53p95M'].lower_bound = gc['growth rate [h-1]'][c]
for j, r in enumerate(m.reactions):
fva_results = flux_variability_analysis(m,reaction_list=[r],
objective_sense='minimize',fraction_of_optimum=1.0001)
fva = pd.DataFrame.from_dict(fva_results)
fluxes[r.id][c,'maximum'] = fva.loc['maximum'][0]
fluxes[r.id][c,'minimum'] = fva.loc['minimum'][0]
print c, i, j, r
rxns['EX_'+gc['media_key'][c]+'_e'].lower_bound = 0
fluxes.dropna(how='all', inplace=True)
fluxes.T.to_csv('../data/flux_variability_[mmol_gCDW_h]_01%.csv')
def test_fva_data_frame(model):
"""Test DataFrame obtained from FVA."""
df = flux_variability_analysis(model)
assert np.all([df.columns.values == ['minimum', 'maximum']])
#too slow
#mymodel_bounds = [([rec.id for rec in mymodel.reactions],
# [rec.lower_bound for rec in mymodel.reactions],
# [rec.upper_bound for rec in mymodel.reactions],
# [flux_variability_analysis(mymodel, reaction_list=rec.id, pfba_factor=1.1) for rec in mymodel.reactions])]
mymodel_flux_minimum = []
mymodel_flux_maximum = []
mymodel_upper_bound = []
mymodel_lower_bound = []
bar = myprogressbar(maxvalue=len(mymodel.reactions))
bar.start()
for i in range(len(mymodel.reactions)):
mymodel_lower_bound.append(mymodel.reactions[i].lower_bound)
mymodel_upper_bound.append(mymodel.reactions[i].upper_bound)
bounds = flux_variability_analysis(mymodel,
reaction_list=mymodel.reactions[i],
pfba_factor=1.1)
mymodel_flux_maximum.append(bounds.loc[mymodel.reactions[i].id, 'maximum'])
mymodel_flux_minimum.append(bounds.loc[mymodel.reactions[i].id, 'minimum'])
bar.update(i + 1)
bar.finish()
mymodel_bounds = zip(mymodel_flux_minimum, mymodel_flux_maximum,
mymodel_lower_bound, mymodel_upper_bound)
import csv
with open("mitocore_bounds.csv", 'w', newline='') as out:
csv_out = csv.writer(out)
csv_out.writerow(
['Minimum Flux', 'Maximum Flux', 'Lower_bound', 'Upper_bound'])
def test_fva_data_frame(model):
"""Test DataFrame obtained from FVA."""
df = flux_variability_analysis(model)
assert np.all([df.columns.values == ['minimum', 'maximum']])
def identify_free_parameters(label_model,parameter_dict={},n_samples=50,add_to_model=True,parameter_precision=1e-5,change_threshold=1e-3,fraction_of_optimum=0,max_d=0.1,key_reactions=[],add_turnover=True,excluded_turnovers=[],turnover_upper_bound=None,debug=True):
free_parameters=copy.deepcopy(parameter_dict)
precision=int(-1*(math.log10(parameter_precision)))
apply_parameters(label_model,parameter_dict=free_parameters)
for reaction in label_model.constrained_model.objective:
fva=flux_variability_analysis(label_model.constrained_model,reaction_list=[reaction], fraction_of_optimum=fraction_of_optimum,tolerance_feasibility=label_model.lp_tolerance_feasibility)
print fva
minimum=round_down(fva[reaction.id]["minimum"],precision)#max(round_up(fva[reaction.id]["minimum"],precision),reaction.lower_bound)
maximum=round_up(fva[reaction.id]["maximum"],precision)#min(round_down(fva[reaction.id]["maximum"],precision),reaction.upper_bound)
value=((minimum+maximum)/2)
#original_objective_list.append(reaction)
free_parameters[reaction.id]={"v":value,"lb":minimum,"ub":maximum ,"type":"flux value","reactions":[reaction.id],"max_d":max_d,"original_lb":reaction.lower_bound,"original_ub":reaction.upper_bound,"original_objective_coefficient":reaction.objective_coefficient}
reaction.lower_bound=minimum
reaction.upper_bound=maximum
reaction.objective_coefficient=0
#print [model.optimize(),minimum,maximum]
original_fva=flux_variability_analysis(label_model.constrained_model,fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility)
#print free_parameters
apply_parameters(label_model,parameter_dict=free_parameters,parameter_precision=parameter_precision)
#print model.optimize()
for i in range(0,n_samples):
print "sample "+str(i)
try:
#original_model=copy.deepcopy(label_model.constrained_model)
free_parameters=identify_free_fluxes(label_model,parameter_dict=free_parameters,fraction_of_optimum=fraction_of_optimum ,change_threshold=change_threshold, parameter_precision=parameter_precision,max_d=max_d,key_reactions=key_reactions,original_fva=original_fva,debug=debug)
flux_value_parameter_list=[]
for parameter in free_parameters:
local_parameter_dict=free_parameters[parameter]
if local_parameter_dict["type"]=="flux value":
flux_value_parameter_list.append(parameter)
#Save the original lower and upper_bounds and turnover
#Generate the samples
for parameter in flux_value_parameter_list:
for reaction_id in free_parameters[parameter]["reactions"]:
reaction=label_model.constrained_model.reactions.get_by_id(reaction_id)
reaction.lower_bound=max(free_parameters[parameter]["original_lb"],free_parameters[parameter]["lb"])
reaction.upper_bound=min(free_parameters[parameter]["original_ub"],free_parameters[parameter]["ub"])
working_flux_value_parameter_list=random.sample(flux_value_parameter_list, len(flux_value_parameter_list))
for reaction_id in working_flux_value_parameter_list:
fva=flux_variability_analysis(label_model.constrained_model, reaction_list=[reaction_id],fraction_of_optimum=0,tolerance_feasibility=label_model.lp_tolerance_feasibility)
#if fva[reaction_id]["maximum"]-fva[reaction_id]["minimum"]>parameter_precision:
new_value=random.uniform(fva[reaction_id]["minimum"],fva[reaction_id]["maximum"])
free_parameters[reaction_id]["v"]=new_value
apply_parameters(label_model,parameter_dict=free_parameters,parameter_precision=parameter_precision,parameter_list=[reaction_id])
reaction=label_model.constrained_model.reactions.get_by_id(reaction_id)
#print[fva,new_value,[reaction.lower_bound,reaction.upper_bound],label_model.constrained_model.optimize()]
except:
print "Error caught"
continue
if add_turnover==True:
"""if turnover_upper_bound==None:
turnover_upper_bound=0
for reaction_id in label_model.reaction_n_dict:
if reaction_id in original_fva:
turnover_upper_bound=max(original_fva[reaction_id]["maximum"],turnover_upper_bound)
turnover_upper_bound=round_up(turnover_upper_bound,0)"""
for turnover in label_model.turnover_flux_dict:
if turnover not in excluded_turnovers and (turnover+"_turnover") not in free_parameters and label_model.turnover_flux_dict[turnover]["ub"]!=label_model.turnover_flux_dict[turnover]["lb"]:
ub=label_model.turnover_flux_dict[turnover]["ub"]
lb=label_model.turnover_flux_dict[turnover]["lb"]
v=label_model.turnover_flux_dict[turnover]["v"]
free_parameters[turnover+"_turnover"]={"v":v,"lb":lb,"ub":ub,"max_d":max_d,"type":"turnover","reactions":[turnover]}
if add_to_model==True:
label_model.parameter_dict=free_parameters
apply_parameters(label_model,parameter_dict=free_parameters)
else:
clear_parameters(label_model,parameter_dict=copy.deepcopy(free_parameters))
return free_parameters
def model_summary(model, threshold=1E-8, fva=None, floatfmt='.3g',
**solver_args):
"""Print a summary of the input and output fluxes of the model.
threshold: float
tolerance for determining if a flux is zero (not printed)
fva: int or None
Whether or not to calculate and report flux variability in the
output summary
floatfmt: string
format method for floats, passed to tabulate. Default is '.3g'.
"""
legacy, _ = choose_solver(model, solver=solver_args.get("solver"))
if legacy:
raise NotImplementedError(
"Summary support for legacy solvers was removed.")
# Create a dataframe of objective fluxes
objective_reactions = linear_reaction_coefficients(model)
obj_fluxes = pd.DataFrame({key: key.flux * value for key, value in
iteritems(objective_reactions)},
index=['flux']).T
obj_fluxes['id'] = obj_fluxes.apply(
lambda x: format_long_string(x.name.id, 15), 1)
# Build a dictionary of metabolite production from the boundary reactions
# collect rxn.flux before fva which invalidates previous solver state
boundary_reactions = model.exchanges
metabolite_fluxes = {}
for rxn in boundary_reactions:
for met, stoich in iteritems(rxn.metabolites):
metabolite_fluxes[met] = {
'id': format_long_string(met.id, 15),
'flux': stoich * rxn.flux}
# Calculate FVA results if requested
if fva:
fva_results = flux_variability_analysis(
model, reaction_list=boundary_reactions, fraction_of_optimum=fva,
**solver_args)
for rxn in boundary_reactions:
for met, stoich in iteritems(rxn.metabolites):
imin = stoich * fva_results.loc[rxn.id]['minimum']
imax = stoich * fva_results.loc[rxn.id]['maximum']
# Correct 'max' and 'min' for negative values
metabolite_fluxes[met].update({
'fmin': imin if abs(imin) <= abs(imax) else imax,
'fmax': imax if abs(imin) <= abs(imax) else imin,
})
# Generate a dataframe of boundary fluxes
metabolite_fluxes = pd.DataFrame(metabolite_fluxes).T
metabolite_fluxes = _process_flux_dataframe(
metabolite_fluxes, fva, threshold, floatfmt)
# Begin building string output table
def get_str_table(species_df, fva=False):
"""Formats a string table for each column"""
if not fva:
return tabulate(species_df.loc[:, ['id', 'flux']].values,
floatfmt=floatfmt, tablefmt='plain').split('\n')
else:
return tabulate(
species_df.loc[:, ['id', 'flux', 'fva_fmt']].values,
floatfmt=floatfmt, tablefmt='simple',
headers=['id', 'Flux', 'Range']).split('\n')
in_table = get_str_table(
metabolite_fluxes[metabolite_fluxes.is_input], fva=fva)
out_table = get_str_table(
metabolite_fluxes[~metabolite_fluxes.is_input], fva=fva)
obj_table = get_str_table(obj_fluxes, fva=False)
# Print nested output table
print_(tabulate(
[entries for entries in zip_longest(in_table, out_table, obj_table)],
headers=['IN FLUXES', 'OUT FLUXES', 'OBJECTIVES'], tablefmt='simple'))
def remove_impossible_emus(label_model):
"""
Removes emus that cannot with labelled substrates used in the experiment
"""
#size_expanded_model2_dict={}
possible_isotopomers=[]
impossible_isotopomers=[]
f=open("impossible_isotopologues","w")
#If some metabolites are marked as input but have no label described assume they all are m0 and add them to initial label
for isotopomer in label_model.isotopomer_object_list:
if isotopomer.input==True:
if isotopomer.id not in label_model.metabolite_emu_dict:
continue
emus=label_model.metabolite_emu_dict[isotopomer.id]
for condition in label_model.initial_label:
present=False
for emu in emus:
for mi in label_model.emu_dict[emu]["mid"]:
if label_model.emu_dict[emu]["mid"][mi] in label_model.initial_label[condition]:
present=True
break
if not present:
for mi in label_model.emu_dict[emu]["mid"]:
if mi==0:
label_model.initial_label[condition][label_model.emu_dict[emu]["mid"][mi]]=1
else:
label_model.initial_label[condition][label_model.emu_dict[emu]["mid"][mi]]=0
present_initial_label=[]
for condition in label_model.initial_label: #For simplicity the label model is shared across all conditions
for label in label_model.initial_label[condition]:
if label_model.initial_label[condition][label]>0.0:
if label not in present_initial_label:
present_initial_label.append(label)
for model_size in sorted(label_model.size_expanded_model_dict.keys()): #needs to be done in order of size
expanded_emu_model=label_model.size_expanded_model_dict[model_size]
label_exchange_reactions=[]
for metabolite in expanded_emu_model.metabolites:
reaction = Reaction("EX_"+metabolite.id)
reaction.name = 'EX'+metabolite.name
reaction.subsystem = 'Test'
reaction.lower_bound = 0
reaction.upper_bound = 1000. # This is the default
reaction.add_metabolites({metabolite:-1})
expanded_emu_model.add_reaction(reaction)
label_exchange_reactions.append(reaction)
for condition in label_model.initial_label: #For simplicity the label model is shared across all conditions
for label in label_model.initial_label[condition]:
ex_id="EX_"+label
if ex_id in expanded_emu_model.reactions:
if label in present_initial_label:
expanded_emu_model.reactions.get_by_id(ex_id).lower_bound=-1000
else:
expanded_emu_model.reactions.get_by_id(ex_id).lower_bound=0
expanded_emu_model.reactions.get_by_id(ex_id).upper_bound=0
"""for isotopomer in label_model.isotopomer_object_list:
if isotopomer.input==True:
if isotopomer.id in label_model.metabolite_emu_dict:
for emu in label_model.metabolite_emu_dict[isotopomer.id]:
mid0=label_model.emu_dict[emu]["mid"][0]
ex_id="EX_"+mid0
if ex_id in expanded_emu_model.reactions:
expanded_emu_model.reactions.get_by_id(ex_id).lower_bound=-1000"""
"""if model_size==1.0:
import cobra
cobra.io.write_sbml_model(expanded_emu_model,"size1.sbml")"""
for isotopomer in possible_isotopomers: #If a isotopomer is produced in a smaller size it should appear as present in this size
ex_id="EX_"+isotopomer
if ex_id in expanded_emu_model.reactions:
#print ex_id
expanded_emu_model.reactions.get_by_id(ex_id).lower_bound=-1000
fva=flux_variability_analysis(expanded_emu_model,reaction_list=label_exchange_reactions,fraction_of_optimum=0)
for x in label_exchange_reactions:
if fva[x.id]["maximum"]>0.00001 or fva[x.id]["minimum"]<-0.00001 :
if x.id[3:] not in possible_isotopomers: #prevent repeated entries
possible_isotopomers.append(x.id[3:])
#print x.id
else:
if x.id[3:] not in impossible_isotopomers: #prevent repeated entries
impossible_isotopomers.append(x.id[3:])
#print x.id
#Remove reactions where those isotopomers participate:
reactions_to_remove=[]
for x in label_exchange_reactions:
x.remove_from_model(remove_orphans=True)
impossible_isotopomers_object_list=[]
for isotopomer_id in impossible_isotopomers:
if isotopomer_id not in expanded_emu_model.metabolites:
continue
isotopomer=expanded_emu_model.metabolites.get_by_id(isotopomer_id)
for x in isotopomer._reaction:
if x not in reactions_to_remove and x.metabolites[isotopomer]<1:
reactions_to_remove.append(x)
isotopomer.remove_from_model(method='subtractive')
for reaction in expanded_emu_model.reactions:
if len(reaction.metabolites)==0 and reaction not in reactions_to_remove:
reactions_to_remove.append(reaction)
for reaction in reactions_to_remove:
print "removing "+reaction.id
f.write("removing "+reaction.id+"\n")
emu_reaction=label_model.expanded_reaction_emu_dict[reaction.id]
label_model.emu_reaction_expanded_dict[emu_reaction].remove(reaction.id)
del label_model.expanded_reaction_emu_dict[reaction.id]
reaction.remove_from_model(remove_orphans=True)
#size_expanded_model2_dict[model_size]=expanded_emu_model
#labek_model=size_expanded_model_dict[model_size]=expanded_emu_model
for emu in impossible_isotopomers:
f.write(emu+"\n")
f.close()
#Remove the reactions where only m0 is transmited and replace them with input reactions
remove_m0_reactions(label_model,impossible_isotopomers,possible_isotopomers)
return label_model.size_expanded_model_dict
def metabolite_summary(met, threshold=0.01, fva=False, floatfmt='.3g',
**solver_args):
"""Print a summary of the reactions which produce and consume this
metabolite
threshold: float
a value below which to ignore reaction fluxes
fva: float (0->1), or None
Whether or not to include flux variability analysis in the output.
If given, fva should be a float between 0 and 1, representing the
fraction of the optimum objective to be searched.
floatfmt: string
format method for floats, passed to tabulate. Default is '.3g'.
"""
rxn_id = list()
flux = list()
reaction = list()
for rxn in met.reactions:
rxn_id.append(format_long_string(rxn.id, 10))
flux.append(rxn.flux * rxn.metabolites[met])
reaction.append(format_long_string(rxn.reaction, 40 if fva else 50))
flux_summary = pd.DataFrame(data={
"id": rxn_id, "flux": flux, "reaction": reaction})
if fva:
fva_results = flux_variability_analysis(
met.model, met.reactions, fraction_of_optimum=fva,
**solver_args)
flux_summary.index = flux_summary["id"]
flux_summary["maximum"] = zeros(len(rxn_id))
flux_summary["minimum"] = zeros(len(rxn_id))
for rid, rxn in zip(rxn_id, met.reactions):
imax = rxn.metabolites[met] * fva_results.loc[rxn.id, "maximum"]
imin = rxn.metabolites[met] * fva_results.loc[rxn.id, "minimum"]
flux_summary.loc[rid, "fmax"] = (imax if abs(imin) <= abs(imax)
else imin)
flux_summary.loc[rid, "fmin"] = (imin if abs(imin) <= abs(imax)
else imax)
assert flux_summary.flux.sum() < 1E-6, "Error in flux balance"
flux_summary = _process_flux_dataframe(flux_summary, fva, threshold,
floatfmt)
flux_summary['percent'] = 0
total_flux = flux_summary[flux_summary.is_input].flux.sum()
flux_summary.loc[flux_summary.is_input, 'percent'] = \
flux_summary.loc[flux_summary.is_input, 'flux'] / total_flux
flux_summary.loc[~flux_summary.is_input, 'percent'] = \
flux_summary.loc[~flux_summary.is_input, 'flux'] / total_flux
flux_summary['percent'] = flux_summary.percent.apply(
lambda x: '{:.0%}'.format(x))
if fva:
flux_table = tabulate(
flux_summary.loc[:, ['percent', 'flux', 'fva_fmt', 'id',
'reaction']].values, floatfmt=floatfmt,
headers=['%', 'FLUX', 'RANGE', 'RXN ID', 'REACTION']).split('\n')
else:
flux_table = tabulate(
flux_summary.loc[:, ['percent', 'flux', 'id', 'reaction']].values,
floatfmt=floatfmt, headers=['%', 'FLUX', 'RXN ID', 'REACTION']
).split('\n')
flux_table_head = flux_table[:2]
met_tag = "{0} ({1})".format(format_long_string(met.name, 45),
format_long_string(met.id, 10))
head = "PRODUCING REACTIONS -- " + met_tag
print_(head)
print_("-" * len(head))
print_('\n'.join(flux_table_head))
print_('\n'.join(
pd.np.array(flux_table[2:])[flux_summary.is_input.values]))
print_()
print_("CONSUMING REACTIONS -- " + met_tag)
print_("-" * len(head))
print_('\n'.join(flux_table_head))
print_('\n'.join(
pd.np.array(flux_table[2:])[~flux_summary.is_input.values]))
def geometric_fba(model, epsilon=1E-06, max_tries=200):
"""
Perform geometric FBA to obtain a unique, centered flux distribution.
Geometric FBA [1]_ formulates the problem as a polyhedron and
then solves it by bounding the convex hull of the polyhedron.
The bounding forms a box around the convex hull which reduces
with every iteration and extracts a unique solution in this way.
Parameters
----------
model: cobra.Model
The model to perform geometric FBA on.
epsilon: float, optional
The convergence tolerance of the model (default 1E-06).
max_tries: int, optional
Maximum number of iterations (default 200).
Returns
-------
cobra.Solution
The solution object containing all the constraints required
for geometric FBA.
References
----------
.. [1] Smallbone, Kieran & Simeonidis, Vangelis. (2009).
Flux balance analysis: A geometric perspective.
Journal of theoretical biology.258. 311-5.
10.1016/j.jtbi.2009.01.027.
"""
with model:
# Variables' and constraints' storage variables.
consts = []
obj_vars = []
updating_vars_cons = []
# The first iteration.
prob = model.problem
add_pfba(model) # Minimize the solution space to a convex hull.
model.optimize()
fva_sol = flux_variability_analysis(model)
mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2
# Set the gFBA constraints.
for rxn in model.reactions:
var = prob.Variable("geometric_fba_" + rxn.id,
lb=0,
ub=mean_flux[rxn.id])
upper_const = prob.Constraint(rxn.flux_expression - var,
ub=mean_flux[rxn.id],
name="geometric_fba_upper_const_" +
rxn.id)
lower_const = prob.Constraint(rxn.flux_expression + var,
lb=fva_sol.at[rxn.id, "minimum"],
name="geometric_fba_lower_const_" +
rxn.id)
updating_vars_cons.append((rxn.id, var, upper_const, lower_const))
consts.extend([var, upper_const, lower_const])
obj_vars.append(var)
model.add_cons_vars(consts)
# Minimize the distance between the flux distribution and center.
model.objective = prob.Objective(Zero, sloppy=True, direction="min")
model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars})
# Update loop variables.
sol = model.optimize()
fva_sol = flux_variability_analysis(model)
mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2
delta = (fva_sol["maximum"] - fva_sol["minimum"]).max()
count = 1
LOGGER.debug("Iteration: %d; delta: %.3g; status: %s.", count, delta,
sol.status)
# Following iterations that minimize the distance below threshold.
while delta > epsilon and count < max_tries:
for rxn_id, var, u_c, l_c in updating_vars_cons:
var.ub = mean_flux[rxn_id]
u_c.ub = mean_flux[rxn_id]
l_c.lb = fva_sol.at[rxn_id, "minimum"]
# Update loop variables.
sol = model.optimize()
fva_sol = flux_variability_analysis(model)
mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2
delta = (fva_sol["maximum"] - fva_sol["minimum"]).max()
count += 1
LOGGER.debug("Iteration: %d; delta: %.3g; status: %s.", count,
delta, sol.status)
if count == max_tries:
raise RuntimeError(
"The iterations have exceeded the maximum value of {}. "
"This is probably due to the increased complexity of the "
"model and can lead to inaccurate results. Please set a "
"different convergence tolerance and/or increase the "
"maximum iterations".format(max_tries))
return sol
def model_summary(model, solution=None, threshold=0.01, fva=None,
floatfmt='.3g'):
"""Print a summary of the input and output fluxes of the model.
solution : cobra.core.Solution
A previously solved model solution to use for generating the
summary. If none provided (default), the summary method will resolve
the model. Note that the solution object must match the model, i.e.,
changes to the model such as changed bounds, added or removed
reactions are not taken into account by this method.
threshold : float
a percentage value of the maximum flux below which to ignore reaction
fluxes
fva : int or None
Whether or not to calculate and report flux variability in the
output summary
floatfmt : string
format method for floats, passed to tabulate. Default is '.3g'.
"""
objective_reactions = linear_reaction_coefficients(model)
boundary_reactions = model.exchanges
summary_rxns = set(objective_reactions.keys()).union(boundary_reactions)
if solution is None:
model.slim_optimize(error_value=None)
solution = get_solution(model, reactions=summary_rxns)
# Create a dataframe of objective fluxes
obj_fluxes = pd.DataFrame({key: solution.fluxes[key.id] * value for key,
value in iteritems(objective_reactions)},
index=['flux']).T
obj_fluxes['id'] = obj_fluxes.apply(
lambda x: format_long_string(x.name.id, 15), 1)
# Build a dictionary of metabolite production from the boundary reactions
metabolite_fluxes = {}
for rxn in boundary_reactions:
for met, stoich in iteritems(rxn.metabolites):
metabolite_fluxes[met] = {
'id': format_long_string(met.id, 15),
'flux': stoich * solution.fluxes[rxn.id]}
# Calculate FVA results if requested
if fva:
fva_results = flux_variability_analysis(
model, reaction_list=boundary_reactions, fraction_of_optimum=fva)
for rxn in boundary_reactions:
for met, stoich in iteritems(rxn.metabolites):
imin = stoich * fva_results.loc[rxn.id]['minimum']
imax = stoich * fva_results.loc[rxn.id]['maximum']
# Correct 'max' and 'min' for negative values
metabolite_fluxes[met].update({
'fmin': imin if abs(imin) <= abs(imax) else imax,
'fmax': imax if abs(imin) <= abs(imax) else imin,
})
# Generate a dataframe of boundary fluxes
metabolite_fluxes = pd.DataFrame(metabolite_fluxes).T
metabolite_fluxes = _process_flux_dataframe(
metabolite_fluxes, fva, threshold, floatfmt)
# Begin building string output table
def get_str_table(species_df, fva=False):
"""Formats a string table for each column"""
if not fva:
return tabulate(species_df.loc[:, ['id', 'flux']].values,
floatfmt=floatfmt, tablefmt='plain').split('\n')
else:
return tabulate(
species_df.loc[:, ['id', 'flux', 'fva_fmt']].values,
floatfmt=floatfmt, tablefmt='simple',
headers=['id', 'Flux', 'Range']).split('\n')
in_table = get_str_table(
metabolite_fluxes[metabolite_fluxes.is_input], fva=fva)
out_table = get_str_table(
metabolite_fluxes[~metabolite_fluxes.is_input], fva=fva)
obj_table = get_str_table(obj_fluxes, fva=False)
# Print nested output table
print_(tabulate(
[entries for entries in zip_longest(in_table, out_table, obj_table)],
headers=['IN FLUXES', 'OUT FLUXES', 'OBJECTIVES'], tablefmt='simple'))
