def run_menecheck(draft_sbml, seeds_sbml, targets_sbml):
print('Reading draft network from ', draft_sbml, '...', end=' ')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end=' ')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
print('done.')
print('Reading targets from ', targets_sbml, '...', end=' ')
sys.stdout.flush()
targets = sbml.readSBMLspecies(targets_sbml, 'target')
print('done.')
print('\nChecking draftnet for unproducible targets ...', end=' ')
sys.stdout.flush()
model = query.get_unproducible(draftnet, targets, seeds)
# print(model)
print('done.')
#utils.print_met(model.to_list())
unprod = []
prod = []
for a in model:
if a.pred() == 'unproducible_target':
unprod.append(a.arg(0))
elif a.pred() == 'producible_target':
prod.append(a.arg(0))
print(str(len(prod)), 'producible targets:')
print(*prod, sep='\n')
print('\n')
print(str(len(unprod)), 'unproducible targets:')
print(*unprod, sep='\n')
utils.clean_up()
return model, unprod, prod
def run_menescope(draft_sbml, seeds_sbml):
print('Reading draft network from ', draft_sbml, '...', end='')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
#print(draftnet)
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end='')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
#print(seeds)
print('done.')
#seeds.to_file("seeds.lp")
print('\nChecking draft network scope ...', end='')
sys.stdout.flush()
model = query.get_scope(draftnet, seeds)
print('done.')
print(' ', len(model), 'compounds on scope:')
utils.print_met(model.to_list())
utils.clean_up()
return model
help="seeds in SBML format",
required=True)
args = parser.parse_args()
draft_sbml = args.draftnet
#repair_sbml = args.repairnetwork
seeds_sbml = args.seeds
print('Reading draft network from ', draft_sbml, '...', end='')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
#print(draftnet)
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end='')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
#print(seeds)
print('done.')
#seeds.to_file("seeds.lp")
print('\nChecking draft network scope ...', end='')
sys.stdout.flush()
model = query.get_scope(draftnet, seeds)
print('done.')
print(' ', len(model), 'compounds on scope:')
utils.print_met(model.to_list())
utils.clean_up()
quit()
seeds_sbml = args.seeds
targets_sbml = args.targets
cofactors_txt = args.cofactors
weights = args.weight
suffix = args.suffix
enumerate = args.enumerate
print('Reading draft network from ', draft_sbml, '...', end='')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
#print(draftnet)
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end='')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
#print(seeds)
print('done.')
#seeds.to_file("seeds.lp")
print('Reading targets from ', targets_sbml, '...', end='')
sys.stdout.flush()
targets = sbml.readSBMLspecies(targets_sbml, 'target')
#print(targets)
print('done.')
#targets.to_file("targets.lp")
if weights and cofactors_txt:
print('Reading cofactors with weights from ',
cofactors_txt,
'...',
def run_menecof(draft_sbml,seeds_sbml,targets_sbml,cofactors_txt=None,weights=None,suffix=None,enumeration=None):
print('Reading draft network from ', draft_sbml, '...', end='')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
#print(draftnet)
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end='')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
#print(seeds)
print('done.')
#seeds.to_file("seeds.lp")
print('Reading targets from ', targets_sbml, '...', end='')
sys.stdout.flush()
targets = sbml.readSBMLspecies(targets_sbml, 'target')
#print(targets)
print('done.')
#targets.to_file("targets.lp")
if weights and cofactors_txt:
print('Reading cofactors with weights from ', cofactors_txt, '...', end='')
sys.stdout.flush()
with open(cofactors_txt,'r') as f:
cofactors_list = f.read().splitlines()
cofactors = TermSet()
for elem in cofactors_list:
try:
data = elem.split('\t')
if suffix != None:
cofactors.add(Term('cofactor', ["\""+convert_to_coded_id(data[0]) + suffix + "\"", data[1]]))
else:
cofactors.add(Term('cofactor', ["\""+convert_to_coded_id(data[0]) + "\"", data[1]]))
except:
print('Input cofactor file is not tabulated (at least not on every line)\
\n Please check the file, maybe you did not mean to use --weight option?\
\nUnsuitable input file... Quitting program')
quit()
#print(cofactors)
print('done.')
#cofactors.to_file("cofactors.lp")
elif cofactors_txt:
print('Reading cofactors from ', cofactors_txt)
with open(cofactors_txt,'r') as f:
cofactors_list = f.read().splitlines()
cofactors = TermSet()
for elem in cofactors_list:
if '\t' in elem:
print('A tabulated file was given as input cofactors. \
\nAre you sure you did not mean to use the weight option? \
\nUnsuitable input file... Quitting program')
quit()
if suffix != None:
cofactors.add(Term('cofactor', ["\""+convert_to_coded_id(elem) + suffix + "\""]))
else:
cofactors.add(Term('cofactor', ["\""+convert_to_coded_id(elem) + "\""]))
#print(cofactors)
print('done.')
#cofactors.to_file("cofactors.lp")
else:
print('No cofactor file is given as input.')
print('Research of cofactors will be done in the network itself')
species_and_weights = sbml.make_weighted_list_of_species(draft_sbml)
cofactors = TermSet()
for elem in species_and_weights:
cofactors.add(Term('cofactor', ["\""+elem+"\"", species_and_weights[elem]]))
weights = True
print('\nChecking draft network for unproducible targets before cofactors selection ...', end='')
sys.stdout.flush()
model = query.get_unproducible(draftnet, targets, seeds)
unprod = [p.arg(0) for p in model if p.pred() == 'unproducible_target']
print('done.')
print(' ',len(unprod),'unproducible targets:')
utils.print_met(model.to_list())
print('\nChecking minimal sets of cofactors to produce all targets ...', end='')
sys.stdout.flush()
if weights:
model = query.get_cofs_weighted(draftnet, targets, seeds, cofactors)
optimum = model.score
if len(optimum) == 2:
# it means that all targets can be produced with the selected cofactors
optimum = [0] + optimum
optimum = ','.join(map(str, optimum))
#print(optimum)
else:
model = query.get_cofs(draftnet, targets, seeds, cofactors)
optimum = model.score
if len(optimum) == 1:
# it means that all targets can be produced with the selected cofactors
optimum = [0] + optimum
optimum = ','.join(map(str, optimum))
#print('done.')
print('Optimum score {}'.format(optimum))
solumodel = model.to_list()
unproduced_targets = []
chosen_cofactors = []
newly_producible_targets = []
for p in solumodel:
if p.pred() == "needed_cof":
cof = p.arg(0)
try:
weight = p.arg(1)
except:
weight = None
chosen_cofactors.append((cof,weight))
elif p.pred() == "still_unprod":
tgt = p.arg(0)
unproduced_targets.append(tgt)
else:
newly_producible_targets.append(p.arg(0))
print('Still '+ str(len(unproduced_targets)) + ' unproducible targets:')
print(*unproduced_targets, sep='\n')
print('\nSelected cofactors:')
for cofactor in chosen_cofactors:
if cofactor[1] == None:
print(cofactor[0])
else:
print(cofactor[0] + ' (' + cofactor[1] + ')')
print('\n' + str(len(newly_producible_targets))+' newly producible targets:')
print(str(newly_producible_targets))
print('\nIntersection of solutions') # with size', optimum, '
intersection_model = query.get_intersection_of_optimal_solutions_cof(draftnet, seeds, targets, cofactors, optimum, weights)
solumodel = intersection_model.to_list()
intersection_icofactors = []
for p in solumodel:
if p.pred() == "needed_cof":
cof = p.arg(0)
try:
weight = p.arg(1)
except:
weight = None
intersection_icofactors.append((cof,weight))
#print('\nSelected cofactors:')
for cofactor in intersection_icofactors:
if cofactor[1] == None:
print(cofactor[0])
else:
print(cofactor[0] + ' (' + cofactor[1] + ')')
print('\nUnion of solutions') # with size', optimum, '
union_model = query.get_union_of_optimal_solutions_cof(draftnet, seeds, targets, cofactors, optimum, weights)
solumodel = union_model.to_list()
union_icofactors = []
for p in solumodel:
if p.pred() == "needed_cof":
cof = p.arg(0)
try:
weight = p.arg(1)
except:
weight = None
union_icofactors.append((cof,weight))
#print('\nSelected cofactors:')
for cofactor in union_icofactors:
if cofactor[1] == None:
print(cofactor[0])
else:
print(cofactor[0] + ' (' + cofactor[1] + ')')
if enumeration:
print('\nComputing all completions with size ',optimum)
models = query.get_optimal_solutions_cof(draftnet, seeds, targets, cofactors, optimum, weights)
count = 1
for model in models:
print('\nSolution '+str(count)+':')
count+=1
ccofactors = []
solumodel = model.to_list()
for p in solumodel:
if p.pred() == "needed_cof":
cof = p.arg(0)
try:
weight = p.arg(1)
except:
weight = None
ccofactors.append((cof,weight))
#print('\nSelected cofactors:')
for cofactor in ccofactors:
if cofactor[1] == None:
print(cofactor[0])
else:
print(cofactor[0] + ' (' + cofactor[1] + ')')
utils.clean_up()
return models, optimum, union_icofactors, intersection_icofactors, chosen_cofactors, unprod, newly_producible_targets
utils.clean_up()
return model, optimum, union_icofactors, intersection_icofactors, chosen_cofactors, unprod, newly_producible_targets
def run_menepath(draft_sbml,
seeds_sbml,
targets_sbml,
min_size=None,
enumeration=None):
print('Reading draft network from ', draft_sbml, '...', end='')
sys.stdout.flush()
draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft')
#print(draftnet)
print('done.')
print('Reading seeds from ', seeds_sbml, '...', end='')
sys.stdout.flush()
seeds = sbml.readSBMLspecies(seeds_sbml, 'seed')
#print(seeds)
print('done.')
#seeds.to_file("seeds.lp")
print('Reading targets from ', targets_sbml, '...', end='')
sys.stdout.flush()
targets = sbml.readSBMLspecies(targets_sbml, 'target')
#print(targets)
print('done.')
#seeds.to_file("targets.lp")
print('\nChecking network for unproducible targets ...', end=' ')
sys.stdout.flush()
model = query.get_unproducible(draftnet, targets, seeds)
print('done.')
unproducible_targets = TermSet()
producible_targets = TermSet()
#print(model)
for p in model:
if p.pred() == "unproducible_target":
tgt = p.arg(0)
#print(tgt)
unproducible_targets.add(Term('unproducible_targets', [tgt]))
elif p.pred() == "producible_target":
tgt = p.arg(0)
producible_targets.add(Term('target', [tgt]))
#producible_targets = TermSet(producible_targets.union(t))
print(' ', len(unproducible_targets), 'unproducible targets:')
utils.print_met(model.to_list())
#print(producible_targets)
for t in producible_targets:
print('\n')
print(t)
single_target = TermSet()
single_target.add(t)
draftfact = String2TermSet('draft("draft")')
lp_instance = TermSet(
draftnet.union(draftfact).union(single_target).union(seeds))
# one solution, minimal or not, depending on option
if min_size:
print(
'\nComputing one solution of cardinality-minimal production paths for ',
t)
else:
print(
'\nComputing one solution of production paths for',
t,
)
one_model = query.get_paths(lp_instance, min_size)
optimum = one_model.score
print('Solution size ', len(one_model), ' reactions')
utils.print_met(one_model.to_list())
# union of solutions
if min_size:
print(
'\nComputing union of cardinality-minimal production paths for ',
t)
else:
print(
'\nComputing union of production paths for',
t,
)
union = query.get_union_of_paths(lp_instance, optimum, min_size)
print('Union size ', len(union), ' reactions')
utils.print_met(union.to_list())
# intersection of solutions
if min_size:
print(
'\nComputing intersection of cardinality-minimal production paths for ',
t)
else:
print(
'\nComputing intersection of production paths for',
t,
)
intersection = query.get_intersection_of_paths(lp_instance, optimum,
min_size)
print('Intersection size (essential reactions) ', len(intersection),
' reactions')
utils.print_met(intersection.to_list())
# if wanted, get union of all solutions
if enumeration:
if min_size:
print(
'\nComputing all cardinality-minimal production paths for ',
t, ' - ', optimum)
else:
print('\nComputing all production paths for ', t, ' - ',
optimum)
all_models = query.get_all_paths(lp_instance, optimum, min_size)
count = 1
for model in all_models:
print('\nSolution ' + str(count) + ' of size :' +
str(len(model)) + ' reactions:')
count += 1
utils.print_met(model.to_list())
utils.clean_up()
return all_models, unproducible_targets, one_model, union, intersection
utils.clean_up()
return model, unproducible_targets, one_model, union, intersection