def build_wil(self, withdb = False, verbose=False):
if self.start_type.upper() == "YAML":
self.model.buildfromyaml(self.infile,self.exchangefname)
self.model.setbiomass(self.biomassfname)
self.model.convertMet2KeggIDs()
self.model.addTransports()
self.model.writeWil(self.outprefix + "Working.wil")
self.write = True
elif self.start_type.upper() == "SBML" or self.start_type.upper() == "SEED":
self.model.read_sbml(self.infile)
self.model.convertMet2KeggIDs() #adds "M_" to metabolite names
name, reversible, notes, equation = self.model.REACTIONS[biomassReactionID]
self.model.add_reaction("R_biomass_target", "BiomassRxn", False, notes, equation)
self.model.delete_reaction(biomassReactionID)
self.model.addTransports()
self.model.writeWil(self.outprefix + "Working.wil")
self.write = True
elif self.start_type.upper() == "TEXT" or self.start_type.upper() == "TXT":
self.model.build_from_textfiles(modelfile=self.infile, exchangesfile=self.exchangefname, biomassfile=self.biomassfname, sourcesfile=self.sourcesfname, escapesfile=self.escapesfname)
self.model.convertMet2KeggIDs()
self.model.addTransports()
self.model.writeWil(self.outprefix + "Working.wil")
self.model.writeWil(self.outprefix + "Stable.wil")
self.write = False
del self.model
self.model = mg.gurobicb()
w2m.wil2metmodel(self.wilfname)
self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes",exchangesfile="")
if self.write:
self.model.writeWil(self.outprefix + "Working.wil")
self.model.writeWil(self.outprefix + "Stable.wil")
self.model.solve(verbose=verbose,out=self.outprefix + 'draft')
if self.model.OBJECTIVE_VALUE < self.epsilon:
model2 = deepcopy(self.model)
if withdb:
model2.fbagapdb(out=self.outprefix)
else:
model2.fbagapnodb(out=self.outprefix)
self.model.writeECfile(self.outprefix + "ec1.txt")
metfile = open(self.outprefix + "metabolites1.txt","w")
numspecies = 0
for species in self.model.SPECIES.keys():
if not species.endswith("_b"):
metfile.write("%s\n" % (species))
numspecies += 1
numreactions = 0
for reactions in self.model.REACTIONS.keys():
if (not reactions.startswith("R_SRC")) and (not reactions.startswith("R_ESC")) and (not reactions.startswith("R_EXCH")):
numreactions += 1
del model2
del self.model
return numspecies,numreactions
def mapFlux(self):
self.model = mg.gurobicb()
w2m.wil2metmodel(self.wilfname)
self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes",exchangesfile="")
self.model.solve(maps=True,out=self.outprefix)
self.model.writeECfile(self.outprefix + "ec4.txt")
metfile = open(self.outprefix+"metabolites4.txt", "w")
numspecies = 0
for species in self.model.SPECIES.keys():
if not species.endswith("_b"):
metfile.write("%s\n" % (species))
numspecies += 1
numreactions = 0
for reactions in self.model.REACTIONS.keys():
if (not reactions.startswith("R_SRC")) and (not reactions.startswith("R_ESC")) and (not reactions.startswith("R_EXCH")):
numreactions += 1
return numspecies,numreactions
def metabolite_integration(self, metfile=args.met, threshold=args.thr):
# if metabolite present, sum of reactions producing that metabolite is at least epsilon
self.model = mg.gurobicb()
w2m.wil2metmodel(self.wilfname)
self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes",exchangesfile="")
self.model.metaboliteIntegration(metfile, threshold)
self.model.writeECfile(self.outprefix + "ec5.txt")
metfile = open(self.outprefix+"metabolites5.txt", "w")
numspecies = 0
for species in self.model.SPECIES.keys():
if not species.endswith("_b"):
metfile.write("%s\n" % (species))
numspecies += 1
numreactions = 0
for reactions in self.model.REACTIONS.keys():
if (not reactions.startswith("R_SRC")) and (not reactions.startswith("R_ESC")) and (not reactions.startswith("R_EXCH")):
numreactions += 1
model.convertMet2KeggIDs() # get this to use the database
model.addTransports()
# write .wil file
model.writeWil(outprefix + "Working.wil")
###############
# build from .wil file
###############
del model
model = SW_metmodel.gurobicb()
wilfname = outprefix + "Working.wil"
wil2metmodelpy.wil2metmodel(wilfname)
modelname = wilfname[0:(len(wilfname)-4)]
model.build_from_textfiles(modelfile=modelname + ".reactions", biomassfile=modelname + ".biomass", sourcesfile=modelname+".sources", escapesfile=modelname +".escapes",exchangesfile="")
# write .wil file with exchanges
model.writeWil(outprefix + "Working.wil")
model.writeWil(outprefix + "Stable.wil")
#####################
# add constraints #
#####################
# reaction id, lower bound, upper bound
#model.set_constraint('R_SRC_C00185',10,10)
model.solve(verbose=False, out=outprefix + "draft")
def data_integration(self,gammas = (-0.1,0.1),exprfile = args.exp,forceBiomass = True, verbose = False):
"""
Ruppin analysis of syw using the datasets from Tetu et al., 2009 about phosphate stress.
"""
gprfname = self.infile + '.gpr'
#gap_Exchanges = "gapSRC_ESC.txt"
def makeCallsDict( allRxns, hirxns, lorxns ):
calls = {}
for r in hirxns:
assert r in allRxns and r not in lorxns
for r in lorxns:
assert r in allRxns and r not in hirxns
for r in allRxns:
if r in hirxns:
calls[r] = "On"
elif r in lorxns:
calls[r] = "Off"
else:
calls[r] = "No call"
for r in allRxns:
assert not calls[r] == None
return calls
organism = raw_input("What is the organism's name in PubMed? ")
self.model = mg.gurobicb()
w2m.wil2metmodel(self.wilfname)
#self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes", exchangesfile="", gapexfile=gap_Exchanges)
self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes", exchangesfile="")
if verbose:
rq = False
else:
rq = True
self.model.gpr2(gprfname,readquiet=rq)
fluxes = {}
calls = {}
self.model.solve(verbose=verbose)
fluxes['fba'] = deepcopy(self.model.REACTION2FLUXVALUE)
self.model.ruppin(exprfile,gammas,forceBiomass=forceBiomass)
#self.model.scoringExpData(exprfile)
#self.model.webScrapping(organism)
calls[self.outprefix] = makeCallsDict(self.model.REACTIONS,self.model.hirxnset,self.model.lowrxnset)
fluxes[self.outprefix] = deepcopy(self.model.REACTION2FLUXVALUE)
cache = {}
for reaction in self.model.REACTIONS:
name, reversible, notes, equation = self.model.REACTIONS[reaction]
reactionequation = eq_current.makestring(equation, reversible)
confidence,gpr = '?','?'
holder = {'pathways':{}, 'ecs':{}}
ref, prr = '.', '.'
for note in notes:
if 'SUBSYSTEM: ' in note:
holder['pathways'][note[11:]] = 1
if 'EC: ' in note:
holder['ecs'][note[4:]] = 1
if 'SCORES: ' in note:
print ("THIS IS THE SCORE", notes['SCORES: '])
Avg = notes['SCORES: '] + Avg
count += 1
if 'CONFIDENCE: ' in note:
confidence = note[12:]
if 'GPR: ' in note:
gpr = note[5:]
if 'Protein_reaction_relation: ' in note:
prr = note[note.find(' == ') + 4:]
if 'PMID: ' in note: # and not 'review' in note and not 'related_organism' in note
ref = ref + note.split(',')[0][6:] + ' '
if not ref == '.':
ref = 'PMIDs: ' + ref[1:-1]
grr = prr
for i in prr.split():
if '(' in i:
i = i[1:]
if ')' in i:
i = i[:-1]
if i in self.model.PROTEIN2GENE:
grr = grr.replace(i, model.PROTEIN2GENE[i])
#add to cache for printing...
for pathwayname in holder['pathways']:
for ec in holder['ecs']:
if not pathwayname in cache:
cache[pathwayname] = {}
if not ec in cache[pathwayname]:
cache[pathwayname][ec] = {}
cache[pathwayname][ec][ ('\t').join((reaction, name, str(reversible), pathwayname, ec, fluxes['fba'][reaction], fluxes[self.outprefix][reaction], reactionequation)) ] = 1
paths = cache.keys()
paths.sort()
outfile = open(self.outprefix+"integrated.xls","w")
for path in paths:
ecs = cache[path].keys()
ecs.sort()
for ec in ecs:
for r in cache[path][ec]:
outfile.write("%s\n" % r)
#Average = float(Avg)/float(count)
#print ('\n\n', "Average Score of Model: ", Avg, '\n')
#print ("Average Score of Model per Gene: ", Average, '\n')
self.model.writeECfile(self.outprefix + "ec3.txt")
metfile = open(self.outprefix + "metabolites3.txt","w")
numspecies = 0
for species in self.model.SPECIES.keys():
if not species.endswith("_b"):
metfile.write("%s\n" % (species))
numspecies += 1
numreactions = 0
for reactions in self.model.REACTIONS.keys():
if (not reactions.startswith("R_SRC")) and (not reactions.startswith("R_ESC")) and (not reactions.startswith("R_EXCH")):
numreactions += 1
del self.model
return numspecies,numreactions
del self.model
def fba_gap(self, verbose=False, withdb=False):
self.model = mg.gurobicb()
w2m.wil2metmodel(self.wilfname)
gapSRC_ESC = open("gapSRC_ESC.txt", 'w')
self.model.build_from_textfiles(modelfile=self.modelname + ".reactions", biomassfile=self.modelname + ".biomass", sourcesfile=self.modelname+".sources", escapesfile=self.modelname +".escapes", exchangesfile="")
try:
print ("Reading reactions to add")
gapfile = open(self.outprefix + ".gap.xls", "r")
reactionsToAdd = []
source = 0
escape = 0
reaction = 0
for line in gapfile.readlines():
if "added sources:" in line:
source = 1
continue
if "added escapes:" in line:
escape = 1
source = 0
continue
if "added reactions:" in line:
reaction = 1
escape = 0
continue
if reaction == 1:
reactionsToAdd.append(line.rstrip())
if escape == 1:
escapeToAdd = line.rstrip()
gapSRC_ESC.write(escapeToAdd)
gapSRC_ESC.write("\n")
self.model.ESCAPES.append(escapeToAdd)
if source == 1:
sourceToAdd = line.rstrip()
gapSRC_ESC.write(sourceToAdd)
gapSRC_ESC.write("\n")
self.model.SOURCES.append(sourceToAdd)
self.model.addReactionsFromDB(reactionsToAdd)
self.model.set_sources(self.model.SOURCES)
self.model.set_escapes(self.model.ESCAPES)
gapfile.close()
except:
print ("No .gap.xls file")
self.model.writeWil(filename=self.outprefix+"Working.wil")
self.model.solve(verbose=verbose,out=self.outprefix + 'draft')
if self.model.OBJECTIVE_VALUE < self.epsilon:
print ("Flux was zero, gap filling")
model2 = deepcopy(self.model)
if withdb:
model2.fbagapdb(out=self.outprefix)
else:
model2.fbagapnodb(out=self.outprefix)
else:
print ("Flux was positive")
gapfile = open(self.outprefix + ".gap.xls", "w")
gapfile.write("added sources:\nadded escapes:\nadded reactions:\n")
gapfile.close()
self.model.writeECfile(self.outprefix + "ec2.txt")
metfile = open(self.outprefix + "metabolites2.txt","w")
numspecies = 0
for species in self.model.SPECIES.keys():
if not species.endswith("_b"):
metfile.write("%s\n" % (species))
numspecies += 1
numreactions = 0
for reactions in self.model.REACTIONS.keys():
if (not reactions.startswith("R_SRC")) and (not reactions.startswith("R_ESC")) and (not reactions.startswith("R_EXCH")):
numreactions += 1
del self.model
return numspecies,numreactions
