def compareModelsStructurally(self, fileName1, fileName2, mapping,
componentMapping):
molecules, _, _ = readBNGXML.parseXML(fileName1)
molecules2, _, _ = readBNGXML.parseXML(fileName2)
moleculeName1 = {x.name: x for x in molecules}
moleculeName2 = {x.name: x for x in molecules2}
intersectionDict = {}
nameIntersection = moleculeIntersection(moleculeName1.keys(),
moleculeName2.keys(), mapping)
componentNameIntersection = defaultdict(lambda: defaultdict(str))
for molecule in nameIntersection:
for component in moleculeName1[molecule].components:
if component.name in [
x.name for x in moleculeName2[
nameIntersection[molecule]].components
]:
componentNameIntersection[nameIntersection[molecule]][
component.name] = component.name
elif (molecule in componentMapping
and component.name in componentMapping[molecule]):
componentNameIntersection[nameIntersection[molecule]][
component.name.lower()] = componentMapping[molecule][
component.name].lower()
return nameIntersection, componentNameIntersection
def askQuestions(inputfile, molecule, center, context=None):
_, rules, _ = readBNGXML.parseXML(inputfile)
transformationCenter = []
transformationContext = []
transformationProduct = []
atomicArray = []
actionNames = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule],True)
if any([
molecule in y and center in y for x in ttransformationCenter
for y in x
]):
if context:
print str(rule[0]).split(':')[0]
print[
y for x in ttransformationContext for y in x
if context in y and molecule in y
]
else:
print rule
def main2():
number = 48
species,rules,parameterDict,observableList= readBNGXML.parseXML('output{0}.xml'.format(number))
graph = nx.Graph()
simpleGraph(graph,species,observableList,number)
nx.write_gml(graph,'graph_{0}.gml'.format(number))
def main():
fileName = 'complex/output19.bngl'
console.bngl2xml(fileName)
species, rules, par = readBNGXML.parseXML('output19.xml')
#print rules
transformationCenter = []
transformationContext = []
#extract the context of such reactions
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
redundantDict, patternDictList = extractRedundantContext(
rules, transformationCenter, transformationContext)
#print redundantDict
#construct rule based patterns based on the reaction patterns they have to match and
#the ones they have to discriminate
for center in patternDictList:
for rate in patternDictList[center]:
match = patternDictList[center][rate]
notContext = []
for cRate in [x for x in patternDictList[center] if x != rate]:
notContext.append([
transformationContext[x]
for x in redundantDict[center][cRate]
])
ruleSet = [rules[x] for x in redundantDict[center][rate]]
createMetaRule(ruleSet, match)
newRules = range(0, len(rules))
for center in redundantDict:
for context in redundantDict[center]:
for element in range(1, len(redundantDict[center][context])):
newRules.remove(redundantDict[center][context][element])
#for element in newRules:
# print str(rules[element][0])
newRulesArray = []
for element in newRules:
newRulesArray.append('{0}\n'.format(str(rules[element][0])))
lines = readFile(fileName)
startR = lines.index('begin reaction rules\n')
endR = lines.index('end reaction rules\n')
startP = lines.index('begin parameters\n')
endP = lines.index('end parameters\n')
newPar = findNewParameters(lines[startP + 1:endP], par)
newLines = lines[0:endP] + newPar + lines[
endP:startR + 1] + newRulesArray + lines[endR:len(lines)]
f = open(fileName + 'reduced.bngl', 'w')
f.writelines(newLines)
'''
def main2():
number = 48
species, rules, parameterDict, observableList = readBNGXML.parseXML(
'output{0}.xml'.format(number))
graph = nx.Graph()
simpleGraph(graph, species, observableList, number)
nx.write_gml(graph, 'graph_{0}.gml'.format(number))
def compareModelsStructurally(self, fileName1, fileName2, mapping,componentMapping):
molecules, _, _ = readBNGXML.parseXML(fileName1)
molecules2, _, _ = readBNGXML.parseXML(fileName2)
moleculeName1 = {x.name: x for x in molecules}
moleculeName2 = {x.name: x for x in molecules2}
intersectionDict = {}
nameIntersection = moleculeIntersection(moleculeName1.keys(), moleculeName2.keys(), mapping)
componentNameIntersection = defaultdict(lambda: defaultdict(str))
for molecule in nameIntersection:
for component in moleculeName1[molecule].components:
if component.name in [x.name for x in moleculeName2[nameIntersection[molecule]].components]:
componentNameIntersection[nameIntersection[molecule]][component.name] = component.name
elif molecule in componentMapping and component.name in componentMapping[molecule]:
componentNameIntersection[nameIntersection[molecule]][component.name.lower()] = componentMapping[molecule][component.name].lower()
return nameIntersection,componentNameIntersection
def main():
fileName = 'complex/output19.bngl'
console.bngl2xml(fileName)
species,rules,par= readBNGXML.parseXML('output19.xml')
#print rules
transformationCenter = []
transformationContext = []
#extract the context of such reactions
for idx,rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
redundantDict,patternDictList = extractRedundantContext(rules,transformationCenter,transformationContext)
#print redundantDict
#construct rule based patterns based on the reaction patterns they have to match and
#the ones they have to discriminate
for center in patternDictList:
for rate in patternDictList[center]:
match = patternDictList[center][rate]
notContext = []
for cRate in [x for x in patternDictList[center] if x != rate]:
notContext.append([transformationContext[x] for x in redundantDict[center][cRate]])
ruleSet = [rules[x] for x in redundantDict[center][rate]]
createMetaRule(ruleSet,match)
newRules = range(0,len(rules))
for center in redundantDict:
for context in redundantDict[center]:
for element in range(1,len(redundantDict[center][context])):
newRules.remove(redundantDict[center][context][element])
#for element in newRules:
# print str(rules[element][0])
newRulesArray = []
for element in newRules:
newRulesArray.append('{0}\n'.format(str(rules[element][0])))
lines = readFile(fileName)
startR = lines.index('begin reaction rules\n')
endR = lines.index('end reaction rules\n')
startP = lines.index('begin parameters\n')
endP = lines.index('end parameters\n')
newPar = findNewParameters(lines[startP+1:endP],par)
newLines = lines[0:endP] + newPar + lines[endP:startR+1] + newRulesArray + lines[endR:len(lines)]
f = open(fileName + 'reduced.bngl','w')
f.writelines(newLines)
'''
def getBondsFromFileName(fileName):
constructedFilename = '../curated/{0}.xml'.format(fileName)
moleculeTypes, rules, _ = readBNGXML.parseXML(constructedFilename)
bonds = set([])
for molecule in moleculeTypes:
for component in molecule.components:
name = [x.name for x in moleculeTypes if x.name.lower() == component.name]
if len(name) > 0:
bonds.add(tuple(sorted([molecule.name, name[0]])))
return bonds
def __init__(self, fileName,rawFileName=None):
self.molecules, self.rules, _ = readBNGXML.parseXML(fileName)
self.dependencies, self.patternXreactions, _, _ = componentGroups.getContextRequirements(fileName, collapse=False)
self.transposePatternsReactions()
self.reverseDependencies = componentGroups.reverseContextDict(self.dependencies)
self.moleculeMotifDict, self.motifMoleculeDict = self.classifyPairsByMotif(self.reverseDependencies)
if rawFileName:
self.processRawSBML(rawFileName)
else:
self.rawRules = {}
def getContextRequirements(inputfile, collapse=True, motifFlag=False, excludeReverse=False):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, parameters = readBNGXML.parseXML(inputfile)
moleculeStateMatrix = {}
label, center, context, product, atomicArray, actions, doubleAction = extractCenterContext(
rules, excludeReverse=excludeReverse
)
return getStateTransitionDiagram(label, center, product, context, actions, molecules, rules, parameters)
def __init__(self, fileName,rawFileName=None):
self.molecules, self.rules, _ = readBNGXML.parseXML(fileName)
self.dependencies, self.patternXreactions, _, _ = componentGroups.getContextRequirements(fileName, collapse=False)
self.transposePatternsReactions()
self.reverseDependencies = componentGroups.reverseContextDict(self.dependencies)
self.moleculeMotifDict, self.motifMoleculeDict = self.classifyPairsByMotif(self.reverseDependencies)
if rawFileName:
self.processRawSBML(rawFileName)
else:
self.rawRules = {}
def getContextRequirements(inputfile,
collapse=True,
motifFlag=False,
excludeReverse=False):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, parameters = readBNGXML.parseXML(inputfile)
moleculeStateMatrix = {}
label, center, context, product, atomicArray, actions, doubleAction = extractCenterContext(
rules, excludeReverse=excludeReverse)
return getStateTransitionDiagram(label, center, product, context, actions,
molecules, rules, parameters)
def askQuestions(inputfile, molecule, center, context=None):
_, rules, _ = readBNGXML.parseXML(inputfile)
ruleArray = []
contextArray = []
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements, tactionNames, tlabelArray = extractAtomic.extractTransformations(
[rule], True)
if any([molecule in y and center in y for x in ttransformationCenter for y in x]):
if context:
ruleArray.append(str(rule[0]).split(':')[0])
contextArray.append([y for x in ttransformationContext for y in x if context in y and molecule in y])
else:
print rule
return ruleArray, contextArray
def getContextRequirements(inputfile):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, _ = readBNGXML.parseXML(inputfile)
center, context, product, atomicArray, actions = extractCenterContext(rules)
reactionCenterStateDictionary = getRestrictedChemicalStates(product, context)
backupstatedictionary = deepcopy(reactionCenterStateDictionary)
#print reactionCenterStateDictionary['STAT3%0']
#chemicalStates = getChemicalStates(rules)
#totalStateDictionary = sortChemicalStates(chemicalStates)
requirementDependencies = detectDependencies(reactionCenterStateDictionary, molecules)
removeIndirectDependencies(requirementDependencies, backupstatedictionary)
getMutualExclusions(requirementDependencies, molecules)
#requirementDependencies = removeCounter(requirementDependencies)\
#raise Exception
return requirementDependencies
def getContextRequirements(inputfile):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, _ = readBNGXML.parseXML(inputfile)
center, context, product, atomicArray, actions = extractCenterContext(
rules)
reactionCenterStateDictionary = getRestrictedChemicalStates(
product, context)
backupstatedictionary = deepcopy(reactionCenterStateDictionary)
#print reactionCenterStateDictionary['STAT3%0']
#chemicalStates = getChemicalStates(rules)
#totalStateDictionary = sortChemicalStates(chemicalStates)
requirementDependencies = detectDependencies(reactionCenterStateDictionary,
molecules)
removeIndirectDependencies(requirementDependencies, backupstatedictionary)
getMutualExclusions(requirementDependencies, molecules)
#requirementDependencies = removeCounter(requirementDependencies)\
#raise Exception
return requirementDependencies
def main():
with open('linkArray.dump', 'rb') as f:
linkArray = pickle.load(f)
with open('xmlAnnotationsExtended.dump','rb') as f:
annotations = pickle.load(f)
speciesEquivalence = {}
onlyDicts = [ x for x in listdir('./complex')]
onlyDicts = [x for x in onlyDicts if '.bngl.dict' in x]
for x in onlyDicts:
with open('complex/{0}'.format(x),'rb') as f:
speciesEquivalence[int(x.split('.')[0][6:])] = pickle.load(f)
for cidx,cluster in enumerate(linkArray):
#FIXME:only do the first cluster
cidx = 0
cluster = linkArray[0]
if len(cluster) == 1:
continue
annotationsDict = {idx:x for idx,x in enumerate(annotations)}
annotationsIncidence = {}
#get the model intersection points
for element in annotationsDict:
for species in annotationsDict[element]:
key = annotationsDict[element][species][1]
if key not in annotationsIncidence:
annotationsIncidence[key] = set([])
annotationsIncidence[key].add(element)
#number = 19
graph = nx.Graph()
nodeDict = {}
superNodeDict = {}
for element in annotationsIncidence:
if len(annotationsIncidence[element]) > 1:
tmpEdgeList = []
names= []
for idx in annotationsIncidence[element]:
for species in [x for x in annotationsDict[idx] if annotationsDict[idx][x][1] == element]:
try:
print speciesEquivalence[idx]
if isinstance(speciesEquivalence[idx][species],str):
tmpEdgeList.append('{0}_{1}'.format(idx,speciesEquivalence[idx][species][0:-2]))
names.append(speciesEquivalence[idx][species][0:-2])
else:
tmpEdgeList.append('{0}_{1}'.format(idx,speciesEquivalence[idx][species].molecules[0].name))
names.append(speciesEquivalence[idx][species].molecules[0].name)
except:
print '----','error',idx,species
pass
names = [x.lower() for x in names]
name = Counter(names).most_common(5)
name = [x for x in name if len(x[0])> 1]
if len(name) == 0:
name = Counter(names).most_common(1)
for edge in tmpEdgeList:
superNodeDict[edge] = name[0][0]
if len(name) > 0:
superNodeDict[name[0][0]] = len(tmpEdgeList)
#pairs = [(x,y,1) for x in tmpEdgeList for y in tmpEdgeList if x!= y]
#graph.add_weighted_edges_from(pairs)
print superNodeDict
for x in cluster:
number = x-1
try:
console.bngl2xml('complex/output{0}.bngl'.format(number))
species,rules,parameterDict,observableList= readBNGXML.parseXML('output{0}.xml'.format(number))
except:
continue
nodeDict[number] = simpleGraph(graph,species,observableList,number,superNodeDict)
nx.write_gml(graph,'cgraph_{0}.gml'.format(cidx))
break
def getContextRequirements(inputfile,
collapse=True,
motifFlag=False,
excludeReverse=False):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, _ = readBNGXML.parseXML(inputfile)
label, center, context, product, atomicArray, actions, doubleAction = extractCenterContext(
rules, excludeReverse=excludeReverse)
reactionCenterStateDictionary, doubleActionDict = getRestrictedChemicalStates(
label, product, context, doubleAction)
#molecule1 = 'socs3'
#molecule2 = 'shp2'
#print(molecule1,molecule2)
#print('---',reactionCenterStateDictionary['gp130%0'][(molecule1,1,'')][molecule2])
#print('---',reactionCenterStateDictionary['gp130%0'][(molecule2,1,'')][molecule1])
#print(reactionCenterStateDictionary['STAT3%0'][('astmod',0,'AST')])
#print('++++')
# print(reactionCenterStateDictionary['Ras%0'][('Ras_GTPmod',0,'Ras_GTP')]['Ras_GDPmod'])
backupstatedictionary = deepcopy(reactionCenterStateDictionary)
# print(reactionCenterStateDictionary['EGFR%1'][('_Pmod',0,'_P')])
# print(reactionCenterStateDictionary['EGFR%0'][('_Pmod',0,'_P')])
# return
# chemicalStates = getChemicalStates(rules)
# totalStateDictionary = sortChemicalStates(chemicalStates)
# print(reactionCenterStateDictionary['Shc%0'][('egfr', 0, '')]['mmod'])
requirementDependencies = detectDependencies(reactionCenterStateDictionary,
molecules)
#print(requirementDependencies['JAK']['requirement'])
# repression
for molecule in reactionCenterStateDictionary:
moleculeName = molecule.split('%')[0]
for element in reactionCenterStateDictionary[molecule]:
if not isActive(element[1:]):
for partner in reactionCenterStateDictionary[molecule][
element]:
for state in reactionCenterStateDictionary[molecule][
element][partner]:
if isActive(state):
repressiveState = (partner, state[0], state[1])
if (reverseState(moleculeName, element,
molecules), repressiveState
) in requirementDependencies[moleculeName][
'requirement']:
# or (repressiveState, element[0]) in requirementDependencies[moleculeName]['independent']:
requirementDependencies[moleculeName][
'repression'].add(
(repressiveState[0], element[0]))
if motifFlag:
requirementDependencies = getMotifRelationships(
requirementDependencies, molecules)
exclusionCliques = getExclusionClusters(requirementDependencies)
removeIndirectDependencies(requirementDependencies,
backupstatedictionary, motifFlag)
else:
removeIndirectDependencies(requirementDependencies,
backupstatedictionary)
getMutualExclusions(requirementDependencies, molecules)
exclusionCliques = {}
processNodes = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
if motifFlag:
# double interactions
print(doubleActionDict['gp130'])
multiInteractionDict = defaultdict(
lambda: defaultdict(lambda: defaultdict(list)))
doubleInteractions = defaultdict(
lambda: defaultdict(lambda: defaultdict(list)))
for molecule in [
x for x in requirementDependencies if x in doubleActionDict
]:
for motif in requirementDependencies[molecule]:
for relationship in requirementDependencies[molecule][motif]:
for combination1 in [True, False]:
for combination2 in [True, False]:
if len(doubleActionDict[molecule][relationship[0]]
[combination1][
relationship[1]][combination2]) > 0:
multiInteractionDict[molecule][(
combination1,
combination2)][relationship] = tuple(
doubleActionDict[molecule][
relationship[0]][combination1][
relationship[1]][combination2])
doubleInteractions[molecule][tuple(
relationship)][motif].append(
(combination1, combination2))
for molecule in doubleInteractions:
for relationship in doubleInteractions[molecule]:
for motif in doubleInteractions[molecule][relationship]:
if motif in [
'partialIndependence-', 'partialIndependence+',
'fullIndependence'
]:
requirementDependencies[molecule][motif].remove(
relationship)
for combination in doubleInteractions[molecule][
relationship][motif]:
label = multiInteractionDict[molecule][combination][
relationship]
if combination[0] and combination[1]:
requirementDependencies[molecule][
'doubleActivation'].append(relationship)
processNodes[molecule]['doubleActivation'][
relationship] = '{0}_{1}'.format(
molecule, '_'.join(label))
elif not combination[0] and combination[1]:
if motif in ['ordering']:
requirementDependencies[molecule][
motif].remove(relationship)
elif motif in ['repression']:
if (
relationship[1], relationship[0]
) in requirementDependencies[molecule][motif]:
requirementDependencies[molecule][
motif].remove(
(relationship[1], relationship[0]))
requirementDependencies[molecule][
'reprordering'].append(relationship)
processNodes[molecule]['reprordering'][
relationship] = '{0}_{1}'.format(
molecule, '_'.join(label))
elif not combination[0] and not combination[1]:
processNodes[molecule]['doubleRepression'][
relationship] = '{0}_{1}'.format(
molecule, '_'.join(label))
if motif == 'repression':
requirementDependencies[molecule][
motif].remove(relationship)
requirementDependencies[molecule][
'doubleRepression'].append(relationship)
elif motif == 'partialIndependence-':
# requirementDependencies[molecule][motif].remove(relationship)
requirementDependencies[molecule][
'doubleRepression'].append(relationship)
return requirementDependencies, backupstatedictionary, exclusionCliques, processNodes
def processRawSBML(self,inputfile):
_, rawrules, _ = readBNGXML.parseXML(inputfile)
self.rawRules = {x[0].label: x[0] for x in rawrules}
def getContextRequirements(inputfile, collapse=True, motifFlag=False, excludeReverse = False):
"""
Receives a BNG-XML file and returns the contextual dependencies implied by this file
"""
molecules, rules, _ = readBNGXML.parseXML(inputfile)
label, center, context, product, atomicArray, actions, doubleAction = extractCenterContext(rules, excludeReverse=excludeReverse)
reactionCenterStateDictionary, doubleActionDict = getRestrictedChemicalStates(label, product, context, doubleAction)
print(reactionCenterStateDictionary['STAT3%0'].keys())
print(reactionCenterStateDictionary['STAT3%0'][('astmod',0,'AST')])
print '++++'
# print reactionCenterStateDictionary['Ras%0'][('Ras_GTPmod',0,'Ras_GTP')]['Ras_GDPmod']
backupstatedictionary = deepcopy(reactionCenterStateDictionary)
# print reactionCenterStateDictionary['EGFR%1'][('_Pmod',0,'_P')]
# print reactionCenterStateDictionary['EGFR%0'][('_Pmod',0,'_P')]
# return
# chemicalStates = getChemicalStates(rules)
# totalStateDictionary = sortChemicalStates(chemicalStates)
# print reactionCenterStateDictionary['Shc%0'][('egfr', 0, '')]['mmod']
requirementDependencies = detectDependencies(reactionCenterStateDictionary, molecules)
print requirementDependencies['JAK']['requirement']
# repression
for molecule in reactionCenterStateDictionary:
moleculeName = molecule.split('%')[0]
for element in reactionCenterStateDictionary[molecule]:
if not isActive(element[1:]):
for partner in reactionCenterStateDictionary[molecule][element]:
for state in reactionCenterStateDictionary[molecule][element][partner]:
if isActive(state):
repressiveState = (partner, state[0], state[1])
if (reverseState(moleculeName, element, molecules), repressiveState) in requirementDependencies[moleculeName]['requirement']:
# or (repressiveState, element[0]) in requirementDependencies[moleculeName]['independent']:
requirementDependencies[moleculeName]['repression'].add((repressiveState[0], element[0]))
if motifFlag:
requirementDependencies = getMotifRelationships(requirementDependencies, molecules)
exclusionCliques = getExclusionClusters(requirementDependencies)
removeIndirectDependencies(requirementDependencies, backupstatedictionary, motifFlag)
else:
removeIndirectDependencies(requirementDependencies, backupstatedictionary)
getMutualExclusions(requirementDependencies, molecules)
exclusionCliques = {}
processNodes = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
if motifFlag:
# double interactions
#print doubleActionDict['MEK']['pmod']
multiInteractionDict = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
doubleInteractions = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
for molecule in [x for x in requirementDependencies if x in doubleActionDict]:
for motif in requirementDependencies[molecule]:
for relationship in requirementDependencies[molecule][motif]:
for combination1 in [True, False]:
for combination2 in [True, False]:
if len(doubleActionDict[molecule][relationship[0]][combination1][relationship[1]][combination2]) > 0:
multiInteractionDict[molecule][(combination1, combination2)][relationship] = tuple(doubleActionDict[molecule][relationship[0]][combination1][relationship[1]][combination2])
doubleInteractions[molecule][tuple(relationship)][motif].append((combination1, combination2))
for molecule in doubleInteractions:
for relationship in doubleInteractions[molecule]:
for motif in doubleInteractions[molecule][relationship]:
if motif in ['partialIndependence-', 'partialIndependence+', 'fullIndependence']:
requirementDependencies[molecule][motif].remove(relationship)
for combination in doubleInteractions[molecule][relationship][motif]:
label = multiInteractionDict[molecule][combination][relationship]
if combination[0] and combination[1]:
requirementDependencies[molecule]['doubleActivation'].append(relationship)
processNodes[molecule]['doubleActivation'][relationship] = '{0}_{1}'.format(molecule, '_'.join(label))
elif not combination[0] and combination[1]:
if motif in ['ordering']:
requirementDependencies[molecule][motif].remove(relationship)
elif motif in ['repression']:
if (relationship[1], relationship[0]) in requirementDependencies[molecule][motif]:
requirementDependencies[molecule][motif].remove((relationship[1], relationship[0]))
requirementDependencies[molecule]['reprordering'].append(relationship)
processNodes[molecule]['reprordering'][relationship] = '{0}_{1}'.format(molecule, '_'.join(label))
elif not combination[0] and not combination[1]:
processNodes[molecule]['doubleRepression'][relationship] = '{0}_{1}'.format(molecule, '_'.join(label))
if motif == 'repression':
requirementDependencies[molecule][motif].remove(relationship)
requirementDependencies[molecule]['doubleRepression'].append(relationship)
elif motif == 'partialIndependence-':
# requirementDependencies[molecule][motif].remove(relationship)
requirementDependencies[molecule]['doubleRepression'].append(relationship)
return requirementDependencies, backupstatedictionary, exclusionCliques, processNodes
def getInformationContent(fileName):
console.bngl2xml(fileName,timeout=30)
xmlFileName = '.'.join(fileName.split('.')[:-1])+'.xml'
xmlFileName =xmlFileName.split('/')[-1]
species,reactions,_= readBNGXML.parseXML(xmlFileName)
calculateSpeciesInformationContent(species)
def extractStatistics():
number = 151
console.bngl2xml('complex/output{0}.bngl'.format(number))
species, rules, parameterDict = readBNGXML.parseXML(
'output{0}.xml'.format(number))
#print rules
transformationCenter = []
transformationContext = []
k = []
actions = Counter()
actionSet = Counter()
for idx, rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
k.append(len(rule[0].actions))
#if len(rule[0].actions) > 3:
# print rule[0].reactants
actions.update([x.action for x in rule[0].actions])
tmp = [x.action for x in rule[0].actions]
tmp.sort()
actionSet.update([tuple(tmp)])
#print actions
#print actionSet
print 'number of species', len(species)
print 'avg number o actions', np.average(k), np.std(k)
centerDict = groupByReactionCenter(transformationCenter)
print 'singletons', len(
{x: centerDict[x]
for x in centerDict if len(centerDict[x]) == 1})
tmp = [[tuple(set(x)), len(centerDict[x])] for x in centerDict]
#reactionCenterGraph(species,tmp)
#tmp.sort(key=lambda x:x[1],reverse=True)
print 'number of reaction centers', len(centerDict.keys())
print 'number of rules', len(rules)
#print 'unique',[x for x in centerDict[element] if len(centerDict[element]) == 1]
redundantDict = groupByReactionCenterAndRateAndActions(rules, centerDict)
#print redundantDict
tmp2 = [('$\\tt{{{0}}}$'.format(tuple(set(x))), tuple(set(y[:-1])), y[-1],
len(redundantDict[x][y])) for x in redundantDict
for y in redundantDict[x] if 'kr' not in y[-1]]
tmp2 = set(tmp2)
tmp2 = list(tmp2)
tmp2.sort(key=lambda x: x[3], reverse=True)
tmp2.sort(key=lambda x: x[0], reverse=True)
tmp2 = [
'{0} & {1} & {2} & {3}\\\\\n'.format(element[0], element[1],
element[2], element[3])
for element in tmp2
]
with open('table.tex', 'w') as f:
f.write('\\begin{tabular}{|cccc|}\n')
f.write('\\hline\n')
f.write('Reaction Centers & Action & Score\\\\\\hline\n')
for element in tmp2:
f.write(element)
f.write('\\hline\n')
f.write('\\end{tabular}\n')
#print redundantDict
x = [len(centerDict[x]) for x in centerDict]
#122,138
print 'average number of reactions with same rate and reaction cneter', np.average(
x), np.std(x)
print 'total number of clusters', len(x)
print x
def main():
with open('linkArray.dump', 'rb') as f:
linkArray = pickle.load(f)
with open('xmlAnnotationsExtended.dump', 'rb') as f:
annotations = pickle.load(f)
speciesEquivalence = {}
onlyDicts = [x for x in listdir('./complex')]
onlyDicts = [x for x in onlyDicts if '.bngl.dict' in x]
for x in onlyDicts:
with open('complex/{0}'.format(x), 'rb') as f:
speciesEquivalence[int(x.split('.')[0][6:])] = pickle.load(f)
for cidx, cluster in enumerate(linkArray):
#FIXME:only do the first cluster
cidx = 0
cluster = linkArray[0]
if len(cluster) == 1:
continue
annotationsDict = {idx: x for idx, x in enumerate(annotations)}
annotationsIncidence = {}
#get the model intersection points
for element in annotationsDict:
for species in annotationsDict[element]:
key = annotationsDict[element][species][1]
if key not in annotationsIncidence:
annotationsIncidence[key] = set([])
annotationsIncidence[key].add(element)
#number = 19
graph = nx.Graph()
nodeDict = {}
superNodeDict = {}
for element in annotationsIncidence:
if len(annotationsIncidence[element]) > 1:
tmpEdgeList = []
names = []
for idx in annotationsIncidence[element]:
for species in [
x for x in annotationsDict[idx]
if annotationsDict[idx][x][1] == element
]:
try:
print speciesEquivalence[idx]
if isinstance(speciesEquivalence[idx][species],
str):
tmpEdgeList.append('{0}_{1}'.format(
idx,
speciesEquivalence[idx][species][0:-2]))
names.append(
speciesEquivalence[idx][species][0:-2])
else:
tmpEdgeList.append('{0}_{1}'.format(
idx, speciesEquivalence[idx]
[species].molecules[0].name))
names.append(speciesEquivalence[idx]
[species].molecules[0].name)
except:
print '----', 'error', idx, species
pass
names = [x.lower() for x in names]
name = Counter(names).most_common(5)
name = [x for x in name if len(x[0]) > 1]
if len(name) == 0:
name = Counter(names).most_common(1)
for edge in tmpEdgeList:
superNodeDict[edge] = name[0][0]
if len(name) > 0:
superNodeDict[name[0][0]] = len(tmpEdgeList)
#pairs = [(x,y,1) for x in tmpEdgeList for y in tmpEdgeList if x!= y]
#graph.add_weighted_edges_from(pairs)
print superNodeDict
for x in cluster:
number = x - 1
try:
console.bngl2xml('complex/output{0}.bngl'.format(number))
species, rules, parameterDict, observableList = readBNGXML.parseXML(
'output{0}.xml'.format(number))
except:
continue
nodeDict[number] = simpleGraph(graph, species, observableList,
number, superNodeDict)
nx.write_gml(graph, 'cgraph_{0}.gml'.format(cidx))
break
def extractStatistics():
number = 151
console.bngl2xml('complex/output{0}.bngl'.format(number))
species,rules,parameterDict= readBNGXML.parseXML('output{0}.xml'.format(number))
#print rules
transformationCenter = []
transformationContext = []
k = []
actions = Counter()
actionSet = Counter()
for idx,rule in enumerate(rules):
tatomicArray, ttransformationCenter, ttransformationContext, \
tproductElements,tactionNames,tlabelArray = extractAtomic.extractTransformations([rule])
#atomicArray.append(tatomicArray)
transformationCenter.append(ttransformationCenter)
transformationContext.append(ttransformationContext)
k.append(len(rule[0].actions))
#if len(rule[0].actions) > 3:
# print rule[0].reactants
actions.update([x.action for x in rule[0].actions])
tmp = [x.action for x in rule[0].actions]
tmp.sort()
actionSet.update([tuple(tmp)])
#print actions
#print actionSet
print 'number of species',len(species)
print 'avg number o actions',np.average(k),np.std(k)
centerDict = groupByReactionCenter(transformationCenter)
print 'singletons',len({x:centerDict[x] for x in centerDict if len(centerDict[x]) == 1})
tmp = [[tuple(set(x)),len(centerDict[x])] for x in centerDict]
#reactionCenterGraph(species,tmp)
#tmp.sort(key=lambda x:x[1],reverse=True)
print 'number of reaction centers',len(centerDict.keys())
print 'number of rules',len(rules)
#print 'unique',[x for x in centerDict[element] if len(centerDict[element]) == 1]
redundantDict = groupByReactionCenterAndRateAndActions(rules,centerDict)
#print redundantDict
tmp2 = [('$\\tt{{{0}}}$'.format(tuple(set(x))),tuple(set(y[:-1])),y[-1],len (redundantDict[x][y])) for x in redundantDict for y in redundantDict[x] if 'kr' not in y[-1]]
tmp2 = set(tmp2)
tmp2 = list(tmp2)
tmp2.sort(key=lambda x:x[3],reverse=True)
tmp2.sort(key=lambda x:x[0],reverse=True)
tmp2 = ['{0} & {1} & {2} & {3}\\\\\n'.format(element[0],element[1],element[2],element[3]) for element in tmp2]
with open('table.tex','w') as f:
f.write('\\begin{tabular}{|cccc|}\n')
f.write('\\hline\n')
f.write('Reaction Centers & Action & Score\\\\\\hline\n')
for element in tmp2:
f.write(element)
f.write('\\hline\n')
f.write('\\end{tabular}\n')
#print redundantDict
x = [len(centerDict[x]) for x in centerDict]
#122,138
print 'average number of reactions with same rate and reaction cneter',np.average(x),np.std(x)
print 'total number of clusters',len(x)
print x
def processRawSBML(self,inputfile):
_, rawrules, _ = readBNGXML.parseXML(inputfile)
self.rawRules = {x[0].label: x[0] for x in rawrules}
