def runSimulations(knockouts=True):
print "starting Simulations"
data = {}
targets = [
'CPK213', 'HATPase', 'NOGC1', 'NAD', 'ABA', 'Microtubule', 'Depolar',
'InsP3', 'SphK12', 'ABH1', 'InsP6', 'CIS', 'AnionEM', 'GAPC',
'KEfflux', 'H2OEfflux', 'VPpase', 'PP2CA', 'MRP5', 'CPK23',
'Vacidification', 'DAG', 'ROP11', 'NO', 'pHc', 'PIP21', 'PEPC',
'NitrocGMP', 'HAB1', 'DAGK', 'PLDd', 'PC', 'CPK6', 'PA', 'PLDa', 'PLC',
'PI3P5K', 'ROS', 'AtRAC1', 'OST1', 'cGMP', 'Ca2ATPase', 'GCR1', 'RCN1',
'PIP2', 'QUAC1', 'S1P', 'Malate', 'KOUT', 'NADPH', 'MAPK912', 'KEV',
'SCAB1', 'CaIM', 'TCTP', 'VATPase', 'GPA1', 'PtdIns35P2', 'GTP',
'GEF1410', 'ARP23', 'PtdInsP4', 'Sph', 'ABI1', 'NIA12', 'ADPRc',
'RCARs', 'PtdInsP3', 'Nitrite', 'ABI2', 'SPP1', 'RBOH', 'Actin',
'ERA1', 'NtSyp121', 'Ca2c', 'GHR1', 'cADPR', 'SLAH3', 'SLAC1'
]
for target in targets:
if knockouts is True:
mtext = boolean2.modify_states(text=text, turnoff=target)
fname = 'knockouts.json'
else:
mtext = boolean2.modify_states(text=text, turnon=target)
fname = 'overexpression.json'
model = boolean2.Model(mode='async', text=mtext)
coll = util.Collector()
for i in xrange(repeat):
model.initialize(missing=util.randbool)
model.iterate(steps=steps)
coll.collect(states=model.states, nodes='Closure')
data[target] = {'Timesteps': coll.get_averages(normalize=True)}
data[target]['Closure AUC'] = sum(data[target]['Timesteps']['Closure'])
with open(fname, 'w') as fp:
json.dump(data, fp)
def runSimulations(knockouts=True):
print "starting Simulations"
data = {}
#use the following set of targets if running the simulation for the reduced model
#targets = ['NOGC1', 'PLDdel', 'InsP3', 'nitrocGMP', 'CIS', 'Actin', 'AnionEM', 'K_efflux', 'PP2CA', 'H2O_Efflux', 'DAG', 'NO', 'pHc', 'PEPC', 'HAB1', 'PA',
#'PLDa', 'PLC', 'PI3P5K', 'OST1', 'SLAH3', 'ABA', 'H_ATPase', 'Malate', 'KOUT', 'Depolarization', 'QUAC1', 'CaIM', 'TCTP', 'AtRAC1', 'SPHK12', 'Ca_ATPase',
#'CPKa', 'CPKb', 'ABI2', 'Microtubule_Depolymerization', 'Ca', 'ABI1', 'NIA12', 'MPK', 'RCARs', 'VATPase', 'Vacuolar_Acidification', 'ROP11', 'RBOH', 'KEV',
#'GHR1', 'SLAC1', 'WT']
#use the following set of targets when running the simulation for the full model
#targets = ['CPK321', 'H_ATPase', 'NOGC1', 'ABA', 'Microtubule_Depolymerization', 'Depolarization', 'InsP3', 'SPHK12', 'ABH1', 'InsP6', 'CIS', 'AnionEM',
#'GAPC12', 'K_efflux', 'H2O_Efflux', 'VPPase', 'PP2CA', 'MRP5', 'CPK23', 'Vacuolar_Acidification', 'DAG', 'ROP11', 'NO', 'pHc', 'PIP', 'PEPC', 'nitrocGMP',
#'HAB1', 'DAGK', 'PLDdel', 'PC', 'CPK6', 'PA', 'PLDa', 'PLC', 'PI3P5K', 'ROS', 'AtRAC1', 'OST1', 'cGMP', 'Ca2_ATPase', 'GCR1', 'RCN1', 'QUAC1', 'S1P',
#'Malate', 'KOUT', 'NADPH', 'MPK912', 'KEV', 'SCAB1', 'CaIM', 'TCTP', 'VATPase', 'GPA1', 'PtdIns35P2', 'GTP', 'GEF', 'ARP_Complex', 'PtdInsP4', 'Sph',
#'ABI1', 'NIA12', 'ADPRc', 'RCARs', 'PtdInsP3', 'Nitrite', 'ABI2', 'SPP1', 'RBOH', 'Actin_Reorganization', 'ERA1', 'NtSyp121', 'Ca2', 'GHR1', 'cADPR',
#'SLAH3', 'SLAC1', 'WT', 'PtdIns45P2']
for target in targets:
print 'Processing target', target
if knockouts is True:
mtext = boolean2.modify_states(text=text, turnoff=target)
fname = 'results_KO.json'
else:
mtext = boolean2.modify_states(text=text, turnon=target)
fname = 'results_CA.json'
model = boolean2.Model(mode='async', text=mtext)
coll = util.Collector()
for i in xrange(repeat):
model.initialize(missing=util.randbool)
model.iterate(steps=steps)
coll.collect(states=model.states, nodes='Closure')
data[target] = {'Timesteps': coll.get_averages(normalize=True)}
data[target]['Closure AUC'] = sum(data[target]['Timesteps']['Closure'])
with open(fname, 'w') as fp:
json.dump(data, fp)
def ics(proteins, ko, oe):
# input proteins and k/o and o/e arrays
ics = []
off = []
on = []
for i in range(len(ko)):
off.append(ko[i])
for j in range(len(oe)):
on.append(oe[j])
text_new = boolean2.modify_states(text = text, turnoff = off, turnon = on)
return text_new
def get_sim_avgs(bn_str, nsim=100, nsteps=20, off=None, on=None):
if off is None:
off_nodes = []
else:
off_nodes = off
if on is None:
on_nodes = []
else:
on_nodes = on
coll = boolean2.util.Collector()
bn_str = boolean2.modify_states(bn_str, turnon=on, turnoff=off)
model = boolean2.Model(text=bn_str, mode='async')
for i in range(nsim):
model.initialize()
model.iterate(steps=nsteps)
coll.collect(states=model.states, nodes=model.nodes)
avgs = coll.get_averages(normalize=True)
return avgs
def get_sim_avgs(bn_str, nsim=100, nsteps=20, off=None, on=None):
if off is None:
off_nodes = []
else:
off_nodes = off
if on is None:
on_nodes = []
else:
on_nodes = on
coll = boolean2.util.Collector()
bn_str = boolean2.modify_states(bn_str, turnon=on, turnoff=off)
model = boolean2.Model(text=bn_str, mode='async')
for i in range(nsim):
model.initialize()
model.iterate(steps=nsteps)
coll.collect(states=model.states, nodes=model.nodes)
avgs = coll.get_averages(normalize=True)
return avgs
def run_mutations(text, repeat, steps):
"Runs the asynchronous model with different mutations"
# WT does not exist so it won't affect anything
data = {}
knockouts = 'WT S1P PA pHc ABI1 ROS'.split()
for target in knockouts:
print('- target %s' % target)
mtext = boolean2.modify_states(text=text, turnoff=target)
model = Model(mode='async', text=mtext)
coll = util.Collector()
for i in range(repeat):
# unintialized nodes set to random
model.initialize(missing=util.randbool)
model.iterate(steps=steps)
coll.collect(states=model.states, nodes=model.nodes)
data[target] = coll.get_averages(normalize=True)
return data
def run_mutations( text, repeat, steps ):
"Runs the asynchronous model with different mutations"
# WT does not exist so it won't affect anything
data = {}
knockouts = 'WT S1P PA pHc ABI1 ROS'.split()
for target in knockouts:
print '- target %s' % target
mtext = boolean2.modify_states( text=text, turnoff=target )
model = Model( mode='async', text=mtext )
coll = util.Collector()
for i in xrange( repeat ):
# unintialized nodes set to random
model.initialize( missing=util.randbool )
model.iterate( steps=steps )
coll.collect( states=model.states, nodes=model.nodes )
data[target] = coll.get_averages( normalize=True )
return data
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['CREB1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CCND1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['KLF4'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['SMAD2'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 10
STEPS = 50
data = []
print('- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS))
# a single overexpressed node
mtext = boolean2.modify_states(text=text, turnon=['Stimuli'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['Stimuli', 'Mcl1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['Stimuli', 'sFas'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text,
turnon=['Stimuli', 'Mcl1', 'sFas'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['ACTB'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['AKT1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CCDC88A'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# parameters for compartment ratios and fluctuations
COMP_PARAMS = helper.read_parameters( 'Bb-compartmental.csv' )
# use data from the sixth row (it is zero based counting!) in the file
CONC = CONC_PARAMS[5]
COMP = COMP_PARAMS[5]
# helper function that Binds the local override to active COMP parameter
def local_override( node, indexer, tokens ):
return overrides.override( node, indexer, tokens, COMP )
#
# there will be two models, one for WT and the other for a BC knockout
#
wt_text = file('Bb.txt').read()
bc_text = boolean2.modify_states( text=wt_text, turnoff= [ "BC" ] )
model1 = Model( text=wt_text, mode='plde' )
model2 = Model( text=bc_text, mode='plde' )
model1.OVERRIDE = local_override
model2.OVERRIDE = local_override
model1.initialize( missing = helper.initializer( CONC ) )
model2.initialize( missing = helper.initializer( CONC ) )
# see localdefs for all function definitions
model1.iterate( fullt=FULLT, steps=STEPS, localdefs='localdefs' )
model2.iterate( fullt=FULLT, steps=STEPS, localdefs='localdefs' )
# saves the simulation resutls into a file
#
# these nodes will be overexpressed (initialized to True)
#
on = []
#
# these nodes will be set to false and their corresponding updating
# rules will be removed
#
off = ["B"]
#
# this modifies the original states to apply to overexpressed and knockouts
#
text = boolean2.modify_states(text, turnon=on, turnoff=off)
#
# see tutorial 3 for more details on what happens below
#
seen = {}
for i in range(10):
model = boolean2.Model(text, mode='sync')
model.initialize()
model.iterate( steps=20 )
size, index = model.detect_cycles()
# fingerprint of the first state
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['miR320a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR223'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR155'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR106a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
#
# these nodes will be overexpressed (initialized to True)
#
on = []
#
# these nodes will be set to false and their corresponding updating
# rules will be removed
#
off = ["B"]
#
# this modifies the original states to apply to overexpressed and knockouts
#
text = boolean2.modify_states(text, turnon=on, turnoff=off)
#
# see tutorial 3 for more details on what happens below
#
seen = {}
for i in range(10):
model = boolean2.Model( text, mode='sync')
model.initialize()
model.iterate( steps=20 )
size, index = model.detect_cycles()
# fingerprint of the first state
# this collects the state of all nodes
NODES = boolean2.all_nodes( text )
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states( text=text, turnon=['miR188'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR143'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR423'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR223'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# use data from the sixth row (it is zero based counting!) in the file
CONC = CONC_PARAMS[5]
COMP = COMP_PARAMS[5]
# helper function that Binds the local override to active COMP parameter
def local_override(node, indexer, tokens):
return overrides.override(node, indexer, tokens, COMP)
#
# there will be two models, one for WT and the other for a BC knockout
#
wt_text = file('Bb.txt').read()
bc_text = boolean2.modify_states(text=wt_text, turnoff=["BC"])
model1 = Model(text=wt_text, mode='plde')
model2 = Model(text=bc_text, mode='plde')
model1.OVERRIDE = local_override
model2.OVERRIDE = local_override
model1.initialize(missing=helper.initializer(CONC))
model2.initialize(missing=helper.initializer(CONC))
# see localdefs for all function definitions
model1.iterate(fullt=FULLT, steps=STEPS, localdefs='localdefs')
model2.iterate(fullt=FULLT, steps=STEPS, localdefs='localdefs')
# saves the simulation resutls into a file
# this collects the state of all nodes
NODES = boolean2.all_nodes( text )
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states( text=text, turnon=['miR130a'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR125b'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR20b'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['miR20a'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# this collects the state of all nodes
NODES = boolean2.all_nodes( text )
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 10
STEPS = 50
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# a single overexpressed node
mtext = boolean2.modify_states( text=text, turnon=['Stimuli'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# multiple overexrpessed nodes
mtext = boolean2.modify_states( text=text, turnon=['Stimuli','Mcl1'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['Stimuli','sFas'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['Stimuli','Mcl1','sFas'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['APP'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DAB1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['NDEL1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['CDK5'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DIXDC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['NDEL1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['RXR'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CCND1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['POU5F1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['TCF3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['miR143'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR106a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR130a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR125b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes( text )
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states( text=text, turnon=['ZNF365'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['GSK3B'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['DISC1'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['NDEL1'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['CCND1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CREB1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['KLF4'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['TCF3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['miR33b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR103a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['miR33b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['miR103a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['PCM1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['BBS4'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['PCM1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['CCND1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CREB1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['POU5F1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['CCND1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['RXR'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['TAL1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['TCF3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['STAT3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
2: i *= n and (dext or jext)
2: n *= i
2: j *= i and not (j and next) or jagged_oe and jagged_ko
2: v *= vr and vext
2: d *= v or not i and not (d and next)
2: vr *= v or not i
3: tip *= d and vr and not i and not n and not j
3: stalk *= i and n and j and not vr and not d
3: tip_stalk *= d and vr and i and n and j
3: hybrid *= not (tip or stalk or tip_stalk)
""".format(n0, d0, j0, i0, vr0, v0, dext0, jext0, vext0, next0)
model = Model(text = text_ics, mode = 'sync')
on = ['jagged_oe', 'j']
off = ['jagged_ko', 'j']
text_mod = boolean2.modify_states(text = text_ics, turnon = on)
model_jag_oe = Model(text = text_mod, mode = 'sync')
text_mod2 = boolean2.modify_states(text = text_ics, turnoff = off)
model_jag_ko = Model(text = text_mod2, mode = 'sync')
model.initialize()
model_jag_oe.initialize()
model_jag_ko.initialize()
model.iterate(steps = 20)
model_jag_oe.iterate(steps = 20)
model_jag_ko.iterate(steps = 20)
n = model.data['n']
d = model.data['d']
j = model.data['j']
i = model.data['i']
vr = model.data['vr']
# this collects the state of all nodes
NODES = boolean2.all_nodes( text )
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states( text=text, turnon=['SMAD2'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['SMAD3'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['POU5F1'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
mtext = boolean2.modify_states( text=text, turnon=['STAT3'] )
avgs = run( text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append( avgs )
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['SOX10'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['FOXD3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['TAL1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['TCF3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['STAT3'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['CREB1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['miR155'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR103a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR20a'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR10b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['RHEB'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['RHEB'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['DISC1'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
# this collects the state of all nodes
NODES = boolean2.all_nodes(text)
#
# raise this for better curves (will take about 2 seconds per repeat)
# plots were made for REPEAT = 1000, STEPS=150
#
REPEAT = 1000
STEPS = 150
data = []
print '- starting simulation with REPEAT=%s, STEPS=%s' % (REPEAT, STEPS)
# multiple overexrpessed nodes
mtext = boolean2.modify_states(text=text, turnon=['miR125b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnon=['miR20b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['miR125b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
mtext = boolean2.modify_states(text=text, turnoff=['miR20b'])
avgs = run(text=mtext, repeat=REPEAT, nodes=NODES, steps=STEPS)
data.append(avgs)
model.initialize()
#model evolution
model.iterate( steps=5 )
for state in model.states:
print state
#Cycle detection
#size, index = model.detect_cycles()
#print "Size =%s, Index %s" % (size, index)
model.report_cycles()
##State modification: specifying gene deletion(s) and observing the change in attractors
on = ["A", "B"]
off = ["C"]
new_text = boolean2.modify_states(text, turnon=on, turnoff=off) ##need to upload rules from a text file?
model = boolean2.Model(new_text, mode='sync')
model.initialize()
#model evolution
model.iterate( steps=5 )
for state in model.states:
print state
#Cycle detection- detecting attractors
model.report_cycles()
