# 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)
fname = 'miRNA3.bin'
util.bsave(data, fname=fname)
print '- data saved into %s' % fname
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
if __name__ == '__main__':
# more repeats - better curve, this run takes about
# plot was made with REPEAT=300, STEPS=10 it took about 5 minutes to run
REPEAT = 300
STEPS = 10
FULLT = 10
text = file('ABA.txt').read()
data = find_stdev(text=text,
node='Closure',
knockouts='WT pHc PA'.split(),
repeat=REPEAT,
steps=STEPS)
muts = run_mutations(text, repeat=REPEAT, steps=STEPS)
obj = dict(data=data, muts=muts)
util.bsave(obj=obj, fname='ABA-run.bin')
print('finished simulation')
# 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
data = [model1.data, model2.data, model1.t]
# it is a binary save ( pickle )
util.bsave(data, 'Bb-run.bin')
# 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 )
fname = 'LGL-run.bin'
util.bsave( data, fname=fname )
print '- data saved into %s' % fname
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
if __name__ == '__main__':
# more repeats - better curve, this run takes about
# plot was made with REPEAT=300, STEPS=10 it took about 5 minutes to run
REPEAT = 300
STEPS = 10
FULLT = 10
text = file( 'ABA.txt').read()
data = find_stdev( text=text, node='Closure',knockouts='WT pHc PA'.split(), repeat=REPEAT, steps=STEPS)
muts = run_mutations( text, repeat=REPEAT, steps=STEPS )
obj = dict( data=data, muts=muts )
util.bsave( obj=obj, fname='ABA-run.bin' )
print 'finished simulation'
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
data = [ model1.data, model2.data, model1.t ]
# it is a binary save ( pickle )
util.bsave(data, 'Bb-run.bin' )
