print '- completed'
avgs = coll.get_averages(normalize=True)
return avgs
if __name__ == '__main__':
# read in the text
text = file('sim1.txt').read()
# the nodes of interest that are collected over the run
# NODES = 'Apoptosis STAT3 FasL Ras'.split()
# 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)
coll.collect( states=engine.states, nodes=nodes )
print '- completed'
avgs = coll.get_averages( normalize=True )
return avgs
if __name__ == '__main__':
# read in the text
text = file( 'LGL.txt').read()
# the nodes of interest that are collected over the run
# NODES = 'Apoptosis STAT3 FasL Ras'.split()
# 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)
