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)