def computeHittingTimes(settings, reverse=False):
myMultistrand = MergeSim()
myMultistrand.setNumOfThreads(NUM_PROCESS)
if settings.type == enum_yurke2:
myMultistrand.setOptionsFactory1(simulationYurke2, settings.nTrials)
if settings.type == enum_bonnet:
myMultistrand.setOptionsFactory2(simulationHairpin, settings.nTrials,
reverse)
if settings.type == enum_flamm:
myMultistrand.setOptionsFactory1(simulationFlamm2000, settings.nTrials)
if settings.type == enum_yurke:
myMultistrand.setOptionsFactory1(simulationYurke, settings.nTrials)
if settings.type == enum_rickettsia:
myMultistrand.setOptionsFactory1(simulationRickettsia,
12 * settings.nTrials)
myMultistrand.setTerminationCriteria(terminationCount=settings.nTrials)
if settings.type == enum_bonnet or settings.type == enum_yurke2:
myMultistrand.setPassageMode() # using the pre-set success / fail
if settings.type == enum_flamm or settings.type == enum_yurke or settings.type == enum_rickettsia:
# non-first stepping mode, no need to store trajectory information
myMultistrand.setPassageMode()
myMultistrand.run()
return myMultistrand.results
def computeRate(selector):
myMultistrand = MergeSim()
myMultistrand.setOptionsFactory2(genOptions, NUMOFPATHS, selector)
myMultistrand.setNumOfThreads(NUMOFTHREADS)
myMultistrand.setTerminationCriteria(TERMINATIONCRIT)
myMultistrand.setLeakMode() # the new leak object -- faster bootstrapping.
myMultistrand.printTrajectory()
return 0.0, 0.0, 0.0
myMultistrand.run()
myFSR = myMultistrand.results
low, high = myFSR.doBootstrap()
return myFSR.k1(), low, high
def estimateSuccessProbability(popularStructure, settings):
# Do a quick first step simulation and see how many end up succeeding.
# Use a new multistrand object so we don't lose the old results
newMultistrand = MergeSim()
newMultistrand.setNumOfThreads(myMultistrand.numOfThreads)
oldTrials = settings.trials
settings.trials = int(settings.trials / 50.0)
settings.initialStructure = popularStructure
first_step_simulation(newMultistrand, settings)
settings.trials = oldTrials
output = np.float(newMultistrand.nForward.value) / np.float(
newMultistrand.trialsPerThread * newMultistrand.numOfThreads)
return output
def first_passage_association(strand_seq, trials, concentration, T=20.0):
thisMS = MergeSim()
thisMS.setNumOfThreads(8)
print "Running first passage time simulations for association of %s at %s..." % (
strand_seq, concentration_string(concentration))
def getOptions(trials):
o = standardOptions(Literals.first_passage_time, TEMPERATURE, trials,
ATIME_OUT)
hybridization(o, strand_seq, trials, True)
setSaltGao2006(o)
o.join_concentration = concentration
o.DNA23Metropolis()
return o
thisMS.setOptionsFactory1(getOptions, trials)
thisMS.setPassageMode()
thisMS.run()
return thisMS
def first_step_simulation(strand_seq, trials, T=20.0):
myMS = MergeSim()
myMS.setNumOfThreads(8)
print(
"Running first step mode simulations for %s (with Boltzmann sampling)..."
% (strand_seq))
def getOptions(trials):
o = standardOptions(Literals.first_step, TEMPERATURE, trials,
ATIME_OUT)
hybridization(o, strand_seq, trials)
setSaltGao2006(o)
o.DNA23Metropolis()
return o
myMS.setOptionsFactory1(getOptions, trials)
myMS.setFirstStepMode() # ensure the right results object is set.
# myMultistrand.setLeakMode()
myMS.setTerminationCriteria(terminationCount=trials)
myMS.run()
return myMS
# FD: This script is now set to use 4 threads and just 50,000 trajectories.
# FD: This is different from the results in case1.pdf
# FD: The results of this study heavily depend on the parameterization of the Metropolis model: JS or DNA23 (see below).
from multistrand.objects import StopCondition, Domain, Complex, Strand
from multistrand.options import Options, Literals
from multistrand.concurrent import MergeSim
from multistrand._options.interface import FirstStepResult
import numpy as np
ATIME_OUT = 10.0
myMultistrand = MergeSim()
myMultistrand.setNumOfThreads(8)
myMultistrand.setLeakMode()
def first_step_simulation(strand_seq, trials, T=25, material="DNA"):
print(
"Running first step mode simulations for %s (with Boltzmann sampling)..."
% (strand_seq))
# Using domain representation makes it easier to write secondary structures.
onedomain = Domain(name="onedomain", sequence=strand_seq)
gdomain = Domain(name="gdomain", sequence="TTTT")
top = Strand(name="top", domains=[onedomain])
bot = top.C
# myMultistrand.clear()
if __name__ == '__main__':
if len(sys.argv) < 1:
print """Usage:
python hybridization_F3 <numOfThreads> <numOfPaths> P0/P3/P4 \n
Example: python hybridization_F3 2 100 P3
"""
sys.exit()
print sys.argv
numOfThreads = np.int(sys.argv[1])
numOfPaths = np.int(sys.argv[2])
toggle = str(sys.argv[3])
myMultistrand.setNumOfThreads(numOfThreads)
if toggle == "test":
doInference('TACCGT', "P0-test", 10, numOfPaths) # P0
if toggle == "test2":
doInference(goa2006_P0, "P0-test", 10, numOfPaths) # P0
if toggle == "P0":
doInference(goa2006_P0, toggle, 14, numOfPaths) # P0
if toggle == "P3":
doInference(goa2006_P3, toggle, 14, numOfPaths) # P3
if toggle == "P4":
doInference(goa2006_P4, toggle, 14, numOfPaths) # P4