def setUp(self):
self.working_directory = os.path.join(os.path.dirname(__file__),
'viz_tests')
if not os.path.isdir(self.working_directory):
os.makedirs(self.working_directory)
copasi_file = os.path.join(self.working_directory, 'test_model.cps')
with model.BuildAntimony(copasi_file) as loader:
self.mod = loader.load("""
model negative_feedback
compartment cell = 1.0
var A in cell
var B in cell
var Signal in cell
var C in cell
vAProd = 0.1
kADeg = 0.2
kBProd = 0.3
kBDeg = 0.4
kCProd = 0.5
kCDeg = 0.6
A = 0
B = 0
C = 0
Signal = 10
AProd: $Signal => A; cell*vAProd*Signal
ADeg: A =>; cell*kADeg*A*B;
BProd: => B; cell*kBProd*A;
BDeg: B => ; cell*kBDeg*B;
CProd: => C ; cell*kCProd*B;
CDeg: C => ; cell*kCDeg*C
end
""")
## initialize empty dict to store TimeCourse objects
self.TC = tasks.TimeCourse(self.mod,
end=10,
intervals=10,
step_size=1,
global_quantities=[],
y=['A', 'B', 'C'])
tasks
## format the time course data
df = utils.format_timecourse_data(self.TC.report_name)
## write data to file
df.to_csv(self.TC.report_name, sep='\t', index=False)
self.MPE = tasks.MultiParameterEstimation(self.mod,
self.TC.report_name,
method='genetic_algorithm',
population_size=25,
number_of_generations=50,
run_mode=True)
self.MPE.write_config_file()
self.MPE.setup()
self.MPE.run()
def setUp(self):
working_directory = os.path.dirname(__file__)
copasi_file = os.path.join(working_directory, 'quick_start_model.cps')
if os.path.isfile(copasi_file):
os.remove(copasi_file)
kf = 0.01
kb = 0.1
kcat = 0.05
with model.Build(copasi_file) as m:
m.name = 'Michaelis-Menten'
m.add('compartment', name='Cell')
m.add('metabolite', name='P', concentration=0)
m.add('metabolite', name='S', concentration=30)
m.add('metabolite', name='E', concentration=10)
m.add('metabolite', name='ES', concentration=0)
m.add('reaction', name='S bind E', expression='S + E -> ES', rate_law='kf*S*E',
parameter_values={'kf': kf})
m.add('reaction', name='S unbind E', expression='ES -> S + E', rate_law='kb*ES',
parameter_values={'kb': kb})
m.add('reaction', name='ES produce P', expression='ES -> P + E', rate_law='kcat*ES',
parameter_values={'kcat': kcat})
michaelis_menten = model.Model(copasi_file)
report = 'parameter_estimation_synthetic_data.txt'
TC = tasks.TimeCourse(
michaelis_menten, start=0, end=100, intervals=10, step_size=10, report_name=report
)
misc.correct_copasi_timecourse_headers(TC.report_name)
fit1 = tasks.MultiParameterEstimation(
michaelis_menten, TC.report_name, copy_number=2, pe_number=2,
lower_bound=1e-3, upper_bound=5e3,
results_directory='fit1',
method='genetic_algorithm_sr', population_size=1, number_of_generations=1,
overwrite_config_file=True
)
fit1.write_config_file()
fit1.setup()
fit1.run()
self.fit1 = fit1
self.model = michaelis_menten
import time
time.sleep(5)
def do_parameter_estimation(self):
MPE = tasks.MultiParameterEstimation(self.mod,
self.TC.report_name,
lower_bound=0.01,
upper_bound=1,
run_mode=True,
pe_number=2,
method='genetic_algorithm',
number_of_generations=10,
population_size=10)
MPE.write_config_file()
MPE.setup()
MPE.run()
return MPE
def test_from_folder_generated_with_chaser_estimations(self):
MPE = tasks.MultiParameterEstimation(self.mod,
self.TC.report_name,
method='genetic_algorithm',
population_size=5,
number_of_generations=5,
run_mode=True,
copy_number=self.copy_number,
pe_number=self.pe_number)
MPE.write_config_file()
MPE.setup()
MPE.run()
CPE = tasks.ChaserParameterEstimations(MPE,
truncate_mode='ranks',
theta=list(range(2)),
tolerance=1e-2,
iteration_limit=5,
run_mode=True)
data = viz.Parse(CPE.results_directory,
copasi_file=CPE.model.copasi_file).data
print(data)
self.assertEqual(data.shape[0], 2)
def setUp(self):
## create model selection directory
self.dire = os.path.join(os.path.dirname(__file__),
'ChaserEstimationTests')
if not os.path.isdir(self.dire):
os.makedirs(self.dire)
self.copasi_file = os.path.join(self.dire, 'negative_feedback.cps')
with model.BuildAntimony(self.copasi_file) as loader:
self.mod = loader.load("""
model model1
compartment cell = 1.0
var A in cell
var B in cell
vAProd = 0.1
kADeg = 0.2
kBProd = 0.3
kBDeg = 0.4
vBasalAProd = 0.001
A = 0
B = 0
AProd: => A; cell*vAProd*B+vBasalAProd
ADeg: A =>; cell*kADeg*A
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
end
""")
self.TC1 = tasks.TimeCourse(self.mod,
end=1000,
step_size=100,
intervals=10,
report_name='report1.txt')
utils.format_timecourse_data(self.TC1.report_name)
## add some noise
data1 = misc.add_noise(self.TC1.report_name)
## remove the data
os.remove(self.TC1.report_name)
## rewrite the data with noise
data1.to_csv(self.TC1.report_name, sep='\t')
self.copy_number = 2
self.pe_number = 3
self.MPE = tasks.MultiParameterEstimation(self.mod,
self.TC1.report_name,
copy_number=self.copy_number,
pe_number=self.pe_number,
method='genetic_algorithm',
population_size=10,
number_of_generations=10,
overwrite_config_file=True,
results_directory='test_mpe',
run_mode=True)
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.MPE.write_config_file()
self.MPE.setup()
self.MPE.run()
