def test_lipo_sites_builder_abbrev():
model_dict = {'builder': 'lipo_sites',
'activation': 1,
'baxtranslocation': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
eq_(bd.model.name, 'ls_Baxtr1Activ1')
def test_one_cpt_builder_abbrev():
model_dict = {'builder': 'one_cpt',
'activation': 1,
'baxtranslocation': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
eq_(bd.model.name, '1c_Baxtr1Activ1')
def test_translocation_1():
"""Check translocation by checking for Bax_mono_translocates_sol_to_ves."""
model_dict = {'baxtranslocation': 1,
'bidtranslocation': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
print bd.model.rules
ok_(bd.model.rules['tBid_translocates_sol_to_ves'])
ok_(bd.model.rules['Bax_mono_translocates_sol_to_ves'])
from tbidbaxlipo.models.one_cpt import Builder
from pylab import *
from pysb.integrate import odesolve
b = Builder()
b.build_model_t()
# Do liposome titration
lipo_concs = logspace (-9, 3, 50)
t = linspace(0, 500, 200)
ion()
figure()
tBid_0 = b.model.parameters['tBid_0'].value
Bax_0 = b.model.parameters['Bax_0'].value
tBid_fracs_bound = np.zeros(len(lipo_concs))
Bax_fracs_bound = np.zeros(len(lipo_concs))
for i, lipo_conc in enumerate(lipo_concs):
b.model.parameters['Vesicles_0'].value = lipo_conc
x = odesolve(b.model, t)
plot(t, x['mtBid'] / tBid_0)
plot(t, x['mBax'] / Bax_0)
tBid_fracs_bound[i] = x['mtBid'][-1] / tBid_0
Bax_fracs_bound[i] = x['mBax'][-1] / Bax_0
show()
figure()
plot(lipo_concs, tBid_fracs_bound)
'Bax_NBD_0': 100.,
'tBid_0': 20.,
'tBid_transloc_kf': 10**-1.89187047,
'tBid_transloc_kr': 10**-4.22923156,
'Bax_transloc_kf': 10**-5.47509186,
'Bax_transloc_kr': 10**-4.50648375,
'tBid_Bax_mem_bh3_kf': 10**-5.07143377,
'tBid_Bax_mem_bh3_kr': 10**3.70925668,
'tBid_Bax_ins_bh3_kc': 10**-1.46660821,
'c1_scaling': 10**0.982916,
'c2_scaling': 10**0.32449226,
'bid_fret1': 10**1.92229914,
}
"""
bd = Builder(params_dict=params_dict)
bd.build_model_from_dict(model_dict)
print(bd.model.parameters)
t = time_36
s = Solver(bd.model, t)
s.run()
n_species = s.y.shape[1]
plt.ion()
for spec_ix in range(n_species):
plt.figure()
plt.title(bd.model.species[spec_ix])
plt.plot(t, s.y[:,spec_ix])
#plt.ion()
'model' not in kwargs or \
'nbd_observables' not in kwargs:
raise Exception('One or more needed arguments was not specified! ' \
'Arguments must include nbd_sites, random_seed, model, ' \
'nbd_observables and nsteps.')
# Because the NBD site(s) to fit has to be specified when the model
# builder object is created, we get this arg first.
# If more than one NBD site has been specified, it should be separated:
nbd_sites = kwargs['nbd_sites'].split(SEP_CHAR)
for site in nbd_sites:
if site not in nbd_site_names:
raise Exception('%s is not an allowed NBD site!' % site)
# Now that we have the NBD site we can instantiate the Builder:
builder = Builder(nbd_sites=nbd_sites)
# The observable associated with the NBD site signal also has to be
# specified:
observables = [o.name for o in builder.model.observables]
# Again, parse in case there is more than one observable:
nbd_observables = kwargs['nbd_observables'].split(SEP_CHAR)
for nbd_obs in nbd_observables:
if nbd_obs not in observables:
raise Exception('%s is not an allowed NBD observable!' % nbd_obs)
# Make sure the number of nbd sites matches the number of observables:
if len(nbd_sites) != len(nbd_observables):
raise Exception('The number of nbd_sites must match the number of '
'nbd_observables!')
from tbidbaxlipo.models.one_cpt import Builder
from pysb.integrate import Solver
import numpy as np
from matplotlib import pyplot as plt
from tbidbaxlipo.util import fitting
b = Builder()
#b.build_model_nbd_2_conf()
b.build_model_nbd_2_conf_dimer()
t = np.linspace(0, 100000, 1e3)
sol = Solver(b.model, t)
plt.ion()
plt.close('all')
bax_concs = np.logspace(0, 3, 30)
k_list = []
fmax_list = []
for bax_ix, bax_conc in enumerate(bax_concs):
b.model.parameters['Bax_0'].value = bax_conc
#b.model.parameters['Vesicles_0'].value = bax_conc
sol.run()
plt.figure('NBD')
plt.plot(t, sol.yexpr['NBD'])
fmax = fitting.Parameter(5.)
k = fitting.Parameter(5e-4)
def exp_func(t):
return fmax()*(1 - np.exp(-k()*t)) + 1.
fitting.fit(exp_func, [k, fmax], sol.yexpr['NBD'], t)
nbd_site = '47'
activator = 'Bid'
rep_index = 1
rt = df[(activator, 'Release', nbd_site, rep_index, 'TIME')].values
ry = df[(activator, 'Release', nbd_site, rep_index, 'VALUE')].values
nt = df[(activator, 'NBD', nbd_site, rep_index, 'TIME')].values
ny = df[(activator, 'NBD', nbd_site, rep_index, 'VALUE')].values
ry = ry / 100.
t = rt
lipo_conc = 1.9
# Build the MCMCOpts
# ==================
from tbidbaxlipo.models.one_cpt import Builder
#from tbidbaxlipo.models.enz_cpt import Builder
builder = Builder()
#builder.build_model_nbd_terbium()
builder.build_model_nbd_terbium_c2_pores()
# We set the random_seed here because it affects our choice of initial
# values
random_seed = 1
np.random.seed(random_seed)
opts = bayessb.MCMCOpts()
opts.model = builder.model
opts.tspan = t
opts.estimate_params = builder.estimate_params
#opts.estimate_params = [p for p in b.model.parameters
# if not p.name.endswith('_0')]
#opts.initial_values = [p.value for p in b.estimate_params]
from tbidbaxlipo.models.one_cpt import Builder import numpy as np from matplotlib import pyplot as plt from pysb.integrate import odesolve b = Builder() b.translocate_Bax() b.basal_Bax_activation() b.Bax_nmerizes(4) b.pores_from_Bax_nmers(4) t = np.linspace(0, 10000, 1000) x = odesolve(b.model, t) plt.ion() plt.figure() plt.plot(t, x['pores'])
def test_error_on_bleach_without_nbd():
model_dict = {'baxtranslocation': 1,
'activation': 1,
'bleach': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
def test_key_error_on_unknown_feature():
model_dict = {'asdf': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
def test_error_on_unknown_implementation():
model_dict = {'baxtranslocation': 9}
bd = Builder()
bd.build_model_from_dict(model_dict)
def test_model_name_sort_order():
model_dict = {'activation': 1,
'baxtranslocation': 1}
bd = Builder()
bd.build_model_from_dict(model_dict)
eq_(bd.model.name, 'Baxtr1Activ1')
