def plot_effect_of_pore_reverse_rate():
ci = color_iter()
pore_reverse_rates = [1e-2, 1e-4, 1e-6]
t = np.linspace(0, 5000, 500)
plt.figure()
for pore_reverse_rate in pore_reverse_rates:
params_dict = {'Bax_0': 50., 'Vesicles_0': 50.,
'pore_reverse_rate_k': pore_reverse_rate}
b = one_cpt.Builder(params_dict=params_dict)
b.build_model_bax_schwarz_reversible()
x = odesolve(b.model, t)
avg_pores = x['pores']/b.model.parameters['Vesicles_0'].value
col = ci.next()
plt.plot(t, avg_pores, color=col, linestyle='-',
label="Pores, reverse rate %g" % pore_reverse_rate)
plt.plot(t, 1 - np.exp(-avg_pores), color=col, linestyle='--',
label="Dye release, reverse rate %g" % pore_reverse_rate)
plt.legend(loc='upper left')
plt.xlabel('Time (sec)')
plt.ylabel('Dye release/Avg. pores')
plt.title('Dye release/pore formation with varying reverse rates')
plt.show()
def run_model_ode(tmax=12000):
from pysb.integrate import odesolve
t = np.linspace(0, tmax, 1000)
x = odesolve(model, t)
ci = color_iter()
plt.figure(1)
# Translocation
plt.plot(t, (x['ctBid'])/tBid_0.value, label='ctBid', color=ci.next())
plt.plot(t, (x['mtBid'])/tBid_0.value, label='mtBid', color=ci.next())
plt.plot(t, (x['cBax'])/Bax_0.value, label='cBax', color=ci.next())
plt.plot(t, (x['mBax'])/Bax_0.value, label='mBax', color=ci.next())
plt.legend()
def run_model(self, tmax=12000, num_sims=1, use_kappa=True,
figure_ids=[0, 1]):
xrecs = [] # The array to store the simulation data
dr_all = [] # TODO: Delete this
# Run multiple simulations and collect data
for i in range(0, num_sims):
# Run simulation using Kappa:
if use_kappa:
ssa_result = kappa.run_simulation(self.model, time=tmax,
points=100,
output_dir='simdata')
xrecs.append(ssa_result)
# Run simulation using BNG SSA implementation:
else:
ssa_result = bng.run_ssa(self.model, t_end=tmax, n_steps=100,
cleanup=True)
xrecs.append(ssa_result)
#dr_all.append(get_dye_release(model, 'pores', ssa_result))
# Convert the multiple simulations in an array...
xall = array([x.tolist() for x in xrecs])
# ...and calculate the Mean and SD across the simulations
x_std = recarray(xrecs[0].shape, dtype=xrecs[0].dtype, buf=std(xall, 0))
x_avg = recarray(xrecs[0].shape, dtype=xrecs[0].dtype,
buf=mean(xall, 0))
# Plotting parameters, aliases
ci = color_iter()
marker = 'x'
linestyle = '-'
tBid_0 = self['tBid_0']
Bax_0 = self['Bax_0']
# Translocation: plot cyto/mito tBid, and cyto/mito Bax
plt.ion()
plt.figure(figure_ids[0])
plt.errorbar(x_avg['time'], x_avg['ctBid']/tBid_0.value,
yerr=x_std['ctBid']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mtBid']/tBid_0.value,
yerr=x_std['mtBid']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['cBax']/Bax_0.value,
yerr=x_std['cBax']/Bax_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mBax']/Bax_0.value,
yerr=x_std['mBax']/Bax_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
# Activation: plot iBax and tBidBax
plt.errorbar(x_avg['time'], x_avg['iBax']/Bax_0.value,
yerr=x_std['iBax']/Bax_0.value, label='iBax',
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['tBidBax']/tBid_0.value,
yerr=x_std['tBidBax']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
# Dye release calculated exactly ----------
#dr_avg = mean(dr_all, 0)
#dr_std = std(dr_all, 0)
#errorbar(x_avg['time'], dr_avg,
# yerr=dr_std, label='dye_release',
# color=ci.next(), marker=marker, linestyle=linestyle)
# Pore Formation
#plot(x['time'], x['pBax']/Bax_0.value, label='pBax')
#leg = legend()
#ltext = leg.get_texts()
#setp(ltext, fontsize='small')
#xlabel("Time (seconds)")
#ylabel("Normalized Concentration")
#ci = color_iter()
# Plot pores/vesicle in a new figure ------
#figure(2)
#errorbar(x_avg['time'], x_avg['pores'] / float(NUM_COMPARTMENTS),
# yerr=x_std['pores']/float(NUM_COMPARTMENTS), label='pores',
# color=ci.next(), marker=marker, linestyle=linestyle)
#F_t = 1 - dr_avg
#pores_poisson = -log(F_t)
#plot(x_avg['time'], pores_poisson, color=ci.next(), label='-ln F(t),
# stoch',
# marker=marker, linestyle=linestyle)
#xlabel("Time (seconds)")
#ylabel("Pores/vesicle")
#title("Pores/vesicle")
#legend()
#xlabel("Time (seconds)")
#ylabel("Dye Release")
#title("Dye release calculated via compartmental model")
return xrecs[0]
def plot_simulation(x_avg, x_std, dr_all, model, num_sims, figure_ids=[0, 1]):
ci = color_iter()
marker = ','
linestyle = ''
tBid_0 = model.parameters['tBid_0'].value
Bax_0 = model.parameters['Bax_0'].value
Vesicles_0 = model.parameters['Vesicles_0'].value
# Translocation
plt.figure(figure_ids[0])
plt.errorbar(x_avg['time'], x_avg['ctBid']/tBid_0,
yerr=(x_std['ctBid']/tBid_0)/np.sqrt(num_sims),
label='ctBid',
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mtBid']/tBid_0,
yerr=(x_std['mtBid']/tBid_0)/np.sqrt(num_sims),
label='mtBid',
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['cBax']/Bax_0,
yerr=(x_std['cBax']/Bax_0)/np.sqrt(num_sims),
label='cBax',
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mBax']/Bax_0,
yerr=(x_std['mBax']/Bax_0)/np.sqrt(num_sims),
label='mBax',
color=ci.next(), marker=marker, linestyle=linestyle)
# Activation
plt.errorbar(x_avg['time'], x_avg['iBax']/Bax_0,
yerr=(x_std['iBax']/Bax_0)/np.sqrt(num_sims),
label='iBax', color=ci.next(), marker=marker,
linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['tBidBax']/tBid_0,
yerr=(x_std['tBidBax']/tBid_0)/np.sqrt(num_sims),
label='tBidBax', color=ci.next(), marker=marker,
linestyle=linestyle)
# Pore Formation
plt.errorbar(x_avg['time'], x_avg['pBax']/Bax_0,
yerr=(x_std['pBax']/Bax_0)/np.sqrt(num_sims),
color=ci.next(),
label='pBax', marker=marker, linestyle=linestyle)
ci = color_iter()
# Dye release calculated exactly ----------
dr_avg = np.mean(dr_all, 0)
dr_std = np.std(dr_all, 0)
plt.errorbar(x_avg['time'], dr_avg,
yerr=dr_std/np.sqrt(num_sims), label='dye_release',
color=ci.next(), marker=marker, linestyle=linestyle)
fontP = FontProperties()
fontP.set_size = ('small')
plt.legend(loc='upper center', prop=fontP, ncol=5,
bbox_to_anchor=(0.5, 1.1), fancybox=True, shadow=True)
plt.xlabel("Time (seconds)")
plt.ylabel("Normalized Concentration")
plt.show()
# Plot pores/vesicle in a new figure ------
ci = color_iter()
plt.figure(figure_ids[1])
plt.errorbar(x_avg['time'], x_avg['pores']/Vesicles_0,
yerr=(x_std['pores']/Vesicles_0)/np.sqrt(num_sims),
label='pores/ves', color=ci.next(), marker=marker,
linestyle=linestyle)
plt.legend(loc='upper center', prop=fontP, ncol=1,
bbox_to_anchor=(0.5, 1.1), fancybox=True, shadow=True)
plt.show()
#F_t = 1 - dr_avg
#pores_poisson = -log(F_t)
#plot(x_avg['time'], pores_poisson, color=ci.next(), label='-ln F(t),
# stoch', marker=marker, linestyle=linestyle)
#xlabel("Time (seconds)")
#ylabel("Pores/vesicle")
#title("Pores/vesicle")
#legend()
#xlabel("Time (seconds)")
#ylabel("Dye Release")
#title("Dye release calculated via compartmental model")
return