def timecourse(run):
print 'ro'
# print run.objectives
print [(i, o.bind.label) for i, o in enumerate(run.objectives)]
tau = run.objectives[0].value
# magnitude = run.objectives[2].value
pi_conc = run.all_parameters['initial_pi_concentration']
ftc = run.all_parameters['filament_tip_concentration']
asymp_val = run.objectives[0].value * ftc
print 'Tau =', tau
# print 'Magnitude =', magnitude
print 'FTC =', ftc
print '[Pi]_0 =', pi_conc
print 'Asymptotic [ADPPi] =', asymp_val
pi = run.analyses['Pi']
adppi_count = run.analyses['ADPPi']
adppi = numerical.measurements.scale(adppi_count, ftc)
# pylab.figure()
# measurements.line(pi, label='Simulated [Pi]', color='red')
measurements.line(adppi, label='Simulated [F-ADPPi]', color='green')
# x = numpy.array(pi[0])
# y = magnitude * (1 - numpy.exp(-x / tau))
# measurements.line((x,y), label='Halftime = %s' % tau, color='blue')
# a = pylab.gca()
# a.set_xscale('log')
# a.set_yscale('log')
rho = run.all_parameters['release_cooperativity']
rate = run.all_parameters['release_rate']
# nh_conc = run.all_parameters['initial_nh_atp_concentration']
pylab.legend(loc=5)
pylab.title('Copolymerization, rho = %s, rate = %s' % (rho, rate))
pylab.xlabel('Time (seconds)')
pylab.ylabel('[Pi] (uM)')
def timecourse(run):
# Plot concentrations
measurements.line(run.analyses['ATP'], color='red',
label='[G-ATP-Actin] uM')
# measurements.line(run.analyses['Pi'], color='green',
# label='[Pi] uM')
measurements.line(run.analyses['ADP'], color='blue',
label='[G-ADP-Actin] uM')
raw_length = run.analyses['length']
scaled_length = numerical.measurements.scale(raw_length,
run.all_parameters['filament_tip_concentration'])
measurements.line(scaled_length, label='[F-actin]')
raw_adppi_count = run.analyses['adppi_count']
scaled_adppi_count = numerical.measurements.scale(raw_adppi_count,
run.all_parameters['filament_tip_concentration'])
measurements.line(scaled_adppi_count, label='[F-ADPPi-actin]', color='green')
# print numpy.array(run.analyses['TAG'])[1]/run.all_parameters['filament_tip_concentration']
# measurements.line(run.analyses['TAG'], color='cyan', label='NX Events')
pylab.legend()
pylab.xlabel('Time (s)')
pylab.ylabel('Concentration (uM)')
def D_vs_concentration(session, cc_scale=False, **kwargs):
# e = session.get_experiment('critical_concentration')
e = session.get_experiment('fujiwara_2002')
D_ob = e.objectives['final_diffusion_coefficient']
D_s = slicing.Slicer.from_objective_bind(D_ob)
Ds, name, concentration_mesh = D_s.minimum_values('atp_concentration')
j_ob = e.objectives['final_elongation_rate']
j_s = slicing.Slicer.from_objective_bind(j_ob)
js, name, concentration_mesh = j_s.minimum_values('atp_concentration')
concentration_mesh = concentration_mesh[0]
if cc_scale:
cc = zero_crossings(concentration_mesh, js)[0]
concentration_mesh = numpy.array(concentration_mesh) / cc
print 'cc =', cc
# pylab.figure()
pylab.subplot(2,1,1)
measurements.line((concentration_mesh, Ds), **kwargs)
if cc_scale:
pylab.axvline(x=1, color='black')
pylab.ylabel('Tip Diffusion Coefficient (mon**2 /s)')
pylab.subplot(2,1,2)
zero_concentrations = [concentration_mesh[0], concentration_mesh[-1]]
zero_values = [0, 0]
measurements.line((zero_concentrations, zero_values))
measurements.line((concentration_mesh, js), **kwargs)
if cc_scale:
pylab.axvline(x=1, color='black')
pylab.ylabel('Elongation Rate (mon /s )')
if cc_scale:
pylab.xlabel('[G-ATP-actin] (critical concentrations)')
else:
pylab.xlabel('[G-ATP-actin] (uM)')
pi_conc = run.all_parameters['initial_pi_concentration']
ftc = run.all_parameters['filament_tip_concentration']
asymp_val = run.objectives[0].value * ftc
print 'Tau =', tau
# print 'Magnitude =', magnitude
print 'FTC =', ftc
print '[Pi]_0 =', pi_conc
print 'Asymptotic [ADPPi] =', asymp_val
pi = run.analyses['Pi']
adppi_count = run.analyses['ADPPi']
adppi = numerical.measurements.scale(adppi_count, ftc)
# pylab.figure()
# measurements.line(pi, label='Simulated [Pi]', color='red')
measurements.line(adppi, label='Simulated [F-ADPPi]', color='green')
# x = numpy.array(pi[0])
# y = magnitude * (1 - numpy.exp(-x / tau))
# measurements.line((x,y), label='Halftime = %s' % tau, color='blue')
# a = pylab.gca()
# a.set_xscale('log')
# a.set_yscale('log')
rho = run.all_parameters['release_cooperativity']
rate = run.all_parameters['release_rate']
# nh_conc = run.all_parameters['initial_nh_atp_concentration']
pylab.legend(loc=5)
pylab.title('Copolymerization, rho = %s, rate = %s' % (rho, rate))
pylab.xlabel('Time (seconds)')
pylab.ylabel('[Pi] (uM)')
def individual_timecourses(run, xmin=10000, xmax=11000, ymin=2600, ymax=3100):
pylab.figure()
pylab.subplot(2,1,1)
measurements.line(run.analyses['filament_0_length'], color='red')
measurements.line(run.analyses['filament_1_length'], color='green')
measurements.line(run.analyses['filament_2_length'], color='blue')
pylab.xlim(xmin, xmax)
pylab.ylim(ymin, ymax)
pylab.subplot(2,1,2)
measurements.line(run.analyses['filament_0_state'], color='red')
measurements.line(run.analyses['filament_1_state'], color='green')
measurements.line(run.analyses['filament_2_state'], color='blue')
pylab.xlim(xmin, xmax)
def validate_D(run):
measurements.line(run.analyses['final_tip_fluctuations'])
