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'])