bax_bgsub = subtract_background(bax_only_set, buffer_background)
# Fret efficiency
fret_means = np.zeros(len(bax_lipos_names))
conc_list = np.zeros(len(bax_lipos_names))
fret_sds = np.zeros(len(bax_lipos_names))
for i, cond_name in enumerate(bax_lipos_names):
conc_list[i] = float(cond_name.split(' ')[1])
f_da = np.mean(bax_lipos_bgsub[cond_name][VALUE])
f_da_sd = np.std(bax_lipos_bgsub[cond_name][VALUE], ddof=1)
f_d = np.mean(bax_bgsub[bax_only_names[i]][VALUE])
f_d_sd = np.std(bax_bgsub[bax_only_names[i]][VALUE], ddof=1)
fret_means[i] = fret = 1 - (f_da / f_d)
fret_sds[i] = error_propagation.calc_ratio_sd(f_da, f_da_sd, f_d, f_d_sd)
def plot_data():
def myfig():
figure(figsize=(9, 6))
myfig()
plot_all(timecourse_wells)
title("Raw timecourses")
myfig()
plot_all(timecourse_averages, errors=timecourse_stds)
title("Raw timecourses, averaged")
if __name__ == '__main__':
color='b', label='Alexa 488 Bax')
plot(np.log10(conc_list), np.log10(bax_lipos_means), marker='o',
color='r', label='Alexa 488 Bax + Rho-PE liposomes')
xlabel('log10([Bax])')
ylabel('log10(RFU)')
title('Steady-state fluorescence vs. [Bax]')
legend(loc='lower right')
# Fret by comparison with controls
figure()
fret = 1 - (bax_lipos_means / bax_only_means)
num_pts = len(bax_lipos_means)
ratio_sds = np.zeros(num_pts)
for i in range(num_pts):
ratio_sds[i] = error_propagation.calc_ratio_sd(
bax_lipos_means[i], bax_lipos_sds[i],
bax_only_means[i], bax_only_sds[i])
# Log scale
figure()
errorbar(np.log10(conc_list), fret, yerr=ratio_sds, color='r')
#plot(np.log10(conc_list), hill(conc_list), color='g')
#fit_concs = 10 ** np.linspace(-1, 3.5, 100)
#plot(np.log10(fit_concs), quad(fit_concs), color='g')
xlabel('log10([Bax]) (nM)')
ylabel('$F_{D+A} / F_D$')
title('Bax/liposome FRET, 30C, 20-60sec avg')
# -----------------------------------
# FRET by comparison with expected F0 from fold-change
fold_changes = []
for well_name in bax_only.trunc_wells.keys():
def plot_initial_rates(activator):
plt.ion()
#nbd_sites = ['WT', '15']
nbd_sites = ['WT', '3', '5', '15', '36', '47', '54', '62', '68', '79',
'120', '122', '126', '138', '151', '175', '179', '184', '188']
replicates = range(1, 4)
count = 0
num_pts = 4
fit_results =[]
for nbd_index, nbd_site in enumerate(nbd_sites):
rn_ratios = []
nr_ratios = []
r_maxs = []
n_maxs = []
rn_errs = []
nr_errs = []
r_errs = []
n_errs = []
for rep_index in replicates:
rt = df[(activator, 'Release', nbd_site, rep_index, 'TIME')].values
ry = df[(activator, 'Release', nbd_site, rep_index, 'VALUE')].values
r_lin = scipy.stats.linregress(rt[0:num_pts], ry[0:num_pts])
r_int = r_lin[1]
r_max = np.max(ry)
r_slope = r_lin[0]
r_maxs.append(r_slope)
r_slope_err = r_lin[4]
r_errs.append(r_slope_err)
fit_results.append(FitResult(None, activator, nbd_site,
rep_index, 'Tb release',
'Initial rate (first %d pts)' % num_pts,
r_slope, r_slope_err))
if nbd_site == 'WT':
n_maxs.append(0)
n_errs.append(0)
rn_ratios.append(0)
rn_errs.append(0)
nr_ratios.append(0)
nr_errs.append(0)
else:
nt = df[(activator, 'NBD', nbd_site, rep_index, 'TIME')].values
ny = df[(activator, 'NBD', nbd_site, rep_index, 'VALUE')].values
# Fit line to first 10 pts
n_lin = scipy.stats.linregress(nt[0:num_pts], ny[0:num_pts])
n_max = np.max(ny)
n_int = n_lin[1]
n_slope = n_lin[0]
n_maxs.append(n_slope)
n_slope_err = n_lin[4]
n_errs.append(n_slope_err)
rn_ratio = r_slope / n_slope
rn_err = calc_ratio_sd(r_slope, r_slope_err, n_slope,
n_slope_err)
rn_ratios.append(rn_ratio)
rn_errs.append(rn_err)
nr_ratio = n_slope / r_slope
nr_err = calc_ratio_sd(n_slope, n_slope_err, r_slope,
r_slope_err)
nr_ratios.append(nr_ratio)
nr_errs.append(nr_err)
fit_results.append(FitResult(None, activator, nbd_site,
rep_index, 'NBD',
'Initial rate (first %d pts)' % num_pts,
n_slope, n_slope_err))
#print "%s, rep %d, Tb slope: %f" % (nbd_site, rep_index, r_slope)
#print "%s, rep %d, NBD slope: %f" % (nbd_site, rep_index, n_slope)
plt.figure('%s, NBD-%s-Bax initial rates' % (activator, nbd_site),
figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(rt[0:num_pts], ry[0:num_pts], linestyle='', marker='.',
color=rep_colors[rep_index])
plt.plot(rt[0:num_pts], r_int + r_slope * rt[0:num_pts],
color=rep_colors[rep_index],
label='%s Rep %d' % (activator, rep_index))
plt.title('NBD-%s-Bax, Tb initial rate' % (nbd_site))
plt.legend(loc='lower right')
if nbd_site != 'WT':
plt.subplot(1, 2, 2)
plt.plot(nt[0:num_pts], ny[0:num_pts], linestyle='', marker='.',
color=rep_colors[rep_index])
plt.plot(nt[0:num_pts], n_int + n_slope * nt[0:num_pts],
color=rep_colors[rep_index],
label='%s Rep %d' % (activator, rep_index))
plt.xlabel('Time (sec)')
plt.ylabel('$F/F_0$')
plt.title('NBD-%s-Bax, NBD initial rate' % (nbd_site))
plt.legend(loc='lower right')
# NBD/Tb ratio
plt.figure("NBD/Tb initial rate ratio", figsize=(12,6))
plt.title("NBD/Tb initial rate ratio")
plt.ylim((-0.08, 0.58))
if activator == 'Bid':
plt.bar(range(nbd_index*7, (nbd_index*7) + 3), nr_ratios,
width=1, color='r', ecolor='k',
yerr=nr_errs)
else:
plt.bar(range(nbd_index*7+3, (nbd_index*7) + 6), nr_ratios,
width=1, color='g', ecolor='k',
yerr=nr_errs)
# Tb/NBD ratio
plt.figure("Tb/NBD initial rate ratio", figsize=(12,6))
plt.title("Tb/NBD initial rate ratio")
plt.ylim((-100, 120))
if activator == 'Bid':
plt.bar(range(nbd_index*7, (nbd_index*7) + 3), rn_ratios,
width=1, color='r', ecolor='k',
yerr=rn_errs)
else:
plt.bar(range(nbd_index*7+3, (nbd_index*7) + 6), rn_ratios,
width=1, color='g', ecolor='k',
yerr=rn_errs)
# Tb initial rate
plt.figure("Tb initial rate", figsize=(12, 6))
plt.title('Tb initial rate')
if activator == 'Bid':
plt.bar(range(nbd_index*7, (nbd_index*7) + 3), r_maxs,
width=1, color='r', ecolor='k',
yerr=r_errs)
else:
plt.bar(range(nbd_index*7+3, (nbd_index*7) + 6), r_maxs,
width=1, color='g', ecolor='k',
yerr=r_errs)
# NBD initial rate
plt.figure("NBD initial rate", figsize=(12, 6))
plt.title('NBD initial rate')
if activator == 'Bid':
plt.bar(range(nbd_index*7, (nbd_index*7) + 3), n_maxs,
width=1, color='r', ecolor='k',
yerr=n_errs)
else:
plt.bar(range(nbd_index*7+3, (nbd_index*7) + 6), n_maxs,
width=1, color='g', ecolor='k',
yerr=n_errs)
num_sites = len(nbd_sites)
fig_names = ["NBD/Tb initial rate ratio", "Tb/NBD initial rate ratio",
"Tb initial rate", "NBD initial rate"]
for fig_name in fig_names:
plt.figure(fig_name)
ax = plt.gca()
ax.set_xticks(np.arange(3, 3 + num_sites * 7, 7))
ax.set_xticklabels(nbd_sites)
return fit_results