data_plot_dr.axes[3][0].set_title(r"IFN$\alpha$ 2019-02-14 data")
data_plot_dr.axes[3][1].set_title(r"IFN$\beta$ 2019-02-14 data")
dr_fig, dr_axes = data_plot_dr.show_figure(save_flag=False)
dr_fig.set_size_inches(10.5, 28.5)
dr_fig.savefig('results/GAB_NewData/compare_data_dr.pdf')
# -------------------------
# Time courses
# -------------------------
alpha_palette_new = sns.color_palette("Reds", 8)
beta_palette_new = sns.color_palette("Greens", 8)
alpha_palette_old = sns.color_palette("Reds", 8)
beta_palette_old = sns.color_palette("Greens", 8)
data_plot_tc = TimecoursePlot((4, 2))
alpha_mask = [0]
beta_mask = [0]
# Add data
# Alpha
# alpha_doses_20190214
for idx, d in enumerate(alpha_doses):
# Optional mask:
if d not in alpha_mask:
atc = IfnData('custom', df=newdata_4.data_set.loc['Alpha', d, :])
data_plot_tc.add_trajectory(atc,
'scatter',
alpha_palette_new[idx], (3, 0),
label='Alpha ' + str(d))
data_plot_tc.add_trajectory(atc,
'plot',
for j in range(len(doses_alpha)):
alpha_IfnData_objects.append(
IfnData('custom',
df=alpha_time_courses[j],
conditions={'Alpha': {
'Ib': 0
}}))
for j in range(len(doses_beta)):
beta_IfnData_objects.append(
IfnData('custom',
df=beta_time_courses[j],
conditions={'Beta': {
'Ia': 0
}}))
# Generate plot
time_course_paper_fig = TimecoursePlot((1, 2))
alpha_mask = [0]
beta_mask = [0]
# Add fits
for j, dose in enumerate(doses_alpha):
if dose not in alpha_mask:
time_course_paper_fig.add_trajectory(alpha_IfnData_objects[j],
'plot', alpha_palette[j],
(0, 0))
for j, dose in enumerate(doses_beta):
if dose not in beta_mask:
time_course_paper_fig.add_trajectory(beta_IfnData_objects[j],
'plot', beta_palette[j],
(0, 1))
# Add data
for j in range(5):
alpha_IfnData_objects.append(
IfnData('custom',
df=alpha_time_courses[j],
conditions={'Alpha': {
'Ib': 0
}}))
for j in range(5):
beta_IfnData_objects.append(
IfnData('custom',
df=beta_time_courses[j],
conditions={'Beta': {
'Ia': 0
}}))
# Generate plot
new_fit = TimecoursePlot((1, 2))
alpha_mask = []
beta_mask = []
# Add fits
for j, dose in enumerate([10, 90, 600, 4000, 8000]):
if dose not in alpha_mask:
new_fit.add_trajectory(alpha_IfnData_objects[j],
'plot',
alpha_palette[j], (0, 0),
label='Alpha ' + str(dose))
for j, dose in enumerate([10, 90, 600, 2000, 11000]):
if dose not in beta_mask:
new_fit.add_trajectory(beta_IfnData_objects[j],
'plot',
beta_palette[j], (0, 1),
}}))
beta_IfnData_objects_int.append(
IfnData('custom',
df=beta_time_courses_int[j],
conditions={'Beta': {
'Ia': 0
}}))
beta_IfnData_objects_rec.append(
IfnData('custom',
df=beta_time_courses_rec[j],
conditions={'Beta': {
'Ia': 0
}}))
# Generate plot
tc_plot = TimecoursePlot((3, 2))
# Add fits
for j, dose in enumerate(alpha_doses):
if dose not in alpha_mask:
tc_plot.add_trajectory(alpha_IfnData_objects[j],
'plot',
alpha_palette[j], (0, 0),
label='Alpha ' + str(dose),
linewidth=2.0)
tc_plot.add_trajectory(alpha_IfnData_objects_int[j],
'plot',
alpha_palette[j], (1, 0),
label='Alpha ' + str(dose),
linewidth=2.0)
tc_plot.add_trajectory(alpha_IfnData_objects_rec[j],
beta_IfnData_objects = []
for j in range(7):
alpha_IfnData_objects.append(
IfnData('custom',
df=alpha_time_courses[j],
conditions={'Alpha': {
'Ib': 0
}}))
beta_IfnData_objects.append(
IfnData('custom',
df=beta_time_courses[j],
conditions={'Beta': {
'Ia': 0
}}))
# Generate plot
new_fit = TimecoursePlot((1, 2))
alpha_mask = [5, 50, 250]
beta_mask = [0.1, 5, 200]
# Add fits
for j, dose in enumerate([5, 50, 250, 500, 5000, 25000, 50000]):
if dose not in alpha_mask:
new_fit.add_trajectory(alpha_IfnData_objects[j],
'plot',
alpha_palette[j + 2], (0, 0),
label='Alpha ' + str(dose))
for j, dose in enumerate([0.1, 1, 5, 10, 100, 200, 1000]):
if dose not in beta_mask:
new_fit.add_trajectory(beta_IfnData_objects[j],
'plot',
beta_palette[j + 2], (0, 1),
label='Beta ' + str(dose))
def experimental_panel(cell_densities,
IFNAlpha_panel,
IFNBeta_panel,
times,
IFN_in_concentration=True):
# ----------
# Run model
# ----------
predictions = run_smooth_trajectories(cell_densities, IFNAlpha_panel,
IFNBeta_panel, times)
# -------------------------------
# Plot model dose response curves
# -------------------------------
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
# pSTAT plot
STAT_plot = TimecoursePlot((len(IFNAlpha_panel), 2))
for dose_idx, dose in enumerate(IFNAlpha_panel):
color_counter = -1
for density_idx, density in enumerate(cell_densities):
color_counter += 1
STAT_plot.add_trajectory(
predictions[density][0],
'plot',
'-',
subplot_idx=(dose_idx, 0),
label=r'IFN$\alpha$ at {}$\times 10^6$ cells/mL'.format(
cell_densities[density_idx] / 1E9),
doseslice=dose,
dose_species='Alpha',
color=alpha_palette[color_counter],
linewidth=2)
for dose_idx, dose in enumerate(IFNBeta_panel):
color_counter = -1
for density_idx, density in enumerate(cell_densities):
color_counter += 1
STAT_plot.add_trajectory(
predictions[density][1],
'plot',
'-',
subplot_idx=(dose_idx, 1),
label=r'IFN$\beta$ at {}$\times 10^6$ cells/mL'.format(
cell_densities[density_idx] / 1E9),
doseslice=dose,
dose_species='Beta',
color=beta_palette[color_counter],
linewidth=2)
fig, axes = STAT_plot.show_figure()
for i in range(len(IFNAlpha_panel)):
axes[i][0].set_title(r'IFN$\alpha$ at {} pM'.format(IFNAlpha_panel[i]))
axes[i][1].set_title(r'IFN$\beta$ at {} pM'.format(IFNBeta_panel[i]))
fig.set_size_inches(12, 9)
fig.tight_layout()
fig.savefig('varying_reaction_volume_STATtc.pdf')
# Free extracellular IFN plot
IFN_plot = TimecoursePlot((len(IFNAlpha_panel), 2))
for ax in IFN_plot.axes.flat:
ax.set_yscale('log')
for i in range(len(IFNAlpha_panel)):
if IFN_in_concentration:
IFN_plot.axes[i][0].set_ylim(bottom=1E-3,
top=max(IFNAlpha_panel) * 1E-2)
IFN_plot.axes[i][1].set_ylim(bottom=1E-3,
top=max(IFNBeta_panel) * 1E-2)
else:
IFN_plot.axes[i][0].set_ylim(bottom=1,
top=max(IFNAlpha_panel) * 10**-12 *
max(volume_panel) * 6.022E23)
IFN_plot.axes[i][1].set_ylim(bottom=1,
top=max(IFNBeta_panel) * 10**-12 *
max(volume_panel) * 6.022E23)
# Add fits
for dose_idx, dose in enumerate(IFNAlpha_panel):
color_counter = -1
for density_idx, density in enumerate(cell_densities):
color_counter += 1
IFN_plot.add_trajectory(predictions[density][2],
'plot',
'-',
subplot_idx=(dose_idx, 0),
label=r'{}$\times 10^6$ cells/mL'.format(
cell_densities[density_idx] / 1E9),
doseslice=dose,
dose_species='Alpha',
color=alpha_palette[color_counter],
linewidth=2)
for dose_idx, dose in enumerate(IFNBeta_panel):
color_counter = -1
for density_idx, density in enumerate(cell_densities):
color_counter += 1
IFN_plot.add_trajectory(predictions[density][3],
'plot',
'-',
subplot_idx=(dose_idx, 1),
label=r'{}$\times 10^6$ cells/mL'.format(
cell_densities[density_idx] / 1E9),
doseslice=dose,
dose_species='Beta',
color=beta_palette[color_counter],
linewidth=2)
fig, axes = IFN_plot.show_figure()
for i in range(len(IFNAlpha_panel)):
axes[i][0].set_title(r'IFN$\alpha$ at {} pM'.format(IFNAlpha_panel[i]))
axes[i][0].set_ylabel(r'Free IFN$\alpha$ (nM)')
axes[i][1].set_title(r'IFN$\beta$ at {} pM'.format(IFNBeta_panel[i]))
axes[i][1].set_ylabel(r'Free IFN$\beta$ (nM)')
fig.set_size_inches(16, 12)
fig.tight_layout()
fig.savefig('varying_reaction_volume_IFNtc.pdf')
def testing_specific_values():
volEC1 = 1 / (25E9)
volEC2 = 1E-5
# --------------------
# Set up Model
# --------------------
# Parameters found by stepwise fitting GAB mean data
# Note: can remove multiplicative factors on all K1, K2, K4 and still get very good fit to data (worst is 5 min beta)
initial_parameters = {
'k_a1': 4.98E-14 * 2,
'k_a2': 8.30e-13 * 2,
'k_d4': 0.006 * 3.8,
'kpu': 0.00095,
'ka2': 4.98e-13 * 2.45,
'kd4': 0.3 * 2.867,
'kint_a': 0.000124,
'kint_b': 0.00086,
'krec_a1': 0.0028,
'krec_a2': 0.01,
'krec_b1': 0.005,
'krec_b2': 0.05
}
dual_parameters = {
'kint_a': 0.00052,
'kSOCSon': 6e-07,
'kint_b': 0.00052,
'krec_a1': 0.001,
'krec_a2': 0.1,
'krec_b1': 0.005,
'krec_b2': 0.05
}
scale_factor = 1.227
Mixed_Model = DualMixedPopulation('Mixed_IFN_ppCompatible', 0.8, 0.2)
Mixed_Model.model_1.set_parameters(initial_parameters)
Mixed_Model.model_1.set_parameters(dual_parameters)
Mixed_Model.model_1.set_parameters({'R1': 12000.0, 'R2': 1511.1})
Mixed_Model.model_2.set_parameters(initial_parameters)
Mixed_Model.model_2.set_parameters(dual_parameters)
Mixed_Model.model_2.set_parameters({'R1': 6755.56, 'R2': 1511.1})
# ---------------------------------
# Make theory dose response curves
# ---------------------------------
# Make predictions
times = np.arange(0, 120, 0.5) # [2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
alpha_doses_20190108 = [0, 10, 100, 300, 1000, 3000, 10000, 100000]
beta_doses_20190108 = [0, 0.2, 6, 20, 60, 200, 600, 2000]
# 1 uL
ka1 = Mixed_Model.model_1.parameters['ka1']
ka2 = Mixed_Model.model_1.parameters['ka2']
k_a1 = Mixed_Model.model_1.parameters['k_a1']
k_a2 = Mixed_Model.model_1.parameters['k_a2']
Mixed_Model.set_global_parameters({
'volEC': volEC1,
'ka1': ka1 * 1E-5 / volEC1,
'ka2': ka2 * 1E-5 / volEC1,
'k_a1': k_a1 * 1E-5 / volEC1,
'k_a2': k_a2 * 1E-5 / volEC1
})
dradf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra15uL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb15uL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# 1 mL
Mixed_Model.set_global_parameters({
'volEC': volEC2,
'ka1': ka1 * 1E-5 / volEC2,
'ka2': ka2 * 1E-5 / volEC2,
'k_a1': k_a1 * 1E-5 / volEC2,
'k_a2': k_a2 * 1E-5 / volEC2
})
dradf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'TotalpSTAT',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra5mL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb5mL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# -------------------------------
# Plot model dose response curves
# -------------------------------
alpha_palette = sns.color_palette("deep", 6)
beta_palette = sns.color_palette("deep", 6)
new_fit = DoseresponsePlot((2, 2))
alpha_mask = [2.5, 5.0, 10.0, 20.0, 60.0]
beta_mask = [2.5, 5.0, 10.0, 20.0, 60.0]
# Add fits
color_counter = -1
for idx, t in enumerate(times):
if t in alpha_mask:
color_counter += 1
new_fit.add_trajectory(dra15uL,
t,
'plot',
alpha_palette[color_counter], (0, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
new_fit.add_trajectory(dra5mL,
t,
'plot',
alpha_palette[color_counter], (1, 0),
'Alpha',
label=str(t) + ' min',
linewidth=2)
if t in beta_mask:
new_fit.add_trajectory(drb15uL,
t,
'plot',
beta_palette[color_counter], (0, 1),
'Beta',
label=str(t) + ' min',
linewidth=2)
new_fit.add_trajectory(drb5mL,
t,
'plot',
beta_palette[color_counter], (1, 1),
'Beta',
label=str(t) + ' min',
linewidth=2)
dr_fig, dr_axes = new_fit.show_figure()
dr_axes[0][0].set_title(r'IFN$\alpha$ at {} L'.format(volEC1))
dr_axes[0][1].set_title(r'IFN$\beta$ at {} L'.format(volEC1))
dr_axes[1][0].set_title(r'IFN$\alpha$ at {} L'.format(volEC2))
dr_axes[1][1].set_title(r'IFN$\beta$ at {} L'.format(volEC2))
dr_fig.tight_layout()
dr_fig.savefig('varying_reaction_volume_dr.pdf')
# -------------------------------
# Plot model dose response curves
# -------------------------------
tc_figure = TimecoursePlot((1, 2))
alpha_testdose = list(logspace(1, 5.2))[24]
beta_testdose = list(logspace(-1, 4))[17]
print("Using {} for IFNalpha".format(alpha_testdose))
print("Using {} for IFNbeta".format(beta_testdose))
tc_figure.add_trajectory(dra15uL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC1),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[1])
tc_figure.add_trajectory(dra5mL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC2),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[4])
tc_figure.add_trajectory(drb15uL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC1),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[0])
tc_figure.add_trajectory(drb5mL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC2),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[2])
tc_figure.show_figure(save_flag=True,
save_dir='varying_reaction_volume_tc.pdf')
# ------------------------------------
# Now let's quickly look at IFN levels
# ------------------------------------
# 1 uL
Mixed_Model.set_global_parameters({
'volEC': volEC1,
'ka1': ka1 * 1E-5 / volEC1,
'ka2': ka2 * 1E-5 / volEC1,
'k_a1': k_a1 * 1E-5 / volEC1,
'k_a2': k_a2 * 1E-5 / volEC1
})
dradf = Mixed_Model.mixed_dose_response(times,
'Free_Ia',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'Free_Ib',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra15uL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb15uL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# 1 mL
Mixed_Model.set_global_parameters({
'volEC': volEC2,
'ka1': ka1 * 1E-5 / volEC2,
'ka2': ka2 * 1E-5 / volEC2,
'k_a1': k_a1 * 1E-5 / volEC2,
'k_a2': k_a2 * 1E-5 / volEC2
})
dradf = Mixed_Model.mixed_dose_response(times,
'Free_Ia',
'Ia',
list(logspace(1, 5.2)),
parameters={'Ib': 0},
sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times,
'Free_Ib',
'Ib',
list(logspace(-1, 4)),
parameters={'Ia': 0},
sf=scale_factor)
dra5mL = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb5mL = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
tc_figure = TimecoursePlot((1, 2))
tc_figure.add_trajectory(dra15uL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC1),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[1])
tc_figure.add_trajectory(dra5mL,
'plot',
'-',
subplot_idx=(0, 0),
label=r'IFN$\alpha$ at {} L'.format(volEC2),
doseslice=alpha_testdose,
dose_species='Alpha',
color=alpha_palette[4])
tc_figure.add_trajectory(drb15uL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC1),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[0])
tc_figure.add_trajectory(drb5mL,
'plot',
'-',
subplot_idx=(0, 1),
label=r'IFN$\beta$ at {} L'.format(volEC2),
doseslice=beta_testdose,
dose_species='Beta',
color=beta_palette[2])
for ax in tc_figure.axes.flatten():
ax.set_yscale('log')
ifn_fig, ifn_axes = tc_figure.show_figure()
ifn_axes[0].set_ylabel('IFN-alpha2')
ifn_axes[1].set_ylabel('IFN-beta')
ifn_fig.savefig('varying_reaction_volume_tc_IFN.pdf')
def timecourse_figure():
# Get experimental data
newdata_1 = IfnData("20190108_pSTAT1_IFN_Bcell")
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([newdata_4, newdata_3, newdata_2, newdata_1])
alignment.align()
alignment.get_scaled_data()
mean_data = alignment.summarize_data()
# Plot
green = sns.color_palette("deep")[2]
red = sns.color_palette("deep")[3]
light_green = sns.color_palette("pastel")[2]
light_red = sns.color_palette("pastel")[3]
plot = TimecoursePlot((1, 1))
plot.add_trajectory(mean_data,
'errorbar',
'o--', (0, 0),
label=r'10 pM IFN$\alpha$2',
color=light_red,
dose_species='Alpha',
doseslice=10.0,
alpha=0.5)
plot.add_trajectory(mean_data,
'errorbar',
'o--', (0, 0),
label=r'6 pM IFN$\beta$',
color=light_green,
dose_species='Beta',
doseslice=6.0,
alpha=0.5)
plot.add_trajectory(mean_data,
'errorbar',
'o-', (0, 0),
label=r'3000 pM IFN$\alpha$2',
color=red,
dose_species='Alpha',
doseslice=3000.0)
plot.add_trajectory(mean_data,
'errorbar',
'o-', (0, 0),
label=r'2000 pM IFN$\beta$',
color=green,
dose_species='Beta',
doseslice=2000.0)
fname = os.path.join(os.getcwd(), 'results', 'Figures', 'Figure_4',
'Timecourse.pdf')
plot.axes.set_ylabel('pSTAT1 (MFI)')
plot.show_figure(show_flag=False, save_flag=True, save_dir=fname)
# -------------------------------
# Plot alignment of Time Course
# -------------------------------
species = alignment.scaled_data[0].get_dose_species()
# Make subtitles
recorded_doses = alignment.scaled_data[0].get_doses()
num_doses = len(recorded_doses[species[0]])
titles = [species[0], species[1]]
titles[0] += "\r\n\r\n{} pM".format(recorded_doses[species[0]][0])
titles[1] += "\r\n\r\n{} pM".format(recorded_doses[species[1]][0])
for d in range(1, num_doses):
for s in species:
titles.append(str(recorded_doses[s][d]) + ' pM')
# Add fits
tc_plot = TimecoursePlot((num_doses, len(species)))
for i in range(num_doses):
for j in range(len(species)):
tc_plot.axes[i][j].set_title(titles[i * len(species) + j])
for d_idx, dataset in enumerate(alignment.scaled_data):
colour_palette = sns.color_palette(clist[d_idx], 1)
for spec_idx, spec in enumerate(species):
for dose_idx, dose in enumerate(recorded_doses[spec]):
dose_dataset = pd.concat([dataset.data_set.loc[spec, dose, :]],
keys=[spec],
names=['Dose_Species'])
dose_IfnData = IfnData('custom',
df=dose_dataset,
conditions=dataset.conditions)
tc_plot.add_trajectory(dose_IfnData,
