'Bax 116 nM, NBD-Bax 96 nM', 'Bax 78 nM, NBD-Bax 96 nM', 'Bax 52 nM, NBD-Bax 96 nM', 'Bax 35 nM, NBD-Bax 96 nM', 'Bax 23 nM, NBD-Bax 96 nM', 'Bax 15 nM, NBD-Bax 96 nM', 'Bax 10 nM, NBD-Bax 96 nM', 'Bax 0 nM, NBD-Bax 96 nM',] nbd_layout = extract(nbd_conditions, layout) nbd_wells = extract(nbd_conditions, timecourse_averages) #nbd_wells = extract([layout[cond][0] for cond in nbd_conditions], # timecourse_wells) # Background subtracted bgsub_wells = subtract_background_set(nbd_wells, bax_bg_wells) # Background subtracted, averaged #(bgsub_averages, bgsub_stds) = averages(bgsub_wells, layout) # First timepoint shifted to 0 (better for fitting) #reset_bgsub_means = reset_first_timepoint_to_zero(bgsub_norm_averages) #Timecourses normalized, BG-subtracted, averaged, then with first point #shifted to t = 0. #reset_bgsub_sds = reset_first_timepoint_to_zero(bgsub_norm_stds) # Get the time vector time = bgsub_wells['Bax 0 nM, NBD-Bax 96 nM'][TIME] # Initialize numpy data matrix data_to_fit = np.zeros((len(bgsub_wells.keys()), 1, len(time))) # Initialize matrix of experimental error values
'Bax 185 nM, Lipos 0.25 mg/ml', 'Bax 185 nM, Lipos 0.125 mg/ml', 'Bax 185 nM, Lipos 0.063 mg/ml', 'Bax 185 nM, Lipos 0.031 mg/ml', 'Bax 185 nM, Lipos 0.016 mg/ml', 'Bax 185 nM, Lipos 0.008 mg/ml', 'Bax 185 nM, Lipos 0.004 mg/ml', 'Bax 185 nM, Lipos 0.002 mg/ml', 'Bax 185 nM, Lipos 0.001 mg/ml', 'Bax 185 nM, Lipos 0 mg/ml', ] bax_lipo_layout = extract(bax_lipo_conditions, layout) bax_lipo_wells = extract([layout[cond][0] for cond in bax_lipo_conditions], timecourse_wells) # Normalized and background subtracted bgsub_wells = subtract_background_set(bax_lipo_wells, lipo_bg_wells) (bgsub_averages, bgsub_sds) = averages(bgsub_wells, bax_lipo_layout) # First timepoint shifted to 0 (better for fitting) #reset_bgsub_means = reset_first_timepoint_to_zero(bgsub_norm_averages) """Timecourses normalized, BG-subtracted, averaged, then with first point shifted to t = 0.""" #reset_bgsub_sds = reset_first_timepoint_to_zero(bgsub_norm_stds) lipo_conc_conv_factor = 15.502 # 1 mg/ml ~= 15.502 nM liposomes bg_tc = bgsub_averages['Bax 185 nM, Lipos 0 mg/ml'][VALUE] bg_time = bgsub_averages['Bax 185 nM, Lipos 0 mg/ml'][TIME] lipo_concs_to_fit = [] lipo_mgs_to_fit = [1., 0.5, 0.25, 0.125, 0.063, 0.031, 0.016, 0.008] # Initialize numpy data matrix