Big update of material for the appendix

code/analysis/data_inference/wildtype_inference.py

@@ -1,12 +1,3 @@
-"""
-Wildtype Inference
-==================
-
-This script executes a bayesian inferential model to estimate size and protein 
-load parameters from measurements of wildtype E. coli.  This generates figures 
-illustrating the posterior predictive checks of the size, growth, and protein 
-inference as well as CSV files of the summarized posteriors for each parameter. 
-"""
 # %%
 import numpy as np
 import pandas as pd
@@ -113,7 +104,7 @@
     ppc = samples.posterior[f'{n}_ppc'].to_dataframe().reset_index()
 
     for g, d in ppc.groupby(f'{n}_ppc_dim_0'):
-        ax[i].plot(1 + np.ones_like(d[::3]) * g, d[f'{n}_ppc'].values[::3] / mod,
+        ax[i].plot(1 + np.ones_like(d[::10]) * g, d[f'{n}_ppc'].values[::10] / mod,
                    '_', markeredgecolor=cor['primary_black'], markeredgewidth=0.1,
                    alpha=0.75)
 
@@ -135,8 +126,8 @@
            phi['phiRb'], '_',
            markeredgecolor=cor['primary_red'],
            markeredgewidth=0.5)
-ax[4].set_title('ribosomal allocation', fontsize=6)
-ax[4].set_ylabel('$\phi_{Rb}$', fontsize=6)
+ax[4].set_title('ribosomal proteome fraction', fontsize=6)
+ax[4].set_ylabel('$\phi_{rib}$', fontsize=6)
 
 ax[5].plot(flow['idx'],
            flow['cells_per_biomass'] / 1E9, '_',
@@ -161,7 +152,7 @@
 for i, q in enumerate(['width', 'length', 'aspect_ratio', 'surface_area', 'volume', 'surface_to_volume']):
     ppc = samples.posterior[f'{q}_ppc'].to_dataframe().reset_index()
     for g, d in ppc.groupby(f'{q}_ppc_dim_0'):
-        ax[i].plot(1 + np.ones_like(d[::3]) * g, d[f'{q}_ppc'].values[::3] / mod,
+        ax[i].plot(1 + np.ones_like(d[::10]) * g, d[f'{q}_ppc'].values[::10] / mod,
                    '_', markeredgecolor=cor['primary_black'], markeredgewidth=0.1,
                    alpha=0.75)
     ax[i].set_title(q, fontsize=6)

code/analysis/data_inference/wildtype_inference.stan

@@ -153,7 +153,6 @@ model {
     surface_to_volume_sigma ~ std_normal();
     surface_to_volumes ~ normal(surface_to_volume_mu[size_idx], surface_to_volume_sigma[size_idx]);
 
-
     aspect_ratio_zeroed_mu ~ std_normal();
     alpha_zeroed_sigma ~ normal(0, 0.1);
     aspect_ratio_zeroed ~ normal(aspect_ratio_zeroed_mu[size_idx], alpha_zeroed_sigma[size_idx]);

code/analysis/model_inference/SAV_model_inference.py

@@ -97,7 +97,7 @@
 # %%
 # Summarize the predicted growth-rate dependencies
 pars = ['pred_lam_prot', 'volume_pred', 'length_pred', 'aspect_ratio_pred', 'width_pred',
-        'm_peri_pred', 'rho_peri_pred', 'rho_mem_pred', 'phi_mem_pred', 'phi_Rb_pred', 'phi_peri_lam_pred',
+        'm_peri_pred', 'rho_peri_pred', 'rho_mem_pred', 'rho_dry_pred', 'phi_mem_pred', 'phi_Rb_pred', 'phi_peri_lam_pred',
         'SA_fit']
 lam_par_percs = pd.DataFrame([])
 for i, p in enumerate(tqdm.tqdm(pars)):

code/analysis/model_inference/SAV_model_inference.stan

@@ -132,13 +132,15 @@ generated quantities {
     vector[N_pred] m_peri_pred;
     vector[N_pred] rho_peri_pred;
     vector[N_pred] rho_mem_pred;
+    vector[N_pred] rho_dry_pred;
     vector[N_pred] phi_mem_pred;
     vector[N_pred] phi_peri_pred;
     vector[N_pred] phi_peri_lam_pred;
     for (i in 1:N_pred) {
         aspect_ratio_pred[i] = alpha_mu;
         m_peri_pred[i] = m_peri;
         rho_mem_pred[i] = rho_mem_mu;
+        rho_dry_pred[i] = drymass_mu;
         phi_mem_pred[i] = phi_mem_mu;
         phi_peri_pred[i] = m_peri / pred_phiRb_prot[i];
         phi_peri_lam_pred[i] = m_peri / pred_lam_prot[i];

code/visualization/FigAX_SA_fit.py

@@ -0,0 +1,25 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/collated_literature_size_data.csv')
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+for g, d in fit[fit['quantity']=='SA_fit'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5)
+
+for g, d in data.groupby('source'):
+    fmt = size.viz.style_point(g)
+    ax.plot(d['growth_rate_hr'], d['surface_area_um2'], **fmt)
+ax.set_ylim([0, 15])
+ax.legend(fontsize=6, loc='upper left')
+ax.set_ylabel('$S_A$ [µm$^2$]\naverage cell surface area', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_sa_bayes.pdf')

code/visualization/FigAX_lit_size_comparison.py

@@ -0,0 +1,43 @@
+#%%
+import numpy as np 
+import pandas as pd
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+
+size_data = pd.read_csv('../../data/literature/collated_literature_size_data.csv')
+size_data = size_data[~size_data['source'].isin(['Si et al. 2017', 'Taher-Araghi et al. 2015', 'Basan et al. 2015'])]
+wt_data = pd.read_csv('../../data/mcmc/wildtype_posterior_parameter_summaries.csv')
+
+fig, ax = plt.subplots(1, 2, figsize=(4, 1.5))
+ax[0].set_xlim([0, 2.2])
+ax[0].set_ylim([0.45, 1.2])
+ax[1].set_ylim([1, 5])
+for a in ax:
+    a.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+ax[0].set_ylabel('$w$ [µm]\naverage cell width', fontsize=6)
+ax[1].set_ylabel('$\ell$ [µm]\naverage cell length', fontsize=6)
+
+for g, d in size_data.groupby('source'):
+    fmt = size.viz.style_point(g)
+    ax[0].plot(d['growth_rate_hr'], d['width_um'], **fmt)
+    ax[1].plot(d['growth_rate_hr'], d['length_um'], **fmt)
+
+
+x = 0
+for g, d in wt_data[wt_data['quantity'].isin(['width', 'length', 'growth_rate'])].groupby('carbon_source'):
+    lam = d[d['quantity']=='growth_rate'] 
+    w = d[d['quantity']=='width']
+    l = d[d['quantity']=='length']
+    if x == 0:
+        label = 'This study'
+    else:
+        label = '__nolegend__'
+    for i, p in enumerate([w, l]):
+        ax[i].vlines(lam['median_value'], p['2.5%'], p['97.5%'], lw=1, color=cor['primary_black'], label='__nolegend__')
+        ax[i].hlines(p['median_value'], lam['2.5%'], lam['97.5%'], lw=1, color=cor['primary_black'], label='__nolegend__')
+        ax[i].plot(lam['median_value'], p['median_value'], 'o', ms=5, markeredgecolor=cor['primary_black'],
+                   markeredgewidth=1, markerfacecolor='w', label=label)
+        x += 1
+ax[1].legend(fontsize=5, bbox_to_anchor=(1,1))
+plt.savefig('../../figures/FigAX_lit_size_comparison.pdf')

code/visualization/FigAX_phi_mem.py

@@ -0,0 +1,27 @@
+#%%
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/collated_mass_fractions_empirics.csv')
+data = data[data['localization']=='membrane']
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+ax.set_ylim([0, 0.2])
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+
+for g, d in fit[fit['quantity']=='phi_mem_pred'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5)
+for g, d in data.groupby('dataset_name'):
+    fmt = size.viz.style_point(g)
+    ax.plot(d['growth_rate_hr'], d['mass_frac'], **fmt)
+
+ax.legend()
+ax.set_ylabel('$\phi_{mem}$ \nmembrane proteome fraction', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_phi_mem_bayes.pdf')

code/visualization/FigAX_phi_peri.py

@@ -0,0 +1,26 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/collated_mass_fractions_empirics.csv')
+data = data[data['localization']=='periplasm']
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+for g, d in fit[fit['quantity']=='phi_peri_lam_pred'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5)
+
+for g, d in data.groupby('dataset_name'):
+    fmt = size.viz.style_point(g)
+    ax.plot(d['growth_rate_hr'], d['mass_frac'], **fmt)
+ax.set_ylim([0, 0.12])
+ax.legend(fontsize=5, loc='upper right')
+ax.set_ylabel('$\phi_{peri}$ \nperiplasmic proteome fraction', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_phi_peri_bayes.pdf')

code/visualization/FigAX_phirib_fit.py

@@ -0,0 +1,25 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/Chure2023/chure2023_collated_mass_fractions.csv')
+data = data[(data['source']!='Si et al. 2017') & (data['source']!='Taher-Araghi et al. 2015')]
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+for g, d in fit[fit['quantity']=='phi_Rb_pred'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5, zorder=1000)
+for g, d in data.groupby('source'):
+    fmt = size.viz.style_point(g, alpha=0.5)
+    ax.plot(d['growth_rate_hr'], d['mass_fraction'], **fmt,ms=5)
+
+ax.legend(fontsize=5, bbox_to_anchor=(1,1))
+ax.set_ylabel('$\phi_{rib}$\nribosomal proteome fraction', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_phi_rib_bayes.pdf')

code/visualization/FigAX_prot_fit.py

@@ -0,0 +1,25 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/collated_protein_per_cell.csv')
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+
+for g, d in fit[fit['quantity']=='pred_lam_prot'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5)
+for g, d in data.groupby('source'):
+    fmt = size.viz.style_point(g)
+    ax.plot(d['growth_rate_hr'], d['fg_protein_per_cell'], **fmt)
+
+ax.legend()
+ax.set_ylabel('$M_{prot}^{(tot)}$ [fg / cell]\ntotal protein', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_protein_bayes.pdf')

code/visualization/FigAX_rho_dry.py

@@ -0,0 +1,26 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/literature/collated_drymass_densities.csv')
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+ax.set_ylim([150, 450])
+
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+for g, d in fit[fit['quantity']=='rho_dry_pred'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5, zorder=1000)
+
+for g, d in data.groupby('source'):
+    fmt = size.viz.style_point(g)
+    ax.plot(d['growth_rate_hr'], d['drymass_density_fg_fL'], **fmt)
+
+ax.legend(fontsize=5, bbox_to_anchor=(1,1))
+ax.set_ylabel(r'$\rho_{dry}$ [fg / fL]'+'\ndrymass density', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_rho_dry_bayes.pdf')

code/visualization/FigAX_sigma_mem.py

@@ -0,0 +1,24 @@
+#%%
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import size.viz 
+cor, pal = size.viz.matplotlib_style()
+data = pd.read_csv('../../data/mcmc/mass_spec_empirical_summaries_wide.csv')
+fit = pd.read_csv('../../data/mcmc/theory_growth_rate_prediction_summaries.csv')
+
+fig, ax = plt.subplots(1, 1, figsize=(3,2))
+ax.set_ylim([0, 6])
+inter_colors = {'95%':'pale_', '75%':'light_', '25%':'primary_', 'median':''}
+
+for g, d in fit[fit['quantity']=='rho_mem_pred'].groupby('interval', sort=False):
+    ax.fill_between(d['growth_rate_hr'], d['lower'], d['upper'], color=cor[f'{inter_colors[g]}red'], alpha=0.5)
+for g, d in data[data['quantity']=='ms_rho_mem'].groupby('source'):
+    fmt = size.viz.style_point(g, alpha=0.5)
+    ax.plot(d['growth_rate_hr'], d['median_value'], **fmt,ms=5)
+
+ax.legend(fontsize=5, bbox_to_anchor=(1,1))
+ax.set_ylabel('$\sigma_{mem}$ [fg / µm$^2$\naverage membrane protein areal density', fontsize=6)
+ax.set_xlabel('growth rate\n$\lambda$ [hr$^{-1}$]', fontsize=6)
+
+plt.savefig('../../figures/FigAX_sigma_mem_bayes.pdf')