for r in index:
c = R.gc['media_key'][R.kmax['condition'][r]]
if c in ['glc', 'ac', 'glyc', 'xyl_D']:
if c == 'xyl_D': c = 'glc'
if saturation[c][r] > 0 and backwrdflx[c][r] > 0:
s[r] = saturation[c][r]
t[r] = backwrdflx[c][r]
invivo_kcat = kmax / (s * t)
new_index = invivo_kcat.dropna().index
names = {k: list(R.rxns[k].genes)[0].name for k in new_index}
fig = plt.figure(figsize=(12.5, 12.5))
ax = plt.axes()
report_before = generate_figure(kcat[new_index],
kmax[new_index],
fig,
ax,
labels=names,
hide_overlap=False)
r1, pval1 = stats.pearsonr(np.log10(kcat[new_index]),
np.log10(kmax[new_index]))
report_after = generate_figure(kcat[new_index],
invivo_kcat[new_index],
fig,
ax,
fit=True,
color='k')
r2, pval2 = stats.pearsonr(np.log10(kcat[new_index]),
np.log10(invivo_kcat[new_index]))
for r in y.index:
c = R.kmax['condition'][r]
v[r] = R.flux_data[c][r]
E = v[ind] / y
min_kmax = y - fva_0.minimum[ind] / E
max_kmax = fva_0.maximum[ind] / E - y
fig, ax = plt.subplots(1, 1, figsize=(6, 6), sharey=True)
fontsize = 20
labels = {'PDX5POi': 'pdxH'}
report = generate_figure(x,
y,
fig,
ax,
labels=labels,
yerr=[min_kmax, max_kmax],
fit=True)
ax.set_ylabel(
r'$k_{\mathrm{max}}^{\mathrm{vivo}}\,\left[s^{-1}\right]$ (in vivo)',
size=fontsize,
style='italic')
ax.set_xlabel(r'$k_{\mathrm{cat}}\,\left[s^{-1}\right]$ (in vitro)',
size=fontsize,
style='italic')
ax.tick_params(axis='both', which='both', top='off', right='off')
[tick.label.set_fontsize(fontsize) for tick in ax.xaxis.get_major_ticks()]
[tick.label.set_fontsize(fontsize) for tick in ax.yaxis.get_major_ticks()]
x = kcat[ind]
y = kmax[ind]
v = pd.Series(index=ind)
for r in y.index:
c = R.kmax['condition'][r]
v[r] = R.flux_data[c][r]
E = v[ind]/y
min_kmax = y - fva_0.minimum[ind]/E
max_kmax = fva_0.maximum[ind]/E - y
fig, ax = plt.subplots(1, 1, figsize=(6,6), sharey=True)
fontsize=20
labels = {'PDX5POi':'pdxH'}
report = generate_figure(x, y, fig, ax,labels=labels, yerr = [min_kmax, max_kmax],fit=True)
ax.set_ylabel(r'$k_{\mathrm{max}}^{\mathrm{vivo}}\,\left[s^{-1}\right]$ (in vivo)',
size=fontsize, style='italic')
ax.set_xlabel(r'$k_{\mathrm{cat}}\,\left[s^{-1}\right]$ (in vitro)',
size=fontsize, style='italic')
ax.tick_params(axis='both', which='both', top='off', right='off')
[tick.label.set_fontsize(fontsize) for tick in ax.xaxis.get_major_ticks()]
[tick.label.set_fontsize(fontsize) for tick in ax.yaxis.get_major_ticks()]
ax.set_xlim(1e-3/4,4*1e3)
ax.set_ylim(1e-3/4,4*1e3)
plt.tight_layout()
t = pd.Series(index=index)
for r in index:
c = R.gc['media_key'][R.kmax['condition'][r]]
if c in ['glc','ac','glyc','xyl_D']:
if c == 'xyl_D': c = 'glc'
if saturation[c][r]>0 and backwrdflx[c][r]>0:
s[r] = saturation[c][r]
t[r] = backwrdflx[c][r]
invivo_kcat = kmax / (s * t)
new_index = invivo_kcat.dropna().index
names = {k:list(R.rxns[k].genes)[0].name for k in new_index}
fig = plt.figure(figsize=(12.5,12.5))
ax= plt.axes()
report_before = generate_figure(kcat[new_index], kmax[new_index], fig, ax,
labels=names, hide_overlap=False)
r1, pval1 = stats.pearsonr(np.log10(kcat[new_index]), np.log10(kmax[new_index]))
report_after = generate_figure(kcat[new_index], invivo_kcat[new_index], fig, ax, fit=True, color='k')
r2, pval2 = stats.pearsonr(np.log10(kcat[new_index]), np.log10(invivo_kcat[new_index]))
def stacked_residual(x, y, saturation_effect, thermodynamic_effect, ax):
for (x0, y0, s, t) in zip(x, y, saturation_effect, thermodynamic_effect):
ax.vlines(x0, y0, y0/s, lw=5, colors='#8383FF')
ax.vlines(x0, y0/s, y0/(s*t), lw=5, colors='#FFDA47')
stacked_residual(kcat[new_index], kmax[new_index], s[new_index], t[new_index], ax)
ax.set_xlim(1e-1*2,1e3*4)
