def save_fba_to_excel(c3_model, c4_model, D_fba, D_exp, theNotebook, file_name='pfba_results'):
writer = pd.ExcelWriter(theNotebook+'/excel/'+file_name+'.xlsx')
L_r_transport = []
for r_obj in c4_model.reactions:
if r_obj.id[0:3] == '[MB]':
L_r_transport.append(r_obj.id)
for exp in D_exp:
DF_fba = pd.DataFrame(index=[r_obj.id for r_obj in c3_model.reactions]+L_r_transport,columns=['rxn','lb','ub','MC-Flux','BC-Flux',])
for r_obj in c3_model.reactions:
r_id = r_obj.id
r_M_flux = D_fba[exp]['[M]_'+r_id]
r_B_flux = D_fba[exp]['[B]_'+r_id]
r_lb = '0' if r_obj.lower_bound == 0. else '-inf' if r_obj.lower_bound == -inf else str(r_obj.lower_bound)
r_ub = '0' if r_obj.upper_bound == 0. else 'inf' if r_obj.upper_bound == inf else str(r_obj.upper_bound)
DF_fba.loc[r_id] = [r_obj.reaction,r_lb,r_ub,r_M_flux,r_B_flux]
DF_fba.loc['[B]_RBC_h_Ex']=[c4_model.reactions.get_by_id('[B]_RBC_h_Ex').reaction,"-inf","inf","0",D_fba[exp]['[B]_RBC_h_Ex']]
for r_id in L_r_transport:
r_obj = c4_model.reactions.get_by_id(r_id)
r_MB_flux = D_fba[exp][r_id]
r_lb = '0' if r_obj.lower_bound == 0. else '-inf' if r_obj.lower_bound == -inf else str(r_obj.lower_bound)
r_ub = '0' if r_obj.upper_bound == 0. else 'inf' if r_obj.upper_bound == inf else str(r_obj.upper_bound)
DF_fba.loc[r_id] = [r_obj.reaction,r_lb,r_ub,r_MB_flux, float('nan')]
sheet = re.sub(r"[\[,:,*,?, \],\\,/]",'_', D_exp[exp])
DF_fba.to_excel(writer,sheet)
writer.save()
def save_fva_to_excel(c3_model, c4_model, D_fva, D_exp, theNotebook, file_name='fva_results'):
writer = pd.ExcelWriter(theNotebook+'/excel/'+file_name+'.xlsx')
L_r_transport = []
for r_obj in c4_model.reactions:
if r_obj.id[0:3] == '[MB]':
L_r_transport.append(r_obj.id)
for exp in D_exp:
DF_fva = pd.DataFrame(index=[r_obj.id for r_obj in c3_model.reactions]+L_r_transport,columns=['rxn','lb','ub','MC-min','MC-max','BC-min','BC-max',])
for r_obj in c3_model.reactions:
r_id = r_obj.id
r_M_max = D_fva[exp].get_value('[M]_'+r_id,'maximum')
r_M_min = D_fva[exp].get_value('[M]_'+r_id,'minimum')
r_B_max = D_fva[exp].get_value('[B]_'+r_id,'maximum')
r_B_min = D_fva[exp].get_value('[B]_'+r_id,'minimum')
r_lb = '0' if r_obj.lower_bound == 0. else '-inf' if r_obj.lower_bound == -inf else str(r_obj.lower_bound)
r_ub = '0' if r_obj.upper_bound == 0. else 'inf' if r_obj.upper_bound == inf else str(r_obj.lower_bound)
DF_fva.loc[r_id] = [r_obj.reaction,r_lb,r_ub,r_M_min,r_M_max,r_B_min,r_B_max]
for r_id in L_r_transport:
r_obj = c4_model.reactions.get_by_id(r_id)
r_MB_max = D_fva[exp].get_value(r_id,'maximum')
r_MB_min = D_fva[exp].get_value(r_id,'minimum')
r_lb = '0' if r_obj.lower_bound == 0. else '-inf' if r_obj.lower_bound == -inf else str(r_obj.lower_bound)
r_ub = '0' if r_obj.upper_bound == 0. else 'inf' if r_obj.upper_bound == inf else str(r_obj.lower_bound)
DF_fva.loc[r_id] = [r_obj.reaction,r_lb,r_ub,r_MB_min,r_MB_max, float('nan'), float('nan')]
sheet = re.sub(r"[\[,:,*,?, \],\\,/]", '_', D_exp[exp])
DF_fva.to_excel(writer,sheet)
writer.save()
def get_fluxes_by_metabolite(D_exp, D_fba, model, m_id, cell):
if cell in ['M', 'B']:
df = pd.DataFrame(columns=['rxn'] + [D_exp[exp] for exp in D_exp])
m_obj = model.metabolites.get_by_id('[' + cell + ']_' + m_id)
for r_obj in m_obj.reactions:
if not r_obj.id in df.index:
df.loc[r_obj.id] = [r_obj.reaction] + [
round(D_fba[exp][r_obj.id], 3) for exp in D_exp
]
return df
else:
return 'Choose either M or B as cell type'
def plot_transport_BM(D_fba, D_exp, L_r, xaxis_title, save_fig, cut_off):
df = pd.DataFrame(index=[],
columns=[D_exp[exp] for exp in sorted(D_fba.keys())])
yTickNames_BM = []
D_rid_meanFlux_BM = {
r_id: abs(
np.mean([0] +
[D_fba[x][r_id] for x in D_exp if D_fba[x][r_id] < 0]))
for r_id in L_r if abs(
np.mean([0] +
[D_fba[x][r_id]
for x in D_exp if D_fba[x][r_id] < 0])) > cut_off
}
D_rid_meanFlux_BM = sorted(D_rid_meanFlux_BM.items(),
key=operator.itemgetter(1))
for entry in D_rid_meanFlux_BM:
r_id = entry[0]
for x in D_exp:
if D_fba[x][r_id] < 0:
df.set_value(
r_id, D_exp[x],
abs(D_fba[x][r_id])
if not abs(D_fba[x][r_id]) < cut_off else float('nan'))
if not r_id[5:-2] in yTickNames_BM:
yTickNames_BM.append(r_id[5:-2])
scl = cl.scales['9']['seq']['Blues']
colorscale = [[float(i) / float(len(scl) - 1), scl[i]]
for i in range(len(scl))]
trace_BM = go.Heatmap(z=df.values.tolist(),
x=df.columns,
y=yTickNames_BM,
colorbar={
'title': 'Flux [µmol/s/m2]',
'titleside': 'right'
},
colorscale=colorscale)
layout_BM = go.Layout(width=500,
margin={'b': 100},
yaxis={
'tickmode': 'array',
'tickvals': range(0, len(yTickNames_BM)),
'ticktext': yTickNames_BM,
'title': 'Transport Metabolites'
},
xaxis={
'title': xaxis_title,
'tickangle': 45 if len(D_exp) >= 8 else 0
},
title='Bundlesheat ==> Mesophyll Transport')
data_BM = [trace_BM]
fig_BM = go.Figure(
data=data_BM,
layout=layout_BM,
)
if save_fig:
iplot(fig_BM,
image='svg',
filename='transport_BM',
image_width=500,
image_height=500)
sleep(5)
else:
iplot(fig_BM)
