In [1]:
import escher
from escher import Builder
import cobra
from time import sleep
In [2]:
escher.rc['never_ask_before_quit'] = True
In [3]:
model=cobra.io.load_json_model("C:/Users/tiann/Desktop/代谢模型/iJO1366.json")
In [4]:
model.solver='cplex'
In [5]:
model_1=model.copy()
In [6]:
model_2=model.copy()
In [7]:
from cobra import Model, Reaction, Metabolite,Gene
In [8]:
from cobra.flux_analysis import flux_variability_analysis
In [9]:
#add metabolites
phpyr_c = Metabolite(
    'phpyr_c',
    formula='C9H7O3',
    name='Phenylpyruvate',
    compartment='c')
bzalc_e = Metabolite(
    'bzalc_e',
    formula='C7H8O',
    name=' Benzyl alcohol',
    compartment='e')
bzalc_c = Metabolite(
    'bzalc_c',
    formula='C7H8O',
    name=' Benzyl alcohol',
    compartment='c')
bzalc_p = Metabolite(
    'bzalc_c',
    formula='C7H8O',
    name=' Benzyl alcohol',
    compartment='p')
co2_c = Metabolite('co2_c', formula='CO2', name='CO2', compartment='c')
o2_c = Metabolite('o2_c', formula='O2', name='O2', compartment='c')
In [10]:
#add Reactions
reaction1 = Reaction('PASY')
reaction1.add_metabolites({
    phpyr_c: -1.0,
    bzalc_c: 1.0,
    o2_c: -1.0,
    co2_c: 2.0
})
reaction1.gene_reaction_rule
reaction1.genes
reaction1.name='Benzyl alcohol_synthase'
reaction1.lower_bound=0.0
reaction1.upper_bound=1000.0
reaction1.subsystem
model_2.add_reactions([reaction1])

reaction2 = Reaction('BZALCpp')
reaction1.add_metabolites({
    bzalc_c: -1.0,
    bzalc_p: 1.0
})
reaction2.gene_reaction_rule
reaction2.genes 
reaction2.name='bzalc transporter via diffusion (periplasm)'
reaction2.lower_bound=0.0
reaction2.upper_bound=1000.0
reaction2.subsystem
model_2.add_reactions([reaction2])

reaction3 = Reaction('BZALCtex')
reaction3.add_metabolites({
    bzalc_e: 1.0,
    bzalc_p: -1.0
})
reaction3.gene_reaction_rule
reaction3.genes 
reaction3.name='bzalc transport via diffusion (extracellular to periplasm)'
reaction3.lower_bound=-1000.0
reaction3.upper_bound=1000.0
reaction3.subsystem
model_2.add_reactions([reaction3])

reaction5 = Reaction('EX_bzalc_e')
reaction5.add_metabolites({
    bzalc_e: -1.0
})
reaction5.gene_reaction_rule
reaction5.genes
reaction5.name='Benzyl alcohol exchange'
reaction5.lower_bound= 0.0
reaction5.upper_bound= 1000.0
reaction5.subsystem

model_2.add_reactions([reaction5])
In [11]:
cobra.io.save_json_model(model_2,"C:/Users/tiann/Desktop/代谢模型/map.json")
In [12]:
builder=escher.Builder(
model_name='iJO1366.json',
map_name=None,
map_json='C:\\Users\\tiann\\Desktop\\代谢模型\\new_map.json')

Downloading Model from https://escher.github.io/1-0-0/6/models/Escherichia%20coli/iJO1366.json
In [13]:
builder
In [14]:
builder.save_html('C:/Users/tiann/Desktop/GSMM_map.html')
In [15]:
model_2.optimize()
Out[15]:
Optimal solution with objective value 0.982
fluxes reduced_costs
DM_4crsol_c 0.000219 0.000000
DM_5drib_c 0.000221 0.000000
DM_aacald_c -0.000000 0.000000
DM_amob_c 0.000002 0.000000
DM_mththf_c 0.000440 0.000000
... ... ...
Zn2tex 0.000335 -0.000000
PASY 0.000000 -0.338034
BZALCpp 0.000000 0.000000
BZALCtex 0.000000 0.000000
EX_bzalc_e -0.000000 0.000000

2587 rows × 2 columns

In [16]:
flux_variability_analysis(model_2, model_2.reactions.get_by_id("EX_bzalc_e"))
Out[16]:
minimum maximum
EX_bzalc_e 0.0 0.0
In [17]:
flux_variability_analysis(model_2, model_2.reactions.get_by_id("EX_phe__L_e"))
Out[17]:
minimum maximum
EX_phe__L_e 0.0 0.0
# build the model_B that the gene b4054(tyrB) is konocked out.
In [18]:
model_B=model_2.copy()
In [19]:
import pandas
from time import time

import cobra.test
from cobra.flux_analysis import (
    single_gene_deletion, single_reaction_deletion, double_gene_deletion,
    double_reaction_deletion)
In [20]:
model_B.genes.b4054.knock_out()
In [21]:
 model_B.optimize()
Out[21]:
Optimal solution with objective value 0.982
fluxes reduced_costs
DM_4crsol_c 0.000219 0.000000
DM_5drib_c 0.000221 0.000000
DM_aacald_c -0.000000 0.000000
DM_amob_c 0.000002 0.000000
DM_mththf_c 0.000440 0.000000
... ... ...
Zn2tex 0.000335 -0.000000
PASY 0.000000 -0.338034
BZALCpp 0.000000 0.000000
BZALCtex 0.000000 -0.000000
EX_bzalc_e -0.000000 0.000000

2587 rows × 2 columns

In [22]:
flux_variability_analysis(model_B, model_B.reactions.get_by_id("EX_bzalc_e"))
Out[22]:
minimum maximum
EX_bzalc_e 0.0 0.0
In [23]:
flux_variability_analysis(model_B, model_B.reactions.get_by_id("EX_phe__L_e"))
Out[23]:
minimum maximum
EX_phe__L_e 0.0 0.0
build the model_A that the gene b2600(tyrA) is konocked out.
In [24]:
model_A=model_2.copy()
In [25]:
model_A.genes.b2600.knock_out()
In [26]:
model_A.optimize()
Out[26]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.0
DM_5drib_c -0.0 0.0
DM_aacald_c -0.0 0.0
DM_amob_c -0.0 0.0
DM_mththf_c -0.0 0.0
... ... ...
Zn2tex 0.0 -0.0
PASY 0.0 0.0
BZALCpp 0.0 0.0
BZALCtex 0.0 -0.0
EX_bzalc_e -0.0 0.0

2587 rows × 2 columns

In [27]:
flux_variability_analysis(model_A, model_A.reactions.get_by_id("EX_bzalc_e"))
Out[27]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [28]:
flux_variability_analysis(model_A, model_A.reactions.get_by_id("EX_phe__L_e"))
Out[28]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_C that the gene b0928(aspC) is konocked out.
In [29]:
model_C=model_2.copy()
In [30]:
model_C.genes.b0928.knock_out()
In [31]:
model_C.optimize()
Out[31]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.0
DM_5drib_c -0.0 0.0
DM_aacald_c -0.0 0.0
DM_amob_c -0.0 0.0
DM_mththf_c -0.0 0.0
... ... ...
Zn2tex 0.0 -0.0
PASY 0.0 0.0
BZALCpp 0.0 0.0
BZALCtex 0.0 -0.0
EX_bzalc_e -0.0 0.0

2587 rows × 2 columns

In [32]:
flux_variability_analysis(model_C, model_C.reactions.get_by_id("EX_bzalc_e"))
Out[32]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [33]:
flux_variability_analysis(model_C, model_C.reactions.get_by_id("EX_phe__L_e"))
Out[33]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_12 that the gene b1264(trpE) is konocked out.
In [34]:
model_E=model_2.copy()
In [35]:
model_E.genes.b1264.knock_out()
In [36]:
model_E.optimize()
Out[36]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.0
DM_5drib_c -0.0 0.0
DM_aacald_c -0.0 0.0
DM_amob_c -0.0 0.0
DM_mththf_c -0.0 0.0
... ... ...
Zn2tex 0.0 -0.0
PASY 0.0 0.0
BZALCpp 0.0 0.0
BZALCtex 0.0 -0.0
EX_bzalc_e -0.0 0.0

2587 rows × 2 columns

In [37]:
flux_variability_analysis(model_E, model_E.reactions.get_by_id("EX_bzalc_e"))
Out[37]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [38]:
flux_variability_analysis(model_E, model_E.reactions.get_by_id("EX_phe__L_e"))
Out[38]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_BC that the gene b4054(tyrB)and b0928(aspC) is konocked out.
In [39]:
model_BC=model_B.copy()
In [40]:
model_BC.genes.b0928.knock_out()
In [41]:
model_BC.optimize()
Out[41]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.000000e+00
DM_5drib_c -0.0 0.000000e+00
DM_aacald_c -0.0 0.000000e+00
DM_amob_c -0.0 0.000000e+00
DM_mththf_c -0.0 0.000000e+00
... ... ...
Zn2tex 0.0 -0.000000e+00
PASY 0.0 7.771605e-16
BZALCpp 0.0 0.000000e+00
BZALCtex 0.0 -0.000000e+00
EX_bzalc_e -0.0 0.000000e+00

2587 rows × 2 columns

In [42]:
flux_variability_analysis(model_BC, model_BC.reactions.get_by_id("EX_bzalc_e"))
Out[42]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [43]:
flux_variability_analysis(model_BC, model_BC.reactions.get_by_id("EX_phe__L_e"))
Out[43]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_BCA that the gene b4054(tyrB),b2600(tyrA) and b0928(aspC) is konocked out.
In [44]:
model_BCA=model_BC.copy()
In [45]:
model_BCA.genes.b2600.knock_out()
In [46]:
model_BCA.optimize()
Out[46]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.000000e+00
DM_5drib_c -0.0 0.000000e+00
DM_aacald_c -0.0 0.000000e+00
DM_amob_c -0.0 0.000000e+00
DM_mththf_c -0.0 0.000000e+00
... ... ...
Zn2tex 0.0 -0.000000e+00
PASY 0.0 2.038300e-17
BZALCpp 0.0 0.000000e+00
BZALCtex 0.0 -0.000000e+00
EX_bzalc_e -0.0 0.000000e+00

2587 rows × 2 columns

In [47]:
flux_variability_analysis(model_BCA, model_BCA.reactions.get_by_id("EX_bzalc_e"))
Out[47]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [48]:
flux_variability_analysis(model_BCA, model_BCA.reactions.get_by_id("EX_phe__L_e"))
Out[48]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_BCE that the gene b4054(tyrB),b1264(trpE) and b0928(aspC) is konocked out.
In [49]:
model_BCE=model_BC.copy()
In [50]:
model_BCE.genes.b1264.knock_out()
In [51]:
model_BCE.optimize()
Out[51]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.000000e+00
DM_5drib_c -0.0 0.000000e+00
DM_aacald_c -0.0 0.000000e+00
DM_amob_c -0.0 0.000000e+00
DM_mththf_c -0.0 0.000000e+00
... ... ...
Zn2tex 0.0 -0.000000e+00
PASY 0.0 2.038300e-17
BZALCpp 0.0 0.000000e+00
BZALCtex 0.0 -0.000000e+00
EX_bzalc_e -0.0 0.000000e+00

2587 rows × 2 columns

In [52]:
flux_variability_analysis(model_BCE, model_BCE.reactions.get_by_id("EX_bzalc_e"))
Out[52]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [53]:
flux_variability_analysis(model_BCE, model_BCE.reactions.get_by_id("EX_phe__L_e"))
Out[53]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_BCE that the gene b4054(tyrB),b1264(trpE),b2600(tyrA) and b0928(aspC) is konocked out.
In [54]:
model_BCAE=model_BCE.copy()
In [55]:
model_BCAE.genes.b2600.knock_out()
In [56]:
model_BCAE.optimize()
Out[56]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.0
DM_5drib_c -0.0 0.0
DM_aacald_c -0.0 0.0
DM_amob_c -0.0 0.0
DM_mththf_c -0.0 0.0
... ... ...
Zn2tex 0.0 -0.0
PASY 0.0 0.0
BZALCpp 0.0 0.0
BZALCtex 0.0 -0.0
EX_bzalc_e -0.0 0.0

2587 rows × 2 columns

In [57]:
flux_variability_analysis(model_BCAE, model_BCAE.reactions.get_by_id("EX_bzalc_e"))
Out[57]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [58]:
flux_variability_analysis(model_BCAE, model_BCAE.reactions.get_by_id("EX_phe__L_e"))
Out[58]:
minimum maximum
EX_phe__L_e 0.0 5.507292
build the model_BCE that the gene b4054(tyrB),b1264(trpE),b2600(tyrA)，b0928(aspC)and b3708(tnaA)is konocked out.
In [59]:
model_BCAET=model_BCAE.copy()
In [60]:
model_BCAET.genes.b3708.knock_out()
In [61]:
model_BCAET.optimize()
Out[61]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.0 0.0
DM_5drib_c -0.0 0.0
DM_aacald_c -0.0 0.0
DM_amob_c -0.0 0.0
DM_mththf_c -0.0 0.0
... ... ...
Zn2tex 0.0 -0.0
PASY 0.0 0.0
BZALCpp 0.0 0.0
BZALCtex 0.0 -0.0
EX_bzalc_e -0.0 0.0

2587 rows × 2 columns

In [62]:
flux_variability_analysis(model_BCAET, model_BCAET.reactions.get_by_id("EX_bzalc_e"))
Out[62]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [63]:
flux_variability_analysis(model_BCAE, model_BCAET.reactions.get_by_id("EX_phe__L_e"))
Out[63]:
minimum maximum
EX_phe__L_e 0.0 5.507292
In [64]:
model_BCAE.metabolites.bzalc_e.summary()

PRODUCING REACTIONS --  Benzyl alcohol (bzalc_e)
------------------------------------------------
%       FLUX  RXN ID      REACTION
----  ------  ----------  -------------------
nan%       0  BZALCtex    bzalc_c <=> bzalc_e
nan%       0  EX_bzalc_e  bzalc_e -->

CONSUMING REACTIONS --  Benzyl alcohol (bzalc_e)
------------------------------------------------
%       FLUX  RXN ID      REACTION
----  ------  ----------  -------------------
In [65]:
model_BCAE.metabolites.phe__L_e.summary()

PRODUCING REACTIONS -- L-Phenylalanine (phe__L_e)
-------------------------------------------------
%       FLUX  RXN ID      REACTION
----  ------  ----------  ---------------------
100%    5.51  PHEtex      phe__L_e <=> phe__L_p

CONSUMING REACTIONS -- L-Phenylalanine (phe__L_e)
-------------------------------------------------
%       FLUX  RXN ID      REACTION
----  ------  ----------  ---------------------
100%    5.51  EX_phe_...  phe__L_e -->
In [66]:
model1_2=model_2.copy()
In [67]:
model1_A=model_A.copy()
In [68]:
model1_B=model_B.copy()
In [69]:
model1_C=model_C.copy()
In [70]:
model1_E=model_E.copy()
In [71]:
model1_BC=model_BC.copy()
In [72]:
model1_BCA=model_BCA.copy()
In [73]:
model1_BCE=model_BCE.copy()
In [74]:
model1_BCAE=model_BCAE.copy()
In [75]:
model1_BCAET=model_BCAET.copy()
In [76]:
modelY_BCAET=model_BCAET.copy()
In [77]:
modelW_BCAET=model_BCAET.copy()
In [78]:
modelD_BCAET=model_BCAET.copy()
In [79]:
modelWY_BCAET=model_BCAET.copy()
In [80]:
modelDY_BCAET=model_BCAET.copy()
In [81]:
modelWD_BCAET=model_BCAET.copy()
In [82]:
modelWDYU_BCAET=model_BCAET.copy()
In [83]:
model1_2.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [84]:
model1_A.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [85]:
model1_B.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [86]:
model1_C.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [87]:
model1_E.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [88]:
model1_BC.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [89]:
model1_BCA.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [90]:
model1_BCE.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [91]:
model1_BCAE.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [92]:
model1_BCAET.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [93]:
modelY_BCAET.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [94]:
modelWY_BCAET.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [95]:
modelDY_BCAET.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [96]:
modelWDYU_BCAET.reactions.get_by_id("EX_tyr__L_e").lower_bound=-0.1
In [97]:
modelWDYU_BCAET.reactions.get_by_id("EX_phe__L_e").lower_bound=-0.1
In [98]:
model1_2.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [99]:
model1_A.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [100]:
model1_B.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [101]:
model1_C.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [102]:
model1_E.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [103]:
model1_BC.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [104]:
model1_BCA.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [105]:
model1_BCE.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [106]:
model1_BCAE.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [107]:
model1_BCAET.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [108]:
modelW_BCAET.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [109]:
modelWY_BCAET.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [110]:
modelWD_BCAET.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [111]:
modelWDYU_BCAET.reactions.get_by_id("EX_trp__L_e").lower_bound=-0.05
In [112]:
model1_2.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [113]:
model1_A.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [114]:
model1_B.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [115]:
model1_C.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [116]:
model1_E.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [117]:
model1_BC.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [118]:
model1_BCA.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [119]:
model1_BCE.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [120]:
model1_BCAE.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [121]:
model1_BCAET.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [122]:
modelD_BCAET.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [123]:
modelDY_BCAET.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [124]:
modelWD_BCAET.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [125]:
modelWDYU_BCAET.reactions.get_by_id("EX_asp__L_e").lower_bound=-1.5
In [126]:
model1_2.optimize()
Out[126]:
Optimal solution with objective value 1.088
fluxes reduced_costs
DM_4crsol_c 0.000243 0.000000
DM_5drib_c 0.000245 0.000000
DM_aacald_c -0.000000 0.000000
DM_amob_c 0.000002 0.000000
DM_mththf_c 0.000487 0.000000
... ... ...
Zn2tex 0.000371 -0.000000
PASY 0.000000 -0.338034
BZALCpp 0.000000 0.000000
BZALCtex 0.000000 -0.000000
EX_bzalc_e -0.000000 0.000000

2587 rows × 2 columns

In [127]:
flux_variability_analysis(model1_2, model1_2.reactions.get_by_id("EX_bzalc_e"))
Out[127]:
minimum maximum
EX_bzalc_e 0.0 0.0
In [128]:
flux_variability_analysis(model1_2, model1_2.reactions.get_by_id("EX_phe__L_e"))
Out[128]:
minimum maximum
EX_phe__L_e 0.0 0.0
In [129]:
model1_B.optimize()
Out[129]:
Optimal solution with objective value 1.088
fluxes reduced_costs
DM_4crsol_c 0.000243 0.000000
DM_5drib_c 0.000245 0.000000
DM_aacald_c -0.000000 0.000000
DM_amob_c 0.000002 0.000000
DM_mththf_c 0.000487 0.000000
... ... ...
Zn2tex 0.000371 -0.000000
PASY 0.000000 -0.338034
BZALCpp 0.000000 0.000000
BZALCtex 0.000000 -0.000000
EX_bzalc_e -0.000000 0.000000

2587 rows × 2 columns

In [130]:
flux_variability_analysis(model1_B, model1_B.reactions.get_by_id("EX_bzalc_e"))
Out[130]:
minimum maximum
EX_bzalc_e 0.0 0.0
In [131]:
flux_variability_analysis(model1_B, model1_B.reactions.get_by_id("EX_phe__L_e"))
Out[131]:
minimum maximum
EX_phe__L_e 0.0 0.0
In [132]:
model1_C.optimize()
Out[132]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.0
DM_5drib_c 0.000115 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c 0.000001 0.0
DM_mththf_c 0.000230 0.0
... ... ...
Zn2tex 0.000175 -0.0
PASY 0.000000 0.0
BZALCpp 0.000000 0.0
BZALCtex 0.000000 -0.0
EX_bzalc_e -0.000000 0.0

2587 rows × 2 columns

In [133]:
flux_variability_analysis(model1_C, model1_C.reactions.get_by_id("EX_bzalc_e"))
Out[133]:
minimum maximum
EX_bzalc_e 0.0 3.326789
In [134]:
flux_variability_analysis(model1_C, model1_C.reactions.get_by_id("EX_phe__L_e"))
Out[134]:
minimum maximum
EX_phe__L_e 0.0 3.189445
In [135]:
model1_E.optimize()
Out[135]:
Optimal solution with objective value 0.880
fluxes reduced_costs
DM_4crsol_c 0.000196 0.0
DM_5drib_c 0.000198 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c 0.000002 0.0
DM_mththf_c 0.000394 0.0
... ... ...
Zn2tex 0.000300 -0.0
PASY 0.000000 0.0
BZALCpp 0.000000 0.0
BZALCtex 0.000000 -0.0
EX_bzalc_e -0.000000 0.0

2587 rows × 2 columns

In [136]:
flux_variability_analysis(model1_E, model1_E.reactions.get_by_id("EX_bzalc_e"))
Out[136]:
minimum maximum
EX_bzalc_e 0.0 1.229529
In [137]:
flux_variability_analysis(model1_E, model1_E.reactions.get_by_id("EX_phe__L_e"))
Out[137]:
minimum maximum
EX_phe__L_e 0.0 1.178769
In [138]:
model1_A.optimize()
Out[138]:
Optimal solution with objective value 0.724
fluxes reduced_costs
DM_4crsol_c 0.000161 0.0
DM_5drib_c 0.000163 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c 0.000001 0.0
DM_mththf_c 0.000324 0.0
... ... ...
Zn2tex 0.000247 -0.0
PASY 0.000000 0.0
BZALCpp 0.000000 0.0
BZALCtex 0.000000 -0.0
EX_bzalc_e -0.000000 0.0

2587 rows × 2 columns

In [139]:
flux_variability_analysis(model1_A, model1_A.reactions.get_by_id("EX_bzalc_e"))
Out[139]:
minimum maximum
EX_bzalc_e 0.0 2.130496
In [140]:
flux_variability_analysis(model1_A, model1_A.reactions.get_by_id("EX_phe__L_e"))
Out[140]:
minimum maximum
EX_phe__L_e 0.0 2.04254
In [141]:
model1_BC.optimize()
Out[141]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.000000e+00
DM_5drib_c 0.000115 0.000000e+00
DM_aacald_c -0.000000 0.000000e+00
DM_amob_c 0.000001 0.000000e+00
DM_mththf_c 0.000230 0.000000e+00
... ... ...
Zn2tex 0.000175 -0.000000e+00
PASY 0.000000 5.928591e-17
BZALCpp 0.000000 0.000000e+00
BZALCtex 0.000000 -0.000000e+00
EX_bzalc_e -0.000000 0.000000e+00

2587 rows × 2 columns

In [142]:
flux_variability_analysis(model1_BC, model1_BC.reactions.get_by_id("EX_bzalc_e"))
Out[142]:
minimum maximum
EX_bzalc_e 0.0 3.326789
In [143]:
flux_variability_analysis(model1_BC, model1_BC.reactions.get_by_id("EX_phe__L_e"))
Out[143]:
minimum maximum
EX_phe__L_e 0.0 3.189445
In [144]:
model1_BCA.optimize()
Out[144]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.000000e+00
DM_5drib_c 0.000115 0.000000e+00
DM_aacald_c -0.000000 0.000000e+00
DM_amob_c 0.000001 0.000000e+00
DM_mththf_c 0.000230 0.000000e+00
... ... ...
Zn2tex 0.000175 -0.000000e+00
PASY 0.000000 1.776357e-15
BZALCpp 0.000000 0.000000e+00
BZALCtex 0.000000 -0.000000e+00
EX_bzalc_e -0.000000 0.000000e+00

2587 rows × 2 columns

In [145]:
flux_variability_analysis(model1_BCA, model1_BCA.reactions.get_by_id("EX_bzalc_e"))
Out[145]:
minimum maximum
EX_bzalc_e 0.0 3.326789
In [146]:
flux_variability_analysis(model1_BCA, model1_BCA.reactions.get_by_id("EX_phe__L_e"))
Out[146]:
minimum maximum
EX_phe__L_e 0.0 3.189445
In [147]:
model1_BCE.optimize()
Out[147]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.000000e+00
DM_5drib_c 0.000115 0.000000e+00
DM_aacald_c -0.000000 0.000000e+00
DM_amob_c 0.000001 0.000000e+00
DM_mththf_c 0.000230 0.000000e+00
... ... ...
Zn2tex 0.000175 -0.000000e+00
PASY 0.000000 -1.665335e-16
BZALCpp 0.000000 0.000000e+00
BZALCtex 0.000000 -0.000000e+00
EX_bzalc_e -0.000000 0.000000e+00

2587 rows × 2 columns

In [148]:
flux_variability_analysis(model_BCE, model_BCE.reactions.get_by_id("EX_bzalc_e"))
Out[148]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [149]:
flux_variability_analysis(model_BCE, model_BCE.reactions.get_by_id("EX_phe__L_e"))
Out[149]:
minimum maximum
EX_phe__L_e 0.0 5.507292
In [150]:
model1_BCAE.optimize()
Out[150]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.000000e+00
DM_5drib_c 0.000115 0.000000e+00
DM_aacald_c -0.000000 0.000000e+00
DM_amob_c 0.000001 0.000000e+00
DM_mththf_c 0.000230 0.000000e+00
... ... ...
Zn2tex 0.000175 -0.000000e+00
PASY 0.000000 1.441407e-15
BZALCpp 0.000000 0.000000e+00
BZALCtex 0.000000 -0.000000e+00
EX_bzalc_e -0.000000 0.000000e+00

2587 rows × 2 columns

In [151]:
flux_variability_analysis(model_BCAE, model_BCAE.reactions.get_by_id("EX_bzalc_e"))
Out[151]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [152]:
flux_variability_analysis(model_BCAE, model_BCAE.reactions.get_by_id("EX_phe__L_e"))
Out[152]:
minimum maximum
EX_phe__L_e 0.0 5.507292
In [153]:
model1_BCAET.optimize()
Out[153]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.0
DM_5drib_c 0.000115 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c 0.000001 0.0
DM_mththf_c 0.000230 0.0
... ... ...
Zn2tex 0.000175 -0.0
PASY 3.040690 0.0
BZALCpp 0.000000 0.0
BZALCtex 3.040690 -0.0
EX_bzalc_e 3.040690 0.0

2587 rows × 2 columns

In [154]:
flux_variability_analysis(model_BCAET, model_BCAET.reactions.get_by_id("EX_bzalc_e"))
Out[154]:
minimum maximum
EX_bzalc_e 0.0 5.746739
In [155]:
flux_variability_analysis(model_BCAET, model_BCAET.reactions.get_by_id("EX_phe__L_e"))
Out[155]:
minimum maximum
EX_phe__L_e 0.0 5.507292
In [156]:
modelY_BCAET.optimize()
Out[156]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.746739 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.746739 -0.0
EX_bzalc_e 5.746739 0.0

2587 rows × 2 columns

In [157]:
modelW_BCAET.optimize()
Out[157]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.746739 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.746739 -0.0
EX_bzalc_e 5.746739 0.0

2587 rows × 2 columns

In [158]:
modelD_BCAET.optimize()
Out[158]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.585714 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.585714 -0.0
EX_bzalc_e 5.585714 0.0

2587 rows × 2 columns

In [159]:
modelWD_BCAET.optimize()
Out[159]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.585714 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.585714 -0.0
EX_bzalc_e 5.585714 0.0

2587 rows × 2 columns

In [160]:
modelWY_BCAET.optimize()
Out[160]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.746739 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.746739 -0.0
EX_bzalc_e 5.746739 0.0

2587 rows × 2 columns

In [161]:
modelDY_BCAET.optimize()
Out[161]:
Optimal solution with objective value 0.000
fluxes reduced_costs
DM_4crsol_c -0.000000 0.0
DM_5drib_c -0.000000 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c -0.000000 0.0
DM_mththf_c -0.000000 0.0
... ... ...
Zn2tex 0.000000 -0.0
PASY 5.714286 0.0
BZALCpp 0.000000 0.0
BZALCtex 5.714286 -0.0
EX_bzalc_e 5.714286 0.0

2587 rows × 2 columns

In [162]:
modelWDYU_BCAET.optimize()
Out[162]:
Optimal solution with objective value 0.512
fluxes reduced_costs
DM_4crsol_c 0.000114 0.0
DM_5drib_c 0.000115 0.0
DM_aacald_c -0.000000 0.0
DM_amob_c 0.000001 0.0
DM_mththf_c 0.000230 0.0
... ... ...
Zn2tex 0.000175 -0.0
PASY 3.028213 0.0
BZALCpp 0.000000 0.0
BZALCtex 3.028213 -0.0
EX_bzalc_e 3.028213 0.0

2587 rows × 2 columns

In [ ]:
In [ ]: