Skip to content

Latest commit

 

History

History
1388 lines (1182 loc) · 29.5 KB

Drugable_rxs_hgu133APlus2.md

File metadata and controls

1388 lines (1182 loc) · 29.5 KB
import cobra
hgu133APlus2_hela=cobra.io.read_sbml_model("Models/hgu133APlus2_hela_model_0418_DEMEM6429_n5.sbml")
hgu133APlus2_hacat=cobra.io.read_sbml_model("Models/hgu133APlus2_hacat_model_0418_DEMEM6429_n5.sbml")

hgu133APlus2_scc=cobra.io.read_sbml_model("Models/hgu133APlus2_cancer_model_0418_westDiet_n5.sbml")
hgu133APlus2_normal=cobra.io.read_sbml_model("Models/hgu133APlus2_normal_model_0418_westDiet_n5.sbml")

import pandas as pd

def get_drugable_targets(normal_Model, disease_Model, model_name,  eps=0.1):
    Nids = [r.id for r in normal_Model.reactions]
    Dids = [r.id for r in disease_Model.reactions]
        
    nmodel=normal_Model.copy()
    dmodel=disease_Model.copy()

    common_rxs = list(set(Nids) & set(Dids))
    print("Common reactions size",len(common_rxs))
    unique_Nrx = list(set(Nids) - set(Dids))
    print("Normal unique reactions size",len(unique_Nrx))
    unique_Drx = list(set(Dids) - set(Nids))
    print("Disease unique reactions size",len(unique_Drx))

    nflx0=normal_Model.optimize().f
    dflx0=disease_Model.optimize().f

    
    results={}



    for rx in common_rxs:
        #print(rx)

        nbounds=nmodel.reactions.get_by_id(rx).bounds
        dbounds=dmodel.reactions.get_by_id(rx).bounds
        
        nmodel.reactions.get_by_id(rx).bounds=(-eps,eps)
        dmodel.reactions.get_by_id(rx).bounds=(-eps,eps)
                
        nfba=nmodel.optimize()    
        dfba=dmodel.optimize()
        
        nflx1=nfba.f
        dflx1=dfba.f
        
        results[rx]={}
        
        results[rx]["model"]=model_name
        
        results[rx]["norm_flux"]=nflx1
        results[rx]["dise_flux"]=dflx1
 
        results[rx]["norm_prolif_ratio"]=nflx1/nflx0
        results[rx]["dise_prolif_ratio"]=dflx1/dflx0
        
        results[rx]["norm_dise_ratio"]=(nflx1/nflx0)/(dflx1/dflx0)
        
        results[rx]["genes"]=normal_Model.reactions.get_by_id(rx).gene_name_reaction_rule

        
        nmodel.reactions.get_by_id(rx).bounds=nbounds
        dmodel.reactions.get_by_id(rx).bounds=dbounds

        
    for rx in unique_Nrx:
        #print(rx)
        
        nbounds=nmodel.reactions.get_by_id(rx).bounds
        
        nmodel.reactions.get_by_id(rx).bounds=(-eps,eps)      
        
        nfba=nmodel.optimize()    
        
        nflx1=nfba.f
        
        results[rx]={}
        
        results[rx]["model"]=model_name
        
        results[rx]["norm_flux"]=nflx1
        results[rx]["dise_flux"]=dflx0
 
        results[rx]["norm_prolif_ratio"]=nflx1/nflx0
        results[rx]["dise_prolif_ratio"]=dflx0
        
        results[rx]["norm_dise_ratio"]=nflx1/nflx0
        
        results[rx]["genes"]=normal_Model.reactions.get_by_id(rx).gene_name_reaction_rule

            
        nmodel.reactions.get_by_id(rx).bounds=nbounds
        
    for rx in unique_Drx:
        #print(rx)
        
        dbounds=dmodel.reactions.get_by_id(rx).bounds
    
        dmodel.reactions.get_by_id(rx).bounds=(-eps,eps)
        dfba=dmodel.optimize()
        dflx1=dfba.f
        
        results[rx]={}

        results[rx]["model"]=model_name

        results[rx]["norm_flux"]=nflx0
        results[rx]["dise_flux"]=dflx1
 
        results[rx]["norm_prolif_ratio"]=nflx0
        results[rx]["dise_prolif_ratio"]=dflx1/dflx0
        
        results[rx]["norm_dise_ratio"]=1/(dflx1/dflx0)
        
        results[rx]["genes"]=disease_Model.reactions.get_by_id(rx).gene_name_reaction_rule

        dmodel.reactions.get_by_id(rx).bounds=dbounds


    return(pd.DataFrame(results).transpose())
R_clines=get_drugable_targets(hgu133APlus2_hacat, hgu133APlus2_hela, "hgu133APlus2_clines")
Common reactions size 2620
Normal unique reactions size 442
Disease unique reactions size 582

R_biopsys=get_drugable_targets(hgu133APlus2_normal, hgu133APlus2_scc, "hgu133APlus2_biopsys" )
Common reactions size 1963
Normal unique reactions size 524
Disease unique reactions size 614

R_biopsys
<style> .dataframe thead tr:only-child th { text-align: right; } 
.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
</style>
dise_flux dise_prolif_ratio genes model norm_dise_ratio norm_flux norm_prolif_ratio
10FTHF5GLUtl 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
10FTHF6GLUtl 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
10FTHF7GLUtl 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
10FTHFtl 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
10FTHFtm 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
12HTACRhr 0.216258 0.216258 HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
12HTACRitr 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
12HTACRtep 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
12HTACRtu 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
13DAMPPOX 0.216258 1 HGNC:549 or HGNC:550 or HGNC:80 hgu133APlus2_biopsys 1 0.128673 1
14HMDZALThr 0.216258 0.216258 HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
14HMDZhr 0.216258 0.216258 HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
14HMDZitr 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
14MDZtev 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
1OHMDZhr 0.216258 0.216258 HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
1OHMDZitr 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
1OHMDZtep 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
1PPDCRp 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
2AMADPTm 0.216258 1 HGNC:14411 hgu133APlus2_biopsys 1 0.128673 0.128673
2HATVACIDOXDhc 0.216258 0.216258 HGNC:2622 or HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
2HATVACIDitr 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
2HATVACIDtep 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
2HATVLACGLUChr 0.216258 0.216258 HGNC:12535 or HGNC:12536 hgu133APlus2_biopsys 1 0.128673 1
2HATVLACGLUCitr 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
2HATVLACGLUCteb 0.216258 0.216258 HGNC:40 or HGNC:53 hgu133APlus2_biopsys 1 0.128673 1
2HATVLACOXDhc 0.216258 0.216258 HGNC:2637 or HGNC:2638 hgu133APlus2_biopsys 1 0.128673 1
2HBO 0.216258 1 HGNC:21481 or HGNC:28335 or HGNC:30866 or (HGN... hgu133APlus2_biopsys 1 0.128673 1
2HBt2 0.216258 1 HGNC:10922 or HGNC:10924 or HGNC:10928 hgu133APlus2_biopsys 1 0.128673 0.128673
2MB2COAc 0.216258 1 HGNC:2690 hgu133APlus2_biopsys 1 0.128673 1
2OXOADOXm 0.216258 1 HGNC:21350 and HGNC:2898 and HGNC:2911 and HGN... hgu133APlus2_biopsys 1 0.128673 0.128673
... ... ... ... ... ... ... ...
r2344 0.216258 1 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 0.128673
r2353 0.216258 1 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 1
r2361 0.216258 0.216258 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 1
r2363 0.216258 0.216258 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 1
r2368 0.216258 1 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 0.128673
r2369 0.216258 1 HGNC:18057 hgu133APlus2_biopsys 1 0.128673 0.128673
r2372 0.216258 0.216258 HGNC:10979 hgu133APlus2_biopsys 1 0.128673 1
r2404 0.216258 0.216258 HGNC:22921 hgu133APlus2_biopsys 1 0.128673 1
r2419 0.216258 1 HGNC:10980 hgu133APlus2_biopsys 1 0.128673 0.128673
r2472 0.216258 1 HGNC:11024 hgu133APlus2_biopsys 1 0.128673 1
r2473 0.216258 1 HGNC:4061 hgu133APlus2_biopsys 1 0.128673 0.128673
r2505 0.216258 1 HGNC:51 hgu133APlus2_biopsys 1 0.128673 0.128673
r2508 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
r2509 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
r2511 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
r2513 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
r2514 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
r2516 0.216258 1 HGNC:10922 hgu133APlus2_biopsys 1 0.128673 0.128673
r2517 0.216258 1 HGNC:67 hgu133APlus2_biopsys 1 0.128673 0.128673
r2518 0.216258 1 HGNC:67 hgu133APlus2_biopsys 1 0.128673 0.128673
r2519 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
r2521 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
r2537 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
r2538 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
sink_citr_LPAREN_c_RPAREN_ 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1
sink_dd2coa_LPAREN_c_RPAREN_ 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
sink_octdececoa_LPAREN_c_RPAREN_ 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
sink_pre_prot_LPAREN_r_RPAREN_ 0.216258 1 hgu133APlus2_biopsys 1 0.128673 0.128673
sink_tetdece1coa_LPAREN_c_RPAREN_ 0.216258 0.216258 hgu133APlus2_biopsys 1 0.128673 1
xmpt 0.216258 1 hgu133APlus2_biopsys 1 0.128673 1

3101 rows × 7 columns

R_biopsys.to_csv("Drugable_targets_hgu133APlus2_biopsys.tsv",sep="\t")
R_clines.to_csv("Drugable_targets_hgu133APlus2_clines.tsv",sep="\t")
hgu133APlus2_biopsys =  R_biopsys
hgu133APlus2_clines  =  R_clines
hgu133APlus2_biopsys_drugable = hgu133APlus2_biopsys[ (hgu133APlus2_biopsys["norm_dise_ratio"] > 1.0000001) ].sort_values(by="norm_dise_ratio", ascending=False)
hgu133APlus2_clines_drugable = hgu133APlus2_clines[ (hgu133APlus2_clines["norm_dise_ratio"] > 1.0000001) ].sort_values(by="norm_dise_ratio", ascending=False)
hgu133APlus2_biopsys_drugable
Recon2.reactions.PCHOLPg_hs.name

Recon2.metabolites.pa_hs_g.name
hgu133APlus2_clines_drugable

reactionlist=["ACACT1x", "ADK1", "AGPAT1", "C14STRr", "C3STKR2r", "C4STMO1r", "CDIPTr", "CEPTC", "CHLPCTD", "CHOLK", "CLS_hs", "DMATTx", "ENO", "GAPD", "GK1", "GLCt4", "GPAM_hs", "GRTTx", "HISt4", "HMGCOARc","INSTt4", "IPDDIx", "LNSTLSr", "LYStiDF","OMPDC", "ORPT", "PGK", "PGM","r0463", "r0781", "r0787", "RPE", "SMS", "SQLEr", "TKT1", "TKT2"]

Recon2.reactions.GAPD.gene_reaction_rule
Recon2.reactions.GAPD.name
Recon2.reactions.PCHOLPg_hs.genes
Recon2.reactions.PCHOLPr_hs.genes
 Recon2.reactions.PCHOLP_hs.genes
Recon2.reactions.ACACT1x.gene_reaction_rule
# results={}
for rx in R_biopsys.index:
    rule=hgu133APlus2_scc.reactions.get_by_id(rx).gene_reaction_rule
    results[rx]={}
    results[rx]["TypeData"]="Biopsys_HGU133APlus2"
    results[rx]["norm_flux"]=R_biopsys.loc[rx]["norm_flux"]
    results[rx]["dise_flux"]=R_biopsys.loc[rx]["dise_flux"]
    results[rx]["norm_prolif_ratio"]=R_biopsys.loc[rx]["norm_prolif_ratio"]
    results[rx]["dise_prolif_ratio"]=R_biopsys.loc[rx]["dise_prolif_ratio"]
    results[rx]["norm_dise_ratio"]=R_biopsys.loc[rx]["norm_dise_ratio"]

results=pd.DataFrame(results).transpose()
results={}
for rx in R_clines[ R_clines["norm_dise_ratio"] > 1.2 ].index:
    rule=hgu133APlus2_hela.reactions.get_by_id(rx).gene_reaction_rule
    if(rule!=''):
        results[rx]={}
        results[rx]["Reaction"]=rx
        results[rx]["TypeData"]="cLines_HGU133APlus2"
        results[rx]["geneRule"]=rule
        results[rx]["norm_flux"]=R_clines.loc[rx]["norm_flux"]
        results[rx]["dise_flux"]=R_clines.loc[rx]["dise_flux"]
        results[rx]["norm_prolif_ratio"]=R_clines.loc[rx]["norm_prolif_ratio"]*100
        results[rx]["dise_prolif_ratio"]=R_clines.loc[rx]["dise_prolif_ratio"]*100
        results[rx]["norm_dise_ratio"]=R_clines.loc[rx]["norm_dise_ratio"]

pd.DataFrame(results).transpose()

#R_biopsys[ R_biopsys["norm_dise_ratio"] >1.1 ].sort_values(by="norm_dise_ratio", ascending=False)
FBA_hela = hgu133APlus2_hela.optimize()
FBA_hacat =hgu133APlus2_hacat.optimize()

FBA_scc = hgu133APlus2_scc.optimize()
FBA_normal = hgu133APlus2_normal.optimize()

print("Hela proliferation:",FBA_hela.f)
print("HaCaT proliferation:",FBA_hacat.f)
print("Scc proliferation:",FBA_scc.f)
print("Normal proliferation:",FBA_normal.f)



print("Hela TPI:",FBA_hela["TPI"])
print("HaCaT TPI:",FBA_hacat["TPI"])
print("Scc TPI:",FBA_scc["TPI"])
print("Normal TPI:",FBA_normal["TPI"])

hgu133APlus2_hela.reactions.TPI.bounds=(-.1,.1) hgu133APlus2_hacat.reactions.TPI.bounds=(-.1,.1) hgu133APlus2_scc.reactions.TPI.bounds=(-.1,.1) hgu133APlus2_normal.reactions.TPI.bounds=(-.1,.1)

FBA_hela = hgu133APlus2_hela.optimize() FBA_hacat =hgu133APlus2_hacat.optimize()

FBA_scc = hgu133APlus2_scc.optimize() FBA_normal = hgu133APlus2_normal.optimize()

print("Hela proliferation:",FBA_hela.f) print("HaCaT proliferation:",FBA_hacat.f) print("Scc proliferation:",FBA_scc.f) print("Normal proliferation:",FBA_normal.f)

print("Hela TPI:",FBA_hela["TPI"]) print("HaCaT TPI:",FBA_hacat["TPI"]) print("Scc TPI:",FBA_scc["TPI"]) print("Normal TPI:",FBA_normal["TPI"])

hgu133APlus2_hela.reactions.PGM.reaction
import cobra
Recon2 = cobra.io.read_sbml_model("Models/recon2.2.xml")

hgu133A_hela=cobra.io.read_sbml_model("hgu133A_hela_model_0418_DEMEM6429_n5.sbml")
hgu133A_hacat=cobra.io.read_sbml_model("hgu133A_hacat_model_0418_DEMEM6429_n5.sbml")

hgu133A_scc=cobra.io.read_sbml_model("hgu133A_scc_model_0418_western_diet_n5.sbml")
hgu133A_normal=cobra.io.read_sbml_model("hgu133A_nc_model_0418_western_diet_n5.sbml")
R_clines=get_drugable_targets(hgu133A_hacat, hgu133A_hela, "hgu133A_clines",0.001)
R_biopsys=get_drugable_targets(hgu133A_normal, hgu133A_scc, "hgu133A_biopsys" ,0.001)

R_biopsys.to_csv("Drugable_targets_hgu133A_biopsys.tsv",sep="\t")
R_clines.to_csv("Drugable_targets_hgu133A_clines.tsv",sep="\t")
hgu133APlus2_biopsys =  pd.read_csv("Drugable_targets_hgu133APlus2_biopsys.tsv",sep="\t", index_col=0)
hgu133APlus2_clines  =  pd.read_csv("Drugable_targets_hgu133APlus2_clines.tsv",sep="\t", index_col=0)
hgu133A_biopsys      =  pd.read_csv("Drugable_targets_hgu133A_biopsys.tsv",sep="\t", index_col=0)
hgu133A_clines       =  pd.read_csv("Drugable_targets_hgu133A_clines.tsv",sep="\t", index_col=0)


hgu133A_clines_drugable = hgu133A_clines[ (hgu133A_clines["norm_dise_ratio"] > 1.0000001) & (hgu133A_clines["dise_flux"] < hgu133A_clines["norm_flux"])].sort_values(by="norm_dise_ratio", ascending=False)


hgu133A_biopsys_drugable = hgu133A_biopsys[ (hgu133A_biopsys["norm_dise_ratio"] > 1.0000001) & (hgu133A_biopsys["dise_flux"] < hgu133A_biopsys["norm_flux"])].sort_values(by="norm_dise_ratio", ascending=False)


hgu133A_clines_drugable


import itertools
import numpy as np

a = np.array(list(itertools.chain(hgu133APlus2_biopsys_drugable.index, hgu133APlus2_clines_drugable.index, hgu133A_clines_drugable.index, hgu133A_biopsys_drugable.index)))
u  = np.unique(a, return_index=False)


for rx in u:
    sets = Recon2.reactions.get_by_id(rx).genes
    if(list(sets)):
        print(Recon2.reactions.get_by_id(rx).id)
        print(list(sets))


ACACT1x	HGNC:94
ADK1	HGNC:365,HGNC:361,HGNC:20091,HGNC:362
AGPAT1	HGNC:326,HGNC:324,HGNC:20886,HGNC:20885,HGNC:25193,HGNC:325,HGNC:20880
C14STRr	HGNC:11863
C3STKR2r	HGNC:5213
C4STMO1r	HGNC:10545
CATm	HGNC:1516
CLS_hs	HGNC:16148
DMATTx	HGNC:3631
ENO	HGNC:3354,HGNC:3353,HGNC:3350
GAPD	HGNC:24864,HGNC:4141
GLCt4	HGNC:23155,HGNC:11038,HGNC:22146,HGNC:23091,HGNC:28750,HGNC:11037
GPAM_hs	HGNC:24865,HGNC:25193
GRTTx	HGNC:3631
HISt4	HGNC:13448,HGNC:11047
HMGCOARc	HGNC:5006
INSTt4	HGNC:11038
IPDDIx	HGNC:5387,HGNC:23487
LNSTLSr	HGNC:6708
LPASE	HGNC:15449,HGNC:9035,HGNC:2014
LYStiDF	HGNC:11057,HGNC:14679,HGNC:11060,HGNC:11061
OMPDC	HGNC:12563
ORPT	HGNC:12563
PGK	HGNC:8896,HGNC:8898
PGM	HGNC:1093,HGNC:8888,HGNC:8889
RPE	HGNC:10293
SMS	HGNC:29799
SQLEr	HGNC:11279
TKT1	HGNC:11835,HGNC:11834,HGNC:25313
TKT2	HGNC:11835,HGNC:11834,HGNC:25313
r0463	HGNC:5007
r0781	HGNC:2649


Recon2.reactions.EX_h2o2_LPAREN_e_RPAREN_.reaction


hgu133APlus2_hela=cobra.io.read_sbml_model("Models/hgu133APlus2_hela_model_0418_DEMEM6429_n5.sbml")
hgu133APlus2_hacat=cobra.io.read_sbml_model("Models/hgu133APlus2_hacat_model_0418_DEMEM6429_n5.sbml")

hgu133APlus2_scc=cobra.io.read_sbml_model("Models/hgu133APlus2_cancer_model_0418_westDiet_n5.sbml")
hgu133APlus2_normal=cobra.io.read_sbml_model("Models/hgu133APlus2_normal_model_0418_westDiet_n5.sbml")



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)

deletion_results=single_reaction_deletion(hgu133APlus2_scc, hgu133APlus2_scc.reactions)

hgu133APlus2_scc_del = deletion_results[deletion_results.status != 'optimal']
hgu133APlus2_scc_del[hgu133APlus2_scc_del.status != 'optimal']



<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}


</style>


  
    

      
      flux
      status
    

  
  
  





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)

deletion_results=single_reaction_deletion(hgu133APlus2_hacat, hgu133APlus2_hacat.reactions)

hgu133APlus2_hacat_del = deletion_results[deletion_results.status != 'optimal']
hgu133APlus2_hacat_del[hgu133APlus2_hacat_del.status != 'optimal']



<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}


</style>


  
    

      
      flux
      status
    

  
  
    

      C14STRr
      0.0
      infeasible
    

    

      C4STMO1r
      0.0
      infeasible
    

    

      CDIPTr
      0.0
      infeasible
    

    

      CHSTEROLtrc
      0.0
      infeasible
    

    

      CLS_hs
      0.0
      infeasible
    

    

      CO2ter
      0.0
      infeasible
    

    

      DMATTx
      0.0
      infeasible
    

    

      DSAT
      0.0
      infeasible
    

    

      EX_lys_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_met_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_pglyc_hs_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_phe_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_pro_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_thr_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      EX_trp_L_LPAREN_e_RPAREN_
      0.0
      infeasible
    

    

      FORtr
      0.0
      infeasible
    

    

      FRDPtr
      0.0
      infeasible
    

    

      GK1
      0.0
      infeasible
    

    

      GRTTx
      0.0
      infeasible
    

    

      HMGCOARc
      0.0
      infeasible
    

    

      IPDDIx
      0.0
      infeasible
    

    

      LNSTLSr
      0.0
      infeasible
    

    

      LYStiDF
      0.0
      infeasible
    

    

      O2ter
      0.0
      infeasible
    

    

      PGI
      0.0
      infeasible
    

    

      PGLYCt
      0.0
      infeasible
    

    

      RE2675C
      0.0
      infeasible
    

    

      SMS
      0.0
      infeasible
    

    

      SQLEr
      0.0
      infeasible
    

    

      biomass_DNA
      0.0
      infeasible
    

    

      biomass_RNA
      0.0
      infeasible
    

    

      biomass_carbohydrate
      0.0
      infeasible
    

    

      biomass_lipid
      0.0
      infeasible
    

    

      biomass_other
      0.0
      infeasible
    

    

      biomass_protein
      0.0
      infeasible
    

    

      r0781
      0.0
      infeasible
    

  





hgu133APlus2_scc


import numpy as np


main_list = np.setdiff1d(hgu133APlus2_hela_del.index,hgu133APlus2_hacat_del.index)

main_list


array(['AGPAT1', 'C3STKR2r', 'GPAM_hs'], dtype=object)