import cobra
Recon2 = cobra.io.read_sbml_model("Models/recon2.2.xml")cobra/io/sbml.py:235 �[1;31mUserWarning�[0m: M_h_x appears as a reactant and product FAOXC220200x
Recon2.reactions.OIVD3m.gene_reaction_rule='HGNC:2698 and HGNC:987 and HGNC:986 and HGNC:2898 or HGNC:2698 and HGNC:2898 and HGNC:987 and HGNC:986'
Recon2.reactions.OIVD1m.gene_reaction_rule='HGNC:2698 and HGNC:987 and HGNC:986 and HGNC:2898 or HGNC:2698 and HGNC:2898 and HGNC:987 and HGNC:986'
Recon2.reactions.OIVD2m.gene_reaction_rule='HGNC:2698 and HGNC:987 and HGNC:986 and HGNC:2898 or HGNC:2698 and HGNC:2898 and HGNC:987 and HGNC:986'print("Reactions:", len(Recon2.reactions))
print("Metabolites:", len(Recon2.metabolites))
print(Recon2.optimize())Reactions: 7785
Metabolites: 5324
<Solution 555.786 at 0x7fbadfdbe780>
bm = Recon2.reactions.get_by_id("biomass_reaction")
print(bm.build_reaction_string())
print(bm.notes)0.014 biomass_DNA_c + 0.058 biomass_RNA_c + 0.071 biomass_carbohydrate_c + 0.097 biomass_lipid_c + 0.054 biomass_other_c + 0.706 biomass_protein_c -->
{'GENE ASSOCIATION': ['']}
Recon2.optimize()<Solution 555.786 at 0x7fbadfb810b8>
for rxex in Recon2.exchanges:
rxex.bounds=(-0.01,10)
### Western Diet
#### Sugars
Recon2.reactions.get_by_id("EX_arab_L_LPAREN_e_RPAREN_").lower_bound=-0.17878295
#Cellobiose not in Humana
#Recon2.reactions.get_by_id("EX_drib_LPAREN_e_RPAREN_").lower_bound=-0.17878295 #????
Recon2.reactions.get_by_id("EX_fru_LPAREN_e_RPAREN_").lower_bound=-0.14898579
Recon2.reactions.get_by_id("EX_fuc_L_LPAREN_e_RPAREN_").lower_bound=-0.14898579
Recon2.reactions.get_by_id("EX_gal_LPAREN_e_RPAREN_").lower_bound=-0.14898579
Recon2.reactions.get_by_id("EX_glc_LPAREN_e_RPAREN_").lower_bound=-0.14898579
#Falta Ex_glcn en Recon2.2
Recon2.reactions.get_by_id("EX_lcts_LPAREN_e_RPAREN_").lower_bound=-0.07449289
Recon2.reactions.get_by_id("EX_malt_LPAREN_e_RPAREN_").lower_bound=-0.07449289
Recon2.reactions.get_by_id("EX_man_LPAREN_e_RPAREN_").lower_bound=-0.14898579
#Melibiose not in Human
#D-Mannitol not in Human
Recon2.reactions.get_by_id("EX_oxa_LPAREN_e_RPAREN_").lower_bound=-0.44695737
Recon2.reactions.get_by_id("EX_rib_D_LPAREN_e_RPAREN_").lower_bound=-0.17878295
#L-Rhamnose not in Human
Recon2.reactions.get_by_id("EX_sucr_LPAREN_e_RPAREN_").lower_bound=-0.07449289
#Estos no estan en sangre
#Recon2.reactions.get_by_id("EX_tre_LPAREN_e_RPAREN_").lower_bound=-0.07449289
#Recon2.reactions.get_by_id("EX_xyl_D_LPAREN_e_RPAREN_").lower_bound=-0.17878295
#Recon2.reactions.get_by_id("EX_strch1_LPAREN_e_RPAREN_").lower_bound=-0.25733909
#fiber not in human
### Western Diet
#### Fat
Recon2.reactions.get_by_id("EX_arachd_LPAREN_e_RPAREN_").lower_bound=-0.00332813
Recon2.reactions.get_by_id("EX_chsterol_LPAREN_e_RPAREN_").lower_bound=-0.00495795
Recon2.reactions.get_by_id("EX_glyc_LPAREN_e_RPAREN_").lower_bound=-1.79965486
Recon2.reactions.get_by_id("EX_hdca_LPAREN_e_RPAREN_").lower_bound=-0.39637090
Recon2.reactions.get_by_id("EX_hdcea_LPAREN_e_RPAREN_").lower_bound=-0.03651697
Recon2.reactions.get_by_id("EX_lnlc_LPAREN_e_RPAREN_").lower_bound=-0.35910921
Recon2.reactions.get_by_id("EX_lnlnca_LPAREN_e_RPAREN_").lower_bound=-0.01756512
Recon2.reactions.get_by_id("EX_lnlncg_LPAREN_e_RPAREN_").lower_bound=-0.01756512
Recon2.reactions.get_by_id("EX_ocdca_LPAREN_e_RPAREN_").lower_bound=-0.16928260
Recon2.reactions.get_by_id("EX_ocdcea_LPAREN_e_RPAREN_").lower_bound=-0.68144465
#No en sangre
#Recon2.reactions.get_by_id("EX_octa_LPAREN_e_RPAREN_").lower_bound=-0.01294272
#Recon2.reactions.get_by_id("EX_ttdca_LPAREN_e_RPAREN_").lower_bound=-0.06867567
### Western Diet
#### Protein
Recon2.reactions.get_by_id("EX_ala_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_arg_L_LPAREN_e_RPAREN_").lower_bound=-0.15
Recon2.reactions.get_by_id("EX_asn_L_LPAREN_e_RPAREN_").lower_bound=-0.225
Recon2.reactions.get_by_id("EX_asp_L_LPAREN_e_RPAREN_").lower_bound=-0.225
Recon2.reactions.get_by_id("EX_cys_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_gln_L_LPAREN_e_RPAREN_").lower_bound=-0.18
Recon2.reactions.get_by_id("EX_glu_L_LPAREN_e_RPAREN_").lower_bound=-0.18
Recon2.reactions.get_by_id("EX_gly_LPAREN_e_RPAREN_").lower_bound=-0.45
Recon2.reactions.get_by_id("EX_his_L_LPAREN_e_RPAREN_").lower_bound=-0.15
Recon2.reactions.get_by_id("EX_ile_L_LPAREN_e_RPAREN_").lower_bound=-0.15
Recon2.reactions.get_by_id("EX_leu_L_LPAREN_e_RPAREN_").lower_bound=-0.15
Recon2.reactions.get_by_id("EX_lys_L_LPAREN_e_RPAREN_").lower_bound=-0.15
Recon2.reactions.get_by_id("EX_met_L_LPAREN_e_RPAREN_").lower_bound=-0.18
Recon2.reactions.get_by_id("EX_phe_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_pro_L_LPAREN_e_RPAREN_").lower_bound=-0.18
Recon2.reactions.get_by_id("EX_ser_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_thr_L_LPAREN_e_RPAREN_").lower_bound=-0.225
Recon2.reactions.get_by_id("EX_trp_L_LPAREN_e_RPAREN_").lower_bound=-0.08181818
Recon2.reactions.get_by_id("EX_tyr_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_val_L_LPAREN_e_RPAREN_").lower_bound=-0.18
### Western Diet
#### Minterals, Vitamins, others
#12dgr180
#26dap_M
#2dmmq8
#2obut
Recon2.reactions.get_by_id("EX_3mop_LPAREN_e_RPAREN_").lower_bound=-1
#4abz
#4hbz
Recon2.reactions.get_by_id("EX_ac_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_acgam_LPAREN_e_RPAREN_").lower_bound=-1
#No en sangre acmana
#Recon2.reactions.get_by_id("EX_acmana_LPAREN_e_RPAREN_").lower_bound=-1
#acnam
Recon2.reactions.get_by_id("EX_ade_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_adn_LPAREN_e_RPAREN_").lower_bound=-1
#adocbl
Recon2.reactions.get_by_id("EX_ala_D_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_amp_LPAREN_e_RPAREN_").lower_bound=-1
#arab_D
#No en sangre btn
#Recon2.reactions.get_by_id("EX_btn_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_ca2_LPAREN_e_RPAREN_").lower_bound=-1
#cbl1
Recon2.reactions.get_by_id("EX_cgly_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_chol_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_cit_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_cl_LPAREN_e_RPAREN_").lower_bound=-1
#cobalt2
Recon2.reactions.get_by_id("EX_csn_LPAREN_e_RPAREN_").lower_bound=-1
#cu2
Recon2.reactions.get_by_id("EX_dad_2_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_dcyt_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_ddca_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_dgsn_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_fe2_LPAREN_e_RPAREN_").lower_bound=-1
# Methemoglobin normalmente no en sangre
#Recon2.reactions.get_by_id("EX_fe3_LPAREN_e_RPAREN_").lower_bound=-1
#fe3dcit
Recon2.reactions.get_by_id("EX_fald_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_fol_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_for_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_fum_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_gam_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_glu_L_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_glyc3p_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_gthox_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_gthrd_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_gua_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_h_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_h2o_LPAREN_e_RPAREN_").lower_bound=-10
Recon2.reactions.get_by_id("EX_h2o2_LPAREN_e_RPAREN_").lower_bound=-0.01
#h2s
Recon2.reactions.get_by_id("EX_hxan_LPAREN_e_RPAREN_").lower_bound=-1
#indole
Recon2.reactions.get_by_id("EX_k_LPAREN_e_RPAREN_").lower_bound=-1
#lanost
Recon2.reactions.get_by_id("EX_meoh_LPAREN_e_RPAREN_").lower_bound=-10
#metsox_S_L
#mg2
#mn2
#mobd
#mqn8
Recon2.reactions.get_by_id("EX_na1_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_nac_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_ncam_LPAREN_e_RPAREN_").lower_bound=-1
#nmn nmn_e doesn't exists in Recon 2.2
Recon2.reactions.get_by_id("EX_no2_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_orn_LPAREN_e_RPAREN_").lower_bound=-1
# No en sangre
#Recon2.reactions.get_by_id("EX_pheme_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_pi_LPAREN_e_RPAREN_").lower_bound=-1
#Pimelate not in Human
Recon2.reactions.get_by_id("EX_pnto_R_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_ptrc_LPAREN_e_RPAREN_").lower_bound=-1
# No en sangre
#Recon2.reactions.get_by_id("EX_pydam_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_pydx_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_pydx5p_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_pydxn_LPAREN_e_RPAREN_").lower_bound=-1
#ubiquinone-8 not in Human
Recon2.reactions.get_by_id("EX_ribflv_LPAREN_e_RPAREN_").lower_bound=-1
#No en sangre
Recon2.reactions.get_by_id("EX_sel_LPAREN_e_RPAREN_").lower_bound=-1
#Siroheme not in Human
Recon2.reactions.get_by_id("EX_so4_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_spmd_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_thm_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_thymd_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_ura_LPAREN_e_RPAREN_").lower_bound=-1
Recon2.reactions.get_by_id("EX_uri_LPAREN_e_RPAREN_").lower_bound=-1
#Xanthine not in Human
#zn2 not in Human
Recon2.reactions.biomass_reaction.lower_bound=0.008
Recon2.reactions.biomass_reaction.upper_bound=0.1
Recon2.optimize()<Solution 0.100 at 0x7fbadfdbe048>
import pandas as pd
ub_scores_matrix =pd.read_csv("Binary_Byposys_RECON2_2.tsv",sep=",",index_col=0,header=0)
confidence_scores_matrix=ub_scores_matrix.copy()
confidence_scores_matrix.head()
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| CancerBiopsyHGU133A | NormalBiopsyHGU133A | CancerBiopsyHGU133Plus2 | NormalBiopsyHGU133APlus2 | MaxCancerBiopsy | MaxNormalBiopsy | MeanCancerBiopsy | MeanNormalBiopsy | |
|---|---|---|---|---|---|---|---|---|
| HGNC:10006 | 0.000000 | 0.0000 | 0.000000 | 0.0 | 0.000000 | 0.0000 | 0.000000 | 0.00000 |
| HGNC:1027 | 0.015625 | 0.0625 | 0.116667 | 0.0 | 0.116667 | 0.0625 | 0.066146 | 0.03125 |
| HGNC:10293 | 0.000000 | 0.0000 | 0.483333 | 0.0 | 0.483333 | 0.0000 | 0.241667 | 0.00000 |
| HGNC:10297 | 0.000000 | 0.0000 | 0.541667 | 0.0 | 0.541667 | 0.0000 | 0.270833 | 0.00000 |
| HGNC:10451 | 0.878472 | 0.6000 | 1.000000 | 0.5 | 1.000000 | 0.6000 | 0.939236 | 0.55000 |
for model in ub_scores_matrix.columns:
#High Confidence rate
HC=ub_scores_matrix[model][ub_scores_matrix[model] >= .7].index.tolist()
confidence_scores_matrix[model][HC]=3
#Medium Confidence Rate
MC=ub_scores_matrix[model][(ub_scores_matrix[model] < .7) & (ub_scores_matrix[model] >= 0.5) ].index.tolist()
confidence_scores_matrix[model][MC]=2
#Low
LC=ub_scores_matrix[model][(ub_scores_matrix[model] < 0.5) & (ub_scores_matrix[model] >=0.4) ].index.tolist()
confidence_scores_matrix[model][LC]=1
#unknown
ZC=ub_scores_matrix[model][(ub_scores_matrix[model] <.4) & (ub_scores_matrix[model] >= 0.2) ].index.tolist()
confidence_scores_matrix[model][ZC]=0
#Negative
NC=ub_scores_matrix[model][(ub_scores_matrix[model] < 0.2 )].index.tolist()
confidence_scores_matrix[model][NC]=-1
from corda import reaction_confidence
conf_MeanCancerBiopsy = {}
conf_MeanNormalBiopsy = {}
for r in Recon2.reactions:
if(r.gene_reaction_rule!=''):
conf_MeanCancerBiopsy[r.id]=reaction_confidence(r.gene_reaction_rule,confidence_scores_matrix["MeanCancerBiopsy"])
conf_MeanNormalBiopsy[r.id]=reaction_confidence(r.gene_reaction_rule,confidence_scores_matrix["MeanNormalBiopsy"])
else:
conf_MeanCancerBiopsy[r.id]=0
conf_MeanNormalBiopsy[r.id]=0conf_MeanCancerBiopsy["biomass_reaction"]=3
conf_MeanCancerBiopsy["biomass_DNA"]=3
conf_MeanCancerBiopsy["biomass_RNA"]=3
conf_MeanCancerBiopsy["biomass_carbohydrate"]=3
conf_MeanCancerBiopsy["biomass_lipid"]=3
conf_MeanCancerBiopsy["biomass_other"]=3
conf_MeanCancerBiopsy["biomass_protein"]=3
conf_MeanCancerBiopsy["DM_atp_c_"]=3
conf_MeanNormalBiopsy["biomass_reaction"]=3
conf_MeanNormalBiopsy["biomass_DNA"]=3
conf_MeanNormalBiopsy["biomass_RNA"]=3
conf_MeanNormalBiopsy["biomass_carbohydrate"]=3
conf_MeanNormalBiopsy["biomass_lipid"]=3
conf_MeanNormalBiopsy["biomass_other"]=3
conf_MeanNormalBiopsy["biomass_protein"]=3
conf_MeanNormalBiopsy["DM_atp_c_"]=3
Recon2.objective="biomass_reaction"metas = ['adp_c', 'atp_c','glc_D_c', 'fru_c', 'nad_c', 'nadh_c','nad_m', 'nadh_m','nadph_c', 'nadph_m', 'nadp_c', 'nadp_m', 'cmp_c', 'HC00342_c', 'glcn_c', 'citr_L_c', 'glyb_c', 'icit_c', '3pg_c', 'accoa_m ->coa_m', 'akg_m', 'e4p_c', 'f6p_c', 'g3p_c', 'g6p_c', 'oaa_m', 'pep_c', 'pyr_c', 'r5p_c', 'succoa_m ->coa_m', 'ala_L_c', 'arg_L_c', 'asn_L_c', 'asp_L_c', 'val_L_c', 'adp_c', 'thr_L_c', 'leu_L_c', 'gln_L_c', 'glu_L_c', 'gly_c', 'pro_D_c', 'pro_L_c', 'ser_L_c', 'ctp_c', 'fdp_c','utp_c', 'pmtcoa_c -> coa_c', 'chsterol_c', 'dag_hs_c', 'tag_hs_c', 'mag_hs_c', 'gthox_c','gthrd_c','ru5p_D_c','crm_hs_c', 'pa_hs_c', 'pe_hs_c', 'ps_hs_c', 'hxan_c', 'creat_c', 'crtn_c', 'chol_c', 'orn_c', '4hpro_LT_c', 's7p_c', 'amp_c', 'udp_c', 'ade_c', 'asp_D_c', 'adn_c', 'ump_c', 'his_L_c', 'tyr_L_c', 'phe_L_c', 'gdp_c', 'ctp_c', 'cys_L_c', 'amet_c', 'cit_c', 'tym_c', 'succ_c', 'CE1936_c', 'gua_c', 'cdp_c', 'g1p_c', 'lys_L_c', 'bhb_c', 'ile_L_c', 'gmp_c', 'dhap_c', 'fum_c', 'mal_L_c', 'spmd_c', 'ala_B_c', 'trp_L_c', 'lac_L_c', 'met_L_c', 'ptrc_c', '4abut_c','0.014 biomass_DNA_c + 0.058 biomass_RNA_c + 0.071 biomass_carbohydrate_c + 0.097 biomass_lipid_c + 0.054 biomass_other_c + 0.706 biomass_protein_c --> ']%%time
#n=5 penalty_factor=900 <Solution 0.056 at 0x7fabebb0f278>
from corda import CORDA
opt_MeanCancerBiopsy = CORDA(model=Recon2, confidence=conf_MeanCancerBiopsy, n=5, met_prod=metas, penalty_factor=1000 )
opt_MeanCancerBiopsy.build()
print(opt_MeanCancerBiopsy)
model_MeanCancerBiopsy=opt_MeanCancerBiopsy.cobra_model(name="MeanCancerBiopsy")
print(model_MeanCancerBiopsy.optimize())build status: reconstruction complete
Inc. reactions: 2041/7886
- unclear: 912/3621
- exclude: 218/2637
- low and medium: 226/845
- high: 685/783
<Solution 0.056 at 0x7fbade0569e8>
CPU times: user 10min 23s, sys: 128 ms, total: 10min 23s
Wall time: 10min 23s
model_MeanCancerBiopsy.summary(fva=True)IN FLUXES OUT FLUXES OBJECTIVES
--------------------------------------------- ---------------------------------------------- -----------------------
id Flux Range id Flux Range biomass_reac... 0.0563
--------------- -------- ------------------ --------------- -------- -------------------
h2o_e 0.499 [-3.46, 7.85] h_e 1.17 [-1, 6.3]
glyc_e 0.32 [0, 1.8] lac_L_e 0.777 [-0.01, 8.1]
cit_e 0.31 [-0.845, 1] ac_e 0.353 [0, 4.72]
glc_D_e 0.149 [0.149, -0.899] mal_L_e 0.32 [-0.01, 3.39]
ser_L_e 0.0495 [0.0124, 1] lac_D_e 0.297 [0, 8.11]
gam_e 0.0384 [0, 1] pi_e 0.127 [-0.106, 0.614]
lys_L_e 0.0333 [0.0333, 0.0333] nh4_e 0.0736 [-0.01, 4.42]
glu_L_e 0.0314 [0, 1] urate_e 0.0462 [0, 0.0506]
leu_L_e 0.0307 [0.0207, 0.15] glyc_S_e 0.037 [0, 0.988]
gly_e 0.0303 [-0.256, 0.45] co2_e 0.027 [-0.01, 4.8]
pro_L_e 0.0232 [0.0232, 0.0232] abt_e 0.02 [0, 0.929]
arg_L_e 0.0202 [0.0102, 0.0202] ribflv_e 0.01 [0, 0.02]
asp_L_e 0.0198 [0, 0.225] glygn4_e 0.01 [0, 0.01]
val_L_e 0.0198 [0.00983, 0.18] strch2_e 0.01 [0, 0.01]
gln_L_e 0.0183 [0.18, -1.93] ascb_L_e 0.01 [-0.01, 0.01]
thr_L_e 0.0176 [0.0176, 0.0176] adn_e 0.01 [0.01, -1]
3mop_e 0.0161 [-0.134, 1] ala_L_e 0.00861 [-0.3, 1.06]
asn_L_e 0.0157 [0.0157, 0.0157] dcmp_e 0.00727 [0.00727, -0.01]
phe_L_e 0.0146 [0.0146, 0.0146] Rtotal_e 0.00672 [-0.01, 0.019]
glyc3p_e 0.0108 [-0.01, 0.434] c51crn_e 0.00548 [0, 0.01]
ps_hs_e 0.01 [0.01, 0.01] am4n9cs_e 0.00475 [0, 0.01]
fad_e 0.01 [0, 0.01] ala_B_e 0.00452 [-0.01, 0.982]
glygn2_e 0.01 [0, 0.01] q10h2_e 0.00452 [0.01, -0.01]
o2s_e 0.01 [0, 0.01] dttp_m 0.00315 [0, 0.0163]
strch1_e 0.01 [0, 0.01] acetone_e 0.00312 [0, 0.0607]
dctp_n 0.01 [-0.00727, 0.01] vacc_e 0.00135 [-0.01, 0.124]
dhdascb_e 0.01 [0.01, -0.01] dag_hs_e 0.000984 [-0.000984, 0.0159]
datp_n 0.01 [0.01, -0.0109] fum_e 0 [-1, 2.4]
dgtp_n 0.01 [0.01, -0.0109] 5oxpro_e 0 [0, 2.38]
atp_e 0.01 [0.01, -0.0232] akg_e 0 [-0.01, 2.25]
xmp_e 0.01 [0.01, -0.0332] fald_e 0 [-1, 1.96]
acac_e 0.01 [0.01, -0.0507] HC00342_e 0 [-0.01, 1.83]
o2_e 0.01 [0.01, -0.114] slfcys_e 0 [-0.01, 1.01]
h2o2_e 0.01 [0.01, -0.125] ile_L_e 0 [-0.15, 0.984]
ha_e 0.01 [0.01, -0.199] xylt_e 0 [-0.01, 0.919]
ppa_e 0.01 [0.01, -0.361] gthrd_e 0 [-0.281, 0.617]
ins_e 0.01 [0.01, -1] Lcystin_c 0 [-0.01, 0.504]
pyr_e 0.01 [0.01, -2.95] fe3_e 0 [-0.01, 0.496]
tyr_L_e 0.00898 [0.00898, 0.00898] ha_pre1_e 0 [-0.01, 0.395]
met_L_e 0.00861 [0.00861, 0.0418] gal_e 0 [0, 0.315]
lpchol_hs_e 0.00771 [0, 0.01] oxa_e 0 [0, 0.265]
his_L_e 0.00711 [0, 0.00711] 3mob_e 0 [-0.01, 0.16]
utp_e 0.00616 [0.00301, 0.01] HC00250_e 0 [0, 0.143]
crn_e 0.00548 [0, 0.01] gthox_e 0 [0, 0.135]
pe_hs_e 0.00508 [-0.00688, 0.01] taur_c 0 [-0.01, 0.134]
am9csa_e 0.00475 [0, 0.01] hdcea_e 0 [0, 0.12]
q10_e 0.00452 [-0.01, 0.01] 4mop_e 0 [-0.01, 0.119]
biomass_other_c 0.00304 [0.00304, 0.00304] ocdcea_e 0 [0, 0.108]
cys_L_e 0.00262 [-0.0274, 1] fuc_L_e 0 [0, 0.0949]
datp_m 0.00256 [0.00699, -0.0209] nrvnc_e 0 [0, 0.0948]
for_e 0.002 [0.00585, -1.48] anth_c 0 [0, 0.0811]
strdnc_e 0.00135 [0, 0.01] pheme_e 0 [0, 0.0567]
inost_e 0.00131 [0.00131, 0.00131] Ser_Thr_l 0 [-0.01, 0.05]
sph1p_e 0.000984 [0.000984, 0.01] ahcys_e 0 [0, 0.0332]
pglyc_hs_e 0.00082 [0.00082, 0.00082] acald_e 0 [-0.01, 0.0309]
trp_L_e 0.000748 [0.000748, 0.0818] adrn_e 0 [0, 0.0301]
dttp_n 0.000736 [-0.00626, 0.01] clpnd_e 0 [0, 0.03]
meoh_e 0 [-1, 1.96] cholate_e 0 [0, 0.0256]
so4_e 0 [-0.13, 1] bilglcur_e 0 [0, 0.024]
fe2_e 0 [-0.01, 0.499] co_e 0 [0, 0.024]
arab_L_e 0 [0, 0.179] prostgh2_e 0 [0, 0.024]
hdca_e 0 [0, 0.131] dgtp_m 0 [0, 0.0209]
ocdca_e 0 [0, 0.125] 1mncam_e 0 [0, 0.02]
lnlc_e 0 [0, 0.0538] nac_e 0 [0, 0.02]
3hpvs_e 0 [0, 0.01] tmndnc_e 0 [-0.01, 0.02]
4hpro_LT_m 0 [0, 0.01] dctp_m 0 [0, 0.0173]
C02470_e 0 [0, 0.01] glyb_e 0 [0, 0.0172]
HC00822_e 0 [0, 0.01] gchola_e 0 [-0.01, 0.0156]
T_antigen_g 0 [0, 0.01] tchola_e 0 [-0.01, 0.0154]
citr_L_c 0 [0, 0.01] core5_g 0 [0, 0.012]
crvnc_e 0 [0, 0.01] core7_g 0 [0, 0.012]
csa_e 0 [0, 0.01] 3ddcrn_e 0 [0, 0.01]
dcsptn1_e 0 [0, 0.01] 3deccrn_e 0 [0, 0.01]
ddca_e 0 [0, 0.01] 3hpvstet_e 0 [0, 0.01]
etoh_e 0 [0, 0.01] 3ivcrn_e 0 [0, 0.01]
fmn_e 0 [0, 0.01] 3octdeccrn_e 0 [0, 0.01]
gluala_e 0 [0, 0.01] 3tdcrn_e 0 [0, 0.01]
leuktrA4_e 0 [0, 0.01] 3ttetddcoacrn_e 0 [0, 0.01]
octa_e 0 [0, 0.01] am4ncs_e 0 [0, 0.01]
retinol_e 0 [0, 0.01] c6crn_e 0 [0, 0.01]
sbt_D_e 0 [0, 0.01] c8crn_e 0 [0, 0.01]
tacr_e 0 [0, 0.01] ddeccrn_e 0 [0, 0.01]
whhdca_e 0 [0, 0.01] epoxtac_e 0 [0, 0.01]
pe_hs_r 0 [-0.00688, 0.01] ivcrn_e 0 [0, 0.01]
Asn_X_Ser_Thr_l 0 [-0.01, 0.01] leuktrB4_e 0 [0, 0.01]
eicostet_e 0 [-0.01, 0.01] leuktrE4_e 0 [0, 0.01]
carn_e 0 [0, 0.00711] retfa_e 0 [0, 0.01]
retn_e 0 [0, 0.01]
HC01609_e 0 [-0.01, 0.01]
HC01610_e 0 [-0.01, 0.01]
cmp_e 0 [-0.01, 0.01]
ctp_e 0 [-0.01, 0.01]
dtmp_e 0 [-0.01, 0.01]
dttp_e 0 [-0.01, 0.01]
n2m2nmasn_e 0 [-0.01, 0.01]
ncam_c 0 [-0.01, 0.01]
fuc14galacgl... 0 [0, 0.00902]
sphs1p_e 0 [0, 0.00902]
man_e 0 [0.03, -0.114]
%%time
#n=5 penalty_factor=900 <Solution 0.051 at 0x7fabebf29160>
#n=5 penalty_factor=500 <Solution 0.050 at 0x7fabebf29160>
from corda import CORDA
opt_MeanNormalBiopsy = CORDA(model=Recon2, confidence=conf_MeanNormalBiopsy, n=5, met_prod=metas, penalty_factor=1000)
opt_MeanNormalBiopsy.build()
print(opt_MeanNormalBiopsy)
model_MeanNormalBiopsy=opt_MeanNormalBiopsy.cobra_model(name="MeanNormalBiopsy")
print(model_MeanNormalBiopsy.optimize())model_MeanNormalBiopsy.summary(fva=True)cobra.io.write_sbml_model(model_MeanCancerBiopsy, "Full_SCC_model_1218_western_diet_n5.sbml")
cobra.io.write_sbml_model(model_MeanNormalBiopsy, "Full_Normal_model_1218_western_diet_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]["gene_rule"]=nmodel.reactions.get_by_id(rx).gene_reaction_rule
results[rx]["norm_flux"]=nflx0
results[rx]["dise_flux"]=dflx0
results[rx]["del_norm_flux"]=nflx1
results[rx]["del_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)
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]["gene_rule"]=nmodel.reactions.get_by_id(rx).gene_reaction_rule
results[rx]["norm_flux"]=nflx0
results[rx]["dise_flux"]=dflx0
results[rx]["del_norm_flux"]=nflx1
results[rx]["del_dise_flux"]=dflx0
# results[rx]["norm_prolif_ratio"]=nflx1/nflx0
# results[rx]["dise_prolif_ratio"]=dflx0
# results[rx]["norm_dise_ratio"]=nflx1/nflx0
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]["gene_rule"]=dmodel.reactions.get_by_id(rx).gene_reaction_rule
results[rx]["norm_flux"]=nflx0
results[rx]["dise_flux"]=dflx0
results[rx]["del_norm_flux"]=nflx1
results[rx]["del_dise_flux"]=dflx1
#results[rx]["norm_prolif_ratio"]=nflx0
# results[rx]["dise_prolif_ratio"]=dflx1/dflx0
# results[rx]["norm_dise_ratio"]=1/(dflx1/dflx0)
dmodel.reactions.get_by_id(rx).bounds=dbounds
return(pd.DataFrame(results).transpose())R_Biopsies=get_drugable_targets(
normal_Model=model_MeanNormalBiopsy,
disease_Model=model_MeanCancerBiopsy, model_name="Biopsys", eps=0.1)
R_Biopsies["dise_prolif_ratio"]=R_Biopsies["del_dise_flux"]/R_Biopsies["dise_flux"]
R_Biopsies["norm_prolif_ratio"]=R_Biopsies["del_norm_flux"]/R_Biopsies["norm_flux"]
R_Biopsies["total_ratio"]=R_Biopsies["dise_prolif_ratio"]/R_Biopsies["norm_prolif_ratio"]R_Biopsies[R_Biopsies["dise_prolif_ratio"]<.999].sort_values(by="total_ratio", ascending=True)import random
import numpy
def shuffle(conf):
lst = [x for x in range(0,len(conf.items()))]
random.shuffle(lst)
return lst
def randomize_model(conf_scores):
lst=shuffle(conf_scores)
a=numpy.array(lst)
(rxindex,rxvalues) = zip(*conf_scores.items())
rxvalues=numpy.array(rxvalues)
nrxval=rxvalues[a]
dictionary = dict(zip(list(rxindex), list(nrxval)))
return(dictionary)
import multiprocessing as mp
from corda import CORDA
import string
output = mp.Queue(maxsize=24)
# define a example function
def multiple_randomized_analysis(modelC, modelN, Recon, pos):
cConf_scores=randomize_model(modelC)
nConf_scores=randomize_model(modelN)
cConf_scores["biomass_reaction"]=3
cConf_scores["biomass_DNA"]=3
cConf_scores["biomass_RNA"]=3
cConf_scores["biomass_carbohydrate"]=3
cConf_scores["biomass_lipid"]=3
cConf_scores["biomass_other"]=3
cConf_scores["biomass_protein"]=3
cConf_scores["DM_atp_c_"]=3
nConf_scores["biomass_reaction"]=3
nConf_scores["biomass_DNA"]=3
nConf_scores["biomass_RNA"]=3
nConf_scores["biomass_carbohydrate"]=3
nConf_scores["biomass_lipid"]=3
nConf_scores["biomass_other"]=3
nConf_scores["biomass_protein"]=3
nConf_scores["DM_atp_c_"]=3
Recon.objective="biomass_reaction"
metas = ['adp_c', 'atp_c','glc_D_c', 'fru_c', 'nad_c', 'nadh_c','nad_m', 'nadh_m','nadph_c', 'nadph_m', 'nadp_c', 'nadp_m', 'cmp_c', 'HC00342_c', 'glcn_c', 'citr_L_c', 'glyb_c', 'icit_c', '3pg_c', 'accoa_m ->coa_m', 'akg_m', 'e4p_c', 'f6p_c', 'g3p_c', 'g6p_c', 'oaa_m', 'pep_c', 'pyr_c', 'r5p_c', 'succoa_m ->coa_m', 'ala_L_c', 'arg_L_c', 'asn_L_c', 'asp_L_c', 'val_L_c', 'adp_c', 'thr_L_c', 'leu_L_c', 'gln_L_c', 'glu_L_c', 'gly_c', 'pro_D_c', 'pro_L_c', 'ser_L_c', 'ctp_c', 'fdp_c','utp_c', 'pmtcoa_c -> coa_c', 'chsterol_c', 'dag_hs_c', 'tag_hs_c', 'mag_hs_c', 'gthox_c','gthrd_c','ru5p_D_c','crm_hs_c', 'pa_hs_c', 'pe_hs_c', 'ps_hs_c', 'hxan_c', 'creat_c', 'crtn_c', 'chol_c', 'orn_c', '4hpro_LT_c', 's7p_c', 'amp_c', 'udp_c', 'ade_c', 'asp_D_c', 'adn_c', 'ump_c', 'his_L_c', 'tyr_L_c', 'phe_L_c', 'gdp_c', 'ctp_c', 'cys_L_c', 'amet_c', 'cit_c', 'tym_c', 'succ_c', 'CE1936_c', 'gua_c', 'cdp_c', 'g1p_c', 'lys_L_c', 'bhb_c', 'ile_L_c', 'gmp_c', 'dhap_c', 'fum_c', 'mal_L_c', 'spmd_c', 'ala_B_c', 'trp_L_c', 'lac_L_c', 'met_L_c', 'ptrc_c', '4abut_c','0.014 biomass_DNA_c + 0.058 biomass_RNA_c + 0.071 biomass_carbohydrate_c + 0.097 biomass_lipid_c + 0.054 biomass_other_c + 0.706 biomass_protein_c --> ']
opt_c= CORDA(model=Recon, confidence=cConf_scores, n=5, met_prod=metas, penalty_factor=1000 )
opt_n= CORDA(model=Recon, confidence=nConf_scores, n=5, met_prod=metas, penalty_factor=1000 )
opt_c.build()
opt_n.build()
model_c=opt_c.cobra_model(name="C")
model_n=opt_n.cobra_model(name="N")
targets=get_drugable_targets(model_n, model_c, "biopsys" )
f="Targets_pf_1000_Biopsys_" + str(pos) + ".tsv"
targets.to_csv(f)
# Setup a list of processes that we want to run
pool = mp.Pool(processes=24)
for i in range(1,49):
pool.apply_async(multiple_randomized_analysis, args=(conf_MeanCancerBiopsy,conf_MeanNormalBiopsy,Recon2,i))
pool.close()
pool.join()Common reactions size 1120
Normal unique reactions size 1059
Disease unique reactions size 1100
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1169
Normal unique reactions size 960
Disease unique reactions size 1116
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1102
Normal unique reactions size 1091
Disease unique reactions size 1226
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1131
Normal unique reactions size 1085
Disease unique reactions size 1162
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1019
Normal unique reactions size 987
Disease unique reactions size 1279
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1121
Normal unique reactions size 1024
Disease unique reactions size 1206
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1154
Normal unique reactions size 1044
Disease unique reactions size 1248
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1121
Normal unique reactions size 1013
Disease unique reactions size 1323
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1123
Normal unique reactions size 1089
Disease unique reactions size 1278
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1104
Normal unique reactions size 1095
Disease unique reactions size 1279
Common reactions size 1092
Normal unique reactions size 1077
Disease unique reactions size 1283
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1150
Normal unique reactions size 1120
Disease unique reactions size 1185
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1080
Normal unique reactions size 967
Disease unique reactions size 1237
Common reactions size 1101
Normal unique reactions size 1090
Disease unique reactions size 1258
Common reactions size 1266
Normal unique reactions size 1116
Disease unique reactions size 1139
Common reactions size 1092
Normal unique reactions size 1032
Disease unique reactions size 1298
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1149
Normal unique reactions size 1148
Disease unique reactions size 1165
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1136
Normal unique reactions size 1119
Disease unique reactions size 1323
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1114
Normal unique reactions size 939
Disease unique reactions size 1315
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1186
Normal unique reactions size 1075
Disease unique reactions size 1189
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1014
Normal unique reactions size 1130
Disease unique reactions size 1294
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1151
Normal unique reactions size 1065
Disease unique reactions size 1299
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1053
Normal unique reactions size 1113
Disease unique reactions size 1126
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 993
Normal unique reactions size 1139
Disease unique reactions size 1254
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1041
Normal unique reactions size 1112
Disease unique reactions size 1212
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
Common reactions size 1054
Normal unique reactions size 1137
Disease unique reactions size 1266
Process ForkPoolWorker-13:
Process ForkPoolWorker-8:
Process ForkPoolWorker-22:
Process ForkPoolWorker-3:
Process ForkPoolWorker-20:
Process ForkPoolWorker-21:
Process ForkPoolWorker-19:
Process ForkPoolWorker-10:
Process ForkPoolWorker-18:
Process ForkPoolWorker-2:
Process ForkPoolWorker-16:
Process ForkPoolWorker-7:
Process ForkPoolWorker-6:
Process ForkPoolWorker-1:
Process ForkPoolWorker-4:
Process ForkPoolWorker-14:
Process ForkPoolWorker-24:
Process ForkPoolWorker-12:
Process ForkPoolWorker-11:
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 236, in build
need = self.associated(include)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in <dictcomp>
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
KeyboardInterrupt
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 244, in build
need = self.associated(include, penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 195, in associated
self.__corda_objective(pen)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 195, in associated
self.__corda_objective(pen)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 195, in associated
self.__corda_objective(pen)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 134, in __corda_objective
self.model.objective.set_linear_coefficients(pen)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 134, in __corda_objective
self.model.objective.set_linear_coefficients(pen)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 134, in __corda_objective
self.model.objective.set_linear_coefficients(pen)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 87, in _index
i = glp_find_col(self.problem.problem, str(self.name))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
KeyboardInterrupt
Traceback (most recent call last):
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 86, in _index
if self.problem is not None:
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 87, in _index
i = glp_find_col(self.problem.problem, str(self.name))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 87, in _index
i = glp_find_col(self.problem.problem, str(self.name))
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 87, in _index
i = glp_find_col(self.problem.problem, str(self.name))
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 86, in _index
if self.problem is not None:
KeyboardInterrupt
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 87, in _index
i = glp_find_col(self.problem.problem, str(self.name))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
KeyboardInterrupt
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 42, in multiple_randomized_analysis
targets=get_drugable_targets(model_n, model_c, "biopsys" )
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "<ipython-input-18-b299aa21be4a>", line 32, in get_drugable_targets
nfba=nmodel.optimize()
File "<ipython-input-23-9847a66c3d0b>", line 42, in multiple_randomized_analysis
targets=get_drugable_targets(model_n, model_c, "biopsys" )
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 236, in build
need = self.associated(include)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/site-packages/cobra/core/model.py", line 840, in optimize
self.solver.optimize()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
Process ForkPoolWorker-9:
File "<ipython-input-18-b299aa21be4a>", line 32, in get_drugable_targets
nfba=nmodel.optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1471, in optimize
status = self._optimize()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/site-packages/cobra/core/model.py", line 840, in optimize
self.solver.optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1471, in optimize
status = self._optimize()
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 236, in build
need = self.associated(include)
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 202, in associated
sol = self.model.solver.primal_values
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 182, in associated
self.__zero_objective()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 138, in __zero_objective
{v: 0 for v in self.model.variables})
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1234, in primal_values
zip(self._get_variables_names(), self._get_primal_values())
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 352, in set_linear_coefficients
glp_set_obj_coef(self.problem.problem, variable._index, float(coefficient))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1222, in _get_variables_names
return [variable.name for variable in self.variables]
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1222, in <listcomp>
return [variable.name for variable in self.variables]
File "/opt/conda/lib/python3.5/site-packages/optlang/container.py", line 76, in __iter__
if original_length != len(self._object_list):
Process ForkPoolWorker-15:
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 202, in associated
sol = self.model.solver.primal_values
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 443, in problem
return getattr(self, '_problem', None)
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1234, in primal_values
zip(self._get_variables_names(), self._get_primal_values())
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
KeyboardInterrupt
KeyboardInterrupt
Process ForkPoolWorker-17:
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
Traceback (most recent call last):
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1222, in _get_variables_names
return [variable.name for variable in self.variables]
File "<ipython-input-23-9847a66c3d0b>", line 42, in multiple_randomized_analysis
targets=get_drugable_targets(model_n, model_c, "biopsys" )
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1222, in <listcomp>
return [variable.name for variable in self.variables]
File "<ipython-input-18-b299aa21be4a>", line 29, in get_drugable_targets
nmodel.reactions.get_by_id(rx).bounds=(-eps,eps)
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 206, in name
return self._name
File "/opt/conda/lib/python3.5/site-packages/cobra/util/context.py", line 69, in wrapper
f(self, new_value)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/cobra/core/reaction.py", line 265, in bounds
update_forward_and_reverse_bounds(self)
File "/opt/conda/lib/python3.5/site-packages/cobra/core/reaction.py", line 1083, in update_forward_and_reverse_bounds
reaction.reverse_variable._lb = None
File "/opt/conda/lib/python3.5/site-packages/cobra/core/reaction.py", line 162, in reverse_variable
self.reverse_id]
File "/opt/conda/lib/python3.5/site-packages/cobra/core/reaction.py", line 111, in reverse_id
self.id.encode('utf-8')).hexdigest()[0:5]))
KeyboardInterrupt
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
Process ForkPoolWorker-23:
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
Traceback (most recent call last):
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
File "/opt/conda/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
File "/opt/conda/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "<ipython-input-23-9847a66c3d0b>", line 38, in multiple_randomized_analysis
opt_n.build()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 272, in build
penalize_medium=False)
KeyboardInterrupt
File "/opt/conda/lib/python3.5/site-packages/corda/corda.py", line 196, in associated
sol = self.model.solver.optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/interface.py", line 1468, in optimize
status = self._optimize()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 669, in _optimize
status = self._run_glp_simplex()
File "/opt/conda/lib/python3.5/site-packages/optlang/glpk_interface.py", line 643, in _run_glp_simplex
return_value = glp_simplex(self.problem, self.configuration._smcp)
KeyboardInterrupt
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-23-9847a66c3d0b> in <module>()
51
52 pool.close()
---> 53 pool.join()
/opt/conda/lib/python3.5/multiprocessing/pool.py in join(self)
508 util.debug('joining pool')
509 assert self._state in (CLOSE, TERMINATE)
--> 510 self._worker_handler.join()
511 self._task_handler.join()
512 self._result_handler.join()
/opt/conda/lib/python3.5/threading.py in join(self, timeout)
1052
1053 if timeout is None:
-> 1054 self._wait_for_tstate_lock()
1055 else:
1056 # the behavior of a negative timeout isn't documented, but
/opt/conda/lib/python3.5/threading.py in _wait_for_tstate_lock(self, block, timeout)
1068 if lock is None: # already determined that the C code is done
1069 assert self._is_stopped
-> 1070 elif lock.acquire(block, timeout):
1071 lock.release()
1072 self._stop()
KeyboardInterrupt:
Common reactions size 1106
Normal unique reactions size 1210
Disease unique reactions size 1165
cobra/util/solver.py:404 �[1;31mUserWarning�[0m: solver status is 'infeasible'
from corda import CORDA
def randomized_analysis(modelC, modelN, Recon): cConf_scores=randomize_model(modelC) nConf_scores=randomize_model(modelN)
metas = ['adp_c', 'atp_c','glc_D_c', 'fru_c', 'nad_c', 'nadh_c','nad_m', 'nadh_m','nadph_c', 'nadph_m', 'nadp_c', 'nadp_m', 'cmp_c', 'HC00342_c', 'glcn_c', 'citr_L_c', 'glyb_c', 'icit_c', '3pg_c', 'accoa_m ->coa_m', 'akg_m', 'e4p_c', 'f6p_c', 'g3p_c', 'g6p_c', 'oaa_m', 'pep_c', 'pyr_c', 'r5p_c', 'succoa_m ->coa_m', 'ala_L_c', 'arg_L_c', 'asn_L_c', 'asp_L_c', 'val_L_c', 'adp_c', 'thr_L_c', 'leu_L_c', 'gln_L_c', 'glu_L_c', 'gly_c', 'pro_D_c', 'pro_L_c', 'ser_L_c', 'ctp_c', 'fdp_c','utp_c', 'pmtcoa_c -> coa_c', 'chsterol_c', 'dag_hs_c', 'tag_hs_c', 'mag_hs_c', 'gthox_c','gthrd_c','ru5p_D_c','crm_hs_c', 'pa_hs_c', 'pe_hs_c', 'ps_hs_c', 'hxan_c', 'creat_c', 'crtn_c', 'chol_c', 'orn_c', '4hpro_LT_c', 's7p_c', 'amp_c', 'udp_c', 'ade_c', 'asp_D_c', 'adn_c', 'ump_c', 'his_L_c', 'tyr_L_c', 'phe_L_c', 'gdp_c', 'ctp_c', 'cys_L_c', 'amet_c', 'cit_c', 'tym_c', 'succ_c', 'CE1936_c', 'gua_c', 'cdp_c', 'g1p_c', 'lys_L_c', 'bhb_c', 'ile_L_c', 'gmp_c', 'dhap_c', 'fum_c', 'mal_L_c', 'spmd_c', 'ala_B_c', 'trp_L_c', 'lac_L_c', 'met_L_c', 'ptrc_c', '4abut_c','0.014 biomass_DNA_c + 0.058 biomass_RNA_c + 0.071 biomass_carbohydrate_c + 0.097 biomass_lipid_c + 0.054 biomass_other_c + 0.706 biomass_protein_c --> ']
opt_c= CORDA(model=Recon, confidence=cConf_scores, n=5, met_prod=metas, penalty_factor=2500 )
opt_n= CORDA(model=Recon, confidence=nConf_scores, n=5, met_prod=metas, penalty_factor=2500 )
opt_c.build()
opt_n.build()
model_c=opt_c.cobra_model(name="C")
model_n=opt_n.cobra_model(name="N")
targets=get_drugable_targets(model_n, model_c, "biopsys" )
targets.to_csv("Targets.tsv",sep="\t")
targets=targets[ (targets["norm_dise_ratio"] > 1.000001) ].sort_values(by="norm_dise_ratio", ascending=False)
targets.to_csv("Targets2_pf_2500.tsv",sep="\t")
randomized_analysis(modelC=conf_CancerBiopsy, modelN=conf_NormalBiopsy, Recon=Recon2)
%matplotlib inline
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')
ax = plt.axes(projection='3d')
# Data for a three-dimensional line
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, 'gray')
# Data for three-dimensional scattered points
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z');import pandas as pd