7.EcModel_ME

Input and output files

ecModel_file="./model/eciML1515.json"
obj='EX_trp__L_e' # EX_lys_L_e
substrate = 'EX_glc__D_e'
substrate_con=10
biomass_id = 'BIOMASS_Ec_iML1515_core_75p37M'
biomass_min=0.1

fluxes_outfile = './analysis/ECMpy_solution_%s_%.2g_pfba.csv'%(obj,substrate_con)
enzcost_cost_file = './analysis/enzcost_%s_%.2g.csv'%(obj,substrate_con)
enzcost_diff_file = './analysis/enzcost_diff_%s_%.2g.csv'%(obj,substrate_con)
enzcost_diff_selcet_file = './analysis/enzcost_diff_%s_%.2g_select.csv'%(obj,substrate_con)
FSEOF_file = './analysis/FESEOF_%s_%.2g.csv'%(obj,substrate_con)

Directly determining targets based on enzyme abundance

enzcost = pd.DataFrame()

enz_model=get_enzyme_constraint_model(ecModel_file)
enz_model.reactions.get_by_id(substrate).bounds=(-substrate_con,0)#EX_glc_e EX_fru_e EX_sucr_e EX_inost_e EX_lac_D_e EX_ac_e
enz_model.reactions.get_by_id('%s_reverse'%substrate).bounds=(0,0)
enz_model.reactions.get_by_id(biomass_id).bounds=(biomass_min,biomass_min)
enz_model.objective=obj
enz_model_pfba_solution = cobra.flux_analysis.pfba(enz_model)

enzcost = get_fluxes_detail_in_model(enz_model,enz_model_pfba_solution,fluxes_outfile,ecModel_file)
enzcost['enz_ratio'] = enzcost['E']/np.sum(enzcost['E'])
enzcost = enzcost.sort_values('enz_ratio', ascending=False)
enzcost_select = enzcost[enzcost['enz_ratio']>0.01]
enzcost_select.to_csv(enzcost_cost_file)
enzcost_select.head(10)

	fluxes	kcat_MW	E	equ	ec-code	enz_ratio
ANS	3.594316	76.627794	0.046906	chor_c + gln__L_c --> anth_c + glu__L_c + h_c ...	4.1.3.27	0.206635
TRPAS2_reverse	3.594316	85.270150	0.042152	indole_c + nh4_c + pyr_c --> h2o_c + trp__L_c	NaN	0.185692
ANPRT	3.594316	158.255810	0.022712	anth_c + prpp_c --> ppi_c + pran_c	2.4.2.18	0.100053
TRPS3	3.594316	249.207417	0.014423	3ig3p_c --> g3p_c + indole_c	4.1.2.8,4.2.1.20	0.063537
PRAIi	3.594316	363.692938	0.009883	pran_c --> 2cpr5p_c	5.3.1.24	0.043537
IGPS	3.594316	363.692938	0.009883	2cpr5p_c + h_c --> 3ig3p_c + co2_c + h2o_c	4.1.1.48	0.043537
GLCDpp_num2	5.074624	1061.316075	0.004781	glc__D_p + h2o_p + q8_c --> glcn_p + h_p + q8h2_c	NaN	0.021064
PRPPS	3.706289	841.654904	0.004404	atp_c + r5p_c --> amp_c + h_c + prpp_c	2.7.6.1	0.019399
GNK_num2	5.074624	1273.803729	0.003984	atp_c + glcn_c --> 6pgc_c + adp_c + h_c	2.7.1.12	0.017550
PDH	2.960121	784.108302	0.003775	coa_c + nad_c + pyr_c --> accoa_c + co2_c + na...	1.2.1.-,1.2.1.51,1.2.4.1,1.8.1.4,2.3.1.12	0.016631

Determine targets based on the fold changes of enzyme cost

biomass_max=0.6
FC_threshold = 1.5

enzcost_diff = get_enz_foldchange(ecModel_file,obj,substrate,substrate_con,biomass_id,biomass_min,biomass_max,FC_threshold,fluxes_outfile, enzcost_diff_file)
enzcost_diff_select = enzcost_diff[enzcost_diff['E_μ0.1']>0.0001]
enzcost_diff_select.to_csv(enzcost_diff_selcet_file)

enzcost_diff_select.head(10)

/home/maozt/anaconda3/envs/ECMpy2/lib/python3.7/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in log2
  result = getattr(ufunc, method)(*inputs, **kwargs)

	E_μ0.1	E_μ0.6	log2_foldchange(max/min)	log2_foldchange(min/max)	type	foldchange(max/min)	equ	ec-code
3OAS160_num1	0.000117	0.000702	2.584963	-2.584963	weaken_target	6.0000	h_c + malACP_c + myrsACP_c --> 3opalmACP_c + A...	2.3.1.86
3OAR160	0.000140	0.000842	2.584963	-2.584963	weaken_target	6.0000	3opalmACP_c + h_c + nadph_c --> 3hpalmACP_c + ...	1.1.1.100,2.3.1.-,2.3.1.85,2.3.1.86
3OAR140	0.000449	0.002693	2.584963	-2.584963	weaken_target	6.0000	3omrsACP_c + h_c + nadph_c --> 3hmrsACP_c + na...	1.1.1.100,2.3.1.-,2.3.1.85,2.3.1.86
3OAS140_num1	0.000115	0.000690	2.584963	-2.584963	weaken_target	6.0000	ddcaACP_c + h_c + malACP_c --> 3omrsACP_c + AC...	2.3.1.86
ACONTa_num1	0.000580	0.002436	2.070767	-2.070767	weaken_target	4.2011	cit_c --> acon_C_c + h2o_c	4.2.1.3
EAR121x	0.000119	0.000714	2.584963	-2.584963	weaken_target	6.0000	h_c + nadh_c + t3c5ddeceACP_c --> cddec5eACP_c...	1.3.1.9
3OAR161	0.000165	0.000992	2.584963	-2.584963	weaken_target	6.0000	3ocpalm9eACP_c + h_c + nadph_c --> 3hcpalm9eAC...	1.1.1.100
3OAR141	0.000165	0.000992	2.584963	-2.584963	weaken_target	6.0000	3ocmrs7eACP_c + h_c + nadph_c --> 3hcmrs7eACP_...	1.1.1.100
EAR160y	0.000153	0.000918	2.584963	-2.584963	weaken_target	6.0000	h_c + nadph_c + tpalm2eACP_c --> nadp_c + palm...	1.3.1.10
EAR161y	0.000179	0.001072	2.584963	-2.584963	weaken_target	6.0000	h_c + nadph_c + t3c9palmeACP_c --> hdeACP_c + ...	1.3.1.10

Determine targets based on FESOF

model=get_enzyme_constraint_model(ecModel_file)

FSEOFdf_done = run_FSEOF(model,substrate,substrate_con,biomass_id,obj,FSEOF_file)

FSEOFdf_done.head(10)

./script/ECMpy_function.py:2653: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  always_down['regulation'] = 'down'
./script/ECMpy_function.py:2656: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  always_up['regulation'] = 'up'

	growth = 0.34	FC growth = 0.34	growth = 0.37	FC growth = 0.37	growth = 0.4	FC growth = 0.4	growth = 0.43	FC growth = 0.43	growth = 0.46	FC growth = 0.46	...	FC growth = 0.55	growth = 0.58	FC growth = 0.58	growth = 0.61	FC growth = 0.61	WT	GPR	FC_mean	regulation	reactions
id
H2Otpp_reverse_num2	39.134690	1000.000000	38.784008	1000.000000	38.433325	1000.000000	38.082642	1000.000000	37.731959	1000.000000	...	1000.000000	36.083873	1000.000000	35.649234	1000.000000	0.000000	b0875	562.598358	up	h2o_c --> h2o_p
H2Otex_reverse_num8	39.134690	1000.000000	38.784008	1000.000000	38.433325	1000.000000	38.082642	1000.000000	37.731959	1000.000000	...	1000.000000	36.083873	1000.000000	35.649234	1000.000000	0.000000	b0929	562.598358	up	h2o_p --> h2o_e
CO2tex_reverse_num4	13.119677	1000.000000	13.437758	1000.000000	13.755840	1000.000000	14.073921	1000.000000	14.392003	1000.000000	...	1000.000000	15.630991	1000.000000	15.937058	1000.000000	0.000000	b2215	551.995516	up	co2_p --> co2_e
Htex_reverse_num4	12.132625	1000.000000	12.272701	1000.000000	12.412777	1000.000000	12.552853	1000.000000	12.692929	1000.000000	...	1000.000000	13.362659	1000.000000	13.540216	1000.000000	0.000000	b2215	551.227208	up	h_p --> h_e
ACtex_reverse_num4	4.749431	1000.000000	4.979100	1000.000000	5.208770	1000.000000	5.438439	1000.000000	5.668108	1000.000000	...	1000.000000	6.749216	1000.000000	7.034352	1000.000000	0.000000	b2215	548.035665	up	ac_p --> ac_e
TRPtex_reverse_num4	2.129569	1000.000000	1.946947	1000.000000	1.764324	1000.000000	1.581701	1000.000000	1.399079	1000.000000	...	1000.000000	0.642086	1000.000000	0.450470	1000.000000	0.000000	b2215	546.089712	up	trp__L_p --> trp__L_e
TRPt2rpp_reverse_num1	2.129569	1000.000000	1.946947	1000.000000	1.764324	1000.000000	1.581701	1000.000000	1.399079	1000.000000	...	1000.000000	0.642086	1000.000000	0.450470	1000.000000	0.000000	b3709	546.089712	up	h_c + trp__L_c --> h_p + trp__L_p
ANS	2.148896	55.574245	1.967978	50.895402	1.787061	46.216558	1.606144	41.537715	1.425226	36.858872	...	22.369518	0.675055	17.458107	0.485145	12.546695	0.038667	b1263 and b1264	22.007804	up	chor_c + gln__L_c --> anth_c + glu__L_c + h_c ...
TRPS3	2.148896	55.574245	1.967978	50.895402	1.787061	46.216558	1.606144	41.537715	1.425226	36.858872	...	22.369518	0.675055	17.458107	0.485145	12.546695	0.038667	b1260 and b1261	22.007804	up	3ig3p_c --> g3p_c + indole_c
ANPRT	2.148896	55.574245	1.967978	50.895402	1.787061	46.216558	1.606144	41.537715	1.425226	36.858872	...	22.369518	0.675055	17.458107	0.485145	12.546695	0.038667	b1263	22.007804	up	anth_c + prpp_c --> ppi_c + pran_c

10 rows × 25 columns