2. Data fetching and processing¶

More details about list_models¶

In [ ]:

import pandas as pd
import numpy as np

import pipeGEM as pg
from pipeGEM.data.fetching import list_models, load_remote_model

import pandas as pd import numpy as np import pipeGEM as pg from pipeGEM.data.fetching import list_models, load_remote_model

In [3]:

# search human models

model_list = list_models(organism="human")

# search human models model_list = list_models(organism="human")

In [4]:

model_list

model_list

Out[4]:

	id	organism	reaction_count	metabolite_count	gene_count	database
1	Human-GEM	Homo sapiens	13024	8363	2920	metabolic atlas
1	iAB_RBC_283	Homo sapiens	469	342	346	BiGG
12	iAT_PLT_636	Homo sapiens	1008	738	636	BiGG
105	RECON1	Homo sapiens	3741	2766	1905	BiGG
106	Recon3D	Homo sapiens	10600	5835	2248	BiGG

In [5]:

# select by number of components

model_list = list_models(organism="mouse", 
                         max_n_genes=2000, max_n_mets=3000, max_n_rxns=5000)

# select by number of components model_list = list_models(organism="mouse", max_n_genes=2000, max_n_mets=3000, max_n_rxns=5000)

In [6]:

model_list

model_list

Out[6]:

	id	organism	reaction_count	metabolite_count	gene_count	database
77	iMM1415	Mus musculus	3726	2775	1375	BiGG

Generate simulated gene data¶

In [7]:

from pipeGEM.data.synthesis import get_syn_gene_data
import seaborn as sns
import numpy as np

from pipeGEM.data.synthesis import get_syn_gene_data import seaborn as sns import numpy as np

In [8]:

mouse = load_remote_model('iMM1415')

mouse = load_remote_model('iMM1415')

Set parameter Username
Academic license - for non-commercial use only - expires 2024-06-25

No objective coefficients in model. Unclear what should be optimized

In [9]:

raw_count = get_syn_gene_data(mouse, n_sample=9, random_state=42)
raw_count.head()

raw_count = get_syn_gene_data(mouse, n_sample=9, random_state=42) raw_count.head()

Out[9]:

	sample_0	sample_1	sample_2	sample_3	sample_4	sample_5	sample_6	sample_7	sample_8
12846	14845	162353	11429	67283	155462	1584067	100515	145758	167789
67689	4406663	5444541	2174195	1115432	13400292	766236	913081	1989966	5588672
237940	1524538	3264187	8463146	1826585	3019458	1613887	1405787	7459855	2358988
222	57165	4878	3100	49435	10238	27138	94524	1704	17945
76507	58703	1289389	187994	2591007	579267	972693	1202149	806222	2110348

In [10]:

sns.displot(np.log2(raw_count + 1)["sample_0"])

sns.displot(np.log2(raw_count + 1)["sample_0"])

C:\Users\qwert\miniconda3\envs\py310\lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

Out[10]:

<seaborn.axisgrid.FacetGrid at 0x1a4bff930a0>

Maping gene data to a pipeGEM model¶

In [11]:

from pipeGEM.data import GeneData

from pipeGEM.data import GeneData

In [12]:

p_mouse = pg.Model("imm1415", mouse)

p_mouse = pg.Model("imm1415", mouse)

In [13]:

gene_data = GeneData(data=raw_count["sample_0"], 
                     data_transform=lambda x: np.log2(x), 
                     absent_expression=0)

gene_data = GeneData(data=raw_count["sample_0"], data_transform=lambda x: np.log2(x), absent_expression=0)

In [14]:

p_mouse.add_gene_data(name_or_prefix="sample_0", 
                      data=gene_data, 
                      or_operation="nanmax",  # alternative: nansum
                      threshold=-np.inf, 
                      absent_value=-np.inf)

p_mouse.add_gene_data(name_or_prefix="sample_0", data=gene_data, or_operation="nanmax", # alternative: nansum threshold=-np.inf, absent_value=-np.inf)

100%|███████████████████████████████████████████████████████████████████████████| 3726/3726 [00:00<00:00, 20452.82it/s]

Finished mapping in 0.20606780052185059 seconds.

In [15]:

p_mouse.reactions.get_by_id("2HBt2")

p_mouse.reactions.get_by_id("2HBt2")

Out[15]:

Reaction identifier	2HBt2
Name	2 hydroxybutyrate cotransport with proton
Memory address	0x1a4bf7efa90
Stoichiometry	2hb_e + h_e <=> 2hb_c + h_c 2 Hydroxybutyrate C4H7O3 + H+ <=> 2 Hydroxybutyrate C4H7O3 + H+
GPR	80879 or 20503 or 20501
Lower bound	-100000.0
Upper bound	100000.0

In [16]:

p_mouse.gene_data["sample_0"].rxn_scores["2HBt2"]

p_mouse.gene_data["sample_0"].rxn_scores["2HBt2"]

C:\Users\qwert\AppData\Local\Temp\ipykernel_34972\3651060646.py:2: RuntimeWarning: divide by zero encountered in log2
  data_transform=lambda x: np.log2(x),

Out[16]:

19.428086140626256

In [17]:

gene_data.transformed_gene_data["80879"], gene_data.transformed_gene_data["20503"], gene_data.transformed_gene_data["20501"]

gene_data.transformed_gene_data["80879"], gene_data.transformed_gene_data["20503"], gene_data.transformed_gene_data["20501"]

C:\Users\qwert\AppData\Local\Temp\ipykernel_34972\3651060646.py:2: RuntimeWarning: divide by zero encountered in log2
  data_transform=lambda x: np.log2(x),

Out[17]:

(17.02433890053839, 17.910098103986154, 19.428086140626256)

Finding data thresholds¶

In [18]:

# modified rFASTCORMICS thresholds
rFASTCORMICS_threshold = gene_data.get_threshold("rFASTCORMICS", return_heuristic=False)

# modified rFASTCORMICS thresholds rFASTCORMICS_threshold = gene_data.get_threshold("rFASTCORMICS", return_heuristic=False)

transform: True
cutting off 115 data since their expression values are below -inf
data's range: [5.0, 28.13763674747688]

C:\Users\qwert\AppData\Local\Temp\ipykernel_34972\3651060646.py:2: RuntimeWarning: divide by zero encountered in log2
  data_transform=lambda x: np.log2(x),

p_score of init values: 0.08537313386787253
original guess:  19.360653430827234 22.139761515007624
best fitted Amps:  0.023454819532955057 0.12048959290393145
best fitted means:  20.62204439000541 19.4026091837788

C:\Users\qwert\PycharmProjects\pipeGEM\pipeGEM\analysis\_threshold.py:158: UserWarning: Fail to find proper parameters, return the best 3 params
  warnings.warn(f"Fail to find proper parameters, return the best {k} params")

In [19]:

rFASTCORMICS_threshold.plot()
print(f"Expression threshold: {rFASTCORMICS_threshold.exp_th}")

rFASTCORMICS_threshold.plot() print(f"Expression threshold: {rFASTCORMICS_threshold.exp_th}")

Expression threshold: 20.62204439000541

In [20]:

# percentile thresholds (which are also called 'global threshold')
p_threshold = gene_data.get_threshold("percentile", p=[90, 75, 50])

# percentile thresholds (which are also called 'global threshold') p_threshold = gene_data.get_threshold("percentile", p=[90, 75, 50])

transform: True

C:\Users\qwert\AppData\Local\Temp\ipykernel_34972\3651060646.py:2: RuntimeWarning: divide by zero encountered in log2
  data_transform=lambda x: np.log2(x),

In [21]:

p_threshold

p_threshold

Out[21]:

PercentileThresholdAnalysis at 0x1a4d9bf68c0

-----------
Parameters:
{'p': [90, 75, 50], 'exp_p': None, 'non_exp_p': None}
-----------
Result keys:
data, exp_th, non_exp_th, threshold_series
-----------
Running time: 
 0.0010004043579101562

In [22]:

p_threshold.threshold_series

p_threshold.threshold_series

Out[22]:

p=90    23.013880
p=75    21.501370
p=50    19.554229
dtype: float64

In [23]:

p_threshold.plot()

p_threshold.plot()

In [24]:

raw_count

raw_count

Out[24]:

	sample_0	sample_1	sample_2	sample_3	sample_4	sample_5	sample_6	sample_7	sample_8
12846	14845	162353	11429	67283	155462	1584067	100515	145758	167789
67689	4406663	5444541	2174195	1115432	13400292	766236	913081	1989966	5588672
237940	1524538	3264187	8463146	1826585	3019458	1613887	1405787	7459855	2358988
222	57165	4878	3100	49435	10238	27138	94524	1704	17945
76507	58703	1289389	187994	2591007	579267	972693	1202149	806222	2110348
...	...	...	...	...	...	...	...	...	...
30955	824385	470969	837334	1383151	2000101	1240493	2203814	216831	85358
18709	330946	2996446	1645833	716716	7043678	13927808	487353	4274073	1665708
20975	1782456	2959058	416373	122911	1537358	1671129	3025042	530628	221207
320634	1302911	179079	1358922	149480	1672668	488008	33415	938677	643217
269437	118270	63514	26325	0	2430	441263	0	201031	0

1375 rows × 9 columns

In [25]:

# To find local threshold, we need to aggregate several GeneData first!
dummy_groups = pd.DataFrame({"gp1": {c: "a" if int(c.split("_")[1]) > 4 else "b" for c in raw_count.columns}})
gene_data_dic = {}

for c in raw_count.columns:

    gene_data_dic[c] = GeneData(data=raw_count[c], 
                                data_transform=lambda x: np.log2(x), 
                                absent_expression=0)
    
agg_gene_data = GeneData.aggregate(gene_data_dic, group_annotation=dummy_groups)
agg_gene_data

# To find local threshold, we need to aggregate several GeneData first! dummy_groups = pd.DataFrame({"gp1": {c: "a" if int(c.split("_")[1]) > 4 else "b" for c in raw_count.columns}}) gene_data_dic = {} for c in raw_count.columns: gene_data_dic[c] = GeneData(data=raw_count[c], data_transform=lambda x: np.log2(x), absent_expression=0) agg_gene_data = GeneData.aggregate(gene_data_dic, group_annotation=dummy_groups) agg_gene_data

Out[25]:

DataAggregation at 0x1a4d9bbcb50

-----------
Parameters:
{'method': 'concat', 'prop': 'data', 'absent_expression': 0, 'group': {'sample_0': ['sample_0'], 'sample_1': ['sample_1'], 'sample_2': ['sample_2'], 'sample_3': ['sample_3'], 'sample_4': ['sample_4'], 'sample_5': ['sample_5'], 'sample_6': ['sample_6'], 'sample_7': ['sample_7'], 'sample_8': ['sample_8']}}
-----------
Result keys:
agg_data, group_annotation

In [26]:

local_th = agg_gene_data.find_local_threshold(p=50, group_name="gp1")

local_th = agg_gene_data.find_local_threshold(p=50, group_name="gp1")

In [27]:

local_th.plot(genes=["76507", "20975"])

local_th.plot(genes=["76507", "20975"])

Visualizing gene data¶

In [28]:

corr_result = agg_gene_data.corr()

corr_result = agg_gene_data.corr()

In [29]:

corr_result

corr_result

Out[29]:

CorrelationAnalysis at 0x1a4f4d683d0

-----------
Parameters:
{'by': 'sample'}
-----------
Result keys:
correlation_result

In [30]:

corr_result.plot(annot=True)

corr_result.plot(annot=True)

In [31]:

pca_result = agg_gene_data.dim_reduction(method="PCA")

pca_result = agg_gene_data.dim_reduction(method="PCA")

In [32]:

pca_result

pca_result

Out[32]:

PCA_Analysis at 0x1a4be1022f0

-----------
Parameters:
{'dr_method': 'PCA', 'method': 'concat', 'prop': 'data', 'absent_expression': 0, 'group': {'sample_0': ['sample_0'], 'sample_1': ['sample_1'], 'sample_2': ['sample_2'], 'sample_3': ['sample_3'], 'sample_4': ['sample_4'], 'sample_5': ['sample_5'], 'sample_6': ['sample_6'], 'sample_7': ['sample_7'], 'sample_8': ['sample_8']}}
-----------
Result keys:
PC, exp_var, components, group_annotation

In [33]:

pca_result.plot(color_by="gp1")

pca_result.plot(color_by="gp1")

In [34]:

pca_result.plot(color_by="default")

pca_result.plot(color_by="default")

In [35]:

tsne_result = agg_gene_data.dim_reduction(method="TSNE")
tsne_result.plot(color_by="gp1")

tsne_result = agg_gene_data.dim_reduction(method="TSNE") tsne_result.plot(color_by="gp1")

In [36]:

tsne_result = agg_gene_data.dim_reduction(method="UMAP")
tsne_result.plot()

tsne_result = agg_gene_data.dim_reduction(method="UMAP") tsne_result.plot()

C:\Users\qwert\miniconda3\envs\py310\lib\site-packages\umap\umap_.py:2344: UserWarning: n_neighbors is larger than the dataset size; truncating to X.shape[0] - 1
  warn(

Medium data¶

In [37]:

from pipeGEM.data import MediumData

from pipeGEM.data import MediumData

In [38]:

human = load_remote_model("Human-GEM")

human = load_remote_model("Human-GEM")

Model Human-GEM is already downloaded

In [39]:

human.summary()

human.summary()

Out[39]:

Objective

1.0 MAR13082 = 187.35362997658078

Uptake

Metabolite	Reaction	Flux	C-Number	C-Flux
MAM01291e	MAR00566	0.3888	22	0.01%
MAM01771e	MAR00567	0.01104	20	0.00%
MAM01371e	MAR00569	1000	10	10.33%
MAM01741e	MAR00571	0.1629	22	0.00%
MAM01689e	MAR00573	0.4173	22	0.01%
MAM00094e	MAR00575	0.1049	22	0.00%
MAM01696e	MAR00576	3.583	20	0.07%
MAM02648e	MAR00577	0.6377	20	0.01%
MAM01432e	MAR00618	0.01104	26	0.00%
MAM02456e	MAR00620	0.01104	17	0.00%
MAM02385e	MAR00621	0.01104	24	0.00%
MAM00656e	MAR00628	23.38	9	0.22%
MAM02564e	MAR00637	0.01104	24	0.00%
MAM02646e	MAR00650	42.91	18	0.80%
MAM02685e	MAR00656	7.903	7	0.06%
MAM02715e	MAR00658	1.247	8	0.01%
MAM02808e	MAR00661	0.7874	8	0.01%
MAM02690e	MAR00662	0.01104	15	0.00%
MAM00135e	MAR00698	0.02208	24	0.00%
MAM00111e	MAR00699	0.01104	24	0.00%
MAM00132e	MAR00700	0.01104	24	0.00%
MAM02494e	MAR00702	0.3672	14	0.01%
MAM01802e	MAR01939	0.007756	27	0.00%
MAM01605e	MAR01965	18.29	36	0.68%
MAM02000e	MAR04148	1000	26	26.86%
MAM03494e	MAR04886	0.01104	10	0.00%
MAM02635e	MAR04889	10.39	11	0.12%
MAM03540e	MAR04923	0.01104	19	0.00%
MAM01583e	MAR04927	0.01104	22	0.00%
MAM01373e	MAR04929	0.01104	22	0.00%
MAM02964e	MAR08959	354.8	26	9.53%
MAM03102e	MAR08967	0.007756	59	0.00%
MAM02559e	MAR09029	0.3895	5	0.00%
MAM02389e	MAR09036	0.3009	18	0.01%
MAM02125e	MAR09038	23.02	6	0.14%
MAM02184e	MAR09039	45.12	6	0.28%
MAM02360e	MAR09040	92.02	6	0.57%
MAM02426e	MAR09041	72.16	6	0.45%
MAM02471e	MAR09042	22.54	5	0.12%
MAM02724e	MAR09043	38.17	9	0.35%
MAM02993e	MAR09044	57.01	4	0.24%
MAM03089e	MAR09045	9.891	11	0.11%
MAM03135e	MAR09046	67.5	5	0.35%
MAM02630e	MAR09048	186.7	0	0.00%
MAM02353e	MAR09051	0.0003146	94027	0.03%
MAM01307e	MAR09061	81.11	3	0.25%
MAM01369e	MAR09062	37.61	4	0.16%
MAM01975e	MAR09063	56.55	5	0.29%
MAM03101e	MAR09064	28.59	9	0.27%
MAM02770e	MAR09068	160.4	5	0.83%
MAM02896e	MAR09069	68.65	3	0.21%
MAM01370e	MAR09070	63.44	4	0.26%
MAM02042e	MAR09103	18.35	0	0.00%
MAM02049e	MAR09107	0.007756	34	0.00%
MAM01704e	MAR09108	666.7	9	6.20%
MAM01401e	MAR09109	0.007756	10	0.00%
MAM02842e	MAR09143	0.007756	17	0.00%
MAM02834e	MAR09147	0.03483	20	0.00%
MAM01330e	MAR09152	0.007756	29	0.00%
MAM01938e	MAR09154	0.007756	28	0.00%
MAM02982e	MAR09159	0.007756	12	0.00%
MAM02394e	MAR09167	0.007756	8	0.00%
MAM01415e	MAR09215	0.0116	27	0.00%
MAM01280e	MAR09254	600.5	10	6.20%
MAM01361e	MAR09269	0.007756	62	0.00%
MAM01397e	MAR09271	0.003867	45	0.00%
MAM01398e	MAR09272	0.05435	39	0.00%
MAM01450e	MAR09285	4.573	27	0.13%
MAM01630e	MAR09295	6.789	9	0.06%
MAM01669e	MAR09300	708.1	10	7.32%
MAM01671e	MAR09305	237.3	10	2.45%
MAM02038e	MAR09348	246.2	10	2.54%
MAM02026e	MAR09351	1.64	10	0.02%
MAM02041e	MAR09354	1000	0	0.00%
MAM02470e	MAR09372	56.07	1	0.06%
MAM02583e	MAR09378	0.3533	6	0.00%
MAM02833e	MAR09404	0.05035	20	0.00%
MAM03130e	MAR09440	66.42	9	0.62%
MAM02814e	MAR09691	0.007756	8	0.00%
MAM03161e	MAR09729	76.1	6	0.47%
MAM00674e	MAR09842	0.3261	3	0.00%
MAM01644e	MAR09847	1.019	9	0.01%
MAM02696e	MAR09858	12.47	3	0.04%
MAM03149e	MAR09861	153.3	10	1.58%
MAM02940e	MAR09924	4.837	25	0.12%
MAM02411e	MAR09925	6.733	23	0.16%
MAM02122e	MAR10250	2.138	27	0.06%
MAM02388e	MAR10254	5.367	25	0.14%
MAM02497e	MAR10265	336.9	6	2.09%
MAM02974e	MAR10345	0.01104	21	0.00%
MAM01584e	MAR10432	0.01104	20	0.00%
MAM00916e	MAR10441	0.2265	3	0.00%
MAM02978e	MAR10496	0.007756	9	0.00%
MAM03684e	MAR11293	1000	6	6.20%
MAM01620e	MAR11349	262.3	4	1.08%
MAM02579e	MAR11420	18.35	0	0.00%
MAM01973e	MAR11424	0.007756	6	0.00%
MAM00350e	MAR11895	0.01938	20	0.00%
MAM01231e	MAR11897	0.003867	20	0.00%
MAM02117e	MAR11949	0.01104	23	0.00%
MAM01045e	MAR11953	0.007756	20	0.00%
MAM02927e	MAR11959	277.7	18	5.16%
MAM01417e	MAR11965	0.003867	27	0.00%
MAM01841e	MAR11974	577.5	6	3.58%
MAM02750e	MAR12127	2.868	11	0.03%
MAM00003e	MAR13040	0.01104	17	0.00%
MAM00008e	MAR13041	0.01104	20	0.00%
MAM00010e	MAR13042	0.01104	20	0.00%
MAM00017e	MAR13043	0.01104	20	0.00%
MAM00021e	MAR13045	0.01104	22	0.00%
MAM00117e	MAR13047	0.01104	14	0.00%
MAM00260e	MAR13048	0.01104	22	0.00%
MAM00265e	MAR13049	0.01104	22	0.00%
MAM00315e	MAR13050	0.01104	24	0.00%
MAM00341e	MAR13051	0.01104	22	0.00%
MAM01207e	MAR13053	0.01104	20	0.00%
MAM01238e	MAR13055	0.01104	17	0.00%
MAM01582e	MAR13056	0.01104	22	0.00%
MAM02053e	MAR13057	0.01104	21	0.00%
MAM02457e	MAR13058	0.01104	20	0.00%
MAM02613e	MAR13059	0.01104	19	0.00%
MAM02745e	MAR13060	0.01104	14	0.00%
MAM03045e	MAR13061	0.01104	23	0.00%
MAM03051e	MAR13062	0.01104	13	0.00%
MAM03153e	MAR13063	0.01104	26	0.00%

Secretion

Metabolite	Reaction	Flux	C-Number	C-Flux
MAM01155e	MAR01871	-666.7	9	6.69%
MAM03127e	MAR01964	-18.29	40	0.82%
MAM02402e	MAR04164	-669	24	17.91%
MAM01965e	MAR09034	-136.7	6	0.92%
MAM02352e	MAR09055	-0.0003146	33247	0.01%
MAM02751e	MAR09072	-1000	0	0.00%
MAM02046e	MAR09078	-34.69	1	0.04%
MAM02819e	MAR09133	-585.1	3	1.96%
MAM02403e	MAR09135	-403.3	3	1.35%
MAM01249e	MAR09242	-1000	2	2.23%
MAM01279e	MAR09253	-1000	5	5.58%
MAM01285e	MAR09255	-592.1	10	6.61%
MAM01396e	MAR09273	-0.05822	33	0.00%
MAM01619e	MAR09290	-262.3	4	1.17%
MAM02037e	MAR09353	-945.8	5	5.28%
MAM02167e	MAR09360	-237.3	10	2.65%
MAM02843e	MAR09406	-1000	5	5.58%
MAM02961e	MAR09418	-354.8	2	0.79%
MAM02997e	MAR09422	-54.57	5	0.30%
MAM01989e	MAR09463	-354.8	26	10.29%
MAM01410e	MAR09809	-12.53	4	0.06%
MAM02120e	MAR09811	-1000	6	6.69%
MAM02642e	MAR09813	-11.23	8	0.10%
MAM03150e	MAR09860	-153.3	10	1.71%
MAM00105e	MAR09922	-27.37	25	0.76%
MAM03971e	MAR10024	-187.4	0	0.00%
MAM00235e	MAR10047	-2.54	5	0.01%
MAM03445e	MAR10513	-285.6	10	3.19%
MAM03190e	MAR11491	-0.02327	6	0.00%
MAM01759e	MAR11942	-519.8	5	2.90%
MAM02929e	MAR11947	-275.2	18	5.53%
MAM03209e	MAR12202	-331	24	8.86%

In [40]:

# load default medium 

dmem_medium = MediumData.from_file("DMEM", id_col_label="human_1")

# load default medium dmem_medium = MediumData.from_file("DMEM", id_col_label="human_1")

In [41]:

human = pg.Model(model=human, name_tag="human")

human = pg.Model(model=human, name_tag="human")

In [42]:

human.add_medium_data(name="dmem", data=dmem_medium)

human.add_medium_data(name="dmem", data=dmem_medium)

In [43]:

human.apply_medium(name="dmem")

human.apply_medium(name="dmem")

MAR09047  is an inorganic exchange reaction (not constrained)
MAR09073  is an inorganic exchange reaction (not constrained)
MAR09079  is an inorganic exchange reaction (not constrained)
MAR09088  is an inorganic exchange reaction (not constrained)
MAR09103  is an inorganic exchange reaction (not constrained)
MAR09148  is an inorganic exchange reaction (not constrained)
MAR09149  is an inorganic exchange reaction (not constrained)
MAR09162  is an inorganic exchange reaction (not constrained)
MAR09169  is an inorganic exchange reaction (not constrained)
MAR09204  is an inorganic exchange reaction (not constrained)
MAR09354  is an inorganic exchange reaction (not constrained)
MAR09381  is an inorganic exchange reaction (not constrained)
MAR09383  is an inorganic exchange reaction (not constrained)
MAR09386  is an inorganic exchange reaction (not constrained)
MAR09432  is an inorganic exchange reaction (not constrained)
MAR09703  is an inorganic exchange reaction (not constrained)
MAR04154  is an inorganic exchange reaction (not constrained)
MAR04155  is an inorganic exchange reaction (not constrained)
MAR11405  is an inorganic exchange reaction (not constrained)
MAR11420  is an inorganic exchange reaction (not constrained)
MAR11906  is an inorganic exchange reaction (not constrained)
MAR11982  is an inorganic exchange reaction (not constrained)
MAR11983  is an inorganic exchange reaction (not constrained)
MAR11984  is an inorganic exchange reaction (not constrained)
MAR11985  is an inorganic exchange reaction (not constrained)
MAR13068  is an inorganic exchange reaction (not constrained)
MAR13072  is an inorganic exchange reaction (not constrained)
MAR10024  is an inorganic exchange reaction (not constrained)
MAR10028  is an inorganic exchange reaction (not constrained)

In [44]:

human.summary()

human.summary()

Out[44]:

Objective

1.0 MAR13082 = 75.83141020981205

Uptake

Metabolite	Reaction	Flux	C-Number	C-Flux
MAM02685e	MAR00656	1000	7	18.49%
MAM01965e	MAR09034	160.2	6	2.54%
MAM02125e	MAR09038	393.7	6	6.24%
MAM02184e	MAR09039	18.26	6	0.29%
MAM02360e	MAR09040	37.25	6	0.59%
MAM02426e	MAR09041	29.21	6	0.46%
MAM02471e	MAR09042	12.98	5	0.17%
MAM02724e	MAR09043	15.45	9	0.37%
MAM02993e	MAR09044	23.08	4	0.24%
MAM03089e	MAR09045	4.003	11	0.12%
MAM03135e	MAR09046	27.32	5	0.36%
MAM02630e	MAR09048	1000	0	0.00%
MAM01975e	MAR09063	2488	5	32.86%
MAM03101e	MAR09064	11.57	9	0.28%
MAM01365e	MAR09066	284.1	6	4.50%
MAM02896e	MAR09069	51.35	3	0.41%
MAM01974e	MAR09071	520.4	5	6.87%
MAM01821e	MAR09076	0.003139	0	0.00%
MAM01513e	MAR09083	9.47	5	0.13%
MAM02042e	MAR09103	698.9	0	0.00%
MAM01401e	MAR09109	0.003139	10	0.00%
MAM02819e	MAR09133	2700	3	21.40%
MAM02842e	MAR09143	0.006279	17	0.00%
MAM02817e	MAR09144	0.003139	8	0.00%
MAM02680e	MAR09145	0.8653	9	0.02%
MAM01830e	MAR09146	62.54	19	3.14%
MAM02834e	MAR09147	0.04389	20	0.00%
MAM01330e	MAR09152	0.003139	29	0.00%
MAM01938e	MAR09154	0.003139	28	0.00%
MAM02982e	MAR09159	0.003139	12	0.00%
MAM02394e	MAR09167	0.003139	8	0.00%
MAM01361e	MAR09269	0.003139	62	0.00%
MAM02041e	MAR09354	1000	0	0.00%
MAM02159e	MAR09358	7.742	5	0.10%
MAM02171e	MAR09361	1.164	6	0.02%
MAM02583e	MAR09378	0.143	6	0.00%
MAM02631e	MAR09383	1000	0	0.00%
MAM02147e	MAR09386	62.53	0	0.00%
MAM02996e	MAR09423	15.7	10	0.41%

Secretion

Metabolite	Reaction	Flux	C-Number	C-Flux
MAM02675e	MAR00617	-354.2	16	10.98%
MAM03560e	MAR04936	-0.3883	22	0.02%
MAM01596e	MAR09058	-1000	1	1.94%
MAM01307e	MAR09061	-1000	3	5.81%
MAM01628e	MAR09065	-689.9	3	4.01%
MAM02751e	MAR09072	-960.1	0	0.00%
MAM02578e	MAR09073	-482.8	0	0.00%
MAM02046e	MAR09078	-1000	1	1.94%
MAM02039e	MAR09079	-1000	0	0.00%
MAM01797e	MAR09084	-988.4	2	3.83%
MAM02658e	MAR09087	-1.71	5	0.02%
MAM02403e	MAR09135	-1000	3	5.81%
MAM10005e	MAR09209	-1000	1	1.94%
MAM01115e	MAR09234	-62.53	20	2.42%
MAM01249e	MAR09242	-7.736	2	0.03%
MAM01833e	MAR09318	-2.14	1	0.00%
MAM02362e	MAR09364	-26.12	20	1.01%
MAM00325e	MAR09443	-1.165	12	0.03%
MAM00371e	MAR09444	-12.91	14	0.35%
MAM00403e	MAR09445	-216.5	16	6.71%
MAM01682e	MAR09685	-116.6	6	1.35%
MAM02642e	MAR09813	-3.204	8	0.05%
MAM03117e	MAR09816	-1.942	11	0.04%
MAM03971e	MAR10024	-75.83	0	0.00%
MAM01209e	MAR10145	-161.6	20	6.26%
MAM01216e	MAR10217	-185.9	18	6.48%
MAM03569e	MAR10228	-0.3883	20	0.02%
MAM00128e	MAR10442	-0.3883	14	0.01%
MAM03415e	MAR10501	-351.4	12	8.17%
MAM03464e	MAR10532	-263.3	13	6.63%
MAM03473e	MAR10541	-3.86	13	0.10%
MAM03903e	MAR10663	-325.8	15	9.47%
MAM01862e	MAR11400	-987.6	4	7.65%
MAM02439e	MAR11404	-683.2	4	5.30%
MAM02579e	MAR11420	-1000	0	0.00%
MAM03777e	MAR11508	-124.7	6	1.45%
MAM00010e	MAR13042	-0.3883	20	0.02%
MAM00021e	MAR13045	-0.3883	22	0.02%
MAM00260e	MAR13048	-0.3883	22	0.02%
MAM00265e	MAR13049	-0.3883	22	0.02%
MAM00315e	MAR13050	-0.3883	24	0.02%
MAM00341e	MAR13051	-0.3883	22	0.02%
MAM01207e	MAR13053	-0.3883	20	0.02%
MAM01235e	MAR13054	-0.3883	20	0.02%
MAM01582e	MAR13056	-0.3883	22	0.02%
MAM02457e	MAR13058	-0.3883	20	0.02%

In [ ]: