1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
|
"""Define module level fixtures."""
import json
from os.path import join
from typing import List
import pandas as pd
import pytest
from cobra.core import Model, Reaction, Solution
from cobra.util import solver as sutil
# The scipy interface is currently unstable and may yield errors or infeasible
# solutions.
@pytest.fixture(
scope="session",
params=[s for s in ["glpk", "cplex", "gurobi", "hybrid"] if s in sutil.solvers],
)
def all_solvers(request) -> List[str]:
"""Return the avaialble solvers."""
return request.param
@pytest.fixture(
scope="session",
params=[s for s in ["cplex", "gurobi", "hybrid"] if s in sutil.solvers],
)
def qp_solvers(request) -> List[str]:
"""Return the available QP solvers."""
return request.param
@pytest.fixture(scope="module")
def fva_results(data_directory) -> pd.DataFrame:
"""Load and return saved FVA results for textbook model."""
with open(join(data_directory, "textbook_fva.json"), "r") as infile:
df = pd.DataFrame(json.load(infile))
df.sort_index(inplace=True)
return df[["minimum", "maximum"]]
@pytest.fixture(scope="module")
def pfba_fva_results(data_directory) -> pd.DataFrame:
"""Load and return saved pFBA FVA results for textbook model."""
with open(join(data_directory, "textbook_pfba_fva.json"), "r") as infile:
df = pd.DataFrame(json.load(infile))
df.sort_index(inplace=True)
return df[["minimum", "maximum"]]
@pytest.fixture(scope="module")
def room_model() -> Model:
"""Generate ROOM model as described in [1]_ .
References
----------
.. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off
minimization of metabolic flux changes after genetic perturbations",
PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102
"""
test_model = Model("papin_2003")
v_1 = Reaction("v1")
v_2 = Reaction("v2")
v_3 = Reaction("v3")
v_4 = Reaction("v4")
v_5 = Reaction("v5")
v_6 = Reaction("v6", upper_bound=0.0)
b_1 = Reaction("b1", upper_bound=10.0, lower_bound=0.0)
b_2 = Reaction("b2")
b_3 = Reaction("b3")
test_model.add_reactions([v_1, v_2, v_3, v_4, v_5, v_6, b_1, b_2, b_3])
v_1.reaction = "A -> B"
v_2.reaction = "2 B -> C + byp"
v_3.reaction = "2 B + cof -> D"
v_4.reaction = "D -> E + cof"
v_5.reaction = "C + cof -> D"
v_6.reaction = "C -> E"
b_1.reaction = "-> A"
b_2.reaction = "E ->"
b_3.reaction = "byp ->"
test_model.objective = "b2"
return test_model
@pytest.fixture(scope="module")
def room_solution() -> Solution:
"""Generate ROOM solution as described in [1]_ .
References
----------
.. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off
minimization of metabolic flux changes after genetic perturbations",
PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102
"""
fluxes = pd.Series(
{
"b1": 10.0,
"b2": 5.0,
"b3": 5.0,
"v1": 10.0,
"v2": 5.0,
"v3": 0.0,
"v4": 0.0,
"v5": 0.0,
"v6": 5.0,
}
)
reduced_costs = pd.Series(
{
"b1": 0.0,
"b2": 0.0,
"b3": 0.0,
"v1": 0.0,
"v2": 0.0,
"v3": 0.0,
"v4": 0.0,
"v5": 0.0,
"v6": 0.0,
}
)
shadow_prices = pd.Series(
{
"b1": 0.0,
"b2": 0.0,
"b3": 0.0,
"v1": 0.0,
"v2": 0.0,
"v3": 0.0,
"v4": 0.0,
"v5": 0.0,
"v6": 0.0,
}
)
sol = Solution(
objective_value=5.000,
status="optimal",
fluxes=fluxes,
reduced_costs=reduced_costs,
shadow_prices=shadow_prices,
)
return sol
|
