-
Notifications
You must be signed in to change notification settings - Fork 3
/
Treg_MN_control.m
215 lines (199 loc) · 12.2 KB
/
Treg_MN_control.m
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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
function output = Treg_MN_control(Treg_model, Phase, solution, Treg_aa_ex, Treg_aa_tran, Treg_glu_ex, Treg_glu_tran, Treg_glutaminolysis, Treg_glycolysis, Treg_lip_ex, Treg_lip_ox, Treg_lip_syn, Treg_ox_phos, Treg_pyruvate_into_mito, Treg_ATP_fluxes, Treg_ATP_coeff, Treg_AMP_fluxes, Treg_AMP_coeff, BN_glycolysis, BN_glu_uptake, BN_aa_tran, BN_mit_ox, BN_lip_eff, BN_glutaminolysis, BN_lip_syn)
%This function uses the outputs of the BN model to parametrise the MN
%model for phenotype 15 (Treg) and then calculates various production
%rates.
%Make a copy of the metabolic model for Treg.
model1=Treg_model;
%Use the converted outputs from the BN model to control the upper and lower
%bounds of different classes of metabolic fluxes by knocking down the
%optimised fluxes. For example, if the converted BN node for glycolysis (lactase)
%is 0.2 at a time step, an optimised flux relevant to the process
%is 100 and the bounds are -1000 and 1000, set its upper bound to 20
%and the lower bound to 0. The idea is to adhere to the direction of
%the optimised flux and use the optimised and constrained value as the limit in that
%direction.
%By default, the flux through the lactase-mediated pathway is stronger
%than that through the mitochondrial pathaway. Therefore, we will knock
%down the lactase-mediated pathway and let the mitochondrial pathway
%respond freely.
dummy=BN_glycolysis;
for i = 1:size(Treg_glycolysis)
if model1.ub(Treg_glycolysis(i))*model1.lb(Treg_glycolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), abs(solution.x(Treg_glycolysis(i))*dummy), 'u');
elseif solution.x(Treg_glycolysis(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), solution.x(Treg_glycolysis(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), 0, 'l');
elseif solution.x(Treg_glycolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glycolysis(i)), solution.x(Treg_glycolysis(i))*dummy, 'l');
end
end
dummy=BN_glu_uptake;
for i = 1:size(Treg_glu_tran)
if model1.ub(Treg_glu_tran(i))*model1.lb(Treg_glu_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), abs(solution.x(Treg_glu_tran(i))*dummy), 'u');
elseif solution.x(Treg_glu_tran(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), solution.x(Treg_glu_tran(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), 0, 'l');
elseif solution.x(Treg_glu_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_tran(i)), solution.x(Treg_glu_tran(i))*dummy, 'l');
end
end
for i = 1:size(Treg_glu_ex)
if model1.ub(Treg_glu_ex(i))*model1.lb(Treg_glu_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), abs(solution.x(Treg_glu_ex(i))*dummy), 'u');
elseif solution.x(Treg_glu_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), solution.x(Treg_glu_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), 0, 'l');
elseif solution.x(Treg_glu_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glu_ex(i)), solution.x(Treg_glu_ex(i))*dummy, 'l');
end
end
dummy=BN_aa_tran;
for i = 1:size(Treg_aa_tran)
if model1.ub(Treg_aa_tran(i))*model1.lb(Treg_aa_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), abs(solution.x(Treg_aa_tran(i))*dummy), 'u');
elseif solution.x(Treg_aa_tran(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), solution.x(Treg_aa_tran(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), 0, 'l');
elseif solution.x(Treg_aa_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_tran(i)), solution.x(Treg_aa_tran(i))*dummy, 'l');
end
end
for i = 1:size(Treg_aa_ex)
if model1.ub(Treg_aa_ex(i))*model1.lb(Treg_aa_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), abs(solution.x(Treg_aa_ex(i))*dummy), 'u');
elseif solution.x(Treg_aa_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), solution.x(Treg_aa_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), 0, 'l');
elseif solution.x(Treg_aa_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_aa_ex(i)), solution.x(Treg_aa_ex(i))*dummy, 'l');
end
end
dummy=BN_lip_eff;
for i = 1:size(Treg_lip_ex)
if model1.ub(Treg_lip_ex(i))*model1.lb(Treg_lip_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), abs(solution.x(Treg_lip_ex(i))*dummy), 'u');
elseif solution.x(Treg_lip_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), solution.x(Treg_lip_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), 0, 'l');
elseif solution.x(Treg_lip_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ex(i)), solution.x(Treg_lip_ex(i))*dummy, 'l');
end
end
dummy=BN_lip_syn;
for i = 1:size(Treg_lip_syn)
if model1.ub(Treg_lip_syn(i))*model1.lb(Treg_lip_syn(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), abs(solution.x(Treg_lip_syn(i))*dummy), 'u');
elseif solution.x(Treg_lip_syn(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), solution.x(Treg_lip_syn(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), 0, 'l');
elseif solution.x(Treg_lip_syn(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_syn(i)), solution.x(Treg_lip_syn(i))*dummy, 'l');
end
end
dummy=BN_mit_ox;
for i = 1:size(Treg_ox_phos)
if model1.ub(Treg_ox_phos(i))*model1.lb(Treg_ox_phos(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), abs(solution.x(Treg_ox_phos(i))*dummy), 'u');
elseif solution.x(Treg_ox_phos(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), solution.x(Treg_ox_phos(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), 0, 'l');
elseif solution.x(Treg_ox_phos(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_ox_phos(i)), solution.x(Treg_ox_phos(i))*dummy, 'l');
end
end
for i = 1:size(Treg_lip_ox)
if model1.ub(Treg_lip_ox(i))*model1.lb(Treg_lip_ox(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), abs(solution.x(Treg_lip_ox(i))*dummy), 'u');
elseif solution.x(Treg_lip_ox(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), solution.x(Treg_lip_ox(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), 0, 'l');
elseif solution.x(Treg_lip_ox(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_lip_ox(i)), solution.x(Treg_lip_ox(i))*dummy, 'l');
end
end
dummy=BN_glutaminolysis;
for i = 1:size(Treg_glutaminolysis)
if model1.ub(Treg_glutaminolysis(i))*model1.lb(Treg_glutaminolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), abs(solution.x(Treg_glutaminolysis(i))*dummy), 'u');
elseif solution.x(Treg_glutaminolysis(i))>0
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), solution.x(Treg_glutaminolysis(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), 0, 'l');
elseif solution.x(Treg_glutaminolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Treg_glutaminolysis(i)), solution.x(Treg_glutaminolysis(i))*dummy, 'l');
end
end
%Set the objective function according to what you want to optimise.
if Phase==1
%Set the objective function to biomass minus DNA (G1).
model1=changeObjective(model1,'Biomass_minusDNA');
model1=changeRxnBounds(model1, model1.rxns(3862), 0, 'l');
model1=changeRxnBounds(model1, model1.rxns(3862), 1000, 'u');
model1=changeRxnBounds(model1, model1.rxns(3855), 0, 'b');
elseif Phase==2
%Set the objective function to DNA (S).
model1=changeObjective(model1,'biomass_DNA');
model1=changeRxnBounds(model1, model1.rxns(3855), 0, 'l');
model1=changeRxnBounds(model1, model1.rxns(3855), 1000, 'u');
model1=changeRxnBounds(model1, model1.rxns(3862), 0, 'b');
end
%Optimise the modified model.
solution1=optimizeCbModel(model1);
%Calculate the production rate of ATP.
ATP=0; %mmol per gram of dry weight per hour.
for i = 1:size(Treg_ATP_fluxes)
ATP=ATP+solution1.x(Treg_ATP_fluxes(i))*Treg_ATP_coeff(i);
end
%Calculate the production rate of AMP.
AMP=0; %mmol per gram of dry weight per hour.
for i = 1:size(Treg_AMP_fluxes)
AMP=AMP+solution1.x(Treg_AMP_fluxes(i))*Treg_AMP_coeff(i);
end
%Extract the other production rates.
Biomass=solution1.x(3855); %Growth rate (per hour).
Biomass_minus_DNA=solution1.x(3862); %Growth rate (per hour).
DNA=solution1.x(3857); %mmol per gram of dry weight per hour.
Protein=solution1.x(3856); %mmol per gram of dry weight per hour.
%Combine the production rates.
output=[Biomass, Biomass_minus_DNA, DNA, Protein, ATP, AMP];
end