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Th1_MN_control.m
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function output = Th1_MN_control(Th1_model, Phase, solution, Th1_aa_ex, Th1_aa_tran, Th1_glu_ex, Th1_glu_tran, Th1_glutaminolysis, Th1_glycolysis, Th1_lip_ex, Th1_lip_ox, Th1_lip_syn, Th1_ox_phos, Th1_pyruvate_into_mito, Th1_ATP_fluxes, Th1_ATP_coeff, Th1_AMP_fluxes, Th1_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 12 (Th1) and then calculates various production
%rates.
%Make a copy of the metabolic model for Th1.
model1=Th1_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(Th1_glycolysis)
if model1.ub(Th1_glycolysis(i))*model1.lb(Th1_glycolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), abs(solution.x(Th1_glycolysis(i))*dummy), 'u');
elseif solution.x(Th1_glycolysis(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), solution.x(Th1_glycolysis(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), 0, 'l');
elseif solution.x(Th1_glycolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glycolysis(i)), solution.x(Th1_glycolysis(i))*dummy, 'l');
end
end
dummy=BN_glu_uptake;
for i = 1:size(Th1_glu_tran)
if model1.ub(Th1_glu_tran(i))*model1.lb(Th1_glu_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), abs(solution.x(Th1_glu_tran(i))*dummy), 'u');
elseif solution.x(Th1_glu_tran(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), solution.x(Th1_glu_tran(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), 0, 'l');
elseif solution.x(Th1_glu_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_tran(i)), solution.x(Th1_glu_tran(i))*dummy, 'l');
end
end
for i = 1:size(Th1_glu_ex)
if model1.ub(Th1_glu_ex(i))*model1.lb(Th1_glu_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), abs(solution.x(Th1_glu_ex(i))*dummy), 'u');
elseif solution.x(Th1_glu_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), solution.x(Th1_glu_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), 0, 'l');
elseif solution.x(Th1_glu_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glu_ex(i)), solution.x(Th1_glu_ex(i))*dummy, 'l');
end
end
dummy=BN_aa_tran;
for i = 1:size(Th1_aa_tran)
if model1.ub(Th1_aa_tran(i))*model1.lb(Th1_aa_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), abs(solution.x(Th1_aa_tran(i))*dummy), 'u');
elseif solution.x(Th1_aa_tran(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), solution.x(Th1_aa_tran(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), 0, 'l');
elseif solution.x(Th1_aa_tran(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_tran(i)), solution.x(Th1_aa_tran(i))*dummy, 'l');
end
end
for i = 1:size(Th1_aa_ex)
if model1.ub(Th1_aa_ex(i))*model1.lb(Th1_aa_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), abs(solution.x(Th1_aa_ex(i))*dummy), 'u');
elseif solution.x(Th1_aa_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), solution.x(Th1_aa_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), 0, 'l');
elseif solution.x(Th1_aa_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_aa_ex(i)), solution.x(Th1_aa_ex(i))*dummy, 'l');
end
end
dummy=BN_lip_eff;
for i = 1:size(Th1_lip_ex)
if model1.ub(Th1_lip_ex(i))*model1.lb(Th1_lip_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), abs(solution.x(Th1_lip_ex(i))*dummy), 'u');
elseif solution.x(Th1_lip_ex(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), solution.x(Th1_lip_ex(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), 0, 'l');
elseif solution.x(Th1_lip_ex(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ex(i)), solution.x(Th1_lip_ex(i))*dummy, 'l');
end
end
dummy=BN_lip_syn;
for i = 1:size(Th1_lip_syn)
if model1.ub(Th1_lip_syn(i))*model1.lb(Th1_lip_syn(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), abs(solution.x(Th1_lip_syn(i))*dummy), 'u');
elseif solution.x(Th1_lip_syn(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), solution.x(Th1_lip_syn(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), 0, 'l');
elseif solution.x(Th1_lip_syn(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_syn(i)), solution.x(Th1_lip_syn(i))*dummy, 'l');
end
end
dummy=BN_mit_ox;
for i = 1:size(Th1_ox_phos)
if model1.ub(Th1_ox_phos(i))*model1.lb(Th1_ox_phos(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), abs(solution.x(Th1_ox_phos(i))*dummy), 'u');
elseif solution.x(Th1_ox_phos(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), solution.x(Th1_ox_phos(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), 0, 'l');
elseif solution.x(Th1_ox_phos(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_ox_phos(i)), solution.x(Th1_ox_phos(i))*dummy, 'l');
end
end
for i = 1:size(Th1_lip_ox)
if model1.ub(Th1_lip_ox(i))*model1.lb(Th1_lip_ox(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), abs(solution.x(Th1_lip_ox(i))*dummy), 'u');
elseif solution.x(Th1_lip_ox(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), solution.x(Th1_lip_ox(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), 0, 'l');
elseif solution.x(Th1_lip_ox(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_lip_ox(i)), solution.x(Th1_lip_ox(i))*dummy, 'l');
end
end
dummy=BN_glutaminolysis;
for i = 1:size(Th1_glutaminolysis)
if model1.ub(Th1_glutaminolysis(i))*model1.lb(Th1_glutaminolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), abs(solution.x(Th1_glutaminolysis(i))*dummy), 'u');
elseif solution.x(Th1_glutaminolysis(i))>0
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), solution.x(Th1_glutaminolysis(i))*dummy, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), 0, 'l');
elseif solution.x(Th1_glutaminolysis(i))==0
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), 0, 'l');
else
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), 0, 'u');
model1=changeRxnBounds(model1, model1.rxns(Th1_glutaminolysis(i)), solution.x(Th1_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(4168), 0, 'l');
model1=changeRxnBounds(model1, model1.rxns(4168), 1000, 'u');
model1=changeRxnBounds(model1, model1.rxns(4161), 0, 'b');
elseif Phase==2
%Set the objective function to DNA (S).
model1=changeObjective(model1,'biomass_DNA');
model1=changeRxnBounds(model1, model1.rxns(4161), 0, 'l');
model1=changeRxnBounds(model1, model1.rxns(4161), 1000, 'u');
model1=changeRxnBounds(model1, model1.rxns(4168), 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(Th1_ATP_fluxes)
ATP=ATP+solution1.x(Th1_ATP_fluxes(i))*Th1_ATP_coeff(i);
end
%Calculate the production rate of AMP.
AMP=0; %mmol per gram of dry weight per hour.
for i = 1:size(Th1_AMP_fluxes)
AMP=AMP+solution1.x(Th1_AMP_fluxes(i))*Th1_AMP_coeff(i);
end
%Extract the other production rates.
Biomass=solution1.x(4161); %Growth rate (per hour).
Biomass_minus_DNA=solution1.x(4168); %Growth rate (per hour).
DNA=solution1.x(4163); %mmol per gram of dry weight per hour.
%RNA=solution1.x(4164); %mmol per gram of dry weight per hour.
Protein=solution1.x(4162); %mmol per gram of dry weight per hour.
%Carb=solution1.x(4165); %mmol per gram of dry weight per hour.
%Lipid=solution1.x(4166); %mmol per gram of dry weight per hour.
%pyruvate_lactase=solution1.x(1466); %mmol per gram of dry weight per hour.
%pyruvate_mito1=solution1.x(3136); %mmol per gram of dry weight per hour.
%pyruvate_mito2=solution1.x(3321); %mmol per gram of dry weight per hour.
%Combine the production rates.
output=[Biomass, Biomass_minus_DNA, DNA, Protein, ATP, AMP];
end