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F5ABC_SF34.m
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F5ABC_SF34.m
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% % Knock-outs to generate
% After the talk with Koen, with the current model, generate in silico
% mutants: (1) what if the trehalose cycle there was knocked down?, (2)
% What if glycerol was knocked down, (3) What if both?, (4) What if only
% one the trehalases (if NTH1, link to dataset FF04).
%% (0) Initial setup.
% startup
clear, %close all
set_paths;
dbstop if error
% assays to run
setup.runSScanelas = 0;
setup.runGSvanHeerden = 0;
setup.plotResultsMode = 0; %30; %20; %10;
setup.conditionsSS = 0;
setup.conditionsGP = 0;
% model structure used
setup.biomass = 0; % biomass
setup.clamp10.Pi = 0;%1;
setup.clamp10.TRE = 0;%1;
setup.clamp10.NADX = 0;%1;
setup.clamp10.AXP = 0;%1;
setup.clamp10.IXP = 1;
setup.clamp10.UDP_GLC = 1; % UDP_GLC_exp in AGT1 rate equation
setup.clamp = setup.clamp10;
% select parameters to study:
parsNTH1 = [122, 123];
parsATH1 = [149, 150];
parsAGT1 = [151:154, 158]; % include par158 (Ki for UDPG)
% parsvacuoleT = [155, 156];
parsvacuoleT = [155, 156, 157];
parsGLT = [35:36];
parsGLK = [28, 29, 30, 31, 32, 33, 34]; % 33 is k_T6P
parsPGI = [57:60];
parsPFK = [43:56,87];
parsPGM1 = [83:86];
parsUGP = [144:148];
parsTPS1 = [124:128];
parsTPS2 = [119:121];
parsPFK_sweep = [43:45,47,51,52,87];
parsPFK_selected = [51, 52];
parCombs = [33, parsNTH1, parsATH1, parsAGT1, parsvacuoleT, parsPGM1, parsUGP, parsTPS1, parsTPS2];
% get datasets (others needed)
[legenda] = legendaFull;
loadData_Y3M1;
% get GPFF datasets
load('TUDdata.mat'); % loads the datasets
dataset.FF01.time_mets = [0;5;10;15;20;25;30;60;90;120;150;180;220;250;300;350;400];
dataset.FF01.timeECgluc= [0;5;11;15;20; 30;60;90; 150;180;220;250;300;350;400];
dataset.FF03.time_mets = [0;5;10;20;30;40;60;90;120;150;200;250;300;400;550;700;800;900;1000;1200;1400;1600;1700;1803];
dataset.FF04.time_mets = [0;5;10;15;20;30;60;90;120;150;180;220;250;300;350;398];
reorganiseTUDdata; % puts it in order for easily coding in the next section
%% (1) simulations (GP suarez)
% simulation FF01
setup.GPdataset.GP400WT = 1;
setup.GPdataset.GP1800WT = 0;
setup.GPdataset.GP400M = 0;
setup.GPdataset.Fructose = 0;
setup.GPdataset.Sucrose = 0;
su = 0;
fn = fieldnames(setup.GPdataset);
for i = 1:numel(fn)
su = su+ setup.GPdataset.(fn{i});
end
if su > 1
disp('too many datasets used')
end
z = 2; %l/gcdw
if setup.GPdataset.GP400WT == 1
InitCond_GP_TUD;
elseif setup.GPdataset.GP1800WT == 1
InitCond_GP_TUD_1800;
elseif setup.GPdataset.GP400M == 1
InitCond_GP_TUD_mutant;
elseif setup.GPdataset.Fructose == 1
FFFruc = readFructose(z);
dataset.FFFruc = FFFruc;
reorganiseFrucData;
IC = ICFrucData(z, FFFruc);
elseif setup.GPdataset.Sucrose == 1
FFSuc = readSucrose(z);
dataset.FFSuc = FFSuc;
reorganiseSucData;
InitConditionsSucrose;
end
NumberCycles = 5;
plotflag = 0;
%% updated parameter set from pE2
% % % % % load('pset_pE2res.mat');
% % % % % x1 = x_pE2_start;
% % % % % x2 = x_pE2_end;
%% glycerol_SynthDeg active
setup.glycSynthDeg = 1; % this adds a reaction to the mass balance (ODEs), reaction calculation in rate equations and the starting parameter value
% % % % % x1(159) = -4; % -10;
% % % % % x2(159) = -4; %-10;
%% extension glycogen metabolism
% load parameters
% % % % % load('pset_pE6_x1res.mat','x_pE6_x1_end'); x1 = x_pE6_x1_end;
% % % % % load('pset_pE6_x2res.mat','x_pE6_x2_end'); x2 = x_pE6_x2_end;
%% adding a more detailed glycogen metabolism
setup.glycSynthDeg_separate = 1;
%% recall last case
% % % % % x = x1;
% % % % % x(162) = 10;
setup.ATHinhibitionT6P = 1;
% % % % % load('pset_pE7_xres.mat','x_pE7_start','x_pE7_end');
% % % % % clear x x1 x2
% % % % % x = x_pE7_end;
%% separating ath1_ec and ath1_vac
% % % % % x(163:164) = x(149:150); % km_tre, k_cat
% % % % % x(165) = x(162); % ki_t6p
setup.ATH_separate_EC_VAC = 1;
%% setting the option
setup.glycogenReactionsSink = 1;
setup.dataset = dataset;
%
% % % % % load('pset_pE10_xres.mat','x_pE10_start','x_pE10_end'); x = x_pE10_end;
%% latest setup
% % % % setup.updated_bmf_Cx_ATH1ec = 1;
setup.TREec_brothOut_OFF = 1;
setup.updated_bmf_Cx_ATH1ec = 1;
%% added for enrichment simulations
load('datasetEnrich.mat');
reorganiseEnrichData;
%% (2021 - 08 - 09) right balance around ATH1ec
setup.TRE_recirculation_rightBalances = 1;
%% (2021 09 17) Adjustment glk
% load('x16a.mat','x16a')
% x = x16a;
% x16c_E_start = x_test(1,:);
% x16c_E_final = x_test(2,:);
% load('x16c_E_TPS2.mat', 'x16c_E_start', 'x16c_E_final')
% x = x16c_E_final;
load('pset_Y3M2.mat','x16d_initial','x16d_final')
x = x16d_final;
%% Directly implementing the Csmin idea in out model
% general clamping and setup options
NumberCycles = 5;
setup.clamp_GLCec = 0;
% setup.clamp_GLCec = 1;
setup.changing_Keq_glt = 3; % here the value number 3.
% changing_Keq_glt = 3; means that CSmin is implemented (FF01 value)
% = 2; manually fixed Keq_glt. p.GLT.KeqGLT = 1 * 10 .^ setup.Keq_glt_inc;
% = 1; estimated Keq_glt. p.GLT.KeqGLT = 1 * 10 .^ x(166);
% x(166) = 0; % % <== change x166 if needed here.
setup.csmin = 0.094;
% setup.csmin = 0;
% tic
% %% testing the change in the mass balance
[legenda] = legendaFull; %legenda for the names needed
metNames = legenda.metabolites;
reactNames = legenda.fluxes;
legendaMetabolites_addEnrichment;
choosedataset
%
setup.experiment = 1;
%% (2021 10 01: adding the sinkPYR decrease)
setup.decrease_sinkPYR = 1;
setup.ratio_decrease_sinkPYR = 0.1;
% % % % %% checkup Simulation
% % % % %
% % % % [T_FF01_1,Y_FF01_1,V_FF01_1] = simulate_FF_enrichment(x,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % %
% % % % % T_FF01_1 = T_FF01_2;
% % % % % Y_FF01_1 = Y_FF01_2;
% % % % % V_FF01_1 = V_FF01_2;
% % % % %
% % % % selResCell{1}.T_FF01 = T_FF01_1;
% % % % selResCell{1}.Y_FF01 = Y_FF01_1;
% % % % selResCell{1}.V_FF01 = V_FF01_1;
% % % %
% % % %
% % % % %% Visualization
% % % % plotMode = 2;
% % % % referencePlotSimulations_enrichment
% % % % % clear selResCell
% % % % plotMode = 0;
%% (1/8) Recall parameter set Y3M1 and Y3M2
load('pset_Y3M1.mat');
x_Y3M1 = x99b;
x_Y3M2 = x; x_Y3M2(35) = x_Y3M2(38); setup.sameKm_Y3M2 = 1; % !!!!!!! (2022 - 01 - 20)
idxs_change = find(x_Y3M1 ~= x_Y3M2(1:length(x_Y3M1)));
% Columns 1 through 8
%
% 28 29 30 31 32 33 34 36
%
% Columns 9 through 16
%
% 37 38 83 84 85 86 109 119
%
% Columns 17 through 24
%
% 120 121 122 123 124 125 126 127
%
% Columns 25 through 32
%
% 128 129 144 145 146 147 148 149
%
% Columns 33 through 35
%
% 150 151 152
% Changes which are:
% % % % parsGLT = [35:36,38];
% % % % parsGLK = [28, 29, 30, 31, 32, 33, 34]; % 33 is k_T6P
% % % % parsPGM1 = [83:86];
% % % % parsUGP = [144:148];
% % % % parsTPS1 = [124:128];
% % % % parsTPS2 = [119:121];
% % % % parsNTH1 = [122, 123];
% % % % parsATH1 = [149, 150];
% % % % parsAGT1 = [151:154, 158]; % include par158 (Ki for UDPG)
% % other parameters
% parsPGI = [57:60];
% parsPFK = [43:56,87];
% %
x_Y3M2(109) = x_Y3M1(109); % ATPase changed here directly
x_Y3M2(129) = x_Y3M1(129) + 1; % ATPase changed here directly
x_Y3M1 = [x_Y3M1, zeros(1,13)];
%
% x2 = x; x2(109) = x_Y3M1(109); x2(129) = x_Y3M1(129)+1;
%% (2/8) Simulate both
% % % % clear selResCell
% % % % [T_FF01_1,Y_FF01_1,V_FF01_1] = simulate_FF_enrichment(x,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % [T_FF01_2,Y_FF01_2,V_FF01_2] = simulate_FF_enrichment(x2,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % [T_FF01_2,Y_FF01_2,V_FF01_2] = simulate_FF_enrichment(x_Y3M1,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % [T_FF01_3,Y_FF01_3,V_FF01_3] = simulate_FF_enrichment(x_Y3M2,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % %
% % % % %
% % % % selResCell{1}.T_FF01 = T_FF01_1;
% % % % selResCell{1}.Y_FF01 = Y_FF01_1;
% % % % selResCell{1}.V_FF01 = V_FF01_1;
% % % % %
% % % % selResCell{2}.T_FF01 = T_FF01_2;
% % % % selResCell{2}.Y_FF01 = Y_FF01_2;
% % % % selResCell{2}.V_FF01 = V_FF01_2;
% % % % %
% % % % selResCell{3}.T_FF01 = T_FF01_3;
% % % % selResCell{3}.Y_FF01 = Y_FF01_3;
% % % % selResCell{3}.V_FF01 = V_FF01_3;
% % % %
% % % % % %% Visualization
% % % % plotMode = 2;
% % % % referencePlotSimulations_enrichment
% % % % plotMode = 0;
%% (2/8)b. Pre-screen for expected changes
% actual changes
parsGLT = [35:36,38];
parsGLK = [28, 29, 30, 31, 32, 33, 34]; % 33 is k_T6P
parsPGM1 = [83:86];
parsUGP = [144:148];
parsTPS1 = [124:128];
parsTPS2 = [119:121];
parsNTH1 = [122, 123];
parsATH1 = [149, 150];
parsAGT1 = [151:154, 158]; % include par158 (Ki for UDPG)
parsvacuoleT = [155, 156, 157];
parsGLYC = 159:161;
parsath1_T6P = 162;
parsATH1ec = 163:165;
% parsAGT1_T6P = 166;
parsLast = [parsvacuoleT, parsGLYC, parsath1_T6P, parsATH1ec];
parsTRE = [parsPGM1, parsUGP, parsTPS1, parsTPS2, parsNTH1, parsATH1, parsAGT1];
parsTRE2 = [parsTRE, parsLast];
% other parameters
parsPGI = [57:60];
parsPFK = [43:56,87];
parsALD = [11:15];
parsGAPDH = [21:27];
% pars...
x_Y3M1_parsGLT = x_Y3M1; x_Y3M1_parsGLT(parsGLT) = x_Y3M2(parsGLT);
x_Y3M1_parsGLT(129) = x_Y3M2(129);
x_Y3M1_parsGLT(37) = x_Y3M2(37);
x_Y3M1_parsGLK = x_Y3M1; x_Y3M1_parsGLK(parsGLK) = x_Y3M2(parsGLK);
x_Y3M1_parsGLK(129) = x_Y3M2(129);
x_Y3M1_parsGLK(37) = x_Y3M2(37);
x_Y3M1_parsTRE = x_Y3M1; x_Y3M1_parsTRE(parsTRE2) = x_Y3M2(parsTRE2);
x_Y3M1_parsTRE(129) = x_Y3M2(129);
x_Y3M1_parsTRE(37) = x_Y3M2(37);
x_Y3M1_parsGLTGLKTRE = x_Y3M1; x_Y3M1_parsGLTGLKTRE([parsGLT,parsGLK,parsTRE2]) = x_Y3M2([parsGLT,parsGLK,parsTRE2]);
x_Y3M1_parsGLTGLKTRE(129) = x_Y3M2(129);
x_Y3M1_parsGLTGLKTRE(37) = x_Y3M2(37);
x_Y3M1_129 = x_Y3M1;
x_Y3M1_129(129) = x_Y3M2(129);
x_Y3M1_129(37) = x_Y3M2(37);
% % % % % % % % % % simulate
% % % % % % % % % clear selResCell
% % % % % % % % [T_FF01_1,Y_FF01_1,V_FF01_1] = simulate_FF_enrichment(x_Y3M2,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % % % % [T_FF01_2,Y_FF01_2,V_FF01_2] = simulate_FF_enrichment(x_Y3M1_parsGLT,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % % % % [T_FF01_3,Y_FF01_3,V_FF01_3] = simulate_FF_enrichment(x_Y3M1_parsGLK,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % [T_FF01_4,Y_FF01_4,V_FF01_4] = simulate_FF_enrichment(x_Y3M1_parsTRE,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % % % % [T_FF01_5,Y_FF01_5,V_FF01_5] = simulate_FF_enrichment(x_Y3M1_parsGLTGLKTRE,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % % % % [T_FF01_6,Y_FF01_6,V_FF01_6] = simulate_FF_enrichment(x_Y3M1_129,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % % % % %%
% % % % % % % % clear selResCell
% % % % % % % % % x_Y3M1_parsGLT = x_Y3M1; x_Y3M1_parsGLT(parsGLT) = x_Y3M2(parsGLT);
% % % % % % % % % Pretty off.
% % % % % % % % % x_Y3M1_parsGLK = x_Y3M1; x_Y3M1_parsGLK(parsGLK) = x_Y3M2(parsGLK);
% % % % % % % % % Pretty off
% % % % % % % % % x_Y3M1_parsTRE = x_Y3M1; x_Y3M1_parsTRE(parsTRE2) = x_Y3M2(parsTRE2);
% % % % % % % % % getting there
% % % % % % % % % % x_Y3M1_parsGLTGLKTRE = x_Y3M1; x_Y3M1_parsGLTGLKTRE([parsGLT,parsGLK,parsTRE2]) = x_Y3M2([parsGLT,parsGLK,parsTRE2]);
% % % % % % % % % Same as initial.
% % % % % % % % % % x_Y3M1_129 = x_Y3M1;
% % % % % % % % % Pretty off, as expected.
% % % % %
% % % % selResCell{1}.T_FF01 = T_FF01_4;
% % % % selResCell{1}.Y_FF01 = Y_FF01_4;
% % % % selResCell{1}.V_FF01 = V_FF01_4;
% % % % % % % % %
% % % % % % % % selResCell{1}.T_FF01 = T_FF01_1;
% % % % % % % % selResCell{1}.Y_FF01 = Y_FF01_1;
% % % % % % % % selResCell{1}.V_FF01 = V_FF01_1;
% % % % % % % % %
% % % % % % % % selResCell{2}.T_FF01 = T_FF01_6;
% % % % % % % % selResCell{2}.Y_FF01 = Y_FF01_6;
% % % % % % % % selResCell{2}.V_FF01 = V_FF01_6;
% % % % % % % % %
% % % % % % % % selResCell{3}.T_FF01 = T_FF01_4;
% % % % % % % % selResCell{3}.Y_FF01 = Y_FF01_4;
% % % % % % % % selResCell{3}.V_FF01 = V_FF01_4;
% % % % % % % % % %
% % % % % % % % % selResCell{2}.T_FF01 = T_FF01_2;
% % % % % % % % % selResCell{2}.Y_FF01 = Y_FF01_2;
% % % % % % % % % selResCell{2}.V_FF01 = V_FF01_2;
% % % % % % % % % %
% % % % % % % % % selResCell{3}.T_FF01 = T_FF01_3;
% % % % % % % % % selResCell{3}.Y_FF01 = Y_FF01_3;
% % % % % % % % % selResCell{3}.V_FF01 = V_FF01_3;
% % % % % % % % % %
% % % % % % % % % selResCell{4}.T_FF01 = T_FF01_4;
% % % % % % % % % selResCell{4}.Y_FF01 = Y_FF01_4;
% % % % % % % % % selResCell{4}.V_FF01 = V_FF01_4;
% % % % % % % % % %
% % % % % % % % % selResCell{5}.T_FF01 = T_FF01_5;
% % % % % % % % % selResCell{5}.Y_FF01 = Y_FF01_5;
% % % % % % % % % selResCell{5}.V_FF01 = V_FF01_5;
% % % % % % % % % %
% % % % % % % % % selResCell{6}.T_FF01 = T_FF01_6;
% % % % % % % % % selResCell{6}.Y_FF01 = Y_FF01_6;
% % % % % % % % % selResCell{6}.V_FF01 = V_FF01_6;
% % % % % % % %
% % % % % %% Visualization
% % % % plotMode = 0;
% % % % referencePlotSimulations_enrichment
% % % % plotMode = 0;
% % % %
% % % %
%%
[T_FF01_ref,Y_FF01_ref,V_FF01_ref] = simulate_FF_enrichment(x_Y3M2,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
%
selResCell_ref{1}.T_FF01 = T_FF01_ref;
selResCell_ref{1}.Y_FF01 = Y_FF01_ref;
selResCell_ref{1}.V_FF01 = V_FF01_ref;
%
% setup to estimate from the reference simulation
% setup.simData = selResCell_ref;
setup.simData = selResCell_ref{1};
setup.pset_Y3M1 = x_Y3M1;
%%
clear selResCell
selResCell = selResCell_ref;
%
plotMode = 0;
referencePlotSimulations_enrichment
plotMode = 0;
%% (2/8)b. Pre-screen for expected changes
%
x_Y3M1_parsTRE_GLT = x_Y3M1; x_Y3M1_parsTRE_GLT([parsGLT, parsTRE2]) = x_Y3M2([parsGLT, parsTRE2]);
x_Y3M1_parsTRE(129) = x_Y3M2(129);
x_Y3M1_parsTRE(37) = x_Y3M2(37);
%
x_Y3M1_parsTRE_GLK = x_Y3M1; x_Y3M1_parsTRE_GLK([parsGLK, parsTRE2]) = x_Y3M2([parsGLK, parsTRE2]);
x_Y3M1_parsTRE(129) = x_Y3M2(129);
x_Y3M1_parsTRE(37) = x_Y3M2(37);
% % % % % % simulate
% % % % % clear selResCell
% % % % [T_FF01_11,Y_FF01_11,V_FF01_11] = simulate_FF_enrichment(x_Y3M2,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % [T_FF01_12,Y_FF01_12,V_FF01_12] = simulate_FF_enrichment(x_Y3M1_parsTRE_GLT,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % [T_FF01_13,Y_FF01_13,V_FF01_13] = simulate_FF_enrichment(x_Y3M1_parsTRE_GLK,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % %
% % % % % %%
% % % % clear selResCell
% % % % %
% % % % selResCell{1}.T_FF01 = T_FF01_11;
% % % % selResCell{1}.Y_FF01 = Y_FF01_11;
% % % % selResCell{1}.V_FF01 = V_FF01_11;
% % % % %
% % % % selResCell{2}.T_FF01 = T_FF01_12;
% % % % selResCell{2}.Y_FF01 = Y_FF01_12;
% % % % selResCell{2}.V_FF01 = V_FF01_12;
% % % % %
% % % % selResCell{3}.T_FF01 = T_FF01_13;
% % % % selResCell{3}.Y_FF01 = Y_FF01_13;
% % % % selResCell{3}.V_FF01 = V_FF01_13;
% % % %
% % % %
% % % % % %% Visualization
% % % % plotMode = 2;
% % % % referencePlotSimulations_enrichment
% % % % plotMode = 0;
%% (3/8) Prepare the multiple combinations of parameters
% blank and constant setup
legendaMetabolites_addEnrichment;
choosedataset
setup.ExpData = ExpData;
%
blankWeight = zeros(1,89); % 85+3 for glycerol, 89 for glucose enrichment
lambdalist = 0;
setup.parEst.lambda = lambdalist(1); lam = setup.parEst.lambda;
% % % options = optimoptions('lsqnonlin','Display','iter','OutputFcn',{@saveIterationsMain},'PlotFcn',{@optimplotx});
% % options = optimoptions('lsqnonlin','Display','iter','OutputFcn',{@saveIterationsMain},'PlotFcn',{@optimplotx},...
% % 'FunctionTolerance', 1e-4, 'OptimalityTolerance', 1e-4);
% options = optimoptions('lsqnonlin','Display','iter',...
% 'OutputFcn',{@saveIterationsMain},'PlotFcn',{@optimplotx},...
% 'FiniteDifferenceStepSize',0.2);
options = optimoptions('lsqnonlin','Display','iter',...
'FunctionTolerance', 1e-4, 'OptimalityTolerance', 1e-4,...
'DiffMinChange',0.1);
% 'OutputFcn',{@saveIterationsMain},...
% 'FunctionTolerance', 1e-4, 'OptimalityTolerance', 1e-4);
% setup.parEst.costfun = 10015;
% creating enzyme combinations
enzNum = 6;
% 1 -> GLT
% 2 -> GLK
% 3 -> PGI
% 4 -> PFK
% 5 -> ALD
% 6 -> GAPDH
nchoosek_cell = cell(enzNum,1);
for i = 1:enzNum
nchoosek_cell{i} = nchoosek(1:enzNum,i);
end
% concatenate
if exist('z','var')
clear z
end
for i = 1:(enzNum-1)
if exist('z','var')
else
z = nchoosek_cell{i}';
end
B = nchoosek_cell{i+1}';
sA = size(z);
sB = size(B);
C = max(sA(1),sB(1));
z = [[z;zeros(abs([C 0]-sA))],[B;zeros(abs([C,0]-sB))]];
end
z = z';
% number of tests + initial parameter set
% z = z([1:3,5:6,8,11,15],:);
[nTests,nZ] = size(z);
xStart = x_Y3M1;
xStart([37,129]) = x_Y3M2([37,129]);
xStart(parsTRE2) = x_Y3M2(parsTRE2);
% x_Y3M1_parsTRE
% % names_cell
names_cell = cell(nTests,1);
for i = 1:nTests
names_cell{i} = sprintf('FF_mFig_7C_n%d.mat',i);
end
% % parComb_cell
parComb_cell = cell(nTests,1);
for i = 1:nTests
% tempPars = parsTRE2;
tempPars = [];
if any(z(i,:) == 1), tempPars = [tempPars, parsGLT]; end
if any(z(i,:) == 2), tempPars = [tempPars, parsGLK]; end
if any(z(i,:) == 3), tempPars = [tempPars, parsPGI]; end
if any(z(i,:) == 4), tempPars = [tempPars, parsPFK]; end
if any(z(i,:) == 5), tempPars = [tempPars, parsALD]; end
if any(z(i,:) == 6), tempPars = [tempPars, parsGAPDH]; end
parComb_cell{i} = tempPars;
end
% % cost_function
% improving 50% for reaction rates (all of UG) and 50% for metabolites (UG and TRE-cycle)
w_glc_ic = 6;
w_glc_ec = 36; % concentrations
w_g1p = 21; % concentrations
w_udpg = 24; % concentrations
w_t6p_tre = [26 25]; % concentrations
w_treRates_glk_pgi = [55 59 57 58 56 40 41]; % rates
w_glt = 39; % rates
w_gly = [86 87 88]; % concentrations
w_tre_ic = 25;
w_tre_ec = 37; % concentrations
w_ath1 = [83 84];
w_ath1_ec = 83;
w_ath1_vac = 84;
w_agt1 = 85;
w_glc_enrich = 89;
% concentrations and rates
wConcentrations = [w_glc_ec, w_g1p, w_udpg, w_t6p_tre, 86, w_tre_ec];
wRates = [w_treRates_glk_pgi, w_glt];
% adding to the cost function matrix
% % warray_cell
warray_cell = cell(nTests,1);
for i = 1:nTests % base,
warray_cell{i} = blankWeight;
% warray_cell{i}(wConcentrations) = 0.5 * ones / length(wConcentrations);
% warray_cell{i}(wRates) = 0.5 * ones / length(wRates);
warray_cell{i}([62 65]) = ones;
end
% % % % %% (4/8) Run them
% % % % % parSet
% % % % % parSet1 = 1:2:63;
% % % % % parSet2 = 2:2:62;
% % % % parSet1 = 1:40;
% % % % parSet2 = 41:63;
% % % % % for o = selectedRuns_idx
% % % % cluster = parcluster('local');
% % % % % pool = parpool(cluster,4);
% % % % pool = parpool(cluster,16);
% % % % parfor o = 1:nTests
% % % % % parfor o = parSet1
% % % % % parfor o = parSet2
% % % % % for o = 1:nTests
% % % %
% % % % % disp
% % % % disp(o);
% % % % % fprintf('o = %f.\n', o) %disp(o)
% % % %
% % % % % find in the precreated arrays
% % % % selPars = parComb_cell{o};
% % % % warray = warray_cell{o};
% % % % saveName = names_cell{o};
% % % % % setup.selPars = selPars;
% % % %
% % % % % core options:
% % % % plength = length(selPars);
% % % % lb = -3*ones(1,plength); %lb(end-12:end) = -5 * ones;
% % % % ub = 3*ones(1,plength); %ub(end-12:end) = 5 * ones;
% % % % x_temp = xStart(selPars);
% % % %
% % % % % % %% run check
% % % % % [error]=costfunSystemY3M1_FF_pRun_enrichment(x_temp,canelas_SS,setup,xStart,data,dataset,NumberCycles,IC,selPars,warray);
% % % % % % errorAnalysis_Y3M1;
% % % % % % %%
% % % % %
% % % % % %
% % % % % [T_FF01_1,Y_FF01_1,V_FF01_1] = simulate_FF_enrichment(xStart,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % % plotMode = 2; % multiple simulations
% % % % % % clear selResCell
% % % % % selResCell{1}.T_FF01 = T_FF01_1;
% % % % % selResCell{1}.Y_FF01 = Y_FF01_1;
% % % % % selResCell{1}.V_FF01 = V_FF01_1;
% % % % % %
% % % % % referencePlotSimulations_enrichment
% % % % % plotMode = 0;
% % % % % %
% % % %
% % % % % parameter estimation
% % % % % NumberCycles = 20;
% % % % sprintf('run num.%d tested',o)
% % % % tic
% % % % % [xres,resnorm,residual,exitflag] = lsqnonlin(@costfunSystemY3M1_FF_pRun,x_temp,lb,ub,options,canelas_SS,setup,x_iter,data,dataset,NumberCycles,IC,selPars,warray);
% % % % [xres,resnorm,residual,exitflag] = lsqnonlin(@costfunSystemY3M1_FF_pRun_enrichment,x_temp,lb,ub,options,canelas_SS,setup,x,data,dataset,NumberCycles,IC,selPars,warray);
% % % % t = toc;
% % % % disp(xres);
% % % %
% % % % % simulating output
% % % % xRound = xStart;
% % % % xRound(selPars) = xres;
% % % %
% % % % [T_FF01,Y_FF01,V_FF01] = simulate_FF_enrichment(xRound,canelas_SS,data,dataset,setup,NumberCycles,plotflag,IC);
% % % % simRes1_T_FF01 = T_FF01;
% % % % simRes1_Y_FF01 = Y_FF01;
% % % % simRes1_V_FF01 = V_FF01;
% % % %
% % % % % plotMode = 2; % multiple simulations
% % % % % % clear selResCell
% % % % % selResCell{1}.T_FF01 = T_FF01_1;
% % % % % selResCell{1}.Y_FF01 = Y_FF01_1;
% % % % % selResCell{1}.V_FF01 = V_FF01_1;
% % % %
% % % %
% % % % % saving
% % % % % parsave_Y3M2_cluster(saveName, xres, resnorm, residual, exitflag, warray, t, selPars);
% % % % parsave_Y3M2_cluster2(saveName, xres, resnorm, residual, ...
% % % % exitflag, warray, t, selPars, ...
% % % % simRes1_T_FF01, simRes1_Y_FF01, simRes1_V_FF01);
% % % % % save(saveName, 'xres', 'resnorm', 'residual', 'exitflag', 'warray', 't', 'selPars');
% % % %
% % % % end
% % % % delete(pool)
% % % % quit
% %% (5/8) Recall results, visualize and select (Option 2)
% %
% xAll1 = xStart;
% %
% namesHits1 = cell(1,1); namesHits1{1} = 'initial';
% %
% idxs = 0;
% %
% simRes1 = selResCell_ref;
% errorRes1 = cell(1,1); errorRes1{1} = zeros(409,1);
% %
% for i = 1:nTests
% % pC1_wA1
% % temp_idx = i;
% % loadName = sprintf(names_cell{temp_idx}, i);
% loadName = names_cell{i};
% if exist(loadName,'file') == 2 % if exist
% load(loadName);
% selPars = parComb_cell{i};
% x3 = xStart;
% x3(selPars) = xres;
% xAll1 = [xAll1; x3];
% namesHits1 = [namesHits1; loadName];
% idxs = [idxs, i];
% %
% temp_simRes.T_FF01 = simRes1_T_FF01;
% temp_simRes.Y_FF01 = simRes1_Y_FF01;
% temp_simRes.V_FF01 = simRes1_V_FF01;
% simRes1 = [simRes1, temp_simRes];
% tempErrCell = cell(1,1); tempErrCell{1} = residual;
% errorRes1 = [errorRes1; tempErrCell];
% %
% end
% end
% % recall
% simRes1_mF7_sims1 = simRes1;
% simRes1_mF7_errorRes1 = errorRes1;
% simRes1_16e_xAll1 = xAll1;
% simRes1_16e_namesHits1 = namesHits1;
% % save
% save('tempRes2_mF7CnewStart.mat', ...
% 'simRes1_mF7_sims1', 'simRes1_mF7_errorRes1', ...
% 'simRes1_16e_xAll1', 'simRes1_16e_namesHits1');
%%
% % % First save
% % load('tempRes2_mF7.mat', ...
% % 'simRes1_mF7_sims1', 'simRes1_mF7_errorRes1', ...
% % 'simRes1_16e_xAll1', 'simRes1_16e_namesHits1');
% % Second save
% load('tempRes2_mF7_b.mat', ...
% 'simRes1_mF7_sims1', 'simRes1_mF7_errorRes1', ...
% 'simRes1_16e_xAll1', 'simRes1_16e_namesHits1');
% Second save
load('tempRes2_mF7CnewStart.mat', ...
'simRes1_mF7_sims1', 'simRes1_mF7_errorRes1', ...
'simRes1_16e_xAll1', 'simRes1_16e_namesHits1');
%% Visualizing results: simulation
% selResCell = simRes1_16e_TRE([1,11:19]);
clear selResCell
selResCell = [selResCell_ref];
selResCell = simRes1_mF7_sims1(1:7);
% selResCell = simRes1_mF7_sims1(1:8);
% selResCell = [selResCell_ref, simRes1_mF7_sims1(2:4)];
% selResCell = simRes1_mF7_sims1(1:3);
% selResCell = simRes1_mF7_sims1(8:12);
% selResCell = simRes1_mF7_sims1;
plotMode = 2;
referencePlotSimulations_enrichment
plotMode = 0;
%% calculate the error
% calculate error 2, based on error rates glycolysis + trehalsoe cycle
% recall idxs
% refSim
time2compare = [0:2:30,40:10:160,180:20:400]; % time2compare
A = repmat(selResCell_ref{1}.T_FF01,[1 length(time2compare')]);
[~,closestIndex] = min(abs(A-time2compare));
% N(closestIndex) % find closest value
vGLTref = selResCell_ref{1}.V_FF01(closestIndex,1);
vGLKref = selResCell_ref{1}.V_FF01(closestIndex,2);
vPGIref = selResCell_ref{1}.V_FF01(closestIndex,3);
vPFKref = selResCell_ref{1}.V_FF01(closestIndex,4);
vALDref = selResCell_ref{1}.V_FF01(closestIndex,5);
vPGM1ref = selResCell_ref{1}.V_FF01(closestIndex,17);
vUGPref = selResCell_ref{1}.V_FF01(closestIndex,18);
vTPS1ref = selResCell_ref{1}.V_FF01(closestIndex,21);
vTPS2ref = selResCell_ref{1}.V_FF01(closestIndex,19);
vNTH1ref = selResCell_ref{1}.V_FF01(closestIndex,20); % fluxes ref
% %
% figure,
% plot(selResCell_ref{1}.T_FF01(closestIndex),vTPS2ref,'.-')
% blaks for the other sims
errorTre = zeros(length(simRes1_16e_namesHits1) - 1, 1); % make blank cell error trehalose
errorGly = zeros(length(simRes1_16e_namesHits1) - 1, 1); % make blank cell error glycolysis
errorTotal = zeros(length(simRes1_16e_namesHits1) - 1, 1); % make blank cell error total
idxs2 = zeros(length(simRes1_16e_namesHits1) - 1, 1); % make blank idxs2
numEnzymes = zeros(length(simRes1_16e_namesHits1) - 1, 1); % make blank num enzumes
enzNames = {'HXT','GLK','PGI','PFK','ALD','TDH'};
cellNamesCombs = cell(length(simRes1_16e_namesHits1) - 1, 1); % make blank cell names combinations
% otherSims
for i = 2:length(simRes1_16e_namesHits1) % other simulations (skip initial point)
% for i = 2:3
A = repmat(simRes1_mF7_sims1{i}.T_FF01,[1 length(time2compare')]);
[~,closestIndex] = min(abs(A-time2compare)); % find closest value
% fluxes exp
vGLTsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,1);
vGLKsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,2);
vPGIsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,3);
vPFKsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,4);
vALDsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,5);
vPGM1sim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,17);
vUGPsim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,18);
vTPS1sim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,21);
vTPS2sim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,19);
vNTH1sim = simRes1_mF7_sims1{i}.V_FF01(closestIndex,20);
% % fluxes error (method 2: denominator = DATAsim)
% vGLTerror = sum(abs((vGLTsim - vGLTref) ./ vGLTsim));
% vGLKerror = sum(abs((vGLKsim - vGLKref) ./ vGLKsim));
% vPGIerror = sum(abs((vPGIsim - vPGIref) ./ vPGIsim));
% vPFKerror = sum(abs((vPFKsim - vPFKref) ./ vPFKsim));
% vALDerror = sum(abs((vALDsim - vALDref) ./ vALDsim));
% vPGM1error = sum(abs((vPGM1sim - vPGM1ref) ./ vPGM1sim));
% vUGPerror = sum(abs((vUGPsim - vUGPref) ./ vUGPsim));
% vTPS1error = sum(abs((vTPS1sim - vTPS1ref) ./ vTPS1sim));
% vTPS2error = sum(abs((vTPS2sim - vTPS2ref) ./ vTPS2sim));
% vNTH1error = sum(abs((vNTH1sim - vNTH1ref) ./ vNTH1sim));
% fluxes error (method 2: denominator = max and single value)
vGLTerror = sum(abs((vGLTsim - vGLTref) ./ max(vGLTref)));
vGLKerror = sum(abs((vGLKsim - vGLKref) ./ max(vGLKref)));
vPGIerror = sum(abs((vPGIsim - vPGIref) ./ max(vPGIref)));
vPFKerror = sum(abs((vPFKsim - vPFKref) ./ max(vPFKref)));
vALDerror = sum(abs((vALDsim - vALDref) ./ max(vALDref)));
vPGM1error = sum(abs((vPGM1sim - vPGM1ref) ./ max(vPGM1ref)));
vUGPerror = sum(abs((vUGPsim - vUGPref) ./ max(vUGPref)));
vTPS1error = sum(abs((vTPS1sim - vTPS1ref) ./ max(vTPS1ref)));
vTPS2error = sum(abs((vTPS2sim - vTPS2ref) ./ max(vTPS2ref)));
vNTH1error = sum(abs((vNTH1sim - vNTH1ref) ./ max(vNTH1ref)));
% %
% figure
% subplot(1,2,1)
% plot(simRes1_mF7_sims1{i}.T_FF01(closestIndex), vTPS2ref, '.-')
% hold on
% plot(simRes1_mF7_sims1{i}.T_FF01(closestIndex), vTPS2sim, '.-')
% legend('ref','sim')
% subplot(1,2,2)
% plot(simRes1_mF7_sims1{i}.T_FF01(closestIndex), (vTPS2sim - vTPS2ref) ./ 1, '.-')
% legend('error')
% get the errors
% errorGly(i-1) = vGLTerror + vGLKerror + vPGIerror + vPFKerror + vALDerror; % to calculate the trehalose errors (weighted)
errorGly(i-1) = vGLTerror; % to calculate the trehalose errors (weighted)
% errorTre(i-1) = vPGM1error + vUGPerror + vTPS1error + vTPS2error + vNTH1error; % to calculate the glycolysis errors (weighted)
errorTre(i-1) = vTPS2error; % to calculate the glycolysis errors (weighted)
errorTotal(i-1) = errorTre(i-1) + errorGly(i-1); % to calculate the total errors (weighted)
idxs2(i-1) = str2double(erase(erase(simRes1_16e_namesHits1{i},".mat"),"FF_mFig_7C_n")); % update idxs2 at each loop (detecit the number in simRes1_16_namesHits1
numEnzymes(i-1) = sum(abs(z(idxs2(i-1),:) ~= 0)); % calculate num error (sum is_num)
tempNamesCombs = [];
for j = 1:enzNum
if ismember(j,z(idxs2(i-1),:))
tempNamesCombs = [tempNamesCombs, enzNames{j}, ', '];
end
end
cellNamesCombs{i-1} = tempNamesCombs; % recall name enzyme combinations (i-1)
end
%
%%
figure,
subplot(1,3,1), plot(1:length(idxs2), errorGly), title('errorGly')
subplot(1,3,2), plot(1:length(idxs2), errorTre), title('errorTre')
subplot(1,3,3), plot(1:length(idxs2), errorTotal), title('errorTotal')
%% visual (1/4) error vs numPars, barplots
% plot per option, as made before
% barplot, orientation vertical
% at first, plot on top errorGLYCOLYSIS and errorTREcycle
% add the plot of the number of enzumes
tempName = categorical(cellNamesCombs);
tempName = reordercats(tempName,cellNamesCombs);
% clf(101)
fig101_h = figure(101);
barh(tempName, [errorGly, errorTre],...
'stacked'),
legend('errorGly','errorTre')
ax = gca;
ax.FontSize = 8;
%% Making the figure plot here
if exist('figh','var')
clf(1001)
end
figh = figure(1001);
figh.Position = [962 42 958 954];
%
% sph = subplot(3,2,[1 3]);
sph = subplot(2,2,[1 3]);
bplot = barh(tempName, errorTotal, 'stacked','facecolor', 'flat');
% bplot.BarWidth = 0.5;
bplot.BarWidth = 0.65;
% bplot.FaceColor = 'black';
% bplot.EdgeColor = 'none';
bplot.EdgeColor = 'black';
sph.XLim = [0 30];
ax = gca;
% ax.FontSize = 6;
set(gca,'YTickLabel',[]);
%
hold on
%
thesis_colopalette
%
% % % % % % % % % % % % % % % % % %
% Get the data for all the bars that were plotted
x_1 = get(bplot,'XData');
y_1 = get(bplot,'YData');
ygap = 1; % Specify vertical gap between the bar and label
% ylimits = get(gca,'YLim');
xlimits = get(gca,'XLim');
set(gca,'XLim',[xlimits(1),xlimits(2)+0.2*max(y_1)]); % Increase y limit for labels
% Create labels to place over bars
labels = cellNamesCombs;
for i = 1:length(x_1) % Loop over each bar
xpos = x_1(i); % Set x position for the text label
ypos = y_1(i) + ygap; % Set y position, including gap
htext = text(-0.2,xpos,labels{i}); % Add text label
htext.Position = htext.Position + [0 -.7 0];
set(htext,'VerticalAlignment','bottom',... % Adjust properties
'HorizontalAlignment','right',...
'FontSize',8)
%
if contains(htext.String, 'HXT, GLK, ')
set(htext, 'color', cpal_broad.blue)
bplot.CData(i,:) = cpal_broad.blue;
else
bplot.CData(i,:) = [0 0 0];
end
end
% re adjustment axis
sph.Position = sph.Position + [0.05 0 -0.05 0];
%
yyaxis right
line([5 5],[0 1],'Color','none','LineStyle','--')
hold on,
%
% % % % line([5 5],[0 0.095],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([5 10],[0.095 0.095],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([10 10],[0.095 0.3325],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([10 15],[0.3325 0.3325],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([15 15],[0.3325 0.65],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([15 20],[0.65 0.65],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([20 20],[0.65 0.89],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([20 25],[0.89 0.89],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([25 25],[0.89 0.985],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([25 30],[0.985 0.985],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
% % % % line([30 30],[0.985 1],'Color',cpal_broad.vermillion,'LineStyle','-','LineWidth',2)
%
annotation('arrow',[0.05 0.115],[0.193 0.193],'color',cpal_broad.blue)
%
set(gca,'YTickLabel',[],'YColor', 'k');%,'color','k');
%
% % % % text(4.5,-0.015,'1','color',cpal_broad.vermillion)
% % % % text(9.5,-0.015,'2','color',cpal_broad.vermillion)
% % % % text(14.5,-0.015,'3','color',cpal_broad.vermillion)
% % % % text(19.5,-0.015,'4','color',cpal_broad.vermillion)
% % % % text(24.5,-0.015,'5','color',cpal_broad.vermillion)
% % % % text(29.5,-0.015,'6','color',cpal_broad.vermillion)
%
% % % % temp = text(17.5,-0.05,'Number of enzymes','color',cpal_broad.vermillion,...
% % % % 'FontSize',12,'HorizontalAlignment','center');
% % % % temp2 = text(17.5,1.05,'Model error','color','black',...
% % % % 'FontSize',12,'HorizontalAlignment','center');
% % % % set(gca,'xaxisLocation','top')
temp2 = text(17.5,-0.05,'Model error','color','black',...
'FontSize',12,'HorizontalAlignment','center');
%% Regularization plot
% To consider changes from here.
%% subplot(2,2,2) -> lambdalist, with pareto front inside
% recall data pars
% -> which is just a backbone, estimated parameters below
load('pset_pE10_xres.mat','x_pE10_start','x_pE10_end'); %x10 = x_pE10_end;
xAll2 = x_pE10_end;
for warray_16a = 1
%
namesHits2 = cell(1,1); namesHits2{1} = 'initial';
%
names_cell2{1} = 'FF_pE16a_glk_wA1.mat';
names_cell2{2} = 'FF_pE16a_glk_wA2.mat';
names_cell2{3} = 'FF_pE16a_glk_wA3.mat';
names_cell2{4} = 'FF_pE16a_glk_wA4.mat';
names_cell2{5} = 'FF_pE16a_glk_wA5.mat';
names_cell2{6} = 'FF_pE16a_glk_wA6.mat';
names_cell2{7} = 'FF_pE16a_glk_wA7.mat';
names_cell2{8} = 'FF_pE16a_glk_wA8.mat';
%
names_cell2{9} = 'FF_pE16a_glk_wA9.mat';
names_cell2{10} = 'FF_pE16a_glk_wA10.mat';
names_cell2{11} = 'FF_pE16a_glk_wA11.mat';
names_cell2{12} = 'FF_pE16a_glk_wA12.mat';
names_cell2{13} = 'FF_pE16a_glk_wA13.mat';
names_cell2{14} = 'FF_pE16a_glk_wA14.mat';
names_cell2{15} = 'FF_pE16a_glk_wA15.mat';
names_cell2{16} = 'FF_pE16a_glk_wA16.mat';
%
parsHXK = 28:34; % hxk
pC16a = parsHXK;
% pC16b = [parsGLT, parsHXK, parsTreCycle];
%
parComb_cell2 = cell(16,1);
%
parComb_cell2{1} = pC16a;
parComb_cell2{2} = pC16a;
parComb_cell2{3} = pC16a;
parComb_cell2{4} = pC16a;
parComb_cell2{5} = pC16a;
parComb_cell2{6} = pC16a;
parComb_cell2{7} = pC16a;
parComb_cell2{8} = pC16a;
parComb_cell2{9} = pC16a;
parComb_cell2{10} = pC16a;
parComb_cell2{11} = pC16a;
parComb_cell2{12} = pC16a;
parComb_cell2{13} = pC16a;
parComb_cell2{14} = pC16a;
parComb_cell2{15} = pC16a;
parComb_cell2{16} = pC16a;
%
for i = 1:16
% pC1_wA1
temp_idx = i;
loadName = sprintf(names_cell2{temp_idx}, i);
if exist(loadName,'file') == 2 % if exist
load(loadName);
selPars = parComb_cell2{temp_idx};
x3 = x_pE10_end;
x3(selPars) = xres;
xAll2 = [xAll2; x3];
namesHits2 = [namesHits2; loadName];
end
end
end
for warray_16a2 = 1