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pbpk_host_microbiome_workflow_v2.m
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pbpk_host_microbiome_workflow_v2.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% define files containing the models and data for first step:
% estimating host parameters (datafilenames1)
% and second step: estimating bacterial parameters (datafilenames2)
dataFolder = ['Data' filesep];
modelfilenames = {'example_basic_model_BRV_mut_wt.csv' ...
'example_basic_model_BRV_GF_CV.csv' ...
'example_basic_model_SRV_GF_CV.csv' ...
'example_basic_model_CLZ_GF_CV.csv' ...
'example_extended_model_CLZ_GF_CV.csv' ...
};
datafilenames1 = {'example_data_BRV_MUT.csv' ...
'example_data_BRV_GF.csv'...
'example_data_SRV_GF.csv'...
'example_data_CLZ_GF.csv'...
'example_data_CLZ_GF_extended.csv'...
};
datafilenames2 = {'example_data_BRV_WT.csv'...
'example_data_BRV_CV.csv'...
'example_data_SRV_CV.csv'...
'example_data_CLZ_CV.csv'...
'example_data_CLZ_CV_extended.csv'
};
% flag indicating whether to perform sensitivity analysis
% (change to 1 to perfrom sensitivity analysis)
perform_sensitivity_analysis_flag = 0;
% build the models
for files_i = 1:length(modelfilenames)
modelfilename = [dataFolder modelfilenames{files_i}];
datafilename1 = [dataFolder datafilenames1{files_i}];
datafilename2 = [dataFolder datafilenames2{files_i}];
% define model output files with out_ preffix and input file name
outfilename1 = [dataFolder 'out_' datafilenames1{files_i}];
outfilename2 = [dataFolder 'out_' datafilenames2{files_i}];
% build model according to the definition in the table
[modelGutUniversal] = create_model_from_file(modelfilename);
% load experimental data
[t,metNamesMap, gd, useForFitting] = load_data_from_file(datafilename1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% extract model parameter names and initial values
parameterNames = cell(size(modelGutUniversal.Parameters));
parameterInitialValues = zeros(size(modelGutUniversal.Parameters));
parameterNames_fitdata = zeros(size(modelGutUniversal.Parameters));
for i=1:length(modelGutUniversal.Parameters)
parameterNames{i} = modelGutUniversal.Parameters(i).Name;
parameterNames_fitdata(i) = str2double(modelGutUniversal.Parameters(i).Notes);
parameterInitialValues(i) = modelGutUniversal.Parameters(i).Value;
if parameterNames_fitdata(i)~=0
modelGutUniversal.Parameters(i).Value = 0;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% extract volumes of the compartments for the first step (GF) and
% second step (CVR)
% Note: GF and CVR are historical names, if both mouse models are
% monocolonized, for example, the volumes for GF and CVR can be defined
% the same
VbloodDRUG = modelGutUniversal.Parameters(ismember(parameterNames, 'Vserum')).Value;
VcecGF = modelGutUniversal.Parameters(ismember(parameterNames, 'Vcecum')).Value;
VcolGF = modelGutUniversal.Parameters(ismember(parameterNames, 'Vcolon')).Value;
VfecGF = modelGutUniversal.Parameters(ismember(parameterNames, 'Vfeces')).Value;
Vsi = modelGutUniversal.Parameters(ismember(parameterNames, 'Vsi')).Value;
VcecCVR = modelGutUniversal.Parameters(ismember(parameterNames, 'VcecumCVR')).Value;
VcolCVR = modelGutUniversal.Parameters(ismember(parameterNames, 'VcolonCVR')).Value;
VfecCVR = modelGutUniversal.Parameters(ismember(parameterNames, 'VfecesCVR')).Value;
if ismember('Vurine', parameterNames)
VurineDRUG = modelGutUniversal.Parameters(ismember(parameterNames, 'Vurine')).Value;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SIMULATE MODEL WITH data from datafile1 to estimate host parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%INITIAL PARAMETER INITIALIZATION
initPar = cell(nnz(parameterNames_fitdata==1),1);
initParValue = zeros(nnz(parameterNames_fitdata==1),1);
idx =1;
for i=1:length(parameterNames)
if parameterNames_fitdata(i)==1
initPar{idx} = ['log(' parameterNames{i}, ')'];
initParValue(idx) = parameterInitialValues(i);
idx = idx+1;
end
end
initParValue(initParValue==0) = rand(nnz(initParValue==0),1);
% Possibly run the model several times to select the best fit
nruns = 1; %number of runs to run the model
randomRuns_init = zeros(nruns, length(initPar));
randomRuns_results = cell(nruns,1);
randomRunsBact_init = zeros(nruns, length(initPar));
randomRunsBact_results = cell(nruns,1);
for run_i = 1:nruns
init = rand(1,length(initPar));
for i=1:length(init)
if initParValue(i)
init(i) = initParValue(i);
end
end
randomRuns_init(run_i,:) = init;
end
for run_i = 1:nruns
init = randomRuns_init(run_i,:);
estimated_parameters = estimatedInfo(initPar, 'InitialValue',init);%'
responseMap = strcat(metNamesMap(useForFitting,1), ' = ',...
metNamesMap(useForFitting,2));
% optimization step
rng('default')
globalMethod = 'ga';
options = optimoptions(globalMethod);
hybridMethod = 'fminsearch';
hybridopts = optimset('Display', 'none');
options = optimoptions(options, 'HybridFcn', {hybridMethod, hybridopts});
resultsHost = sbiofit(modelGutUniversal, gd,responseMap,estimated_parameters,[],...
globalMethod,options,'pooled',true);
% save fitting results
randomRuns_results{run_i} = resultsHost;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set model parameters to optimized values and simulate
resultsHost = randomRuns_results{run_i};
for i=1:length(resultsHost.ParameterEstimates.Name)
modelGutUniversal.Parameters(ismember(parameterNames, resultsHost.ParameterEstimates.Name(i))).Value =...
resultsHost.ParameterEstimates.Estimate(i);
end
% define volumes according to the compartments in the model
if files_i<5
curVolumes = [Vsi Vsi Vsi Vsi Vsi...
VbloodDRUG VbloodDRUG VcecGF VcolGF VfecGF...
VcecGF VcolGF VfecGF VbloodDRUG];
else % adapt volumes for extended model
% curVolumes = [Vsi Vsi Vsi Vsi Vsi...
% VbloodDRUG 1 VbloodDRUG VcecGF VcolGF VfecGF...
% Vsi Vsi Vsi...
% VcecGF VcolGF VfecGF 1 ...
% VcecGF VcolGF VfecGF VbloodDRUG ...
% VcecGF VcolGF VfecGF VbloodDRUG...
% VurineDRUG ...
% VbloodDRUG 1 ...
% VurineDRUG VurineDRUG ...
% VurineDRUG 1 ...
% VbloodDRUG VurineDRUG ...
% VurineDRUG ...
% ];
curVolumes = [Vsi Vsi ...
VbloodDRUG VurineDRUG ...
Vsi Vsi Vsi...
VbloodDRUG VbloodDRUG...
1 1 ...
VcecGF VcolGF VfecGF ...
VcecGF VcolGF VfecGF ...
VbloodDRUG ...
VurineDRUG ...
VurineDRUG ...
VurineDRUG ...
Vsi Vsi Vsi ...
VbloodDRUG 1 1 ...
VcecGF VcolGF VfecGF ...
VcecGF VcolGF VfecGF...
VbloodDRUG...
VurineDRUG ...
VurineDRUG ...
];
end
% simulate the model according to the set parameters
simulate_pbpk_model(t, curVolumes, modelGutUniversal, ...
metNamesMap, useForFitting)
suptitle(datafilename1)
% save model fitting and predictions to a file
prepare_data_for_tables_public(t, curVolumes, modelGutUniversal, resultsHost, ...
metNamesMap, useForFitting, outfilename1, initParValue)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Run the model with the data from datafile2 to estimate bacterial
% coefficients
modelGutUniversalBact = copyobj(modelGutUniversal);
[t,metNamesMap, gd, useForFitting] = load_data_from_file(datafilename2);
%INITIAL PARAMETER INITIALIZATION
initPar = cell(nnz(parameterNames_fitdata==2),1);
initParValue = 1+rand(nnz(parameterNames_fitdata==2),1);
idx =1;
for i=1:length(parameterNames)
if parameterNames_fitdata(i)==2
initPar{idx} = ['log(' parameterNames{i}, ')'];
initParValue(idx) = parameterInitialValues(i);
idx = idx+1;
end
end
initParValue(initParValue==0) = rand(nnz(initParValue==0),1);
estimated_parameters = estimatedInfo(initPar, 'InitialValue',initParValue);%'
responseMap = strcat(metNamesMap(useForFitting,1), ' = ',...
metNamesMap(useForFitting,2));
rng('default')
globalMethod = 'ga';
options = optimoptions(globalMethod);
hybridMethod = 'fminsearch';
hybridopts = optimset('Display', 'none');
options = optimoptions(options, 'HybridFcn', {hybridMethod, hybridopts});
resultsHostBact = sbiofit(modelGutUniversalBact, gd,responseMap,estimated_parameters,[],...
globalMethod,options,'pooled',true);
randomRunsBact_init(run_i,1:length(init)) = init;
randomRunsBact_results{run_i} = resultsHostBact;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set model parameters to optimized values and simulate
parameterNames = cell(size(modelGutUniversalBact.Parameters));
for i=1:length(modelGutUniversalBact.Parameters)
parameterNames{i} = modelGutUniversalBact.Parameters(i).Name;
end
for i=1:length(resultsHostBact.ParameterEstimates.Name)
modelGutUniversalBact.Parameters(ismember(parameterNames, resultsHostBact.ParameterEstimates.Name(i))).Value =...
resultsHostBact.ParameterEstimates.Estimate(i);
end
% define tissue weights depending on the model
if files_i<5
curVolumes = [Vsi Vsi Vsi Vsi Vsi...
VbloodDRUG VbloodDRUG VcecCVR VcolCVR VfecCVR...
VcecCVR VcolCVR VfecCVR VbloodDRUG];
else
% curVolumes = [Vsi Vsi Vsi Vsi Vsi...
% VbloodDRUG 1 VbloodDRUG VcecCVR VcolCVR VfecCVR...
% Vsi Vsi Vsi...
% VcecCVR VcolCVR VfecCVR 1 ...
% VcecCVR VcolCVR VfecCVR VbloodDRUG ...
% VcecCVR VcolCVR VfecCVR VbloodDRUG...
% VurineDRUG ...
% VbloodDRUG 1 ...
% VurineDRUG VurineDRUG ...
% VurineDRUG 1 ...
% VbloodDRUG VurineDRUG ...
% VurineDRUG ...
% ];
curVolumes = [Vsi Vsi ...
VbloodDRUG VurineDRUG ...
Vsi Vsi Vsi...
VbloodDRUG VbloodDRUG...
1 1 ...
VcecCVR VcolCVR VfecCVR ...
VcecCVR VcolCVR VfecCVR ...
VbloodDRUG ...
VurineDRUG ...
VurineDRUG ...
VurineDRUG ...
Vsi Vsi Vsi ...
VbloodDRUG 1 1 ...
VcecCVR VcolCVR VfecCVR ...
VcecCVR VcolCVR VfecCVR...
VbloodDRUG...
VurineDRUG ...
VurineDRUG ...
];
end
% simulate the model according to the set parameters
simulate_pbpk_model(t, curVolumes, modelGutUniversalBact, ...
metNamesMap, useForFitting)
suptitle(datafilename2)
% save model fitting and predictions to a file
prepare_data_for_tables_public(t, curVolumes, modelGutUniversalBact, resultsHostBact, ...
metNamesMap, useForFitting, outfilename2, initParValue)
if perform_sensitivity_analysis_flag
perform_global_sensitivity_analysis(modelGutUniversal,...
resultsHost,...
resultsHostBact,...
1000,...
0,... % change F flag
0,... % change dose flag
0); % simulate within estimated flag
end
end