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Add comments, use FIM results for combinedGRModel in MH proposal dist…
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…ribution, save results, remove redundant code
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@alexpopinga
alexpopinga authored Oct 31, 2024
1 parent 60cf301 commit ba56a7c
Showing 1 changed file with 34 additions and 191 deletions.
225 changes: 34 additions & 191 deletions WorkSpace/EricModel/a0_Fit_GR_and_DUSP1_models.m
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% Define stoichiometry.
ModelGR.stoichiometry = [-1,1,1,-1,0;...
1,-1,0,0,-1];

% Specify parameter guesses.
ModelGR.parameters = ({'koff',0.1;'kon',0.1;'kr',1;'gr',0.02;...
'kcn0',0.005;'kcn1',0.02;'gDex',0.003;'knc',0.01;'kg1',14e-5;...
'gg1',1e-5;'gg2',1e-6;'MDex',5;'Dex0',100});

% Print visual summary of the model
ModelGR.summarizeModel

%% The log prior will be applied to the fit to multiple models as an additional constraint.
log10PriorMean = [-1 -1 0 -2,...
-1 -3 -2 -1 -2 -2 -2 0.5, 2];
log10PriorStd = 2*ones(1,13);
% the log prior will be applied to the fit to multiple models as an
% additional constraint.
ModelGR.fittingOptions.logPrior = [];

% So it is left out of the prior, since we only want it to be calculated once.
ModelGR.fittingOptions.logPrior = [];


ModelGR.fspOptions.initApproxSS = true;
ModelGR.fittingOptions.modelVarsToFit = (5:12);
Expand All @@ -87,7 +90,6 @@
[FSPGrSoln,ModelGR.fspOptions.bounds] = ModelGR.solve(FSPGrSoln.stateSpace);

% STEP 0.B.2. -- Define GR parameters
fitIters = 3;
GRpars = cell2mat(ModelGR.parameters(5:12,2))';

% STEP 0.B.3. -- Associate GR Data with Different Instances of Model (10,100nm Dex)
Expand Down Expand Up @@ -118,6 +120,8 @@
% First N are lower bounds. Next N is upper bound. Remaining are
% custom.
end
% Set for STEP1 -- Fit GR Models
fitIters = 30;
end

%% STEP 1 -- Fit GR Models
Expand All @@ -129,36 +133,50 @@
% STEP 1.B. -- Specify log prior (NOTE: must transpose due to Matlab update that
% no longer correctly assumes format when adding single value vector to
% column vector).
%logPriorGR = @(x)-sum((log10(x)-log10PriorMean(5:12)').^2./(2*log10PriorStd(5:12)'.^2));

logPriorGR = @(x)-sum((log10(x)-log10PriorMean(5:12)').^2./(2*log10PriorStd(5:12)'.^2));

% STEP 1.C. -- Combine all three GR models and fit using a single parameter set.
for jj = 1:fitIters
%combinedGRModel = SSITMultiModel(ModelGRfit,ModelGRparameterMap,logPriorGR);
combinedGRModel = SSITMultiModel(ModelGRfit,ModelGRparameterMap);
combinedGRModel = SSITMultiModel(ModelGRfit,ModelGRparameterMap,logPriorGR);
combinedGRModel = combinedGRModel.initializeStateSpaces(boundGuesses);
combinedGRModel = combinedGRModel.updateModels(GRpars,false);
GRpars = combinedGRModel.maximizeLikelihood(...
GRpars, fitOptions);
save('EricModel_MMDex','GRpars')
end

save('EricModelGR_MMDex','GRpars','combinedGRModel')

%% STEP 1.D. -- Compute FIM for GR parameters.
combinedGRModel = combinedGRModel.computeFIMs([],'log');
fimGR_withPrior = combinedGRModel.FIM.totalFIM+... % the FIM in log space.
diag(1./(log10PriorStd(ModelGR.fittingOptions.modelVarsToFit)*log(10)).^2); % Add prior in log space.

%% STEP 1.C. -- Run MH on GR Models.
if min(eig(fimGR_withPrior))<1
disp('Warning -- FIM has one or more small eigenvalues. Reducing proposal width to 10x in those directions. MH Convergence may be slow.')
fimGR_withPrior = fimGR_withPrior + 1*eye(length(fimGR_withPrior));
end
covFree = fimGR_withPrior^-1;
covFree = 0.5*(covFree+covFree');

%% STEP 1.E. -- Run MH on GR Models.
%GRpars = GRpars';
MHFitOptions.thin=1;
MHFitOptions.numberOfSamples=1000;
MHFitOptions.burnIn=0;
MHFitOptions.numberOfSamples=3000;
MHFitOptions.burnIn=1000;
MHFitOptions.progress=true;
MHFitOptions.numChains = 1;

% Use FIM computed above rather than making SSIT call 'useFIMforMetHast'
% which forces SSIT.m to compute it within (no prior, etc.)
MHFitOptions.useFIMforMetHast = false;
MHFitOptions.proposalDistribution=@(x)mvnrnd(x,covFree);

MHFitOptions.saveFile = 'TMPEricMHGR.mat';
[~,~,MHResultsGR] = combinedGRModel.maximizeLikelihood(...
GRpars, MHFitOptions, 'MetropolisHastings');
delete(MHFitOptions.saveFile)
%delete(MHFitOptions.saveFile)
%%
figNew = figure;
ModelGR.plotMHResults(MHResultsGR,[],'log',[],figNew)
Expand All @@ -171,9 +189,11 @@
end
end

%% STEP 1.D. -- Make Plots of GR Fit Results
%% STEP 1.F. -- Make Plots of GR Fit Results
makeGRPlots(combinedGRModel,GRpars)

save('EricModelGR_MMDex','GRpars','combinedGRModel','MHResultsGR')

%% STEP 2 -- Extend Model to Include DUSP1 Activation, Production, and Degradation
if loadPrevious
varNamesDUSP1 = {'ModelGRDusp100nM'
Expand Down Expand Up @@ -265,6 +285,7 @@
ModelGRDusp100nM.parameters(1:4,2) = num2cell(DUSP1pars);
save('EricModelDusp1_MMDex','GRpars','DUSP1pars')
end

%% STEP 2.C. -- Plot predictions for other Dex concentrations.
showCases = [1,1,1,1];
makePlotsDUSP1({ModelGRDusp100nM},ModelGRDusp100nM,DUSP1pars,Dusp1FitCases,showCases)
Expand Down Expand Up @@ -310,185 +331,7 @@
delete('TMPEricMHDusp1.mat')
ModelGRDusp100nM.parameters(1:4,2) = num2cell(DUSP1pars);
end
%% STEP 1.B. -- Compute FIM for GR parameters.
combinedGRModel = combinedGRModel.computeFIMs([],'log');
fimGR_withPrior = combinedGRModel.FIM.totalFIM+... % the FIM in log space.
diag(1./(log10PriorStd(ModelGR.fittingOptions.modelVarsToFit)*log(10)).^2); % Add prior in log space.
%% STEP 1.C. -- Run MH on GR Models.
%GRpars = GRpars';
MHFitOptions.proposalDistribution=@(x)mvnrnd(x,covFree);
MHFitOptions.thin=1;
MHFitOptions.numberOfSamples=1000;
MHFitOptions.burnIn=0;
MHFitOptions.progress=true;
MHFitOptions.numChains = 1;
MHFitOptions.useFIMforMetHast = true;
MHFitOptions.saveFile = 'TMPEricMHGR.mat';
[GRpars,~,MHResultsGR] = combinedGRModel.maximizeLikelihood(...
GRpars, MHFitOptions, 'MetropolisHastings');
delete(MHFitOptions.saveFile)
%%
figNew = figure;
ModelGR.plotMHResults(MHResultsGR,[],'log',[],figNew)
for i = 1:7
for j = i+1:7
subplot(7,7,(i-1)*7+j-1)
CH = get(gca,'Children');
CH(1).Color=[1,0,1]; %
CH(1).LineWidth = 3;
end
end

%% STEP 1.D. -- Make Plots of GR Fit Results
makeGRPlots(combinedGRModel,GRpars)
%%
%% STEP 2 -- Extend Model to Include DUSP1 Activation, Production, and Degradation
if loadPrevious
varNamesDUSP1 = {'ModelGRDusp100nM'
'GRfitCases'
'log10PriorMean'
'log10PriorStd'
'duspLogPrior'
'DUSP1pars'
'Dusp1FitCases'};
load(savedWorkspace,varNamesDUSP1{:})
fitOptions = optimset('Display','iter','MaxIter',10);
fitIters = 1;
try
ModelGRDusp100nM.propensitiesGeneral{1}.stateDependentFactor(0);
catch
ModelGRDusp100nM = ModelGRDusp100nM.formPropensitiesGeneral('DUSP1_Model');
end
else
%% STEP 2.A.1. -- Add DUSP1 to the existing GR model.
% Copy parameters from the 100nM Dex stim case in GR.
fitIters = 3;
ModelGRDusp100nM = ModelGRfit{3};
ModelGRDusp100nM.species = {'offGene';'onGene';'cytGR';'nucGR';'rna'};
ModelGRDusp100nM.initialCondition = [2;0;24;1;5];
ModelGRDusp100nM.propensityFunctions = {'kon*offGene*nucGR';'koff*onGene';
'(kcn0 + (t>0)*kcn1*IDex/(MDex+IDex)) * cytGR';'knc*nucGR';'kg1';'gg1*cytGR';'gg2*nucGR';...
'kr*onGene';'gr*rna'};
ModelGRDusp100nM.stoichiometry = [-1,1,0,0,0,0,0,0,0;...
1,-1,0,0,0,0,0,0,0;...
0,0,-1,1,1,-1,0,0,0;...
0,0,1,-1,0,0,-1,0,0;...
0,0,0,0,0,0,0,1,-1];
ModelGRDusp100nM.useHybrid = true;
ModelGRDusp100nM.hybridOptions.upstreamODEs = {'cytGR','nucGR'};
ModelGRDusp100nM.solutionScheme = 'FSP';
ModelGRDusp100nM.customConstraintFuns = [];
ModelGRDusp100nM.fspOptions.bounds = [0;0;0;2;2;400];
ModelGRDusp100nM.fittingOptions.modelVarsToFit = 1:4;
ModelGRDusp100nM = ModelGRDusp100nM.formPropensitiesGeneral('EricModDusp1');
duspLogPrior = @(x)-sum((log10(x(:))'-log10PriorMean(1:4)).^2./(2*log10PriorStd(1:4).^2));
ModelGRDusp100nM.fittingOptions.logPrior = duspLogPrior;

%% STEP 2.A.2. -- Load pre-fit parameters into model.
if loadPrevious
load('EricModelDusp1_MMDex','DUSP1pars')
else
%% Pull the DUSP1 parameters from the Model
% Find the indices of the desired parameter names
knc_idx = find(strcmp(ModelGRDusp100nM.parameters(:,1), 'knc'));
kg1_idx = find(strcmp(ModelGRDusp100nM.parameters(:,1), 'kg1'));
gg1_idx = find(strcmp(ModelGRDusp100nM.parameters(:,1), 'gg1'));
gg2_idx = find(strcmp(ModelGRDusp100nM.parameters(:,1), 'gg2'));

% Extract the values and store them in DUSP1pars
DUSP1pars = [ModelGRDusp100nM.parameters{knc_idx, 2}, ...
ModelGRDusp100nM.parameters{kg1_idx, 2}, ...
ModelGRDusp100nM.parameters{gg1_idx, 2}, ...
ModelGRDusp100nM.parameters{gg2_idx, 2}];
end
ModelGRDusp100nM.parameters(ModelGRDusp100nM.fittingOptions.modelVarsToFit,2) = num2cell(DUSP1pars);
%% STEP 2.A.3. -- Load and Associate with DUSP1 smFISH Data (100nM Dex Only)
% The commented code below would be needed to fit multiple conditions,
% but that is not used in this case. It is left here in case it is
% needed in later stages of the project.
% % % Dusp1FitCases = {'100','100',201,'DUSP1 Fit (100nM Dex)'};
% % % ModelDusp1Fit = cell(size(Dusp1FitCases,1),1);
% % % ModelDusp1parameterMap = cell(1,size(GRfitCases,1));
% % % for i = 1:size(Dusp1FitCases,1)
% % % ModelDusp1Fit{i} = ModelGRDusp100nM.loadData('EricData/pdoCalibrationData_EricIntensity_DexSweeps.csv',...
% % % {'rna','totalNucRNA'},...
% % % {'Dex_Conc','100'});
% % % ModelDusp1Fit{i}.inputExpressions = {'IDex','Dex0*exp(-gDex*t)'};
% % %
% % % ModelDusp1parameterMap{i} = (1:4);
% % % % Set Dex concentration.
% % % ModelDusp1Fit{i}.parameters{13,2} = str2num(Dusp1FitCases{i,1});
% % % ModelDusp1Fit{i} = ModelDusp1Fit{i}.formPropensitiesGeneral(['EricModDusp1_',num2str(i),'_FSP']);
% % % end
% % % DUSP1pars = [ModelDusp1Fit{i}.parameters{ModelGRDusp100nM.fittingOptions.modelVarsToFit,2}];
ModelGRDusp100nM = ModelGRDusp100nM.loadData('EricData/pdoCalibrationData_EricIntensity_DexSweeps.csv',...
{'rna','totalNucRNA'},{'Dex_Conc','100'});
DUSP1pars = [ModelGRDusp100nM.parameters{ModelGRDusp100nM.fittingOptions.modelVarsToFit,2}];
end
%% STEP 2.B. -- Fit DUSP1 model at 100nM Dex.
for i = 1:fitIters
fitOptions.suppressFSPExpansion = true;
DUSP1pars = ModelGRDusp100nM.maximizeLikelihood(...
DUSP1pars, fitOptions);
ModelGRDusp100nM.parameters(1:4,2) = num2cell(DUSP1pars);
save('EricModelDusp1_MMDex','GRpars','DUSP1pars')
end
%% STEP 2.C. -- Plot predictions for other Dex concentrations.
showCases = [1,1,1,1];
makePlotsDUSP1({ModelGRDusp100nM},ModelGRDusp100nM,DUSP1pars,Dusp1FitCases,showCases)
%% STEP 2.D. -- Sample uncertainty for Dusp1 Parameters
%% STEP 2.D.1. -- Compute sensitivity of the FSP solution
ModelGRDusp100nM.solutionScheme = 'fspSens';
sensSoln = ModelGRDusp100nM.solve();
ModelGRDusp100nM.solutionScheme = 'FSP';
%% STEP 2.D.2. -- Compute FIM
% define which species in model are not observed.
ModelGRDusp100nM.pdoOptions.unobservedSpecies = {'offGene';'onGene'};
% compute the FIM
fimResults = ModelGRDusp100nM.computeFIM(sensSoln.sens,'log');
% In the following, the log-prior is used as a prior co-variance matrix.
% This will be used in the FIM calculation as an FIM without new evidence
% being set equal to the inverse of this covariance matrix. More rigorous
% justification is needed to support this heuristic.
fimTotal = ModelGRDusp100nM.evaluateExperiment(fimResults,ModelGRDusp100nM.dataSet.nCells,...
diag(log10PriorStd(1:13).^2));
FIMfree = fimTotal{1}(1:4,1:4);
if min(eig(FIMfree))<1
disp('Warning -- FIM has one or more small eigenvalues. Reducing proposal width to 10x in those directions. MH Convergence may be slow.')
FIMfree = FIMfree + 1*eye(length(FIMfree));
end
covFree = FIMfree^-1;
covFree = 0.5*(covFree+covFree');
%
%% STEP 2.D.3. -- Run Metropolis Hastings Search
if loadPrevious
MHDusp1File = 'MHDusp1_Jul22';
load(MHDusp1File)
else
MHFitOptions.proposalDistribution=@(x)mvnrnd(x,covFree);
MHFitOptions.thin=1;
MHFitOptions.numberOfSamples=1000;
MHFitOptions.burnIn=0;
MHFitOptions.progress=true;
MHFitOptions.numChains = 1;
MHFitOptions.saveFile = 'TMPEricMHDusp1.mat';
[DUSP1pars,~,MHResultsDusp1] = ModelGRDusp100nM.maximizeLikelihood(...
[], MHFitOptions, 'MetropolisHastings');
delete('TMPEricMHDusp1.mat')
ModelGRDusp100nM.parameters(1:4,2) = num2cell(DUSP1pars);
end
%% STEP 2.D.4. -- Plot the MH results
figNew = figure;
ModelGRDusp100nM.plotMHResults(MHResultsDusp1,[],'log',[],figNew)
for i = 1:3
for j = i:3
subplot(3,3,(i-1)*3+j)
CH = get(gca,'Children');
CH(1).Color=[1,0,1]; %
CH(1).LineWidth = 3;
end
end
%%
%% STEP 3. -- Model Extensions using ODE Analyses
if loadPrevious
vaNamesExtended = {'ModelGRfit'
Expand Down

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