Merge pull request #5 from MunskyGroup/main

Catchup merge

Examples/example_ExtrinsicNoiseModels.m

@@ -29,4 +29,5 @@
     2+0.4*randn,...
     1+0.2*randn];
 extrinsicModel = extrinsicSSIT(Model1,parDistributions,20);
-Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3])
+Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3])
+legend('Intrinsic Only','Intrinsic + Extrinsic','Location','EastOutside')

Examples/example_FIMCalculation.m

@@ -13,21 +13,35 @@
 %% (2) Solve FSP for model
 F2.solutionScheme = 'FSP';    % Set solution scheme to FSP.
 [FSPsoln,F2.fspOptions.bounds] = F2.solve;  % Solve the FSP analysis
+% Plot the results from the FSP analysis
+fig1 = figure(1);clf; set(fig1,'Name','Marginal Distributions, mRNA');
+fig2 = figure(2);clf; set(fig2,'Name','Marginal Distributions, Protein');
+F2.makePlot(FSPsoln,'marginals',[1:4:21],false,[fig1,fig2],{'r-','linewidth',2})
 
 %% (3) Solve FSP Sensitivity
 F2.solutionScheme = 'fspSens'; % Set solutions scheme to FSP Sensitivity
 [sensSoln,bounds] = F2.solve(FSPsoln.stateSpace);  % Solve the sensitivity problem
+% Plot the results from the sensitivity analysis
+fig3 = figure(3);clf; set(fig3,'Name','Marginal Sensitivity, mRNA');
+fig4 = figure(4);clf; set(fig4,'Name','Marginal Sensitivity, Protein');
+F2.makePlot(sensSoln,'marginals',[],false,[fig3,fig4],{'b','linewidth',2})
 
 %% (4) Compute FIM using FSP Sensitivity Results
 fimResults = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
 cellCounts = 10*ones(size(F2.tSpan));  % Number of cells in each experiment.
 [fimTotal,mleCovEstimate,fimMetrics] = F2.evaluateExperiment(fimResults,cellCounts)
+fig5 = figure(5);clf; set(fig5,'Name','Fim-Predicted Uncertainty Ellipses');
+F2.plotMHResults([],fimTotal,'lin',[],fig5)
+legend('FIM - Full Observation')
 
 %% (5) Compute FIM for Partial Observations
 F2.pdoOptions.PDO=[];
 F2.pdoOptions.unobservedSpecies = 'x1';
 [fimResults_partialObs] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
 [fimTotal_partialObs,mleCovEstimate_partialObs,fimMetrics_partialObs] = F2.evaluateExperiment(fimResults_partialObs,cellCounts)
+fig6 = figure(6);clf; set(fig6,'Name','Fim-Predicted Uncertainty Ellipses');
+F2.plotMHResults([],[fimTotal,fimTotal_partialObs],'lin',[],fig6)
+legend('FIM - Full Observation','FIM - Protein Only')
 
 %% (6) Compute FIM for Distorted Observation (Probabilistic Distortion Operator)
 F2.pdoOptions.unobservedSpecies = 'x1';
@@ -36,17 +50,20 @@
 pdoOptions.props.CaptureProbabilityS1 = 0;  % Use zero for unobserved species.
 pdoOptions.props.CaptureProbabilityS2 = 0.9;
 
-% call method to generate the PDO.
+% Call method to generate the PDO.
 F2.pdoOptions.PDO = F2.generatePDO(pdoOptions,[],FSPsoln.fsp);
 
-% plot the PDO
+% Plot the PDO
 N = size(F2.pdoOptions.PDO.conditionalPmfs{1});
-figure;
+fig7 = figure(7); set(fig7,'Name','Probabilistic Distortion Operator for Protein');
 contourf([0:N(1)-1],[0:N(2)-1],log10(F2.pdoOptions.PDO.conditionalPmfs{1}));
 % Here we wanted the first PDO for 'x2' because 'x1' was unobserved.
 xlabel('Actual');ylabel('Observable');colorbar;
 
-% solve FIM using the specified PDO
+% Solve FIM using the specified PDO
 [fimResults_BinomialPDO] = F2.computeFIM(sensSoln.sens); % Compute the FIM for full observations and no distortion.
 [fimTotal_BinomialPDO,mleCovEstimate_BinomialPDO,fimMetrics_BinomialPDO] =...
-    F2.evaluateExperiment(fimResults_BinomialPDO,cellCounts)
+    F2.evaluateExperiment(fimResults_BinomialPDO,cellCounts)
+fig8 = figure(8);clf; set(fig8,'Name','Fim-Predicted Uncertainty Ellipses');
+F2.plotMHResults([],[fimTotal,fimTotal_partialObs,fimTotal_BinomialPDO],'lin',[],fig8)
+legend('FIM - Full Observation','FIM - Protein Only','FIM - Protein with Error')

Examples/example_HybridModel.m

@@ -27,6 +27,7 @@
 Model2.hybridOptions.upstreamODEs = {'rna'};
 [fspSoln2, Model2.fspOptions.bounds] = Model2.solve;
 Model2.makePlot(fspSoln2,'marginals',[],[],3)
+legend('Full','QSSA for (n)','Location','eastoutside')
 
 %% Example 2 - 5-species MAPK induction Model
 % In this example, we consider a model of MAPK translocation to the nucleus
@@ -71,4 +72,4 @@
 Model5.hybridOptions.upstreamODEs = {'mapkCyt','mapkNuc'};
 [fspSoln5, Model5.fspOptions.bounds] = Model5.solve;
 Model5.makePlot(fspSoln5,'marginals',[],[],[11,12,15])
-
+legend('Full','QSSA for (gene,MAKP)','QSSA for (MAKP)','Location','eastoutside')

Examples/example_IterativeBayesianExperimentDesign.m

@@ -34,6 +34,8 @@
         fitParameters = [1:4];
 end
 
+experimentDesignCriteria = 
+
 saveFileName = ['IterativeExperimentResults_',example];
 
 %% Generate Model Propensity Functions and Solve True Model

Examples/example_ModelReductionTool.m

@@ -135,7 +135,10 @@
 % Make Figures to compare the results. Here, we will plot the original
 % model in blue and the reduced model in red lines.
 Model1.makePlot(fspSoln,'meansAndDevs',[],[],1,{'Color',[0,0,1]})
-Model1.makePlot(fspSoln,'marginals',[],[],[2,3],{'Color',[0,0,1]})
-
 Model2.makePlot(fspSolnRed,'meansAndDevs',[],[],1,{'Color',[1,0,0]})
-Model2.makePlot(fspSolnRed,'marginals',[],[],[2,3],{'Color',[1,0,0]})
+figure(1);legend('Full','Reduced','Location','southeast')
+
+Model1.makePlot(fspSoln,'marginals',[],[],[2,3],{'Color',[0,0,1]})
+Model2.makePlot(fspSolnRed,'marginals',[],[],[2,3],{'Color',[1,0,0]})
+figure(2);legend('Full','Reduced','Location','eastoutside')
+figure(3);legend('Full','Reduced','Location','eastoutside')

Examples/html/example_EscapeTimes.html

@@ -6,7 +6,7 @@
    <!--
 This HTML was auto-generated from MATLAB code.
 To make changes, update the MATLAB code and republish this document.
-      --><title>example_EscapeTimes</title><meta name="generator" content="MATLAB 9.14"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2023-10-21"><meta name="DC.source" content="example_EscapeTimes.m"><style type="text/css">
+      --><title>example_EscapeTimes</title><meta name="generator" content="MATLAB 9.14"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2023-11-15"><meta name="DC.source" content="example_EscapeTimes.m"><style type="text/css">
 html,body,div,span,applet,object,iframe,h1,h2,h3,h4,h5,h6,p,blockquote,pre,a,abbr,acronym,address,big,cite,code,del,dfn,em,font,img,ins,kbd,q,s,samp,small,strike,strong,tt,var,b,u,i,center,dl,dt,dd,ol,ul,li,fieldset,form,label,legend,table,caption,tbody,tfoot,thead,tr,th,td{margin:0;padding:0;border:0;outline:0;font-size:100%;vertical-align:baseline;background:transparent}body{line-height:1}ol,ul{list-style:none}blockquote,q{quotes:none}blockquote:before,blockquote:after,q:before,q:after{content:'';content:none}:focus{outine:0}ins{text-decoration:none}del{text-decoration:line-through}table{border-collapse:collapse;border-spacing:0}
 
 html { min-height:100%; margin-bottom:1px; }
@@ -69,7 +69,7 @@
 
   </style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#1">example_EscapeTimes</a></li><li><a href="#2">Example 1 - a simple transcription/translation model</a></li><li><a href="#3">Next, specify a boundary for the escape calculation</a></li><li><a href="#4">Example 2 - escape time with time varying transcription rate</a></li><li><a href="#5">Here we solve for the escape time:</a></li><li><a href="#6">Example 3 - More complex escape thresholds.</a></li><li><a href="#7">Example 4 - Time until making a specific decision.</a></li></ul></div><h2 id="1">example_EscapeTimes</h2><p>In this script, we demonstrate how to create and solve a fiorst passage time problem.</p><pre class="codeinput">close <span class="string">all</span>
 clear <span class="string">all</span>
-addpath(<span class="string">'../CommandLine'</span>);
+addpath(genpath(<span class="string">'../src'</span>));
 </pre><h2 id="2">Example 1 - a simple transcription/translation model</h2><p>First create a full model (e.g., for mRNA and protein)</p><pre class="codeinput">Model1 = SSIT;
 Model1.species = {<span class="string">'rna'</span>,<span class="string">'protein'</span>};
 Model1.initialCondition = [0;0];
@@ -112,7 +112,7 @@
 % time problem.
 close all
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 %% Example 1 - a simple transcription/translation model
 % First create a full model (e.g., for mRNA and protein)
 Model1 = SSIT;

Examples/html/example_ExtrinsicNoiseModels.html

@@ -6,7 +6,7 @@
    <!--
 This HTML was auto-generated from MATLAB code.
 To make changes, update the MATLAB code and republish this document.
-      --><title>example_ExtrinsicNoiseModels</title><meta name="generator" content="MATLAB 9.14"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2023-10-21"><meta name="DC.source" content="example_ExtrinsicNoiseModels.m"><style type="text/css">
+      --><title>example_ExtrinsicNoiseModels</title><meta name="generator" content="MATLAB 9.14"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2023-11-15"><meta name="DC.source" content="example_ExtrinsicNoiseModels.m"><style type="text/css">
 html,body,div,span,applet,object,iframe,h1,h2,h3,h4,h5,h6,p,blockquote,pre,a,abbr,acronym,address,big,cite,code,del,dfn,em,font,img,ins,kbd,q,s,samp,small,strike,strong,tt,var,b,u,i,center,dl,dt,dd,ol,ul,li,fieldset,form,label,legend,table,caption,tbody,tfoot,thead,tr,th,td{margin:0;padding:0;border:0;outline:0;font-size:100%;vertical-align:baseline;background:transparent}body{line-height:1}ol,ul{list-style:none}blockquote,q{quotes:none}blockquote:before,blockquote:after,q:before,q:after{content:'';content:none}:focus{outine:0}ins{text-decoration:none}del{text-decoration:line-through}table{border-collapse:collapse;border-spacing:0}
 
 html { min-height:100%; margin-bottom:1px; }
@@ -69,7 +69,7 @@
 
   </style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#1">example_ExtrinsicNoiseModels</a></li><li><a href="#2">Example 1 - transcription and translation</a></li><li><a href="#3">Generate, solve and plot results for an extrinsic noise version</a></li></ul></div><h2 id="1">example_ExtrinsicNoiseModels</h2><p>In this example, we show how to sample an FSM  model over intrinsic noise in its various parameters.</p><pre class="codeinput">close <span class="string">all</span>
 clear <span class="string">all</span>
-addpath(<span class="string">'../CommandLine'</span>);
+addpath(genpath(<span class="string">'../src'</span>));
 </pre><h2 id="2">Example 1 - transcription and translation</h2><p>First create a full model (e.g., for mRNA and protein)</p><pre class="codeinput">Model1 = SSIT();
 Model1.species = {<span class="string">'rna'</span>,<span class="string">'protein'</span>};
 Model1.initialCondition = [0;0];
@@ -80,21 +80,23 @@
 Model1.fspOptions.initApproxSS = false;
 Model1.tSpan = linspace(0,5,10);
 [fspSoln1,Model1.fspOptions.bounds] = Model1.solve;
+Model1 = Model1.formPropensitiesGeneral(<span class="string">'Model1'</span>);
 Model1.makePlot(fspSoln1,<span class="string">'marginals'</span>,[],[],[2,3])
 </pre><img vspace="5" hspace="5" src="example_ExtrinsicNoiseModels_01.png" alt=""> <img vspace="5" hspace="5" src="example_ExtrinsicNoiseModels_02.png" alt=""> <h2 id="3">Generate, solve and plot results for an extrinsic noise version</h2><p>Specify the rules for the extrinsic noise.  This must be a function that returns a  vector of parameters, that are in the same order as the parameters provided. You can choose a different distribution for the extrinsic noise in each parameter.</p><pre class="codeinput">parDistributions = @()[10+2*randn,<span class="keyword">...</span>
     0.5+0.1*randn,<span class="keyword">...</span>
     2+0.4*randn,<span class="keyword">...</span>
     1+0.2*randn];
 extrinsicModel = extrinsicSSIT(Model1,parDistributions,20);
 Model1.makePlot(extrinsicModel.averagedResults,<span class="string">'marginals'</span>,[],[],[2,3])
+legend(<span class="string">'Intrinsic Only'</span>,<span class="string">'Intrinsic + Extrinsic'</span>,<span class="string">'Location'</span>,<span class="string">'EastOutside'</span>)
 </pre><img vspace="5" hspace="5" src="example_ExtrinsicNoiseModels_03.png" alt=""> <img vspace="5" hspace="5" src="example_ExtrinsicNoiseModels_04.png" alt=""> <p class="footer"><br><a href="https://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2023a</a><br></p></div><!--
 ##### SOURCE BEGIN #####
 %% example_ExtrinsicNoiseModels
 % In this example, we show how to sample an FSM  model over intrinsic noise
 % in its various parameters. 
 close all 
 clear all
-addpath('../CommandLine');
+addpath(genpath('../src'));
 %% Example 1 - transcription and translation
 % First create a full model (e.g., for mRNA and protein)
 Model1 = SSIT();
@@ -107,6 +109,7 @@
 Model1.fspOptions.initApproxSS = false; 
 Model1.tSpan = linspace(0,5,10);
 [fspSoln1,Model1.fspOptions.bounds] = Model1.solve;
+Model1 = Model1.formPropensitiesGeneral('Model1');   
 Model1.makePlot(fspSoln1,'marginals',[],[],[2,3])
 
 %% Generate, solve and plot results for an extrinsic noise version
@@ -120,5 +123,6 @@
     1+0.2*randn];
 extrinsicModel = extrinsicSSIT(Model1,parDistributions,20);
 Model1.makePlot(extrinsicModel.averagedResults,'marginals',[],[],[2,3])
+legend('Intrinsic Only','Intrinsic + Extrinsic','Location','EastOutside')
 ##### SOURCE END #####
 --></body></html>