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LoosCLoosC
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add figure routines
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‎Matlab_figures/Benchmark_analysis.m‎

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% This script generates the plots for the analyses of the paper "Benchmark
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% Problems for Dynamic Modeling of Intracellular Processes".
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% Load results for the checks of convexity in linear and log scale.
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load('Benchmark_paper_convex.mat','Convex_analysis');
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% Load fitting results of all models
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loadOptimizationAnalysis
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% Load table with all the characteristics of the
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% models including number of data points, observables, parameters, etc.
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load('Benchmark_paper_modelcharacteristics.mat','modelcharacteristics');
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%% Comparison of performance criterion linlog vs. log for different thresholds to consider a start as converged
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chi2_threshold = 1e-1;
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plot_Benchmark('convergence-linloglog',chi2_threshold)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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% if ~(exist('Manuscript_figures'))
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% mkdir('Manuscript_figures')
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% end
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% print('-depsc',['Manuscript_figures/Fig2A'])
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chi2_threshold = 10;
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plot_Benchmark('convergence-linloglog',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Supplement_figures/linlog_threshold10'])
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chi2_threshold = 50;
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plot_Benchmark('convergence-linloglog',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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% if ~(exist('Supplement_figures'))
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% mkdir('Supplement_figures')
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% end
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%print('-depsc',['Supplement_figures/linlog_threshold50'])
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%% Comparison of convexity between different sampling and parameter transformation strategies
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plot_Benchmark('convex')
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 5 6.5])
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%print('-depsc',['Manuscript_figures/Fig2C'])
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%% Interior-point vs. trust-region-reflective performance metrics for different thresholds
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chi2_threshold = 1e-1;
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plot_Benchmark('trust-ip',chi2_threshold)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Manuscript_figures/Fig3'])
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chi2_threshold = 0.5;
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plot_Benchmark('trust-ip',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Supplement_figures/ip_trust_threshold0_5'])
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chi2_threshold = 1;
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plot_Benchmark('trust-ip',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Supplement_figures/ip_trust_threshold1'])
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chi2_threshold = 10;
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plot_Benchmark('trust-ip',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Supplement_figures/ip_trust_threshold10'])
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chi2_threshold = 50;
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plot_Benchmark('trust-ip',chi2_threshold)
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title(['threshold = ' num2str(chi2_threshold)],'FontSize',7)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Supplement_figures/ip_trust_threshold50'])
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%% Iterations needed in optimization vs. parameter number
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plot_Iterations(0,modelcharacteristics,6)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Manuscript_figures/Fig4A'])
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%% Time needed for optimization vs. parameter number
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plot_Iterations(1,modelcharacteristics,6)
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 8 8])
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%print('-depsc',['Manuscript_figures/Fig4B'])
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%% Waterfall plots for the Supplementary Information
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plot_WaterfallScatter
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%% Convexity plots for the Supplementary Information
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plot_Convexity_suppl
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set(gcf, 'PaperUnits','centimeters', 'PaperPosition',[0 0 16 15])
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%print('-depsc',['Supplement_figures/convexity'])

‎Matlab_figures/Benchmark_paper_convex.mat‎

86.7 MB
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‎Matlab_figures/Benchmark_paper_modelcharacteristics.mat‎

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‎Matlab_figures/Benchmark_paper_optimization_1.mat‎

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‎Matlab_figures/Benchmark_paper_optimization_2.mat‎

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‎Matlab_figures/loadModelColors.m‎

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% Colors for the benchmark models
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colors = [
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245,184,255; % Bachmann
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0,139,228; % Becker
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172,82,0; % Beer
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96,21,111; % Boehm
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0,109,64; % Brannmark
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42,212,90; % Bruno
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172,22,0; % Chen
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255,214,170; % Crauste
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163,252,195; % Fiedler
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162,141,0; % Fujita
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255,179,0; % Hass
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173,211,0; % Isensee
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0,168,161; % Lucarelli
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208,41,155; % Merkle
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0,70,148; % Raia
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255,186,200; % Schwen
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255,38,16; % Sobotta
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107,243,246; % Swameye
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255,245,0; % Weber
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138,56,255]./255; % Zheng
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% Colors for the convexity plots
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colors_convexity = [17,122,218;
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157,77,203;
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238,48,117;
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255,117,16]./255;

‎Matlab_figures/loadOptimizationAnalysis.m‎

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% Load and merge results of optimization analysis
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load('Benchmark_paper_optimization_1.mat','Optimization_analysis_part1');
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load('Benchmark_paper_optimization_2.mat','Optimization_analysis_part2');
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global Optimization_analysis
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fn1=fieldnames(Optimization_analysis_part1);
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fn2=fieldnames(Optimization_analysis_part2);
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for i = 1:10
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Optimization_analysis.(fn1{i}) = Optimization_analysis_part1.(fn1{i});
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Optimization_analysis.(fn2{i}) = Optimization_analysis_part2.(fn2{i});
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end

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