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model_p53_script.m
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clear all;
close all;
addpath(genpath('lib'))
% model name
name='p53';
% crating model equations based on model equations; should be uncommented after used once
create(name);
addpath(genpath(['models/',name]))
% initial condition
y0(1)=0;
y0(2)=0;
y0(3)=0;
y0(4)=1;
y0(5)=1;
y0(6)=1;
y0(7)=0;
y0(8)=0;
y0(9)=0;
% plotting model trajectories. Parametrs are as follows: model name, number
% of observations, time distance between observations, time of initial
% observation,initial conditions, vector with indices of observed variables
plottraj(name,20,1,8,y0,[1, 2, 3])
% Calculating Fisher Information Matrices (FIMs). Parametrs are as follows: model name, number
% of observations, time distance between observations, time of initial
% observation,initial conditions, vector with indices of observed
% variables, measurement error variance, 'All@ for all three data types,
% 'TRUE' for logarithmic parametrisation
F=Fisher(name,20,1,8,y0,[1, 2, 3],10,'All','T');
% extracting the FIMs for three data types
FIM_TS=F{1};
FIM_TP=F{2};
FIM_DT=F{3};
% Contour plot of the FIM for time-series (TS) and deterministic model (DT) for
% parameters 1 and 2
figure
i=1;
j=2;
plotdFMcc(name, 1,1,FIM_TS,FIM_DT,i,j,'TS','DT');
% The same as above but as a heatmap
figure
plotdFM(name, 1,1,FIM_TS,FIM_DT,i,j);
% FIMs for all parameters as heatmap
plotdFMAll(name, 1,1,FIM_TS,FIM_DT); % First element plotted (TS) polted as heatmap, second (DT) as contourplot
% The same as above but as a contour plot
plotdFMAllcc(name, 1,1,FIM_TS,FIM_DT,'TS','DT')
% sensitivity coeffitients
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculating sensitifity coeffitients for based on FIM for TS data
SC1=sensitivities(FIM_TS);
% Plotting sensitifity coeffitients for all data types
sensitivitiesAll(name, F);
% plotting Cramer-Rao bounds for all parameters for TS and time-point data (TP)
diaginv(name,F);
% plotting contributions of individual parameters to each of the eigenvalues
decomp(name,F);