function populationSunitinibFits

plotSunitinibData

groupColors = [0 0 0; 0 0 255; 255 0 0;0 206 209;255 165 0 ; 0 0 255; 199 21 133]/255;

colTIME = [unique(G1(:,2));unique(G3(:,2))];

%% Calculate with Means
% colG1 = nanmean(G1ind(:,:,3),1)';
% colG3 = nanmean(G3ind(:,:,3),1)';

%% Calculate with Medians
colG1 = nanmedian(G1ind(:,:,3),1)';
colG3 = nanmedian(G3ind(:,:,3),1)';
varG1 = nanstd(G1ind(:,:,3),1)';
varG3 = nanstd(G3ind(:,:,3),1)';
 
D0 = 1.76;
K0 = 7.43;
lambda0 = 1.02;
bb0 = 0.00168;
ps = 2.12;
betas = 0.0237;
 
b0 = [D0 K0 lambda0 bb0 ps betas];
 
Y = [colG1;colG3];

yhat = runModelSutent(b0,colTIME);

close all
hFig = figure;


% Set Figure Properties
hold all
fontSize = 26;
set(gcf,'defaultlinelinewidth',1,'DefaultAxesFontSize', fontSize,'DefaultAxesFontName', 'Times')
set(gca,'fontsize',fontSize,'fontname','times')
set(hFig, 'Position', [10 800 1000 560])
set(gca,'XTick',[0:20:100]) 
set(gca,'yTick',[0:2:16])

subplot(2,4,[1 2 5 6])
hold all

h(1) = plot(colTIME(1:19),yhat(1:19),'-','color',groupColors(1,:),'LineWidth',4);
h(2) = plot(colTIME(1:19),colG1,'ko','LineWidth',2,...
         'Color','k','MarkerEdgeColor','k','MarkerFaceColor','k','MarkerSize',8);

h(3) = plot(colTIME(20:46),yhat(20:46),'--','color',groupColors(2,:),'LineWidth',4);
h(4) = plot(colTIME(20:46),colG3,'ko','LineWidth',2,...
         'Color',groupColors(2,:),'MarkerEdgeColor','k','MarkerFaceColor',groupColors(2,:),'MarkerSize',8);

legend([h(1) h(3)], {'G1 : Control','G2 : Sunitinib'},'location','northwest');
axis square
xlabel('Time (days)')
ylabel('Mean Tumor Diameter (mm)')
ylim([0 16])

xa = [41 37]; % X-CooFrdinates in data space 
ya = [6.5 7.8]; % Y-Coordinates in data space
[xaf,yaf] = ds2nfu(xa,ya); %Convert to normalized figure units
annotation('textarrow',xaf,yaf,'string','Day 0 ','fontweight','bold',...
    'fontsize',fontSize,'headwidth',14,'headlength',14,'fontName','times')

% Run VPC code
cd('./SunitinibVPC/code')
generateVPCs
end
 
function yhat = runModelSutent(beta,x)
 
V0 = beta(1); 
K0 = beta(2);
lambda = beta(3);
b = beta(4);
ps = beta(5);
betas = beta(6);
 
treattime = 37;
 
x1 = x(1:19,1); % Time values G1
x3 = x(20:46,1); % Time values G3
 
S0 = [0 V0 K0];
 
param = [lambda;...
    b;...
    ps;...
    betas];
 
tspan = [0 treattime];
 
options_ode15s=odeset('Vectorized','{on,on}');
 
G =  deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',x1(x1<=tspan(end)));
GSPAN = deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',tspan(end));
 
YY1 = G(1,:);
Y1end = GSPAN(1,end);
 
YY2 = G(2,:);
Y2end = GSPAN(2,end);
 
YY3 = G(3,:);
Y3end = GSPAN(3,end);
 
ttime = treattime;
 
% For group 1
tspan = [ttime ceil(x1(end))];
 
if tspan(1) ~= tspan(2)
    
    S0 = [Y1end ; Y2end ; Y3end];
    
    H =  deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',x1(x1>tspan(1)));
    
    Y1 = cat(2,YY1,H(1,1:end));
    Y2 = cat(2,YY2,H(2,1:end));
    Y3 = cat(2,YY3,H(3,1:end));
 
end
 
yhat = Y2';
 
% For group 3
S0 = [0 V0 K0];
tspan = [0 treattime];
 
G =  deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',x3(x3<=tspan(end)));
GSPAN = deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',tspan(end));
 
YY1 = G(1,:);
Y1end = GSPAN(1,end);
 
YY2 = G(2,:);
Y2end = GSPAN(2,end);
 
YY3 = G(3,:);
Y3end = GSPAN(3,end);
 
ttime = treattime;
nbcures = 12;
 
for j=1:nbcures-1
    
    ttime = treattime-1+j;
    
    tspan = [treattime-1+j treattime+j];

    S0 = [Y1end+1 ; Y2end ; Y3end];
    
    time1 = x3(x3>tspan(1));
    time2 = x3(x3<=tspan(end));
    timei = intersect(time1,time2);
    
    if ~isempty(timei)
        H = deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',timei);
        HSPAN = deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',tspan(end));
        
        Y1 = cat(2,YY1,H(1,:));
        Y1end = HSPAN(1,end);
        
        Y2 = cat(2,YY2,H(2,:));
        Y2end = HSPAN(2,end);
        
        Y3 = cat(2,YY3,H(3,:));
        Y3end = HSPAN(3,end);
        
        YY1 = Y1;
        YY2 = Y2;
        YY3 = Y3;
        
    end
    
    HSPAN = deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',tspan(end));
    
    Y1end = HSPAN(1,end);
    Y2end = HSPAN(2,end);
    Y3end = HSPAN(3,end);
    
end
 
ttime = 48;
 
tspan = [48 ceil(x3(end))];
 
if tspan(1) ~= tspan(2)
    
    S0 = [Y1end+1 ; Y2end ; Y3end];
    
    H =  deval(ode15s(@modelSutent,tspan,S0,options_ode15s,param)',x3(x3>tspan(1)));
    
    Y1 = cat(2,YY1,H(1,1:end));
    Y2 = cat(2,YY2,H(2,1:end));
    Y3 = cat(2,YY3,H(3,1:end));
    
end
 
yhat = cat(1,yhat,Y2');
 
end
 
function dSdt = modelSutent(t,S,param)
  
    lambda = param(1);
    b = param(2);
    kde = param(3);
    beta = param(4);
    
    C = S(1);
    V = S(2);
    K = S(3);
    
    alfa = 0.1;
    
   dCdt = -kde*C; 
   dVdt = lambda*V*(1-((V/K)^alfa));
   dKdt = b*(V^2) - beta*kde*C*K; 
   
   dSdt = [dCdt; dVdt; dKdt];
  
end
 
function [varargout] = nlinfitsome(fixed,x,y,fun,beta0,varargin)
% This function was downloaded from MATHWORKS (www.mathworks.com)
 
% "fixed" indicates which values of beta0 should not change
% Get separate arrays of coefficients to fix and to estimate
bfixed = beta0(fixed); beta0 = beta0(~fixed);
 
% Estimate only the non-fixed ones
[varargout{1:max(1,nargout)}] = nlinfit(x,y,@localfit,beta0,varargin{:});
 
% Re-create array combining fixed and estimated coefficients
b(~fixed) = varargout{1};
b(fixed) = bfixed;
varargout{1} = b;
 
% Nested function takes just the parameters to be estimated as inputs
% It inherits the following from the outer function:
%   fixed = logical index for fixed elements
%   bfixed = fixed values for these elements
    function y=localfit(beta,x)
 
        b(fixed) = bfixed;
        b(~fixed) = beta;
        y = fun(b,x);
    end
end