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Team:SUSTC-Shenzhen/Modeling/matlabcode
From 2014.igem.org
matlab code
a simple example
main
% This document is the main file for simulate the multi-agent interaction
% of HIV, HSC and immune system.
% author: Rongpeng Li, Mingmeng Geng, Fan Jiang
% Date: September, 7, 2014
%Version 1.0
delete(gcf);
%Initialize variables and their explainations.
thymus = 0; % the stem cell generation capability.
hiv = 0; % the hiv counting.
high = 0; % the high-resistance T cell counting,
low = 0; % the low-resistance T cell counting.
interveneTime = 16000; % the intervene time of CRISPR mechanism
method = 0; % 1 for using CRISPR mechnism, 0 for not. At the beginning, start without CRISPR mechanism.
%Initialize parameters and their explainations.
t = 0.01; % the simulation time step.
T = 350; %the total time that the programs is going to simulate.
% parameter for thymus update.
global thymusGrowthRate; % the self-generation rate of stem cell
global N_thymus ; % the possible total thymus counting.
global thymusSelfPresure ; % the enviroment pressure for thymus growth in M-model.
global hiv2thymus ; % the hiv-caused thymus counting decreasing factor.
global highlow2thymus ; % the high and low immune cell induced increasing factor.
% parameter for hiv update.
global hivGrowthRate; %the self-generation rate of hiv virus.
global hivMutation ; % the mutation factor.
global N_hiv ; % the possible total hiv counting, can be set to be infinity.
global hivSelfPressure ; % Almost no self-pressure for hiv virus.
% parameter for high update.
global thymus2high ; %the generation rate by thymus counting.
global highResist ; % the resistance factor, can be invicible if CRISPR is 100% effective.
global decayHigh ; % the decay(death) rate from high resistant T cell to low ones.
global gainLow ; % the gain rate from low resistant T cell to high ones.
% parameter for low update.
global thymus2low; %generation rate from thymus counting.
global lowResist; % the resistance factor.
global hiv2low; % the death rate caused by hiv.
global gain4high % the gain rate by the immune decaying of high resistant T cells.
% choose parameter sets by user.
OK = 0;
while OK == 0
% set = input('Please choose the initial parameter set. 1, 2 or 3?');
set =1 ;
if set ==1
[thymusGrowthRate, N_thymus,thymusSelfPresure, hiv2thymus, highlow2thymus] =deal(0.8,0.94,0.3,0.22,0.2);
[hivGrowthRate, hivMutation, N_hiv, hivSelfPressure] = deal(8,0.15,20,0.1);
[thymus2high, highResist, decayHigh , gainLow] = deal(0.2,0.6,0.4,0.1);
[thymus2low, lowResist, hiv2low, gain4high] = deal(0.2,0.2,0.8,0); [thymus, hiv, high, low]= deal(1,0.2,0.0,0.1); OK = 1;
elseif set ==2
[thymusGrowthRate, N_thymus, thymusSelfPresure, hiv2thymus, highlow2thymus] =deal();
[hivGrowthRate, hivMutation, N_hiv, hivSelfPressure] = deal();
[thymus2high, highResist, decayHigh , gainLow] = deal();
[thymus2low, lowResist, hiv2low, gain4high] = deal();
[thymus, hiv, high, low]= deal(); OK = 1;
elseif set ==3
[thymusGrowthRate, N_thymus, thymusSelfPresure, hiv2thymus, highlow2thymus] =deal();
[hivGrowthRate, hivMutation, N_hiv, hivSelfPressure] = deal();
[thymus2high, highResist, decayHigh , gainLow] = deal();
[thymus2low, lowResist, hiv2low, gain4high] = deal(); [thymus, hiv, high, low]= deal(); OK = 1;
else
OK = 0;
end
end
% Initialize zero-filled arrays to store the data generated.
Len = floor(T/t);
[Time,Thymus,HIV,High,Low] = deal(zeros(1,Len));
%update the status
for i = 1:Len
if i < interveneTime
[Time(i),Thymus(i),HIV(i),High(i),Low(i)] = deal(t*i,thymus,hiv,high,low);
[thymus, high, low, hiv,hivMutation,highResist,lowResist ] = update(t,thymus, high, low, hiv, method );
else
[Time(i),Thymus(i),HIV(i),High(i),Low(i)] = deal(t*i,thymus,hiv,high,low);
method = 1;
[thymus, high, low, hiv, hivMutation,highResist,lowResist] = update(t,thymus, high, low, hiv, method );
end
% hivMutation
end
% Draw pictures
subplot(2,2,1)
plot(Time,Thymus,'*');
xlabel('Time')
ylabel('Thymus counting')
title('Thymus counting versus Time');
grid;
hold on;
subplot(2,2,2)
plot(Time,High,'+');
xlabel('Time')
ylabel('High-resistance T cell counting')
title('High-resistance T cell counting versus Time');
grid;
hold on;
subplot(2,2,3)
plot(Time,Low,'<');
xlabel('Time')
ylabel('Low-resistance T cell counting')
title('Low-resistance T cell counting versus Time');
grid;
hold on;
subplot(2,2,4)
plot(Time,HIV,'>');
xlabel('Time')
ylabel('HIV counting')
title('HIV counting versus Time');
grid;
saveas(gcf,'Trend Graph','bmp');
%delete(gcf);
%Draw phase graph.
update
function [thymus, high, low, hiv,hivMutation,highResist,lowResist ] = update(t,thymus, high, low, hiv, method )
% the function update all the status.
% Here are the key of the program. The following function handlers decribes
% the interaction details of the agents. You can modify them later to make
% them more physical and realistic.
global thymusGrowthRate; % the self-generation rate of stem cell
global N_thymus ; % the possible total thymus counting.
global thymusSelfPresure ; % the enviroment pressure for thymus growth in M-model.
global hiv2thymus ; % the hiv-caused thymus counting decreasing factor.
global highlow2thymus ; % the high and low immune cell induced increasing factor.
% parameter for hiv update.
global hivGrowthRate; %the self-generation rate of hiv virus.
global hivMutation ; % the mutation factor.
global N_hiv ; % the possible total hiv counting, can be set to be infinity.
global hivSelfPressure ; % Almost no self-pressure for hiv virus.
% parameter for high update.
global thymus2high ; %the generation rate by thymus counting.
global highResist ; % the resistance factor, can be invicible if CRISPR is 100% effective.
global decayHigh ; % the decay(death) rate from high resistant T cell to low ones.
global gainLow ; % the gain rate from low resistant T cell to high ones.
% parameter for low update.
global thymus2low; %generation rate from thymus counting.
global lowResist; % the resistance factor.
global hiv2low; % the death rate caused by hiv.
global gain4high % the gain rate by the immune decaying of high resistant T cells.
% To avoid duplicity of variables, I ignore the means of symbol.
f1a = @(hiv)(3*hiv); % HIV's existence,thymus counting is decreasing.
f1b = @(hiv)(3 * hiv); % HIV's existence,thymus counting is decreasing. CRISPR exsits. following 'b' means the same logic.
f2a = @(high,low)(0.01*high+0.01*low); % The high and low can boost the thymus increasing.
f2b = @(high,low)(0.01* high + 0.01*low);
f3a = @(low,high,hivMutation)((low + high) * hivMutation); % T cell as hosts can boost the increasing of HIV
f3b = @(low,high,hivMutation)(( low+ 0.3 * high)* hivMutation);
f4a = @(high,hiv,hivMutation)((1-hivMutation) * hiv * high );% high resistance ones can decrease HIV counting.
% f4a = @(high,hiv,hivMut)(0);
f4b = @(high,hiv,hivMutation)( 1.2* (1-hivMutation) * hiv * high);
f5a = @(thymus)(0.001*thymus); %high resistance ones increase due to thymus counting
f5b = @(thymus)(0.001*thymus);
f6a = @(hiv)((1- 0.4 * hiv));% HIV caused the high ones increasing slower than normal
f6b = @(hiv)(1- 0.2 * hiv);
f7a = @(high, highResist,hiv)(0.8*high*exp(-1*highResist)*hiv);% Immune decay rate for high ones due to mutation
f7b = @(high, highResist,hiv)(0.5*high*exp(-0.5*highResist)*hiv);
f8a = @(low,lowResist)(0.2*low*exp(lowResist));% high ones increase due to low ones that gain immune capacity
f8b = @(low,lowResist)(0.3*low*exp(lowResist));
f9a = @(thymus)(0.2*thymus);% low ones increase due to thymus counting
f9b = @(thymus)(0.2*thymus);
f10a = @(hiv)(1- 0.5 * hiv);% HIV caused the high ones increasing slower than normal
f10b = @(hiv)(1- 0.5 * hiv);
f11a = @(low,lowResist,hiv)(1.5*low*exp(-1*lowResist)*hiv);% death formula for low ones due to HIV counting.
f11b = @(low,lowResist,hiv)(1.5*low*exp(-1*lowResist)*hiv);
f12a = @(high,decayHigh)(0);% growth rate due to decay from high ones.
f12b = @(high,decayHigh)(0);
if method == 1 % implement with CRISPR mechnism.
thymus = thymus + t* (thymusGrowthRate*thymus*(1-thymusSelfPresure*thymus/N_thymus) - hiv2thymus*f1b(hiv) + highlow2thymus*f2b(high,low));
if thymus >1
thymus = 1;
elseif thymus < 0
%thymus =0.1; %thymus system should not be broken
end
hiv = hiv + t*(hivGrowthRate*hiv*(1-hivSelfPressure*hiv/N_hiv)*f3b(low,high,hivMutation) - f4b(high,hiv,hivMutation));
if hiv >1
hiv = 1;
elseif hiv < 0
hiv =0.05; % hiv can not be extinguished becasue there are hiv in other tissues.
end high = high + t*( thymus2high*f5b(thymus)*f6b(hiv) - decayHigh*f7b(high, highResist, hiv ) + gainLow*f8b(low,lowResist));
if high >1
high = 1;
elseif high < 0
high =0;
end
low = low + t* ( thymus2low*f9b(thymus)*f10b(hiv) - hiv2low*f11b(low,lowResist,hiv) + gain4high*f12b(high,decayHigh) );
if low >1
low = 1;
elseif low < 0
low =0;
end
highResist = 0.8 * thymus;
hivMutation = 0.1*(high/( high + low ) + 0.3*rand(1)); % update add random elements to the mutation level
elseif method ==0 %implement without CRISPR mechnism.
thymus = thymus + t* (thymusGrowthRate*thymus*(1-thymusSelfPresure*thymus/N_thymus) - hiv2thymus*f1a(hiv) + highlow2thymus*f2a(high,low));
if thymus >1
thymus = 1;
elseif thymus < 0
thymus =0;
end hiv = hiv + t*(hivGrowthRate*hiv*(1-hivSelfPressure*hiv/N_hiv)*f3a(low,high,hivMutation) - f4a(high,hiv,hivMutation) );
if hiv >1
hiv = 1;
elseif hiv < 0
hiv =0;
end % high = high + t*( thymus2high*f5a(thymus)*f6a(hiv) - decayHigh*f7a(high, highResist, hiv ) + gainLow*f8a(low,lowResist));
high = 0;
if high >1
high = 1;
elseif high < 0
high =0;
end low = low + t* ( thymus2low*f9a(thymus)*f10a(hiv) - hiv2low*f11a(low,lowResist,hiv) + gain4high*f12a(high,decayHigh) );
if low >1
low = 1;
elseif low < 0
low =0;
end
hivMutation = 0.1*(high/( high + low ) + 0.3*rand(1)); % update add random elements to the mutation level
end
% update some important global factors. If necessary, many other global
% variables can be updated here.
end
