Team:Colombia/Scripting
From 2014.igem.org
(Difference between revisions)
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%xlabel('Time [h]'); % | %xlabel('Time [h]'); % | ||
%ylabel('Concentration [nanomolar]'); % | %ylabel('Concentration [nanomolar]'); % | ||
- | %title('Response'); | + | %title('Response'); % |
% | % | ||
y=[R(95*12),R(100*12),R(105*12),R(110*12),R(115*12),R(120*12),R(125*12),R(130*12),R(135*12),R(140*12),R(145*12),R(150*12),R(155*12),R(160*12),R(165*12),R(170*12)]; | y=[R(95*12),R(100*12),R(105*12),R(110*12),R(115*12),R(120*12),R(125*12),R(130*12),R(135*12),R(140*12),R(145*12),R(150*12),R(155*12),R(160*12),R(165*12),R(170*12)]; | ||
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function y=input_Metropolis(t) | function y=input_Metropolis(t) | ||
%--------------------------------------------% | %--------------------------------------------% | ||
- | % CAI-1 (C) pulse simulation | + | % CAI-1 (C) pulse simulation % |
%--------------------------------------------% | %--------------------------------------------% | ||
if t>100 && t<200 | if t>100 && t<200 | ||
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%--------------------------------------------% | %--------------------------------------------% | ||
% | % | ||
- | kcc=1; | + | kcc=1; % CAI1 and CqsS coupling Rate |
- | kcd=1; | + | kcd=1; % CAI1 and CqsS decoupling Rate |
- | kcu=2; | + | kcu=2; % Michaelis Menten Constant for the phoshporylation CqsS-LuxU |
- | kuc=1; | + | kuc=1; % Michaelis Menten Constant of the phosphase process CqSS-LuxU |
- | kuo=2; | + | kuo=2; % Michaelis Menten Constant for the phoshporylation LuxU-LuxO |
- | kou=1; | + | kou=1; % Michaelis Menten Constant of the phosphase process LuxU-LuxO |
- | kttr=5; | + | kttr=5; % Dimerization rate of TetR ---[Sensitivity Analysis]--- [5-50/5] |
% | % | ||
- | gcs=0.5; | + | gcs=0.5; % CqsS protein decay rate |
- | gcsa=1; | + | gcsa=1; % CqsS* protein decay rate |
- | guf=0.12; | + | guf=0.12; % LuxU* protein decay rate |
- | gu=0.65; | + | gu=0.65; % LuxU protein decay rate |
- | gof=0.12; | + | gof=0.12; % LuxO* protein decay rate |
- | go=0.12; | + | go=0.12; % LuxO protein decay rate |
- | gtr=0.12; | + | gtr=0.12; % Ptet Repressor protein decay rate |
- | gttr=0.12; | + | gttr=0.12; % Ptet2 dimer Repressor decay rate |
- | gta=0.12; | + | gta=0.12; % Ptet Activator protein decay rate |
- | gr=0.12; | + | gr=0.12; % Response molecule decay rate |
% | % | ||
- | acs=5; | + | acs=5; % CS basal production rate |
- | au=5; | + | au=5; % LuxU basal production rate |
- | ao=5; | + | ao=5; % LuxO basal production rate |
- | ar=0; | + | ar=0; % response molecule basal production rate |
- | atr=1; | + | atr=1; % TR basal production rate ---[Sensitivity Analysis]--- [0.5-5/0.5] |
- | ata=0.1; | + | ata=0.1; % TA basal production rate ---[Sensitivity Analysis]--- [0.1-10/0.5] |
% | % | ||
- | bcu=1; | + | bcu=1; % Phosphorylation rate of CqsS to LuxU |
- | buc=0.1; | + | buc=0.1; % Phosphase rate of CqsS to LuxU |
- | buo=3.2; | + | buo=3.2; % Phosphorylation rate LuxU-LuxO |
- | bou=1.58; | + | bou=1.58; % Phosphase rate LuxU-LuxO |
- | btr=5; | + | btr=5; % Maximum rate of TR expression ---[Sensitivity Analysis]--- [1-10/1] |
- | bta=5; | + | bta=5; % Maximum rate of TA expression ---[Sensitivity Analysis]--- [1-10/1] |
% | % | ||
- | ho=1.5; | + | ho=1.5; % LuxO*- DNA coupling rate ---[Sensitivity Analysis]--- [0.5-2.5/0.5] |
- | htr=5; | + | htr=5; % TRdomain-DNA coupling rate ---[Sensitivity Analysis]--- [1-10/1] |
% | % | ||
- | n=2; | + | n=2; % Hill's coefficient ---[Sensitivity Analysis]--- [1-4/0.5] |
% | % | ||
- | h=400; | + | h=400; % Maximum time |
- | m=1/10; | + | m=1/10; % Step lenght [h] |
% | % | ||
- | param= (5:50:5); | + | param= (5:50:5); % Range for the parameter to vary, evaluate at least 2 orders of magnitude chose maximum 20 points. |
- | rta=zeros(3,length(param)); | + | rta=zeros(3,length(param)); % Array that saves the canche in concentration of LuxOF, the response and time of response. |
- | for w=1:length(param) | + | for w=1:length(param) % Varying the value of the parameter |
- | kttr= param(w); | + | kttr= param(w); % New parameter value (This MUST be changed everytime a parameter is being evaluated) |
% | % | ||
% | % | ||
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% | % | ||
% | % | ||
- | conInd=[1,0,1,1,1,1,0,1,1,1]; | + | conInd=[1,0,1,1,1,1,0,1,1,1]; % |
- | l=(0:m:h)'; | + | l=(0:m:h)'; % Time vector |
% | % | ||
- | x=zeros(length(l),length(conInd)); | + | x=zeros(length(l),length(conInd)); % Variable's Matrix, the variables in each column and time in each row |
- | C=zeros(1,length(l)); | + | C=zeros(1,length(l)); % zero vector to be filled |
- | x(1,:)=conInd; | + | x(1,:)=conInd; % |
% | % | ||
for k=1:length(l)-1 | for k=1:length(l)-1 | ||
- | xk=x(k,:); | + | xk=x(k,:); % Capture of the last value in the matrix, the actual variable values |
- | k1=difeq(l(k),xk); | + | k1=difeq(l(k),xk); % First slope of the RK4 method |
- | k2=difeq(l(k)+m/2,xk+(m/2*k1)'); | + | k2=difeq(l(k)+m/2,xk+(m/2*k1)'); % Second slope of the RK4 method |
- | k3=difeq(l(k)+m/2,xk+(m/2*k2)'); | + | k3=difeq(l(k)+m/2,xk+(m/2*k2)'); % Third slope of the RK4 method |
- | k4=difeq(l(k)+m,xk+(m*k3)'); | + | k4=difeq(l(k)+m,xk+(m*k3)'); % Fourth slope of the RK4 method |
xk1=xk+m/6*(k1+2*k2+2*k3+k4)'; % New value calculation for the variables | xk1=xk+m/6*(k1+2*k2+2*k3+k4)'; % New value calculation for the variables | ||
- | xk2=zeros(1,length(xk1)); | + | xk2=zeros(1,length(xk1)); % |
% | % | ||
for p=1:length(xk1) | for p=1:length(xk1) | ||
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end | end | ||
end | end | ||
- | x(k+1,:)=xk2; | + | x(k+1,:)=xk2; % Actualizacion del nuevo vector de variables en la matriz |
end | end | ||
% | % | ||
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% | % | ||
% | % | ||
- | if (R(it)-R(1011))/(R(1011))> 1.5 | + | if (R(it)-R(1011))/(R(1011))> 1.5 % If the response changes 1.5-fold relative to previous iteration |
% | % | ||
rta(1,w)= l(it)-100; | rta(1,w)= l(it)-100; |
Revision as of 04:14, 16 October 2014
Scripting
Feel free to expand and scroll through the text boxes in order to further examine the code.
Deterministic Model
This code creates the differential equations governing the concentration dinamics of each protein in our model, finds the steady state solutions and then solves them using the numerical aproximation method Runge-Kutta
Metropolis-Hastings Algorithm
This code determines which set of values for missing parameters yield the most desirable response.
Sensitivity Analysis
This code points out which are the critical parameters in the system (those that change the response drastically).
Stochastic Model
Sometimes probabilistic models better describe certain systems