Team:Colombia Uniandes/Scripting

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  <li class="active"><a href="https://2013.igem.org/Team:Colombia_Uniandes/Scripting">Scripting</a></li>
 
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<li><a href="https://2013.igem.org/Team:Colombia_Uniandes/Scripting#chimi">Glucocorticoid detection system</a></li>
 
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  <li><a href="https://2013.igem.org/Team:Colombia_Uniandes/Scripting#nico">Nickel removal system</a></li>
 
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<h2>Scripting</h2>
<h2>Scripting</h2>
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<h3 id="chimi"> Glucocorticoid Detection System </h3>
<h3 id="chimi"> Glucocorticoid Detection System </h3>
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      Deterministic model
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<h4>Equations</h4>
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===Deterministic model===
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====Equations====
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  function y = EcuacionesGluco(t,x)
  function y = EcuacionesGluco(t,x)
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====Equation solver====
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      Equation solver
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<h4>Equation solver</h4>
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  plot(l,GRI)%,l,GRO)
  plot(l,GRI)%,l,GRO)
  legend('GRI')%,'Glucocorticoid')
  legend('GRI')%,'Glucocorticoid')
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===Stochastic===
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    Stochastic
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<h4>Stochastic</h4>
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  %Stochastic Simulation of the Chemical Reactions of the Glucocorticoid
  %Stochastic Simulation of the Chemical Reactions of the Glucocorticoid
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  <h3 id="nico"> Nickel removal system </h3>
  <h3 id="nico"> Nickel removal system </h3>

Revision as of 14:59, 26 September 2013


Scripting

Glucocorticoid Detection System

Deterministic model

Equations

function y = EcuacionesGluco(t,x) global gammaGR mGRIR mCC deltaGRI alfaR deltaR deltaCC betaCC k n deltaS H % %---------Parameters------% % % GRO=funcImpulso(t); % %------ Variables%------% % GRI= x(1); %Glucocorticoid inside the cell R=x(2); %Receptor in the cytoplasm CC=x(3); %Receptor -Glucocorticoid complex V=x(4); %Violacein % % %---Equations---% dGRI=gammaGR*(GRO-GRI)-mGRIR*GRI*R+mCC*CC-deltaGRI*GRI; dR=alfaR-mGRIR*GRI*R+mCC*CC-deltaR*R; dCC=mGRIR*GRI*R-mCC*CC-deltaCC*CC-(betaCC*CC.^n)/(k^n+CC.^n);%Revisar dV=H*(betaCC*CC^n)/(k^n+CC^n)-deltaS*V; % y1(1)=dGRI; y1(2)=dR; y1(3)=dCC; y1(4)=dV; % % y= y1'; % end
Equation solver

Equation solver

%global gammaGR mGRIR mCC deltaGRI alfaR deltaR deltaCC betaCC k n deltaS H % % gammaGR= 0.1; %Diffussion rate of glucocorticoid inside the cell (mm/min) mGRIR=1.080e-3; % GRI-R complex formation kinetic constant (1/umol min) mCC=1.14*10^-8; %GRI-R Complex formation reverse kinetic constant (1/min) deltaGRI=0.00833; %Glucocorticoids Destruction rate inside the cell (1/min) alfaR= 0.8e3; %Basal production rate of the receptor (umol/min) deltaR=0.004166; %Receptor destruction rate inside the cell (1/min) deltaCC=0.004166; % GRI-R complex Destruction rate (1/min) betaCC=0.5e3; % GRI-R complex maximum expression rate (umol/min) k=0.05e3; %Hill's constant for the GRI-R complex dimmer binding to his respective region (umol) n=2; %Hill coefficient (cooperation constant) deltaS=0.04166; %Signal destruction rate (1/min) H=2; %Correction constant for the signal % % % h=60; %maximum time m=0.01; %step length [s] t=0:m:h; %time vector % xi=[0 0 0 0]; % y=fsolve(@CondIndGluco,xi,optimset('algorithm','levenberg-marquardt','maxiter',100000,'tolfun',1e-9)); % conInd=y; assignin('base','conInd',conInd); l=(0:m:h)'; %Vector de tiempo % x=zeros(length(l),length(conInd)); %Matriz de variables, en las columnas varia %la variable y en las filas varia el tiempo % GRO=zeros(1,length(l)); % x(1,:)=conInd; % for u=1:length(l)-1 % xk=x(u,:); %Takes the last position of the matrix (i.e. the actual values of the variables) % % k1=EcuacionesGluco(l(u),xk); %first slope of the RK4 method % k2=EcuacionesGluco(l(u)+m/2,xk+(m/2*k1)'); %second slope of the RK4 method % k3=EcuacionesGluco(l(u)+m/2,xk+(m/2*k2)'); %third slope of the RK4 method % k4=EcuacionesGluco(l(u)+m,xk+(m*k3)'); %ffourth slope of the RK4 method % % xk1=xk+m/6*(k1+2*k2+2*k3+k4)'; %new vectors for the variables % % % xk2=zeros(1,length(xk1)); % % % for p=1:length(xk1) % % if(xk1(p)<0.00000001) % % xk2(p)=0; % else % % xk2(p)=xk1(p); % end % % end % % % x(u+1,:)=xk2; %Actualization of the new vector of variables int the martrix % % % % % end % for j=1:length(l) % % if (l(j)<(10) || l(j)>(30)) % % GRO(j)=155; % % else % % GRO(j)=155*1.3; % % % end % % end % GRI=x(:,1); R=x(:,2); CC=x(:,3); V=x(:,4); % % figure(1) plot(l,R)%,l,GRO)%,l,CC,l,V) legend('Receptor')%,'Glucocorticoid') %, 'Complex', 'Signal') xlabel('Time') ylabel('Concetration (micromolar)') title('Glucocorticoid model') % figure(2) plot(l,CC)%,l,GRO) legend('Complejo')%,'Glucocorticoid') % figure(3) plot(l,V)%,l,GRO) legend('Senal')%,'Glucocorticoid') % figure(4) plot(l,GRI)%,l,GRO) legend('GRI')%,'Glucocorticoid') %
Stochastic

Stochastic

% %Stochastic Simulation of the Chemical Reactions of the Glucocorticoid %System % % %%----Parameters of the System---- global gammaGR mGRIR mCC deltaGRI alfaR deltaR deltaCC betaCC k n deltaS H % %%-----Initial Conditions of the glucocorticoid system----- %Run CondIndGluco.m with the parameters of the system. % con=condiciones(); % GRO_0= round(10000); %Glucocorticoid Outside the Cell GRI_0 = round(con(1)); %Glucocorticoid Inside the Cell R_0= round(con(2)); %Receptor CC_0= round(con(3)); %Receptor- Glucocorticoid Complex V_0= round(con(4));%Signal % %%----Simulation Constants---- numCells = 10; numEvents= 60000; numCurves = 5; % Restriction(numCurves

Nickel removal system

===Deterministic model=== ====Equations==== % function y = EqNick(x,t) % %--Parameters---% % global gammaN Kp beta Kd Kx alfaR deltaR Kt deltaT n alfaP deltaP % % % No=x(1); % Niquel outside the cell % %------ Variables%------% % Ni= x(2); %Nickel Inside the cell R=x(3); %RcnR (Repressor) T=x(4); %RcnR Tetramer P=x(5); %Porine % % %---Equations---% % dNo=-gammaN*(No-Ni) - Kp*P*No; dNi=gammaN*(No-Ni) + Kp*P*No- beta/(1 + (T/(Kd*(1+(Ni/Kx))^n))); dR=alfaR-deltaR*R - Kt*R^4; dT= Kt*R^4 - deltaT*T - beta/(1 + (T/(Kd*(1+(Ni/Kx))^n))); dP=alfaP - deltaP*P + beta/(1 + (T/(Kd*(1+(Ni/Kx))^n))); % y1(1)=dNo; y1(2)=dNi; y1(3)=dR; y1(4)=dT; y1(5)=dP; % y= y1'; % end ====Equation solver==== % % clear all clc % % % %---------Parameters------% % % % global gammaN Kp beta Kd Kx alfaR deltaR Kt deltaT n alfaP deltaP % gammaN=0.5034e-4; %Diffussion rate of Nickel (1/min) % Kp=0.000634; %Dynamic constant for the entrance of nickel to the cell % beta=0.166; %Porine maximum expression rate (nM/min) % Kd=276e-3; %Association constant DNA and repressor (nM) % Kx=25e-3; %Association constant of the repressor with nickel (nM) % alfaR= 5; %Represor basal production rate (nM/min) % deltaR=1/1200; % Represor destruction rate (1/min) % Kt=820e-3;% Rate constant for the formation of the tetramer (Giraldo et al) % deltaT=1/1200; %Tetramer destruction rate (!/min) % n=1; %Hill coefficient (cooperation constant) % alfaP=0.031; %Porine basal production rate (nM/min) % deltaP=1/1200;%Porine destruction rate (1/min) % % % yo=[0 0 0 0]; % con=fsolve(@(x)CondIniciales(x),yo, optimset('display','iter','MaxIter',1000000,'algorithm','levenberg-marquardt','tolfun',1e-9)); % assignin('base','ini',con); % % cond=abs(con); % h=30; %Tiempo maximo % m=0.01; %Paso l=(0:m:h);%Vector de tiemp % condI=[9.88e3 cond]; x=zeros(length(l),length(condI)); x(1,:)=condI; % for k=1:length(l)-1 % xk=x(k,:); %Captura de la ultima posicion de la matirz, es decir, los valores actuales de las variables % k1=EqNick(xk,l(k)); %Primera pendiente del metodo de RK4 k2=EqNick(xk+(m/2*k1)',l(k)+m/2); %Segunda pendiente del metodo de RK4 k3=EqNick(xk+(m/2*k2)',l(k)+m/2); %Tercera pendiente del metodo de RK4 k4=EqNick(xk+(m*k3)',l(k)+m); %Cuarta pendiente del metodo de RK4 % xk1=xk+m/6*(k1+2*k2+2*k3+k4)'; %Calculo de nuevos valores para las %variables % %xk1=xk+m*ecuaDif(l(k),xk)'; %Method of Newton % xk2=zeros(1,length(xk1)); % % for p=1:length(xk1) % if(xk1(p)<0.00000001) % xk2(p)=0; else % xk2(p)=xk1(p); end % end % % x(k+1,:)=xk2; %Actualizacion del nuevo vector de variables en la matriz % % % % end % No=x(:,1); Ni=x(:,2); assignin('base','No',No); assignin('base','Nistable',Ni(length(Ni))); disp(Ni(length(Ni))) R=x(:,3); assignin('base','Rstable',R(length(R))); T=x(:,4); assignin('base','Tstable',T(length(T))); P=x(:,5); assignin('base','Pstable',P(length(P))); cond=[R(length(R)) T(length(T)) P(length(P))]; assignin('base','cond',cond); figure(1) plot(l,No,l,P) legend('No','P') xlabel('Time (min)') ylabel('Concentration (nM)') figure(2) plot(l,Ni) legend('Ni') xlabel('Time (min)') ylabel('Concentration (nM)') figure(3) plot(l,R) legend('R') xlabel('Time (min)') ylabel('Concentration (nM)') figure(4) plot(l,T) legend('T') xlabel('Time (min)') ylabel('Concentration (nM)') figure(5) plot(l,P) legend('P') xlabel('Time (min)') ylabel('Concentration (nM)') % ===Stochastic===

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