function AmpVsPol % This function simulates the steady state Levchenko and Iglesias model [1] % on ellipses with increasing polarity. % % % Matt Onsum % % Reference: % [1] Levchenko A., Iglesias, P.A. Models of Eukaryotic Gradient Sensing: % Applications to Chemotaxis of Amoebae and Neutrophils, Biophys J, 2002 % 82: p 50-63 % % close all %As defined in text polarity = [1:.1:2]; n_p = length(polarity); Amp = zeros(n_p,1); for ind_p = 1:n_p %Define ellipse with polarity(ind_p) %and put into polar coordinates p = polarity(ind_p); a = sqrt(2/(p^2+1)); b = p*a; theta = linspace(-pi,pi); x = a*cos(theta); y = b*sin(theta); r = sqrt(x.^2 + y.^2); figure(1) hold on plot(x,y,'LineWidth',2); axis equal title('Set of Ellipses'); xlabel('X','Fontsize',12); ylabel('Y','Fontsize',12); %Define Linear Field of attractant %L(x,y) = a + by; c = 1; d = .4; %Apply to ellipse G1 = d*r.*sin(theta)+c; % Solve LI Model with above input %Linear Amplifier-- Epsilon =0 s0 = Iglesias02(r,c,d,0); %Nonlinear Amplifier-- Epsilon =.45 s1 = Iglesias02(r,c,d,0.45); %Calculate Amp linear [P0,S] = polyfit(G1,s0,1); alpha = P0(1); Amp0(ind_p)= alpha*min(G1)/(P0(1)*min(G1)+P0(2)); %Calculate Amp, nonlinear [P1,S] = polyfit(G1,s1,1); alpha = P1(1); Amp1(ind_p)= alpha*min(G1)/(P1(1)*min(G1) + P1(2)); end figure(2); plot(polarity,Amp0/min(Amp0),'LineWidth',2); hold on plot(polarity,Amp1/min(Amp1),'g','LineWidth',2); axis([1 2 0 2.5]) xlabel('Polarity','FontSize',12); ylabel('Amplification', 'FontSize', 12); function [r1] = Iglesias02(R,a,b,epsilon); % Steady State Solution to L&I's model % Reference %1.0 Biochem Parameters-% % 1.1 Activator Dynamics ka = 1; %S-[+ka]-> A k_a = 1; %S<-[-k_a]- A KDa = ka/k_a; % 1.2 Inhibitor Dynamics ki = 0.05; %S -[+ki]-> I k_i = 0.05; %S <-[-k_i]-I KDi = ki/k_i; D = 20; %Diffusion of I % 1.3 Lumped Parameters alpha = 1; beta = 1; gamma = 1; %2.0 Chemoattractant about ellipse theta = linspace(-pi,pi,length(R)); s = a + b*R.*sin(theta); %3.0 Steady State Values of Activator and Inhibitor in polar coordinates A = KDa*(s); I = KDi*a + ki*inv(k_i+D)*b*sin(theta); F = A./I; %4.0 distribution of response element r1* r1 = alpha*gamma*F./(beta*(1-alpha*epsilon*F));