From 2012.igem.org

Revision as of 13:53, 21 October 2012

Yeast Growth Model

Code

data1=[1,1.3625,2.875,5.25];
t=[0,80,270,450];

fun = @(k,data) sum((exp(k*t)-data).^2);

[k, RN] = fminsearch(@(k) fun(k(1),data1)/k(2) - length(t)*log(1/(sqrt(2*pi*k(2)))),[0.005 1]);

% needs to be adapted such that acceptance rate is close to23%
kc=0.01;
acc=0;
nacc=0;
n=10000;
sample=zeros(n,1);
prob=zeros(n,1);
lh=zeros(n,1);
accepted=zeros(n,1);

kprev = k(1);
s=1;
while (s<n+1)
    if(mod(s,500)==0)
        (s)/n;
        display(['Acceptance Rate: ' num2str(acc/(nacc+acc)*100) '%'])
    end
    try
    % the step is sampled from a multivariate normal distribution and
    % scaled with kc
    kcur = kprev + kc*mvnrnd(0,0.001);
    sample(s,:) = kcur;
    % calculate the a-posteriori probability of the new sample
    prob(s) = -fun(kcur,data1)/k(2);
    if s>1
        % check whether we reject the sample or not. mind that the
        % probability is log-scaled
        if log(rand(1)) < +prob(s)-prob(s-1)  
            % update current sample
            kprev = kcur;
            % count accepted samples
            acc=acc+1;
            % update waitbar
            %waitbar((k-1)/n)
            % save current acceptance rate for post-processing
            lh(s)=acc/(nacc+acc)*100;
            s=s+1;
            %p = [kcur sigma];
            %plotETOHPROM
            %drawnow
        else
            % count not accepted samples
            nacc=nacc+1;
        end
    else
        % we need to do something else for the first sample.
        if log(rand(1)) < prob(s)+RN
            kprev = kcur;
            acc=acc+1;
            %waitbar((k-1)/n)
            lh(s)=acc/(nacc+acc)*100;
            s=s+1;
        else
            nacc=nacc+1;
        end  
    end

    catch ME
        disp(ME)
    end
end
disp(['Doubling Time: ' num2str(log(2)/k(1)) ' STD: ' num2str(std(log(2)./sample))])