Exercise5_interactive_parfor.m

%% Information about the application
% A MATLAB application from the field of systems biology was chosen for
% experimental runs. The application was created using SimBiology which
% extends MATLAB with functionality for modeling, simulating, and
% analyzing biochemical pathways.
%
% The application allows a user to model and simulate the dynamics of a
% simple gene regulation network, and understand the effect of
% stochasticity on the final state of the system.  A ensemble of stochastic
% runs were performed on a gene-regulation model, and the distribution of
% state was studied at any given time point. Several hundred thousand
% simulations might need to be performed to obtain a smooth probability
% distribution function, requiring several days of computation on one
% MATLAB.

% The model used in this script was adpated from the following publication :
% Kepler TB, Elston TC. Stochasticity in transcriptional regulation:
% Origins, consequences and % mathematical representations. Biophys J.
% 2001;81:31163136.

%% Serial version of the code

clear all

%% Define simulation settings

% Total number of runs
numberOfRuns = 20;

% Load SimBiology model
sbioloadproject GeneRegulation m1

% Pre-allocate memory
data   = zeros(numberOfRuns, numel(m1.Species));

%% Running an ensemble of stochastic simulations

tic

for i = 1:numberOfRuns
    data(i, :) =  simulation;
end

elapsedTimeSerial = toc;  
display(elapsedTimeSerial) 

%% Running SimBiology with parfor

% Open matlabpool using default configuration

if matlabpool('size') == 0
    matlabpool open 2
else
    warning('MATLAB:poolOpen', 'matlabpool already open')
end


%% Define simulation settings

% Pre-allocate memory
dataParfor = zeros(numberOfRuns, numel(m1.Species));


%% Running an ensemble of stochastic simulations

tic

% change the for loop to a parfor to run in parallel

parfor i = 1:numberOfRuns
    dataParfor(i, :) =  simulation;
end

elapsedTimeParallel = toc;  
display(elapsedTimeParallel) 


%% Collect and plot results from parfor

hist(dataParfor(:,1))
title('Histogram of protein state at t = 1000 s')
xlabel('Protein State')
ylabel('Frequency')

%% Calculating speedup

speedUp = elapsedTimeSerial/elapsedTimeParallel;
display(speedUp); 

%% Closing the matlabpool

if matlabpool('size') ~= 0
    matlabpool close
else
    warning('MATLAB:poolClose', 'matlabpool already closed')
end