Background

The model you create in this example simulates the first-order elimination of a protein that is produced at a specified rate. The production rate contains two discontinuities. To simulate the model accurately, you must create events that are triggered at the time of the discontinuity.

The production rate has three "modes" of production, as illustrated in the following plot:

plot([0 3 3 6 6 10], [5 5 3 3 0 0]);
ylim([-0.5 5.5]);
xlabel('Time');
ylabel('Rate');
title('Discontinuous Protein Production Rate');

Initially ("Mode 1"), the production rate is a constant value of 5. From 3 to 6 seconds ("Mode 2"), the production rate is 3. After 6 seconds ("Mode 3"), the production rate is 0. These production rates are implemented in a MATLAB function discontSimBiologyRateFunction.m, which requires two arguments, simulation time and production mode.

In this example, you will add events to the model to change the mode of protein production. This approach ensures that discontinuities in the model occur only via events, which further ensures that the ODE solver accurately calculates the numerical behavior near the discontinuities.

Note that to simulate a model accurately you must use events to handle any discontinuity, whether related to function values or their derivatives.

Construct the Model, Compartment, and Species

model = sbiomodel('discontinuous rate');
central = addcompartment(model,'Central');
addspecies(central,'protein')

ans = 
   SimBiology Species Array

   Index:    Compartment:    Name:      Value:    Units:
   1         Central         protein    0               

Construct the Reaction for First-Order Elimination

reaction1 = addreaction(model,'protein -> null')

reaction1 = 
   SimBiology Reaction Array

   Index:    Reaction:      
   1         protein -> null

ke = addparameter(model,'ke', 0.5);
kineticLaw1 = addkineticlaw(reaction1,'MassAction');
kineticLaw1.ParameterVariableNames = {ke.Name};
reaction1.ReactionRate;

Construct the Events That Are Triggered at the Time of Discontinuities

These events set a parameter mode that controls the mode of protein production. The mode is initially 1, changes to 2 at time 3, and changes to 3 at time 6.

counter = addparameter(model,'mode', 1, 'ConstantValue', false);
addevent(model,'time > 3', 'mode = 2')

ans = 
   SimBiology Event Array

   Index:    Trigger:    EventFcns:
   1         time > 3    mode = 2  

addevent(model,'time > 6', 'mode = 3')

ans = 
   SimBiology Event Array

   Index:    Trigger:    EventFcns:
   1         time > 6    mode = 3  

Construct the Reaction for Protein Production

We assign this rate to a parameter using a repeated assignment rule. This lets us store the production rate in the simulation results.

reaction2 = addreaction(model, 'null -> protein');
rate2 = addparameter(model,'rate2', 0, 'ConstantValue', false);
reaction2.ReactionRate = 'rate2'

reaction2 = 
   SimBiology Reaction Array

   Index:    Reaction:      
   1         null -> protein

addrule(model,'rate2 = discontSimBiologyRateFunction(time, mode)', 'repeatedAssignment')

ans = 
   SimBiology Rule Array

   Index:    RuleType:             Rule:                                            
   1         repeatedAssignment    rate2 = discontSimBiologyRateFunction(time, mode)

View the Contents of discontSimBiologyRateFunction

type discontSimBiologyRateFunction

function rate = discontSimBiologyRateFunction(time, mode)
%discontSimBiologyRateFunction - Helper function for discontSimBiologyModel demo
% RATE = discontSimBiologyRateFunction(TIME, MODE);

%   Copyright 2010 The MathWorks, Inc.

% Mode is a double precision number subject to round-off errors. We need to
% round to the nearest integer to correctly handle this issue.
mode = round(mode);
switch mode
    case 1
        rate = 5;
    case 2
        rate = 3;
    case 3
        rate = 0;
    otherwise
        error('Invalid mode.');
end
    

Simulate and Plot the Model

model

model = 
   SimBiology Model - discontinuous rate 

   Model Components:
     Compartments:      1
     Events:            2
     Parameters:        3
     Reactions:         2
     Rules:             1
     Species:           1
     Observables:       0

sd = sbiosimulate(model);
plot(sd.Time, sd.Data);
ylim([-0.5 8]);
xlabel('Time');
ylabel('State');
title('Simulation Results');
legend(sd.DataNames);

Conclusion

This example illustrates how to create a SimBiology model that contains discontinuities. It illustrates how to add events to the model to address the discontinuities, so you can simulate the model accurately.