Scanning Simbiology variants in Matlab for virtual population simulations

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Hello,
I have a Simbioogy variant that I want to scan it's parameter values for 2-fold changes over 10 runs. First, how can I scan all parameters included in the variant for 2-fold changes? Can this be done using Simbiology variants or do I need to select each parameter from my model using: p1= sbioselect(m2, 'Name', 'kNeu2NA0', 'Type', 'parameter'); %m2 is my model
kNeu2NA0 is a parameter value I'm selecting.
I know this can be simulated using the Simbiology model analyzer app but I would like to use Matlab code for now. Also, is using a simbiology variant object a good way of creating 10 different patient parameters in a for loop?
Thanks in advance,
some code:
%simulating the virtual population for 5FU
sbioloadproject human_model_5FU;
v=getvariant(m2);
set(v(1), 'Active'); %set variant that contains top 5 param.
p1= sbioselect(m2, 'Name', 'kNeu2NA0', 'Type', 'parameter');
p2= sbioselect(m2, 'Name', 'kE2D0', 'Type', 'parameter');
p3= sbioselect(m2, 'Name', 'kDgCXC0', 'Type', 'parameter');
p4= sbioselect (m2, 'Name', 'kNeu_LP2L0', 'Type', 'parameter');
%two fold changes for first variable
for i= 1:10
a= [p1.value/2 p1.value*2];
b= [p2.value/2 p2.value*2];
c= [p3.value/2 p3.value*2];
d= [p4.value/2 p4.value*2];
r(i)=(a(2)-a(1).*(rand+a(1)));
t(i)=(b(2)-b(1).*(rand+b(1)));
u(i)=(c(2)-c(1).*(rand+c(1)));
p(i)=(d(2)-d(1).*(rand+d(1)));
vObj = addvariant(m2, 'vsim');
addcontent(vObj, {'parameter', 'kNeu2NA', 'Value', r(i)});
addcontent(vObj{'parameter', 'kE2D0', 'Value', t(i)});
addcontent(vObj{'parameter', 'kDgCXC0', 'Value', u(i)});
addcontent(vObj{'parameter', 'kNeu_LP2L0', 'Value', p(i)});
vObj.Active = true;
end

Accepted Answer

Arthur Goldsipe
Arthur Goldsipe on 3 Sep 2021
There are lots of ways you could do this. And you certainly could use variants. But I personally would do this by creating a SimFunction from the model and using Scenarios to represent the parameter samples. This is similar to the code that is used in the Model Analyzer. In fact, I created this example by viewing and modifying code created by the Model Analyzer.
sbioloadproject lotka m1
paramNames = ["x", "y1", "y2"];
outputNames = ["y1", "y2"];
simfunc = createSimFunction(m1, paramNames, outputNames, [])
simfunc =
SimFunction Parameters: Name Value Type ______ _____ ___________ {'x' } 1 {'species'} {'y1'} 900 {'species'} {'y2'} 900 {'species'} Observables: Name Type ______ ___________ {'y1'} {'species'} {'y2'} {'species'} Dosed: None
paramValues = simfunc.Parameters.Value;
samples = SimBiology.Scenarios();
for i = 1:numel(paramNames)
probdist = makedist("Uniform", "lower", 0.5*paramValues(i), "upper", 2*paramValues(i));
add(samples, "elementwise", paramNames(i), probdist, "Number", 10);
end
simdata = simfunc(samples, 2);
sbioplot(simdata);
I hope this helps.
-Arthur
  5 Comments
Fearass Zieneddin
Fearass Zieneddin on 9 Sep 2021
Edited: Fearass Zieneddin on 9 Sep 2021
Thank you so much! I was able to get the mean simulation behavior to compare with min and max behavior with similar code like this:
stopTime = simdata(1).Time(end);
timeVector = linspace(0,stopTime,843);
simdataResampled = resample(simdata, timeVector);
% "Stack" the matrices of simulation results and average them
stackedData = cat(3, simdataResampled.Data);
meanData = mean(stackedData, 3);
maxData = max(stackedData, [],3);
minData = min(stackedData, [],3);
for i= 1:numel(outputNames)
figure(i);
plot(timeVector, meanData(:,i),'color', 'red');
hold on;
plot(timeVector, maxData(:, i), 'color', 'blue');
hold on;
plot(timeVector, minData(:, i), 'color', 'green');
hold on;
xlabel('time (day)');
set(gcf,'color','w');
set(gca,'fontsize',11)
set(gca,'box','off');
end
hold off;

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