SimBiology.Variant
Store alternate component values
Description
The SimBiology.Variant object stores the names and values of model
components and allows you to use the values stored in a variant object as the alternate value
to be applied during a simulation. You can store values for species InitialAmount, parameter Value, and compartment Capacity in a variant object. Simulating using a variant does not alter the
model component values. The values specified in the variant temporarily apply during
simulation.
Using one or more variant objects associated with a model allows you to evaluate model
behavior during simulation, with different values for the various model components without
having to search and replace these values, or having to create additional models with these
values. If you determine that the values in a variant object accurately define your model, you
can permanently replace the values in your model with the values stored in the variant object,
using the commit function.
To use a variant in a simulation you must add the variant object to the model object and
set the Active property of the variant to true. Set the
Active property to true if you always want the variant to be applied
before simulating the model. You can also enter the variant object as an argument to sbiosimulate; this applies the variant only for the current simulation and
supersedes any active variant objects on the model.
When there are multiple active variant objects on a model, if there are duplicate
specifications for a property's value, the last occurrence for the property value in the array
of variants, is used during simulation. You can find out which variant is applied last by
looking at the indices of the variant objects stored on the model. Similarly, in the
Content property, if there are duplicate specifications for a property's
value, the last occurrence for the property in the Content property, is
used during simulation.
Use dot notation to query the object properties or change properties that are not
read-only. You can also use the get and set commands.
The SimBiology Model Builder app also enables you to add variants to your model and edit them. For an example, see Represent Biological Variability Using Variants.
Creation
Use sbiovariant to create a
SimBiology.Variant object. Use addvariant (model) to add a SimBiology.Variant object to a
SimBiology model.
Properties
Object Functions
addcontent (variant) | Append content to variant object |
commit (variant) | Commit variant contents to model |
copyobj | Copy SimBiology object and its children |
delete | Delete SimBiology object |
display | Display summary of SimBiology object |
get | Get SimBiology object properties |
rename | Rename SimBiology model component and update expressions |
rmcontent (variant) | Remove contents from variant object |
set | Set SimBiology object properties |
verify (model, variant) | Validate and verify SimBiology model |
Examples
Create a model containing three species in one compartment.
modelObj = sbiomodel('mymodel'); compObj = addcompartment(modelObj,'comp1'); A = addspecies(compObj,'A'); B = addspecies(compObj,'B'); C = addspecies(compObj,'C');
Add a variant object that varies the species InitialAmount property.
variantObj = addvariant(modelObj,'v1'); addcontent(variantObj,{{'species','A','InitialAmount', 5}, ... {'species','B','InitialAmount',10}})
ans = SimBiology Variant - v1 (inactive) ContentIndex: Type: Name: Property: Value: 1 species A InitialAmount 5.0 2 species B InitialAmount 10.0
Add another species data to the Content property.
addcontent(variantObj,{'species','C','InitialAmount',15})ans = SimBiology Variant - v1 (inactive) ContentIndex: Type: Name: Property: Value: 1 species A InitialAmount 5.0 2 species B InitialAmount 10.0 3 species C InitialAmount 15.0
Remove a species from the Content property.
rmcontent(variantObj,3)
ans = SimBiology Variant - v1 (inactive) ContentIndex: Type: Name: Property: Value: 1 species A InitialAmount 5.0 2 species B InitialAmount 10.0
Replace the data in the Content property.
variantObj.Content = {'species','C','InitialAmount',15}variantObj = SimBiology Variant - v1 (inactive) ContentIndex: Type: Name: Property: Value: 1 species C InitialAmount 15.0
This example shows how to create and apply a variant to the G protein model of a wild-type strain. The variant represents a parameter value for the G protein model of a mutant strain. Thus, when you simulate the model without applying the variant, you see results for the wild type strain, and when you simulate the model with the variant, you see results for the mutant strain.
The value of the parameter kGd is 0.11 for the wild-type strain and 0.004 for the mutant strain. To represent the mutant strain, you will store an alternate value of 0.004 for the kGd parameter in a variant object, and apply this variant when simulating the model.
Load the gprotein.sbproj project, which includes the variable m1, a SimBiology.Model object.
sbioloadproject gproteinYou can create a variant of the original model by specifying a different parameter value for the kGd parameter of the model. First, add a variant to the m1 model object.
v1 = addvariant(m1,'mutant_strain');Next, add a parameter kGd with a value of 0.004 to the variant object v1.
addcontent(v1,{'parameter','kGd','Value',0.004});Simulate the wild type model.
[t,x,names] = sbiosimulate(m1);
Simulate the mutant strain model by applying the variant.
[tV,xV,names] = sbiosimulate(m1,v1);
Plot and compare the simulated results.
subplot(1,2,1) plot(t,x); legend(names); xlabel('Time'); ylabel('Amount'); title('Wild Type'); subplot(1,2,2) plot(tV,xV); legend(names); xlabel('Time'); ylabel('Amount'); title('Mutant Strain');
This example shows how to simulate the glucose-insulin responses for the normal and diabetic subjects.
Load the model of glucose-insulin response. For details about the model, see the Background section in Simulate the Glucose-Insulin Response.
sbioloadproject('insulindemo', 'm1')
The model contains different initial conditions stored in various variants.
variants = getvariant(m1);
Get the initial conditions for the type 2 diabetic patient.
type2 = variants(1)
type2 = SimBiology Variant - Type 2 diabetic (inactive) ContentIndex: Type: Name: Property: Value: 1 parameter Plasma Volume ... Value 1.49 2 parameter k1 Value .042 3 parameter k2 Value .071 4 parameter Plasma Volume ... Value .04 5 parameter m1 Value .379 6 parameter m2 Value .673 7 parameter m4 Value .269 8 parameter m5 Value .0526 9 parameter m6 Value .8118 10 parameter Hepatic Extrac... Value .6 11 parameter kmax Value .0465 12 parameter kmin Value .0076 13 parameter kabs Value .023 14 parameter kgri Value .0465 15 parameter f Value .9 16 parameter a Value 6e-05 17 parameter b Value .68 18 parameter c Value .00023 19 parameter d Value .09 20 parameter kp1 Value 3.09 21 parameter kp2 Value .0007 22 parameter kp3 Value .005 23 parameter kp4 Value .0786 24 parameter ki Value .0066 25 parameter [Ins Ind Glu U... Value 1.0 26 parameter Vm0 Value 4.65 27 parameter Vmx Value .034 28 parameter Km Value 466.21 29 parameter p2U Value .084 30 parameter K Value .99 31 parameter alpha Value .013 32 parameter beta Value .05 33 parameter gamma Value .5 34 parameter ke1 Value .0007 35 parameter ke2 Value 269.0 36 parameter Basal Plasma G... Value 164.18 37 parameter Basal Plasma I... Value 54.81
Suppress an informational warning that is issued during simulations.
warnSettings = warning('off','SimBiology:DimAnalysisNotDone_MatlabFcn_Dimensionless');
Create SimFunction objects to simulate the glucose-insulin response for the normal and diabetic subjects.
Specify an empty array
{}for the second input argument to denote that the model will be simulated using the base parameter values (that is, no parameter scanning will be performed).Specify the plasma glucose and insulin concentrations as responses (outputs of the function to be plotted).
Specify the species
Doseas the dosed species. This species represents the initial concentration of glucose at the start of the simulation.
normSim = createSimFunction(m1,{},...
{'[Plasma Glu Conc]','[Plasma Ins Conc]'},'Dose')normSim =
SimFunction
Parameters:
Name Value Type Units
____ _____ ____ _____
Observables:
Name Type Units
_____________________ ___________ _______________________
{'[Plasma Glu Conc]'} {'species'} {'milligram/deciliter'}
{'[Plasma Ins Conc]'} {'species'} {'picomole/liter' }
Dosed:
TargetName TargetDimension
__________ _____________________
{'Dose'} {'Mass (e.g., gram)'}
TimeUnits: hour
For the diabetic patient, specify the initial conditions using the variant type2.
diabSim = createSimFunction(m1,{},...
{'[Plasma Glu Conc]','[Plasma Ins Conc]'},'Dose',type2)diabSim =
SimFunction
Parameters:
Name Value Type Units
____ _____ ____ _____
Observables:
Name Type Units
_____________________ ___________ _______________________
{'[Plasma Glu Conc]'} {'species'} {'milligram/deciliter'}
{'[Plasma Ins Conc]'} {'species'} {'picomole/liter' }
Dosed:
TargetName TargetDimension
__________ _____________________
{'Dose'} {'Mass (e.g., gram)'}
TimeUnits: hour
Select a dose that represents a single meal of 78 grams of glucose at the start of the simulation.
singleMeal = sbioselect(m1,'Name','Single Meal');
Convert the dosing information to the table format.
mealTable = getTable(singleMeal);
Simulate the glucose-insulin response for a normal subject for 24 hours.
sbioplot(normSim([],24,mealTable));
Simulate the glucose-insulin response for a diabetic subject for 24 hours.
sbioplot(diabSim([],24,mealTable));
Perform a Scan Using Variants
Suppose you want to perform a parameter scan using an array of variants that contain different initial conditions for different insulin impairments. For example, the model m1 has variants that correspond to the low insulin sensitivity and high insulin sensitivity. You can simulate the model for both conditions via a single call to the SimFunction object.
Select the variants to scan.
varToScan = sbioselect(m1,'Name',... {'Low insulin sensitivity','High insulin sensitivity'});
Check which model parameters are being stored in each variant.
varToScan(1)
ans = SimBiology Variant - Low insulin sensitivity (inactive) ContentIndex: Type: Name: Property: Value: 1 parameter Vmx Value .0235 2 parameter kp3 Value .0045
varToScan(2)
ans = SimBiology Variant - High insulin sensitivity (inactive) ContentIndex: Type: Name: Property: Value: 1 parameter Vmx Value .094 2 parameter kp3 Value .018
Both variants store alternate values for Vmx and kp3 parameters. You need to specify them as input parameters when you create a SimFunction object.
Create a SimFunction object to scan the variants.
variantScan = createSimFunction(m1,{'Vmx','kp3'},...
{'[Plasma Glu Conc]','[Plasma Ins Conc]'},'Dose');Simulate the model and plot the results. Run 1 include simulation results for the low insulin sensitivity and Run 2 for the high insulin sensitivity.
sbioplot(variantScan(varToScan,24,mealTable));
Low insulin sensitivity lead to increased and prolonged plasma glucose concentration.
Restore warning settings.
warning(warnSettings);
