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Add observable object to SimBiology model



obsObj = addobservable(modelObj,obsName,obsExpression) adds an observable object to a SimBiology® model modelObj. The inputs obsName and obsExpression are the observable object name and its expression, respectively.


obsObj = addobservable(modelObj,obsName,obsExpression,Name,Value) sets the property values of obsObj using one or more name-value pair arguments. Name is the property name and Value is the corresponding value Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN. For a list of properties, see observable object properties.


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Load the Target-Mediated Drug Disposition (TMDD) Model.

sbioloadproject tmdd_with_TO.sbproj

Set the target occupancy (TO) as a response.

cs = getconfigset(m1);
cs.RuntimeOptions.StatesToLog = 'TO';

Get the dosing information.

d = getdose(m1,'Daily Dose');

Scan over different dose amounts using a SimBiology.Scenarios object. To do so, first parameterize the Amount property of the dose. Then vary the corresponding parameter value using the Scenarios object.

amountParam = addparameter(m1,'AmountParam','Units',d.AmountUnits);
d.Amount = 'AmountParam';
d.Active = 1;
doseSamples = SimBiology.Scenarios('AmountParam',linspace(0,300,31));

Create a SimFunction to simulate the model. Set TO as the simulation output.

% Suppress informational warnings that are issued during simulation.
f = createSimFunction(m1,doseSamples,'TO',d)
f = 


         Name          Value        Type            Units    
    _______________    _____    _____________    ____________

    {'AmountParam'}      1      {'parameter'}    {'nanomole'}


     Name         Type               Units      
    ______    _____________    _________________

    {'TO'}    {'parameter'}    {'dimensionless'}


      TargetName                 TargetDimension                  Amount         AmountValue    AmountUnits 
    _______________    ___________________________________    _______________    ___________    ____________

    {'Plasma.Drug'}    {'Amount (e.g., mole or molecule)'}    {'AmountParam'}         1         {'nanomole'}


Simulate the model using the dose amounts generated by the Scenarios object. In this case, the object generates 31 different doses; hence the model is simulated 31 times and generates a SimData array.

doseTable = getTable(d);
sd = f(doseSamples,cs.StopTime,doseTable)
   SimBiology Simulation Data Array: 31-by-1
   ModelName:        TMDD
   Logged Data:
     Species:        0
     Compartment:    0
     Parameter:      1
     Sensitivity:    0
     Observable:     0

Plot the simulation results. Also add two reference lines that represent the safety and efficacy thresholds for TO. In this example, suppose that any TO value above 0.85 is unsafe, and any TO value below 0.15 has no efficacy.

h = sbioplot(sd);
time = sd(1).Time;
h.NextPlot = 'add';
safetyThreshold = plot(h,[min(time), max(time)],[0.85, 0.85],'DisplayName','Safety Threshold');
efficacyThreshold = plot(h,[min(time), max(time)],[0.15, 0.15],'DisplayName','Efficacy Threshold');

Figure contains an axes. The axes with title States versus Time contains 33 objects of type line. These objects represent Run 1 - TO, Run 2 - TO, Run 3 - TO, Run 4 - TO, Run 5 - TO, Run 6 - TO, Run 7 - TO, Run 8 - TO, Run 9 - TO, Run 10 - TO, Run 11 - TO, Run 12 - TO, Run 13 - TO, Run 14 - TO, Run 15 - TO, Run 16 - TO, Run 17 - TO, Run 18 - TO, Run 19 - TO, Run 20 - TO, Run 21 - TO, Run 22 - TO, Run 23 - TO, Run 24 - TO, Run 25 - TO, Run 26 - TO, Run 27 - TO, Run 28 - TO, Run 29 - TO, Run 30 - TO, Run 31 - TO, Safety Threshold, Efficacy Threshold.

Postprocess the simulation results. Find out which dose amounts are effective, corresponding to the TO responses within the safety and efficacy thresholds. To do so, add an observable expression to the simulation data.

% Suppress informational warnings that are issued during simulation.
newSD = addobservable(sd,'stat1','max(TO) < 0.85 & min(TO) > 0.15','Units','dimensionless')
   SimBiology Simulation Data Array: 31-by-1
   ModelName:        TMDD
   Logged Data:
     Species:        0
     Compartment:    0
     Parameter:      1
     Sensitivity:    0
     Observable:     1

The addobservable function evaluates the new observable expression for each SimData in sd and returns the evaluated results as a new SimData array, newSD, which now has the added observable (stat1).

SimBiology stores the observable results in two different properties of a SimData object. If the results are scalar-valued, they are stored in SimData.ScalarObservables. Otherwise, they are stored in SimData.VectorObservables. In this example, the stat1 observable expression is scalar-valued.

Extract the scalar observable values and plot them against the dose amounts.

scalarObs = vertcat(newSD.ScalarObservables);
doseAmounts = generate(doseSamples);

Figure contains an axes. The axes contains an object of type line.

The plot shows that dose amounts ranging from 50 to 180 nanomoles provide TO responses that lie within the target efficacy and safety thresholds.

You can update the observable expression with different threshold amounts. The function recalculates the expression and returns the results in a new SimData object array.

newSD2 = updateobservable(newSD,'stat1','max(TO) < 0.75 & min(TO) > 0.30');

Rename the observable expression. The function renames the observable, updates any expressions that reference the renamed observable (if applicable), and returns the results in a new SimData object array.

newSD3 = renameobservable(newSD2,'stat1','EffectiveDose');

Restore the warning settings.


Input Arguments

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SimBiology model, specified as a SimBiology model object.

Name of the observable object, specified as a character vector or string.

The name

  • Cannot contain the characters [ ], ->, or <->.

  • Cannot be empty, the word time, the word null, or all whitespace.

  • Must be unique in a model, meaning no observable object can have the same name as another observable, species, compartment, parameter, reaction, variant, or dose in the model.

For details, see Guidelines for Naming Model Components.

Example: 'AUC_obs'

Data Types: char | string

Expression of the observable object, specified as a character vector or string.

Example: 'trapz(time,drug)'

Data Types: char | string

Output Arguments

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Observable object, returned as an observable object.

Introduced in R2020a