updateobservable

Update observable expressions or units in SimData

Since R2020a

Syntax

sdout = updateobservable(sdin,obsNames,obsExpressions)

sdout = updateobservable(sdin,obsNames,obsExpressions,'Units',units)

sdout = updateobservable(sdin,obsNames,'Units',units)

Description

sdout = updateobservable(sdin,obsNames,obsExpressions) returns a new SimData object (or array of objects) sdout after copying the input SimData sdin and recalculating the observables using updated expressions. obsNames and obsExpressions are the existing observable names and their corresponding expressions to update, respectively. The number of expressions must match the number of observable names.

SimBiology^® reevaluates existing observable expressions every time you call updateobservable.

sdout = updateobservable(sdin,obsNames,obsExpressions,'Units',units) recalculates the observables obsNames using the updated expressions obsExpressions and the specified units. The number of units must match the number of observable names.

sdout = updateobservable(sdin,obsNames,'Units',units) recalculates the observables obsNames using the specified units. The number of units must match the number of observable names.

Examples

Calculate Statistics After Model Simulation Using Observables

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.
warning('off','SimBiology:SimFunction:DOSES_NOT_EMPTY');
f = createSimFunction(m1,doseSamples,'TO',d)

f = 
SimFunction

Parameters:

         Name          Value        Type            Units    
    _______________    _____    _____________    ____________

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

Observables: 

     Name         Type               Units      
    ______    _____________    _________________

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

Dosed: 

      TargetName                 TargetDimension                  Amount         AmountValue    AmountUnits 
    _______________    ___________________________________    _______________    ___________    ____________

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


TimeUnits: day

warning('on','SimBiology:SimFunction:DOSES_NOT_EMPTY');

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');

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.
warning('off','SimBiology:sbservices:SB_DIMANALYSISNOTDONE_MATLABFCN_UCON');
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
plot(doseAmounts.AmountParam,scalarObs.stat1,'o','MarkerFaceColor','b')

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.

warning('on','SimBiology:sbservices:SB_DIMANALYSISNOTDONE_MATLABFCN_UCON');

Input Arguments

sdin — Input simulation data

SimData object | array of SimData objects

Input simulation data, specified as a SimData object or array of objects.

obsNames — Names of existing observable expressions

character vector | string | string vector | cell array of character vector

Names of existing observable expressions, specified as a character vector, string, string vector, or cell array of character vector.

Example: {'max_drug','mean_drug'}

Data Types: char | string | cell

obsExpressions — Observable expressions

character vector | string | string vector | cell array of character vectors

Observable expressions, specified as a character vector, string, string vector, or cell array of character vectors. The number of expressions must match the number of observable names.

Example: {'max(drug)','mean(drug)'}

Data Types: char | string | cell

units — Units for observable expressions

character vector | string | string vector | cell array of character vectors

Units for the observable expressions, specified as a character vector, string, string vector, or cell array of character vectors. The number of units must match the number of observable names.

Example: {'nanomole/liter','nanomole/liter'}

Data Types: char | string | cell

Output Arguments

sdout — Simulation data with observable results

SimData object | array of SimData objects

Simulation data with observable results, returned as a SimData object or array of objects.

Version History

Introduced in R2020a