renameobservable
Description
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.
oldNames
— Existing names of observables
character vector | string | string vector | cell array of character vectors
Existing names of the observables, specified as a character vector, string, string vector, or cell array of character vectors.
Example: {'max_drug','mean_drug'}
Data Types: char
| string
| cell
newNames
— New names for observables
character vector | string | string vector | cell array of character vectors
New names for the observables, specified as a character vector, string, string vector, or cell array of character vectors. The number of new names must match the number of old names.
Each new name must be unique in the SimData
object, meaning it
cannot match the name of any other observable, species, compartment, parameter, or
reaction referenced in the SimData
object.
Example: {'MAX','MEAN'}
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
See Also
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