Simulate SimBiology model, adding variations by sampling error model
[ynew,parameterEstimates]
= random(resultsObj)
[ynew,parameterEstimates]
= random(resultsObj,data,dosing)
[
returns simulation
results ynew
,parameterEstimates
]
= random(resultsObj
)ynew
with added noise using the error
model information specified by the resultsObj.ErrorModelInfo
property
and estimated parameter values parameterEstimates
.
[
uses
the specified ynew
,parameterEstimates
]
= random(resultsObj
,data
,dosing
)data
and dosing
information.
The noise is only added to states that are responses which are
the states included in the responseMap
input argument
when you called sbiofit
. If
there is a separate error model for each response, the noise is added
to each response separately using the corresponding error model.
resultsObj
— Estimation resultsOptimResults
object  NLINResults
objectEstimation results, specified as an OptimResults
object
or NLINResults object
,
which contains estimation results returned by sbiofit
.
It must be a scalar object.
data
— Grouped data or output timesgroupedData
object  vector  cell array of vectorsGrouped data or output times, specified as a groupedData object
, vector, or cell array
of vectors of output times.
If it is a vector of time points, random
simulates
the model with new time points using the parameter estimates from
the results object resultsObj
.
If it is a cell array of vectors of time points, random
simulates
the model n times using the output times from each
time vector, where n is the length of data
.
If it is a groupedData
object, it must have
an independent variable such as Time. It must also have a group variable
if the training data used for fitting has such variable. You can use
a groupedData
object to query different combinations
of categories if the resultsObj
contains parameter
estimates for each category. random
simulates the
model for each group with the specified categories. For instance,
suppose you have a set of parameter estimates for sex category (males
versus females), and age category (young versus old) in your training
data. You can use random
to simulate the responses
of an old male (or any other combination) although such patient may
not exist in the training data.
If the resultsObj
is from estimating categoryspecific
parameters, data
must be a groupedData
object.
If UnitConversion
is turned on for the
underlying SimBiology model that was used for fitting and data
is
a groupedData
object, data
must
specify valid variable units via data.Properties.VariableUnits
property.
If it is a numeric vector or cell array of vectors of time points, random
uses
the model’s TimeUnits
.
dosing
— Dosing information[]
 2D matrix of SimBiology dose objectsDosing information, specified as an empty array []
or 2D matrix of
SimBiology dose objects (ScheduleDose object
or RepeatDose object
). If dosing
is a matrix of dose
objects, the matrix must contain default doses or be consistent with the original dosing
data used with sbiofit
. That is, dose objects in
dosing
must have the same values for dose properties (such as
TargetName
) or must be parameterized in
the same way as the original dosing data. For instance, suppose that the original dosing
matrix has two columns of doses, where the doses in the first column target species
x and those in the second column target species y.
Then dosing
must have doses in the first column targeting species
x and those in the second column targeting species
y.
If empty, no doses are applied during simulation, even if the model has active doses.
If not empty, the matrix must have a single row or one row per group in the input data. If it has a single row, the same doses are applied to all groups during simulation. If it has multiple rows, each row is applied to a separate group, in the same order as the groups appear in the input data. If some groups (or time vectors) require more doses than others, then fill in the matrix with default (dummy) doses.
Multiple columns are allowed so that you can apply multiple dose objects to
each group or time vector. All doses in a column must be default doses or must
reference the same components in the model (for instance, the doses must have
the same dose target TargetName
) and must have consistent
parameterized properties as in the original dosing data used with
sbiofit
. For example, if the
Amount
property of a dose used in the original
sbiofit
call is parameterized to a modelscoped
parameter 'A'
, all doses for the corresponding group (column)
in dosing
must have the Amount
property parameterized to 'A'
.
A default dose has default values for all properties, except for the
Name
property. Create a default dose as
follows.
d1 = sbiodose('d1');
In addition to manually constructing dose objects using
sbiodose
, if the input data is a
groupedData
object and has dosing information, you can
use the createDoses
method to construct
doses from it.
The number of rows in the dosing
matrix and the number of groups or
output time vectors in data
determine the total number of simulation
results in the output ynew
. For details, see the table in the
ynew
argument description.
If UnitConversion
is turned on for the underlying SimBiology^{®} model that was used for fitting, dosing
must specify valid amount and time units.
ynew
— Simulation results with noiseSimData
objectsSimulation results, returned as a vector of SimData
objects. The states reported in ynew
are the states that were included in the responseMap
input argument of sbiofit
as well as any other states listed in the StatesToLog
property of the runtime options (RuntimeOptions
) of the SimBiology model.
The total number of simulation results in ynew
depends on the number of groups or output time vectors in data
and the number of rows in the dosing
matrix.
Number of groups or output time vectors in data  Number of rows in the dosing matrix  Simulation results 


 The total number of No doses are applied during simulation. 

 The total number of The given row of doses is applied during the simulation. 
 N  The total number of Each row of 
N 
 The total number of No doses are applied during simulation. 
N 
 The total number of The same row of doses is applied to each simulation. 
N  N  The total number of Each row of 
M  N  The function throws an error when M ≠ N. 
parameterEstimates
— Estimated parameter valuesEstimated parameter values, returned as a table. This is identical
to resultsObj.ParameterEstimates
property.
This example uses the yeast heterotrimeric G protein model and experimental data reported by [1]. For more details about the model, see the Background section in Parameter Scanning, Parameter Estimation, and Sensitivity Analysis in the Yeast Heterotrimeric G Protein Cycle.
Load the G protein model.
sbioloadproject gprotein
Enter the experimental data containing the time course for the fraction of active G protein, as reported in the reference paper [1].
time = [0 10 30 60 110 210 300 450 600]'; GaFracExpt = [0 0.35 0.4 0.36 0.39 0.33 0.24 0.17 0.2]';
Create a groupedData
object based on
the experimental data.
tbl = table(time,GaFracExpt); grpData = groupedData(tbl);
Map the appropriate model component to the experimental
data. In other words, indicate which species in the model corresponds
to which response variable in the data. In this example, map the model
parameter GaFrac
to the experimental data variable GaFracExpt
from grpData
.
responseMap = 'GaFrac = GaFracExpt';
Use an estimatedInfo
object to define
the model parameter kGd
as a parameter to be estimated.
estimatedParam = estimatedInfo('kGd');
Perform the parameter estimation. Use the namevalue pair
argument 'ErrorModel'
to specify the error model
that adds error to simulation data.
fitResult = sbiofit(m1,grpData,responseMap,estimatedParam,'ErrorModel','proportional');
View the estimated parameter value of kGd
.
fitResult.ParameterEstimates
ans = Name Estimate StandardError _____ ________ _____________ 'kGd' 0.11 0.00064116
Use the random
method to retrieve the
simulation data with added noise using the proportional error model
which was specified by sbiofit
. Note that the
noise is added only to the response state, that is the GaFrac
parameter.
[ynew,paramEstim] = random(fitResult);
Select the simulation data for the GaFrac
parameter.
GaFracNew = select(ynew,{'Name','GaFrac'});
Plot the simulation results.
plot(GaFracNew.Time,GaFracNew.Data)
hold on
Plot the experimental data to compare it with the simulated data.
plot(time,GaFracExpt,'Color','k','Marker','o') legend('GaFracNew','GaFracExpt')
[1] Yi, TM., Kitano, H., and Simon, M. (2003). A quantitative characterization of the yeast heterotrimeric G protein cycle. PNAS. 100, 10764–10769.
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