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generate

Generate scenarios from SimBiology.Scenarios object and return table

Description

example

scenariosTable = generate(sObj) generates scenarios from the SimBiology.Scenarios object sObj and returns a table, where each row represents a scenario and each column represents an entry.

example

scenariosTable = generate(sObj,n) returns only the specified nth row (scenario) of the scenarios table.

example

scenariosTable = generate(___,'StandardizedOutput',tf) enables standardization of doses in the output table.

Examples

collapse all

Load the model of glucose-insulin response. For details about the model, see the Background section in Simulating the Glucose-Insulin Response.

sbioloadproject('insulindemo','m1');

The model contains different parameter values and initial conditions that represents different insulin impairments (such as Type 2 diabetes, low insulin sensitivity, and so on) stored in five variants.

variants = getvariant(m1)
variants = 
   SimBiology Variant Array

   Index:  Name:             Active:
   1       Type 2 diabetic   false
   2       Low insulin se... false
   3       High beta cell... false
   4       Low beta cell ... false
   5       High insulin s... false

Suppress an informational warning that is issued during simulations.

warnSettings = warning('off','SimBiology:DimAnalysisNotDone_MatlabFcn_Dimensionless');

Select a dose that represents a single meal of 78 grams of glucose.

singleMeal = sbioselect(m1,'Name','Single Meal');

Create a Scenarios object to represent different initial conditions combined with the dose. That is, create a scenario object where each variant is paired (or combined) with the dose, for a total of five simulation scenarios.

sObj = SimBiology.Scenarios;
add(sObj,'cartesian','variants',variants);
add(sObj,'cartesian','dose',singleMeal)
ans = 
  Scenarios (5 scenarios)

                   Name            Content          Number
                 ________    ___________________    ______

    Entry 1      variants    SimBiology variants      5   
    x Entry 2    dose        SimBiology dose          1   

  See also Expression property.

sObj contains two entries. Use the generate function to combine the entries and generate five scenarios. The function returns a scenarios table, where each row represents a scenario and each column represents an entry of the Scenarios object.

scenariosTbl = generate(sObj)
scenariosTbl=5×2 table
           variants                     dose           
    ______________________    _________________________

    1x1 SimBiology.Variant    1x1 SimBiology.RepeatDose
    1x1 SimBiology.Variant    1x1 SimBiology.RepeatDose
    1x1 SimBiology.Variant    1x1 SimBiology.RepeatDose
    1x1 SimBiology.Variant    1x1 SimBiology.RepeatDose
    1x1 SimBiology.Variant    1x1 SimBiology.RepeatDose

Change the entry name of the first entry.

rename(sObj,1,'Insulin Impairements')
ans = 
  Scenarios (5 scenarios)

                         Name                  Content          Number
                 ____________________    ___________________    ______

    Entry 1      Insulin Impairements    SimBiology variants      5   
    x Entry 2    dose                    SimBiology dose          1   

  See also Expression property.

Create a SimFunction object to simulate the generated scenarios. Use the Scenarios object as the input and specify the plasma glucose and insulin concentrations as reponses (outputs of the function to be plotted). Specify [] for the dose input argument since the Scenarios object already has the dosing information.

f = createSimFunction(m1,sObj,{'[Plasma Glu Conc]','[Plasma Ins Conc]'},[])
f = 
SimFunction

Parameters:

              Name               Value         Type                            Units                   
    _________________________    ______    _____________    ___________________________________________

    {'Plasma Volume (Glu)'  }      1.88    {'parameter'}    {'deciliter'                              }
    {'k1'                   }     0.065    {'parameter'}    {'1/minute'                               }
    {'k2'                   }     0.079    {'parameter'}    {'1/minute'                               }
    {'Plasma Volume (Ins)'  }      0.05    {'parameter'}    {'liter'                                  }
    {'m1'                   }      0.19    {'parameter'}    {'1/minute'                               }
    {'m2'                   }     0.484    {'parameter'}    {'1/minute'                               }
    {'m4'                   }    0.1936    {'parameter'}    {'1/minute'                               }
    {'m5'                   }    0.0304    {'parameter'}    {'minute/picomole'                        }
    {'m6'                   }    0.6469    {'parameter'}    {'dimensionless'                          }
    {'Hepatic Extraction'   }       0.6    {'parameter'}    {'dimensionless'                          }
    {'kmax'                 }    0.0558    {'parameter'}    {'1/minute'                               }
    {'kmin'                 }     0.008    {'parameter'}    {'1/minute'                               }
    {'kabs'                 }    0.0568    {'parameter'}    {'1/minute'                               }
    {'kgri'                 }         0    {'parameter'}    {'1/minute'                               }
    {'f'                    }       0.9    {'parameter'}    {'dimensionless'                          }
    {'a'                    }         0    {'parameter'}    {'1/milligram'                            }
    {'b'                    }      0.82    {'parameter'}    {'dimensionless'                          }
    {'c'                    }         0    {'parameter'}    {'1/milligram'                            }
    {'d'                    }      0.01    {'parameter'}    {'dimensionless'                          }
    {'kp1'                  }       2.7    {'parameter'}    {'milligram/minute'                       }
    {'kp2'                  }    0.0021    {'parameter'}    {'1/minute'                               }
    {'kp3'                  }     0.009    {'parameter'}    {'(milligram/minute)/(picomole/liter)'    }
    {'kp4'                  }    0.0618    {'parameter'}    {'(milligram/minute)/picomole'            }
    {'ki'                   }    0.0079    {'parameter'}    {'1/minute'                               }
    {'[Ins Ind Glu Util]'   }         1    {'parameter'}    {'milligram/minute'                       }
    {'Vm0'                  }    2.5129    {'parameter'}    {'milligram/minute'                       }
    {'Vmx'                  }     0.047    {'parameter'}    {'(milligram/minute)/(picomole/liter)'    }
    {'Km'                   }    225.59    {'parameter'}    {'milligram'                              }
    {'p2U'                  }    0.0331    {'parameter'}    {'1/minute'                               }
    {'K'                    }      2.28    {'parameter'}    {'picomole/(milligram/deciliter)'         }
    {'alpha'                }      0.05    {'parameter'}    {'1/minute'                               }
    {'beta'                 }      0.11    {'parameter'}    {'(picomole/minute)/(milligram/deciliter)'}
    {'gamma'                }       0.5    {'parameter'}    {'1/minute'                               }
    {'ke1'                  }    0.0005    {'parameter'}    {'1/minute'                               }
    {'ke2'                  }       339    {'parameter'}    {'milligram'                              }
    {'Basal Plasma Glu Conc'}     91.76    {'parameter'}    {'milligram/deciliter'                    }
    {'Basal Plasma Ins Conc'}     25.49    {'parameter'}    {'picomole/liter'                         }

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

Simulate the model for 24 hours and plot the simulation data. The data contains five runs, where each run represents a scenario in the Scenarios object.

sd = f(sObj,24);
sbioplot(sd)

Figure contains an axes object. The axes object with title States versus Time contains 10 objects of type line. These objects represent Run 1 - Glucose appearance.Plasma Glu Conc, Run 1 - Insulin secretion.Plasma Ins Conc, Run 2 - Glucose appearance.Plasma Glu Conc, Run 2 - Insulin secretion.Plasma Ins Conc, Run 3 - Glucose appearance.Plasma Glu Conc, Run 3 - Insulin secretion.Plasma Ins Conc, Run 4 - Glucose appearance.Plasma Glu Conc, Run 4 - Insulin secretion.Plasma Ins Conc, Run 5 - Glucose appearance.Plasma Glu Conc, Run 5 - Insulin secretion.Plasma Ins Conc.

ans = 
  Axes (SbioPlot) with properties:

             XLim: [0 30]
             YLim: [0 450]
           XScale: 'linear'
           YScale: 'linear'
    GridLineStyle: '-'
         Position: [0.0956 0.1100 0.2509 0.8150]
            Units: 'normalized'

  Show all properties

If you have Statistics and Machine Learning Toolbox™, you can also draw sample values for model quantities from various probability distributions. For instance, suppose that the parameters Vmx and kp3, which are known for the low and high insulin sensitivity, follow the lognormal distribution. You can generate sample values for these parameters from such a distribution, and perform a scan to explore model behavior.

Define the lognormal probability distribution object for Vmx.

pd_Vmx = makedist('lognormal')
pd_Vmx = 
  LognormalDistribution

  Lognormal distribution
       mu = 0
    sigma = 1

By definition, the parameter mu is the mean of logarithmic values. To vary the parameter value around the base (model) value of the parameter, set mu to log(model_value). Set the standard deviation (sigma) to 0.2. For a small sigma value, the mean of a lognormal distribtion is approximately equal to log(model_value). For details, see Lognormal Distribution (Statistics and Machine Learning Toolbox).

Vmx = sbioselect(m1,'Name','Vmx');
pd_Vmx.mu = log(Vmx.Value);
pd_Vmx.sigma = 0.2
pd_Vmx = 
  LognormalDistribution

  Lognormal distribution
       mu = -3.05761
    sigma =      0.2

Similarly define the probability distribution for kp3.

pd_kp3 = makedist('lognormal');
kp3 = sbioselect(m1,'Name','kp3');
pd_kp3.mu = log(kp3.Value);
pd_kp3.sigma = 0.2
pd_kp3 = 
  LognormalDistribution

  Lognormal distribution
       mu = -4.71053
    sigma =      0.2

Now define a joint probability distribution to draw sample values for Vmx and kp3, with a rank correlation to specify some correlation between these two parameters. Note that this correlation assumption is for the illustration purposes of this example only and may not be biologically relevant.

First remove the variants entry (entry 1) from sObj.

remove(sObj,1)
ans = 
  Scenarios (1 scenarios)

               Name        Content        Number
               ____    _______________    ______

    Entry 1    dose    SimBiology dose      1   

  See also Expression property.

Add an entry that defines the joint probability distribution with a rank correlation matrix.

add(sObj,'cartesian',["Vmx","kp3"],[pd_Vmx, pd_kp3],'RankCorrelation',[1,0.5;0.5,1])
ans = 
  Scenarios (2 scenarios)

                    Name           Content              Number   
                    ____    ______________________    ___________

    Entry 1         dose    SimBiology dose           1          
    x (Entry 2.1    Vmx     Lognormal distribution    2 (default)
    + Entry 2.2)    kp3     Lognormal distribution    2 (default)

  See also Expression property.

By default, the number of samples to draw from the joint distribution is set to 2. Increase the number of samples.

updateEntry(sObj,2,'Number',50)
ans = 
  Scenarios (50 scenarios)

                    Name           Content            Number
                    ____    ______________________    ______

    Entry 1         dose    SimBiology dose             1   
    x (Entry 2.1    Vmx     Lognormal distribution      50  
    + Entry 2.2)    kp3     Lognormal distribution      50  

  See also Expression property.

Verify that the Scenarios object can be simulated with the model. The verify function throws an error if any entry does not resolve uniquely to an object in the model or the entry contents have inconsistent lengths (sample sizes). The function throws a warning if multiple entries resolve to the same object in the model.

verify(sObj,m1)

Generate the simulation scenarios. Plot the sample values using plotmatrix. You can see the value of Vmx is varied around its model value 0.047 and that of kp3 around 0.009.

sTbl = generate(sObj);
[s,ax,bigax,h,hax] = plotmatrix([sTbl.Vmx,sTbl.kp3]);
ax(1,1).YLabel.String = "Vmx";
ax(2,1).YLabel.String = "kp3";
ax(2,1).XLabel.String = "Vmx";
ax(2,2).XLabel.String = "kp3";

MATLAB figure

Simulate the scenarios using the same SimFunction you created previously. You do not need to create a new SimFunction object even though the Scenarios object has been updated.

sd2 = f(sObj,24);
sbioplot(sd2);

Figure contains an axes object. The axes object with title States versus Time contains 100 objects of type line. These objects represent Run 1 - Glucose appearance.Plasma Glu Conc, Run 1 - Insulin secretion.Plasma Ins Conc, Run 2 - Glucose appearance.Plasma Glu Conc, Run 2 - Insulin secretion.Plasma Ins Conc, Run 3 - Glucose appearance.Plasma Glu Conc, Run 3 - Insulin secretion.Plasma Ins Conc, Run 4 - Glucose appearance.Plasma Glu Conc, Run 4 - Insulin secretion.Plasma Ins Conc, Run 5 - Glucose appearance.Plasma Glu Conc, Run 5 - Insulin secretion.Plasma Ins Conc, Run 6 - Glucose appearance.Plasma Glu Conc, Run 6 - Insulin secretion.Plasma Ins Conc, Run 7 - Glucose appearance.Plasma Glu Conc, Run 7 - Insulin secretion.Plasma Ins Conc, Run 8 - Glucose appearance.Plasma Glu Conc, Run 8 - Insulin secretion.Plasma Ins Conc, Run 9 - Glucose appearance.Plasma Glu Conc, Run 9 - Insulin secretion.Plasma Ins Conc, Run 10 - Glucose appearance.Plasma Glu Conc, Run 10 - Insulin secretion.Plasma Ins Conc, Run 11 - Glucose appearance.Plasma Glu Conc, Run 11 - Insulin secretion.Plasma Ins Conc, Run 12 - Glucose appearance.Plasma Glu Conc, Run 12 - Insulin secretion.Plasma Ins Conc, Run 13 - Glucose appearance.Plasma Glu Conc, Run 13 - Insulin secretion.Plasma Ins Conc, Run 14 - Glucose appearance.Plasma Glu Conc, Run 14 - Insulin secretion.Plasma Ins Conc, Run 15 - Glucose appearance.Plasma Glu Conc, Run 15 - Insulin secretion.Plasma Ins Conc, Run 16 - Glucose appearance.Plasma Glu Conc, Run 16 - Insulin secretion.Plasma Ins Conc, Run 17 - Glucose appearance.Plasma Glu Conc, Run 17 - Insulin secretion.Plasma Ins Conc, Run 18 - Glucose appearance.Plasma Glu Conc, Run 18 - Insulin secretion.Plasma Ins Conc, Run 19 - Glucose appearance.Plasma Glu Conc, Run 19 - Insulin secretion.Plasma Ins Conc, Run 20 - Glucose appearance.Plasma Glu Conc, Run 20 - Insulin secretion.Plasma Ins Conc, Run 21 - Glucose appearance.Plasma Glu Conc, Run 21 - Insulin secretion.Plasma Ins Conc, Run 22 - Glucose appearance.Plasma Glu Conc, Run 22 - Insulin secretion.Plasma Ins Conc, Run 23 - Glucose appearance.Plasma Glu Conc, Run 23 - Insulin secretion.Plasma Ins Conc, Run 24 - Glucose appearance.Plasma Glu Conc, Run 24 - Insulin secretion.Plasma Ins Conc, Run 25 - Glucose appearance.Plasma Glu Conc, Run 25 - Insulin secretion.Plasma Ins Conc, Run 26 - Glucose appearance.Plasma Glu Conc, Run 26 - Insulin secretion.Plasma Ins Conc, Run 27 - Glucose appearance.Plasma Glu Conc, Run 27 - Insulin secretion.Plasma Ins Conc, Run 28 - Glucose appearance.Plasma Glu Conc, Run 28 - Insulin secretion.Plasma Ins Conc, Run 29 - Glucose appearance.Plasma Glu Conc, Run 29 - Insulin secretion.Plasma Ins Conc, Run 30 - Glucose appearance.Plasma Glu Conc, Run 30 - Insulin secretion.Plasma Ins Conc, Run 31 - Glucose appearance.Plasma Glu Conc, Run 31 - Insulin secretion.Plasma Ins Conc, Run 32 - Glucose appearance.Plasma Glu Conc, Run 32 - Insulin secretion.Plasma Ins Conc, Run 33 - Glucose appearance.Plasma Glu Conc, Run 33 - Insulin secretion.Plasma Ins Conc, Run 34 - Glucose appearance.Plasma Glu Conc, Run 34 - Insulin secretion.Plasma Ins Conc, Run 35 - Glucose appearance.Plasma Glu Conc, Run 35 - Insulin secretion.Plasma Ins Conc, Run 36 - Glucose appearance.Plasma Glu Conc, Run 36 - Insulin secretion.Plasma Ins Conc, Run 37 - Glucose appearance.Plasma Glu Conc, Run 37 - Insulin secretion.Plasma Ins Conc, Run 38 - Glucose appearance.Plasma Glu Conc, Run 38 - Insulin secretion.Plasma Ins Conc, Run 39 - Glucose appearance.Plasma Glu Conc, Run 39 - Insulin secretion.Plasma Ins Conc, Run 40 - Glucose appearance.Plasma Glu Conc, Run 40 - Insulin secretion.Plasma Ins Conc, Run 41 - Glucose appearance.Plasma Glu Conc, Run 41 - Insulin secretion.Plasma Ins Conc, Run 42 - Glucose appearance.Plasma Glu Conc, Run 42 - Insulin secretion.Plasma Ins Conc, Run 43 - Glucose appearance.Plasma Glu Conc, Run 43 - Insulin secretion.Plasma Ins Conc, Run 44 - Glucose appearance.Plasma Glu Conc, Run 44 - Insulin secretion.Plasma Ins Conc, Run 45 - Glucose appearance.Plasma Glu Conc, Run 45 - Insulin secretion.Plasma Ins Conc, Run 46 - Glucose appearance.Plasma Glu Conc, Run 46 - Insulin secretion.Plasma Ins Conc, Run 47 - Glucose appearance.Plasma Glu Conc, Run 47 - Insulin secretion.Plasma Ins Conc, Run 48 - Glucose appearance.Plasma Glu Conc, Run 48 - Insulin secretion.Plasma Ins Conc, Run 49 - Glucose appearance.Plasma Glu Conc, Run 49 - Insulin secretion.Plasma Ins Conc, Run 50 - Glucose appearance.Plasma Glu Conc, Run 50 - Insulin secretion.Plasma Ins Conc.

By default, SimBiology uses the random sampling method. You can change it to the Latin hypercube sampling (or sobol or halton) for a more systematic space-filling approach.

entry2struct = getEntry(sObj,2)
entry2struct = struct with fields:
               Name: {'Vmx'  'kp3'}
            Content: [2x1 prob.LognormalDistribution]
             Number: 50
    RankCorrelation: [2x2 double]
         Covariance: []
     SamplingMethod: 'random'
    SamplingOptions: [0x0 struct]

entry2struct.SamplingMethod = 'lhs'
entry2struct = struct with fields:
               Name: {'Vmx'  'kp3'}
            Content: [2x1 prob.LognormalDistribution]
             Number: 50
    RankCorrelation: [2x2 double]
         Covariance: []
     SamplingMethod: 'lhs'
    SamplingOptions: [0x0 struct]

You can now use the updated structure to modify entry 2.

updateEntry(sObj,2,entry2struct)
ans = 
  Scenarios (50 scenarios)

                    Name           Content            Number
                    ____    ______________________    ______

    Entry 1         dose    SimBiology dose             1   
    x (Entry 2.1    Vmx     Lognormal distribution      50  
    + Entry 2.2)    kp3     Lognormal distribution      50  

  See also Expression property.

Visualize the sample values.

sTbl2 = generate(sObj);
[s,ax,bigax,h,hax] = plotmatrix([sTbl2.Vmx,sTbl2.kp3]);
ax(1,1).YLabel.String = "Vmx";
ax(2,1).YLabel.String = "kp3";
ax(2,1).XLabel.String = "Vmx";
ax(2,2).XLabel.String = "kp3";

MATLAB figure

Simulate the scenarios.

sd3 = f(sObj,24);
sbioplot(sd3);

Figure contains an axes object. The axes object with title States versus Time contains 100 objects of type line. These objects represent Run 1 - Glucose appearance.Plasma Glu Conc, Run 1 - Insulin secretion.Plasma Ins Conc, Run 2 - Glucose appearance.Plasma Glu Conc, Run 2 - Insulin secretion.Plasma Ins Conc, Run 3 - Glucose appearance.Plasma Glu Conc, Run 3 - Insulin secretion.Plasma Ins Conc, Run 4 - Glucose appearance.Plasma Glu Conc, Run 4 - Insulin secretion.Plasma Ins Conc, Run 5 - Glucose appearance.Plasma Glu Conc, Run 5 - Insulin secretion.Plasma Ins Conc, Run 6 - Glucose appearance.Plasma Glu Conc, Run 6 - Insulin secretion.Plasma Ins Conc, Run 7 - Glucose appearance.Plasma Glu Conc, Run 7 - Insulin secretion.Plasma Ins Conc, Run 8 - Glucose appearance.Plasma Glu Conc, Run 8 - Insulin secretion.Plasma Ins Conc, Run 9 - Glucose appearance.Plasma Glu Conc, Run 9 - Insulin secretion.Plasma Ins Conc, Run 10 - Glucose appearance.Plasma Glu Conc, Run 10 - Insulin secretion.Plasma Ins Conc, Run 11 - Glucose appearance.Plasma Glu Conc, Run 11 - Insulin secretion.Plasma Ins Conc, Run 12 - Glucose appearance.Plasma Glu Conc, Run 12 - Insulin secretion.Plasma Ins Conc, Run 13 - Glucose appearance.Plasma Glu Conc, Run 13 - Insulin secretion.Plasma Ins Conc, Run 14 - Glucose appearance.Plasma Glu Conc, Run 14 - Insulin secretion.Plasma Ins Conc, Run 15 - Glucose appearance.Plasma Glu Conc, Run 15 - Insulin secretion.Plasma Ins Conc, Run 16 - Glucose appearance.Plasma Glu Conc, Run 16 - Insulin secretion.Plasma Ins Conc, Run 17 - Glucose appearance.Plasma Glu Conc, Run 17 - Insulin secretion.Plasma Ins Conc, Run 18 - Glucose appearance.Plasma Glu Conc, Run 18 - Insulin secretion.Plasma Ins Conc, Run 19 - Glucose appearance.Plasma Glu Conc, Run 19 - Insulin secretion.Plasma Ins Conc, Run 20 - Glucose appearance.Plasma Glu Conc, Run 20 - Insulin secretion.Plasma Ins Conc, Run 21 - Glucose appearance.Plasma Glu Conc, Run 21 - Insulin secretion.Plasma Ins Conc, Run 22 - Glucose appearance.Plasma Glu Conc, Run 22 - Insulin secretion.Plasma Ins Conc, Run 23 - Glucose appearance.Plasma Glu Conc, Run 23 - Insulin secretion.Plasma Ins Conc, Run 24 - Glucose appearance.Plasma Glu Conc, Run 24 - Insulin secretion.Plasma Ins Conc, Run 25 - Glucose appearance.Plasma Glu Conc, Run 25 - Insulin secretion.Plasma Ins Conc, Run 26 - Glucose appearance.Plasma Glu Conc, Run 26 - Insulin secretion.Plasma Ins Conc, Run 27 - Glucose appearance.Plasma Glu Conc, Run 27 - Insulin secretion.Plasma Ins Conc, Run 28 - Glucose appearance.Plasma Glu Conc, Run 28 - Insulin secretion.Plasma Ins Conc, Run 29 - Glucose appearance.Plasma Glu Conc, Run 29 - Insulin secretion.Plasma Ins Conc, Run 30 - Glucose appearance.Plasma Glu Conc, Run 30 - Insulin secretion.Plasma Ins Conc, Run 31 - Glucose appearance.Plasma Glu Conc, Run 31 - Insulin secretion.Plasma Ins Conc, Run 32 - Glucose appearance.Plasma Glu Conc, Run 32 - Insulin secretion.Plasma Ins Conc, Run 33 - Glucose appearance.Plasma Glu Conc, Run 33 - Insulin secretion.Plasma Ins Conc, Run 34 - Glucose appearance.Plasma Glu Conc, Run 34 - Insulin secretion.Plasma Ins Conc, Run 35 - Glucose appearance.Plasma Glu Conc, Run 35 - Insulin secretion.Plasma Ins Conc, Run 36 - Glucose appearance.Plasma Glu Conc, Run 36 - Insulin secretion.Plasma Ins Conc, Run 37 - Glucose appearance.Plasma Glu Conc, Run 37 - Insulin secretion.Plasma Ins Conc, Run 38 - Glucose appearance.Plasma Glu Conc, Run 38 - Insulin secretion.Plasma Ins Conc, Run 39 - Glucose appearance.Plasma Glu Conc, Run 39 - Insulin secretion.Plasma Ins Conc, Run 40 - Glucose appearance.Plasma Glu Conc, Run 40 - Insulin secretion.Plasma Ins Conc, Run 41 - Glucose appearance.Plasma Glu Conc, Run 41 - Insulin secretion.Plasma Ins Conc, Run 42 - Glucose appearance.Plasma Glu Conc, Run 42 - Insulin secretion.Plasma Ins Conc, Run 43 - Glucose appearance.Plasma Glu Conc, Run 43 - Insulin secretion.Plasma Ins Conc, Run 44 - Glucose appearance.Plasma Glu Conc, Run 44 - Insulin secretion.Plasma Ins Conc, Run 45 - Glucose appearance.Plasma Glu Conc, Run 45 - Insulin secretion.Plasma Ins Conc, Run 46 - Glucose appearance.Plasma Glu Conc, Run 46 - Insulin secretion.Plasma Ins Conc, Run 47 - Glucose appearance.Plasma Glu Conc, Run 47 - Insulin secretion.Plasma Ins Conc, Run 48 - Glucose appearance.Plasma Glu Conc, Run 48 - Insulin secretion.Plasma Ins Conc, Run 49 - Glucose appearance.Plasma Glu Conc, Run 49 - Insulin secretion.Plasma Ins Conc, Run 50 - Glucose appearance.Plasma Glu Conc, Run 50 - Insulin secretion.Plasma Ins Conc.

Restore warning settings.

warning(warnSettings);

Input Arguments

collapse all

Simulation scenarios, specified as a SimBiology.Scenarios object.

Index of a scenario to return as the output, specified as a positive integer. n must be less than or equal to the total number of scenarios (rows) in the scenarios table.

Example: 4

Data Types: double

Flag to enable standardization of doses in the output, specified as true or false.

Set tf to true if you plan to pass in a dose table as an input to a SimFunction object. The standardization procedure expands the dose samples to a cell array of dose tables with consistent target names within each column. For example, let d1 and d2 have different dose targets. The doses get standardized to:

{getTable(d1),[];[],getTable(d2)}

Example: true

Data Types: logical

Output Arguments

collapse all

Table of simulation scenarios, returned as a table. Each row represents a scenario and each column represents an entry.

Introduced in R2019b