getEntry

Get entry contents from SimBiology.Scenarios object

Syntax

entryStruct = getEntry(sObj,entryNameOrIndex)

entryStruct = getEntry(sObj,entryIndex,subIndex)

Description

entryStruct = getEntry(sObj,entryNameOrIndex) returns a structure containing the contents of the entry (or subentry) specified by entryNameOrIndex.

entryStruct = getEntry(sObj,entryIndex,subIndex) returns a structure containing the contents of a subentry specified by entryIndex and subIndex.

Examples

Generate Different Simulation Scenarios for Glucose-Insulin Response

Load the model of glucose-insulin response. For details about the model, see the Background section in Simulate 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 responses (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)

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'

  Use GET to 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 distribution 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";

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);

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";

Simulate the scenarios.

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

Restore warning settings.

warning(warnSettings);

Input Arguments

sObj — Simulation scenarios

SimBiology.Scenarios object

Simulation scenarios, specified as a SimBiology.Scenarios object.

entryNameOrIndex — Entry name or index

character vector | string | scalar positive integer

Entry name or index, specified as a character vector, string, or scalar positive integer. You can also specify the name of a subentry.

If you are specifying an index, it must be smaller than or equal to the number of entries in the object.

Data Types: double | char | string

entryIndex — Entry index

scalar positive integer

Entry index, specified as a scalar positive integer. The entry index must be smaller than or equal to the number of entries in the object.

Data Types: double

subIndex — Entry subindex

scalar positive integer

Entry subindex, specified as a scalar positive integer. The subindex must be smaller than or equal to the number of subentries in the entry.

Data Types: double

Output Arguments

entryStruct — Entry content

structure

Entry content, returned as a structure.

entryStruct has the following fields:

• Name – Character vector or cell array of character vectors specifying the names of entries.

• Content – Vector of numeric values, dose objects, variant objects, or probability distribution objects.

If you query a random sampling entry without specifying a subentry, or if the random sampling entry references only one model component, entryStruct has the following additional fields:

• Number – Number of samples drawn from the distribution. If this field is empty [], the number of samples is inferred from other entries. The default value is 2.

• RankCorrelation – Rank correlation matrix. This field is empty [] if no correlation matrix is specified.

• Covariance – Covariance matrix. This field is empty [] if no covariance matrix is specified.

• SamplingMethod – Character vector specifying the sampling method. Values are:

  • 'random' – Random sampling (default).

  • 'lhs' – Latin hypercube sampling.

  • 'copula' – Multivariate sample using a copula.

  • 'sobol' – Sobol quasirandom sample set.

  • 'halton' – Halton quasirandom sample set.

  For details, see Sampling Methods.

Version History

Introduced in R2019b