Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

construct (PKModelDesign)

Construct SimBiology model from PKModelDesign object

Syntax

[modelObj, pkModelMapObject] = construct(pkModelDesignObject)
[modelObj, pkModelMapObject, CovModelObj] = construct(pkModelDesignObject)

Arguments

modelObjSimBiology® model object specifying a pharmacokinetic model.
pkModelMapObjectDefines the roles of the components in modelObj. For details, see PKModelMap object.
CovModelObjDefines the relationship between parameters and covariates. For details, see CovariateModel object.

Description

[modelObj, pkModelMapObject] = construct(pkModelDesignObject) constructs a SimBiology model object, modelObj, containing the model components (such as compartments, species, reactions, and rules) required to represent the pharmacokinetic model specified in pkModelDesignObject. It also constructs pkModelMapObject, a PKModelMap object, which defines the roles of the model components.

The newly constructed model object, modelObj, is named 'Generated Model' (which you can change). It contains one compartment for each compartment specified in the PKCompartment property of pkModelDesignObject. Each compartment contains a species that represents a drug concentration. The compartments are connected with reversible reactions that models flux between compartments.

[modelObj, pkModelMapObject, CovModelObj] = construct(pkModelDesignObject) constructs CovModelObj, a CovariateModel object, which defines the relationship between parameters and covariates. Within the Expression property of CovModelObj, each parameter being estimated has an expression of the form parameterName = exp(theta1 + eta1) (without covariate dependencies), where theta1 is a fixed effect, and eta1 is a random effect. You can modify the expressions to add covariate dependencies. For details, see CovariateModel object.

Introduced in R2009a

Was this topic helpful?