Mixed graphical models for simultaneous model identification and control applied to the glucose-insulin metabolism
In this paper a method for model identification of biological systems described by stochastic linear differential equations using a new computational technique for statistical Bayesian inference, namely mixed graphical models in the sense of Lauritzen and Wermuth, is presented. The model is identified in terms of biological model parameters and noise parameters. This non-linear estimation problem is solved by means of an exact inference algorithm. The parameter estimates are given as a-posteriori distributions which can be interpreted as fuzzy possibility distributions. For model-based simulations of the underlying biological system the model parameters are represented as uncertain parameters with the distributions obtained from the estimation procedure, We apply the presented methods to a model for the glucose-insulin metabolism: the Karlsburg model for type I diabetes. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.
mixed graphical models glucose-insulin metabolism biological systems