title: Genetic algorithms for parameter estimation in mathematical modeling of glucose metabolism.
abstract: Direct measurement of hormones secretion and kinetics in glucose metabolism is not feasible in the clinical practice, being highly invasive. As their knowledge is important in the diagnosis of metabolic disorders, thanks to mathematical models based on non-invasive tests, estimation of hormones behaviour is obtained. Unfortunately, traditional model estimation can suffer for convergence problems, and it can be strongly dependent on the parameters initial value. To overcome these limitations, Genetic algorithms (GAs) were tested on a group of 49 subjects. The stochastic nature of GAs allowed overcoming the initialization problem. Moreover, GAs significantly improved the accuracy of fit.
authors.affiliation: Laboratory of Biomedical Engineering, Istituto Superiore di Sanità, Rome, Italy.
authors.forename: Umberto
authors.initials: U
authors.lastname: Morbiducci
authors.forename: Andrea
authors.initials: A
authors.lastname: Tura
authors.forename: Mauro
authors.initials: M
authors.lastname: Grigioni
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D002096:C-Peptide / Q000378:metabolism; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D007328:Insulin / Q000378:metabolism; D000078790:Insulin Secretion; D008954:Models, Biological
chemical_list: D002096:C-Peptide; D007328:Insulin; D005947:Glucose