title: Identification of quadratic nonlinear models oriented to genetic network analysis.
abstract: The goal of this paper is to provide a novel procedure for the identification of nonlinear models which exhibit a quadratic dependence on the state variables. These models turn out to be very useful for the description of a large class of biochemical processes with particular reference to the genetic networks regulating the cell cycle. The proposed approach is validated through extensive computer simulations on randomly generated systems.
authors.affiliation: School of Computer and Biomedical Engineering, Università degli Studi Magna Graecia di Catanzaro, Campus di Germaneto, Viale Europa, 88100 Catanzaro, Italy; amato@unicz.it.
authors.forename: F
authors.initials: F
authors.lastname: Amato
authors.forename: M
authors.initials: M
authors.lastname: Bansal
authors.forename: C
authors.initials: C
authors.lastname: Cosentino
authors.forename: W
authors.initials: W
authors.lastname: Curatola
authors.forename: D
authors.initials: D
authors.lastname: Di Bernardo