Identification of quadratic nonlinear models oriented to genetic network analysis.
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.
Amato
F
F
School of Computer and Biomedical Engineering, Università degli Studi Magna Graecia di Catanzaro, Campus di Germaneto, Viale Europa, 88100 Catanzaro, Italy; amato@unicz.it.
Bansal
M
M
Cosentino
C
C
Curatola
W
W
Di Bernardo
D
D