Title :
Identification of Quadratic Nonlinear Models Oriented to Genetic Network Analysis
Author :
Amato, F. ; Bansal, M. ; Cosentino, C. ; Curatola, W. ; Di Bernardo, D.
Author_Institution :
Sch. of Comput. & Biomed. Eng., Univ. degli Studi Magna Gracia di Catanzaro
fDate :
6/27/1905 12:00:00 AM
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
Keywords :
biochemistry; cellular biophysics; genetics; molecular biophysics; physiological models; biochemical processes; cell cycle; genetic network analysis; quadratic nonlinear models; state variables; Biological system modeling; Cellular networks; Computer networks; Computer simulation; Differential equations; Genetic expression; Limit-cycles; Mathematical model; Network topology; Proteins;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1615759
