Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Identification of Genetic Regulatory Networks - A stochastic Hybrid Approach

Most functions of a living cell are regulated by the biochemical interaction of several genes through the synthesis of proteins and other essential molecules. Thorough investigation of these biochemical control mechanisms is an essential step in the development of medical therapies and is expected to enable design and implementation of artificial biochemical circuitry. Classical modelling and identification have been developed in the framework of deterministic nonlinear (hybrid) systems. However, it is being recognized the stochasticity plays an essential role in the regulation of gene expression. In this talk, we discuss stochastic hybrid modelling and identification of genetic regulatory networks. A piecewise deterministic model is introduced where deterministic protein synthesis is induced by the random activation of gene expression. In turn, gene expression follows the laws of a finite Markov chain which depend on current protein concentration levels. By exploiting the structure of the model, we establish a data-driven identification procedure that allows very efficient estimation of several unknown parameters of the model. Numerical results on synthetically generated data are presented to show the effectiveness of our method.

Type of Seminar:
IfA Seminar
Eugenio Cinquemani
Aug 09, 2007   11am-12am

Contact Person:

No downloadable files available.
Biographical Sketch: