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Identification of Genetic Regulatory Networks: A Stochastic Hybrid Approach


E. Cinquemani, A. Milias, J. Lygeros

IFAC World Congress, Seoul, Korea

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. Modeling and identification methods for such systems have been developed in the framework of deterministic nonlinear (hybrid) systems. However, it is being recognized that stochasticity plays a fundamental role in the regulation of gene expression. In this paper we discuss stochastic hybrid modeling 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 state Markov chain whose transition rates depend on current protein concentration levels. By exploiting the structure of the model, we establish an identification procedure that allows very efficient estimation of several unknown parameters of the model. Numerical results on simulated data are presented to show the effectiveness of our method.


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% Autogenerated BibTeX entry
@InProceedings { CinMil:2008:IFA_3018,
    author={E. Cinquemani and A. Milias and J. Lygeros},
    title={{Identification of Genetic Regulatory Networks: A Stochastic
	  Hybrid Approach}},
    booktitle={IFAC World Congress},
    address={Seoul, Korea},
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