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Structural Identification of Unate-Like Genetic Network Models from Time-Lapse Protein Concentration Measurements

Author(s):

R. Porreca, E. Cinquemani, J. Lygeros, G. Ferrari-Trecate
Conference/Journal:

IEEE Conference on Decision and Control, Atlanta, Georgia, USA
Abstract:

We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [Porreca et al,Bioinformatics,2010], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [Porreca et al,Bioinformatics,2010] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae.

Year:

2010
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { PorEtal:2010:IFA_3672,
    author={R. Porreca and E. Cinquemani and J. Lygeros and G. Ferrari-Trecate},
    title={{Structural Identification of Unate-Like Genetic Network
	  Models from Time-Lapse Protein Concentration Measurements}},
    booktitle={IEEE Conference on Decision and Control},
    pages={},
    year={2010},
    address={Atlanta, Georgia, USA},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3672}
}
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