Learning the structure of genetic network dynamics : A geometric approach

Riccardo Porreca 1 Eugenio Cinquemani 2, * John Lygeros 1 Giancarlo Ferrari-Trecate 3
* Auteur correspondant
2 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
Abstract : This work concerns the identification of the structure of a genetic network model from measurements of gene product concentrations and synthesis rates. In earlier work, for a wide family of network models, we developed a data preprocessing algorithm that is able to reject many hypotheses on the network structure by testing certain monotonicity properties of the models. Here we develop a geometric analysis of the method. Then, for a relevant subclass of genetic network models, we extend our approach to the combined testing of monotonicity and convexity-like properties associated with the network structures. Theoretical achievements as well as performance of the enhanced methods are illustrated by way of numerical results.
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Soumis le : jeudi 21 février 2013 - 14:33:35
Dernière modification le : mercredi 11 avril 2018 - 01:53:04

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Riccardo Porreca, Eugenio Cinquemani, John Lygeros, Giancarlo Ferrari-Trecate. Learning the structure of genetic network dynamics : A geometric approach. Proceedings of the 18th IFAC World Congress, 2011, Milan, Italy. pp.11654-11659, 2011, 〈http://www.ifac-papersonline.net/Detailed/51299.html〉. 〈10.3182/20110828-6-IT-1002.01578〉. 〈hal-00793040〉

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