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Learning the structure of genetic network dynamics : A geometric approach

Riccardo Porreca 1 Eugenio Cinquemani 2, * John Lygeros 1 Giancarlo Ferrari-Trecate 3 
* Corresponding author
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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Submitted on : Thursday, February 21, 2013 - 2:33:35 PM
Last modification on : Monday, November 28, 2022 - 8:22:06 PM

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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, ⟨10.3182/20110828-6-IT-1002.01578⟩. ⟨hal-00793040⟩



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