Learning the structure of genetic network dynamics : A geometric approach - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

Learning the structure of genetic network dynamics : A geometric approach

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.
No file

Dates and versions

hal-00793040 , version 1 (21-02-2013)

Identifiers

Cite

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⟩
321 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More