R. Porreca, E. Cinquemani, J. Lygeros, and G. Ferrari-Trecate. Learning the structure of genetic network dynamics : A geometric approach. 18th IFAC World Congress on Automatic Control , pages 11654--11659, 2011. Milan, Italy, Aug. 28 - Sept. 2.
This work concerns the identification of the structure of a genetic network modelfrom measurements of gene product concentrations and synthesis rates. In earlier work, for awide family of network models, we developed a data preprocessing algorithm that is able toreject many hypotheses on the network structure by testing certain monotonicity properties ofthe models. Here we develop a geometric analysis of the method. Then, for a relevant subclassof genetic network models, we extend our approach to the combined testing of monotonicityand convexity-like properties associated with the network structures. Theoretical achievementsas well as performance of the enhanced methods are illustrated by way of numerical results.