Publication details

Reference

E. Cinquemani, R. Porreca, J. Lygeros, and G. Ferrari-Trecate. Canalizing structure of genetic network dynamics: modelling and identification via mixed-integer programming. Proc. 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference , pages 5618--5623, 2009. Shanghai, China, 16-18 December.

Abstract

We discuss the identification of genetic networks based on a class of boolean gene activation rules known as hierarchically canalizing functions. We introduce a class of kinetic models for the concentration of the proteins in the network built on a family of canalizing functions that has been shown to capture the vastmajority of the known interaction networks. The simultaneous identification of the structure and of the parameters of the model from experimental data is addressed based on a mixed integerparametrization of the model class. The resulting regressionproblem is solved numerically via standard branch-and-boundtechniques. The performance of the method is tested on simulateddata generated by a simple model of Escherichia colinutrient stress response.