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Identification of genetic network dynamics with unate structure

Riccardo Porreca 1 Eugenio Cinquemani 2, * John Lygeros 3 Giancarlo Ferrari-Trecate 1 
* 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 : Motivation : Modern experimental techniques for time-course measurement of gene expression enable the identification of dynamicalmodels of genetic regulatory networks. In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structuresis clearly prohibitive. Modelling and identification methods for the a priori selection of network structures compatible with biological knowledge and experimental data are necessary to make the identificationproblem tractable. RESULTS : We propose a differential equation modelling framework where the regulatory interactions among genes are expressed interms of unate functions, a class of gene activation rules commonly encountered in Boolean network modelling. We establish analytical properties of the models in the class and exploit them to devise a two-step procedure for gene network reconstruction from product concentration and synthesis rate time series. The first step isolates a family of model structures compatible with the data from a set of most relevant biological hypotheses. The second step explores this family and returns a pool of best fitting models along with estimates of their parameters. The method is tested on a simulated network and compared to state-of-the-art network inference methods on the benchmark synthetic network IRMA.
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Submitted on : Thursday, February 21, 2013 - 2:33:01 PM
Last modification on : Saturday, August 6, 2022 - 3:46:30 AM

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Riccardo Porreca, Eugenio Cinquemani, John Lygeros, Giancarlo Ferrari-Trecate. Identification of genetic network dynamics with unate structure. Bioinformatics, Oxford University Press (OUP), 2010, 26, pp.1239-1245. ⟨10.1093/bioinformatics/btq120⟩. ⟨hal-00793025⟩



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