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Journal Articles Bioinformatics Year : 2010

Identification of genetic network dynamics with unate structure


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.

Dates and versions

hal-00793025 , version 1 (21-02-2013)



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