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Identification of genetic network dynamics with unate structure

Porreca, Riccardo ; Cinquemani, Eugenio ; Lygeros, John ; Ferrari-Trecate, Giancarlo

In: Bioinformatics, 2010, vol. 26, no. 9, p. 1239-1245

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    Titre
    • Identification of genetic network dynamics with unate structure
    Auteur
    • Porreca, Riccardo. Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland, INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France and Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, 27100 Pavia, Italy
    • Cinquemani, Eugenio. Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland, INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France and Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, 27100 Pavia, Italy
    • Lygeros, John. Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland, INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France and Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, 27100 Pavia, Italy
    • Ferrari-Trecate, Giancarlo. Institut für Automatik, ETH Zürich, 8092 Zürich, Switzerland, INRIA Grenoble-Rhône-Alpes, Montbonnot, 38334 Saint-Ismier Cedex, France and Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, 27100 Pavia, Italy
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:295675
    Summary
    Motivation: Modern experimental techniques for time course measurement of gene expression enable the identification of dynamical models 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 structures is 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 identification problem tractable. Results: We propose a differential equation modelling framework where the regulatory interactions among genes are expressed in terms 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 with state-of-the-art network inference methods on the benchmark synthetic network IRMA. Contact: eugenio.cinquemani@inria.fr Supplementary information: Supplementary data are available at Bioinformatics online