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Identification of genetic network dynamics with unate structure

R. Porreca, E. Cinquemani, J. Lygeros, G. Ferrari-Trecate

Bioinformatics, 26(9):1239-1245, 2010.

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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