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Coexpression of genes or, more generally, similarity in the expressionprofiles poses an unsurmountable obstacle to inferring the gene regulatorynetwork (GRN) based solely on data from DNA microarray time series. Clusteringof genes with similar expression profiles allows for a course-grained view ofthe GRN and a probabilistic determination of the connectivity among theclusters. We present a model for the temporal evolution of a gene clusternetwork which takes into account interactions of gene products with genes and,through a non-constant degradation rate, with other gene products. The numberof model parameters is reduced by using polynomial functions to interpolatetemporal data points. In this manner, the task of parameter estimation isreduced to a system of linear algebraic equations, thus making the computationtime shorter by orders of magnitude. To eliminate irrelevant networks, we testeach GRN for stability with respect to parameter variations, and imposerestrictions on its behavior near the steady state. We apply our model andmethods to DNA microarray time series' data collected on Escherichia coliduring glucose-lactose diauxie and infer the most probable cluster network fordifferent phases of the experiment.

Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli