Identification of regulatory network motifs from gene expression data

The modern systems biology approach to the study of molecular cellular biology, consists in the development of computational tools to support the formulation of new hypotheses on the molecular mechanisms underlying the observed cell behavior. Recent biotechnologies are able to provide precise measures of gene expression time courses in response to a large variety of internal and environmental perturbations. In this paper, we propose a simple algorithm for the selection of the "best" regulatory network motif among a number of alternatives, using the expression time course of the genes which are the final targets of the activated signalling pathway. To this aim, we considered the Hill nonlinear ODEs model to simulate the behavior of two ubiquitous motifs: the single input motif and the multi output feed-forward loop motif. Our algorithm has been tested on simulated noisy data assuming the presence of a step-wise regulatory signal. The results clearly show that our method is potentially able to robustly discriminate between alternative motifs, thus providing a useful in silico identification tool for the experimenter.