A recursive network approach can identify constitutive regulatory circuits in gene expression data

The activity of the cell is often coordinated by the organisation of proteins into regulatory circuits that share a common function. Genome-wide expression profiles might contain important information on these circuits. Current approaches for the analysis of gene expression data include clustering the individual expression measurements and relating them to biological functions as well as modelling and simulation of gene regulation processes by additional computer tools. The identification of the regulative programmes from microarray experiments is limited, however, by the intrinsic difficulty of linear methods to detect low-variance signals and by the sensitivity of the different approaches. Here we face the problem of recognising invariant patterns of correlations among gene expression reminiscent of regulation circuits. We demonstrate that a recursive neural network approach can identify genetic regulation circuits from expression data for ribosomal and genome stability genes. The proposed method, by greatly enhancing the sensitivity of microarray studies, allows the identification of important aspects of genetic regulation networks and might be useful for the discrimination of the different players involved in regulation circuits. Our results suggest that the constitutive regulatory networks involved in the generic organisation of the cell display a high degree of clustering depending on a modular architecture.