Inferring gene networks from steady-state response to single-gene perturbations.

Inferring gene networks from gene expression data is an important step in understanding the molecular machinery of life. Three methods for establishing and quantifying causal relationships between genes based on steady-state measurements in single-gene perturbation experiments have recently been proposed: the regulatory strength method, the local regulatory strength method, and Gardner's method. The theoretical basis of these methods is presented here in a thorough and consistent fashion. In principle, for the same data set all three methods would generate identical networks, but they would quantify the strengths of connections in different ways. The regulatory strength method is shown here to be topology-dependent. It adopts the format of the data collected in gene expression microarray experiments and therefore can be immediately used with this technology. The regulatory strengths obtained by this method can also be used to compute local regulatory strengths. In contrast, Gardner's method requires both measurements of mRNA concentrations and measurements of the applied rate perturbations, which is not usually part of a standard microarray experimental protocol. The results generated by Gardner's method and by the two regulatory strengths methods differ only by scaling constants, but Gardner's method requires more measurements. On the other hand, the explicit use of rate perturbations in Gardner's approach allows one to address new questions with this method, like what perturbations caused given responses of the system. Results of the application of the three techniques to real experimental data are presented and discussed. The comparative analysis presented in this paper can be helpful for identifying an appropriate technique for inferring genetic networks and for interpreting the results of its application to experimental data.