Transcriptional Network Structure Assessment Via the Data Processing Inequality

Whole genome transcriptional regulation involves an enormous number of physicochemical processes responsible for phenotypic variability and organismal function. The actual mechanisms of regulation are only partially understood. In this sense, an extremely important conundrum is related with the probabilistic inference of gene regulatory networks. A plethora of different methods and algorithms exists. Many of these algorithms are inspired in statistical mechanics and rely on information theoretical grounds. However, an important shortcoming of most of these methods, when it comes to deconvolute the actual, functional structure of gene regulatory networks lies in the presence of indirect interactions. We present a proposal to discover and assess for such indirect interactions within the framework of information theory by means of the data processing inequality. We also present some actual examples of the applicability of the method in several instances in the field of functional genomics.