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To uncover potential disease molecular pathways and signaling networks, we do not only need undirected maps but also we need to infer the directionality of functional or physical interactions between cellular components. A wide range of methods for identifying functional interactions between genes relies on correlations between experimental gene expression measurements to some extent. However, the standard Pearson or Spearman correlation-based approaches can only determine undirected correlations between cellular components. Here, we apply a volatility-constrained correlation method for gene expression profiles that offers a new metric to capture directionality of interactions between genes. To evaluate the predictions we used four datasets distributed by the DREAM5 network inference challenge including an in silico-constructed network and three organisms such as S. aureus , E. coli and S. cerevisiae . The predictions performed by our proposed method were compared to a gold standard of experimentally verified directionality of genetic regulatory links. Our findings show that our method successfully predicts the genetic interaction directionality with a success rate higher than 0.5 with high statistical significance. [ABSTRACT FROM AUTHOR]

Abstract: The widespread use of microarrays provided a first glimpse at some simple laws and organizing principles that govern the transcriptome. Previous analyses have shown that the transcriptional organization is very heterogeneous and characterized by a power–law decay for gene expression levels. Moreover, a simple law was unveiled suggesting that gene expression dynamic changes under stress are proportional to their initial expression values. However, to elucidate and assess the underlying governing principles of transcriptional organization, we do not only need to identify them, but also provide theoretical models that are able to faithfully capture and reproduce them. Here we present a method to investigate the gene expression dynamics inspired by the theory of nonlinear transformation of random signals and noise. The model is able to explain not only the well-known power–law decay for abundance of expression levels, but also to reproduce the linear dependence of the standard deviation of gene expression change with respect to the initial expression value (also known as rich-travels-more dynamics). To our knowledge, this is the first model applied to gene expression dynamics that is able to simultaneously predict both statistical features. The theoretical framework derives an indicator to measure the coupling between gene expression and specific perturbations. Using genome-wide transcriptional data, this indicator identifies genes strongly coupled to specific inflammatory responses to different pathogens. The novel application of signal and noise theory to study intracellular responses and gene expression changes offers not only a new theoretical avenue to study transcriptional responses to environmental stresses and chemical signals but also provides predictive capability at the genome scale. [Copyright &y& Elsevier]