Kevin Kontos

Information-theoretic inference of large transcriptional regulatory networks

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Effect of 21 different nitrogen sources on global gene expression in the yeast Saccharomyces cerevisiae.

ABSTRACT We compared the transcriptomes of Saccharomyces cerevisiae cells growing under steady-state conditions on 21 unique sources of nitrogen. We found 506 genes differentially regulated by nitrogen and estimated the activation degrees of all identified nitrogen-responding transcriptional controls according to the nitrogen source. One main group of nitrogenous compounds supports fast growth and a highly active nitrogen catabolite repression (NCR) control. Catabolism of these compounds typically yields carbon derivatives directly assimilable by a cells metabolism. Another group of nitrogen compounds supports slower growth, is associated with excretion by cells of nonmetabolizable carbon compounds such as fusel oils, and is characterized by activation of the general control of amino acid biosynthesis (GAAC). Furthermore, NCR and GAAC appear interlinked, since expression of the GCN4 gene encoding the transcription factor that mediates GAAC is subject to NCR. We also observed that several transcriptional-regulation systems are active under a wider range of nitrogen supply conditions than anticipated. Other transcriptional-regulation systems acting on genes not involved in nitrogen metabolism, e.g., the pleiotropic-drug resistance and the unfolded-protein response systems, also respond to nitrogen. We have completed the lists of target genes of several nitrogen-sensitive regulons and have used sequence comparison tools to propose functions for about 20 orphan genes. Similar studies conducted for other nutrients should provide a more complete view of alternative metabolic pathways in yeast and contribute to the attribution of functions to many other orphan genes.

Nested q-Partial Graphs for Genetic Network Inference from ”Small n, Large p” Microarray Data

Gaussian graphical models are widely used to tackle the important and challenging problem of inferring genetic regulatory networks from expression data. These models have gained much attention as they encode full conditional relationships between variables, i.e. genes. Unfortunately, microarray data are characterized by a low number of samples compared to the number of genes. Hence, classical approaches to estimate the full joint distribution cannot be applied. Recently, limited-order partial correlation approaches have been proposed to circumvent this problem. It has been shown both theoretically and experimentally that such graphs provide accurate approximations of the full conditional independence structure between the variables thanks to the sparsity of genetic networks. Alas, computing limited-order partial correlation coefficients for large networks, even for small order values, is computationally expensive, and often even intractable. Moreover, problems deriving from multiple statistical testing arise, and one should expect that most of the edges are removed. We propose a procedure to tackle both problems by reducing the dimensionality of the inference tasks. By adopting a screening procedure, we iteratively build nested graphs by discarding the less relevant edges. Moreover, by conditioning only on relevant variables, we diminish the problems related to multiple testing. This procedure allows us to faster infer limited-order partial correlation graphs and to consider higher order values, increasing the accuracy of the inferred graph. The effectiveness of the proposed procedure is demonstrated on simulated data.

Gaussian Graphical Models to Infer Putative Genes Involved in Nitrogen Catabolite Repression in S. cerevisiae

Nitrogen is an essential nutrient for all life forms. Like most unicellular organisms, the yeast Saccharomyces cerevisiae transports and catabolizes good nitrogen sources in preference to poor ones. Nitrogen catabolite repression (NCR) refers to this selection mechanism. We propose an approach based on Gaussian graphical models (GGMs), which enable to distinguish direct from indirect interactions between genes, to identify putative NCR genes from putative NCR regulatory motifs and over-represented motifs in the upstream noncoding sequences of annotated NCR genes. Because of the high-dimensionality of the data, we use a shrinkage estimator of the covariance matrix to infer the GGMs. We show that our approach makes significant and biologically valid predictions. We also show that GGMs are more effective than models that rely on measures of direct interactions between genes.