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

Authors:
Kevin Kontos;Bruno André;Jacques Helden;Gianluca Bontempi
Affiliations:
Machine Learning Group, Faculté des Sciences, Université Libre de Bruxelles (ULB), Brussels, Belgium 1050;Physiologie Moléculaire de la Cellule, IBMM, Faculté des Sciences, ULB, Gosselies, Belgium 6041;Laboratoire de Bioinformatique des Génomes et des Réseaux, Faculté des Sciences, ULB, Brussels, Belgium 1050;Machine Learning Group, Faculté des Sciences, Université Libre de Bruxelles (ULB), Brussels, Belgium 1050
Venue:
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2009

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Abstract

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.