Assessing the Role of Multi-Protein Complexes in Determining Phenotype
Nolwenn Le Meur, Robert Gentleman
InfoBioStat, EHESP, Rennes, France; Bioinformatics and Computational Biology, Genentech, South San Francisco, CA, United States

In Saccharomyces cerevisiae, we showed that molecular interactions within and between multi-protein complexes are critical for cell fate. Recent studies also suggest that some control of phenotype can be usefully attributed to multi-protein complexes rather than genes or pathways. Indeed, while phenotypic changes are often measured by the manipulation of single genes (deletion, up-regulation, etc.), the biological mechanisms that underlie the change in phenotype might depend on higher levels of organization, such as multi-protein complexes. In this work we thus attempted to assess the role of multi-protein complexes in determining phenotype. We tested whether gene products known to be associated with a phenotype are randomly distributed in the interactome or cluster in specific multi-protein complexes. In addition, since the expression of phenotype highly depends on the environmental conditions, we investigated different datasets to evaluate if similar phenomena (random distribution or cluster) could be observed and thus associate multi-protein complex activity (fitness) to environmental conditions.

Keywords: Interactome; Graph Theory; Yeast

Biography: I am an associate professor in the computational and biostatistics department at Ecole des Hautes Etudes en Santé Publique (EHESP - Rennes). I did my thesis in Nantes, France, under Dr. Jean Leger supervision at INSERM U533 (currently Nantes Thorax Institute). During my thesis, I worked on microarray pre-processing and design methods to assess and preprocess homemade microarray data. Since my thesis (2005) I have been interested in data quality assessment in *omics and other high throughput data (microarray, flow cytometry...). During my post-doctoral training in the computational biology group of Dr. Gentleman at the Fred Hutchinson Cancer Research Center (in Seattle) I have started to be interested in data integration, and more especially in the integration of omics data and meta-data using statistical and modeling techniques. This brought me to coordinate in 2010 the integrative genomic project of the western France region (Brittany and Loire regions).