Systematic analysis of large-scale networks Investigating biological functions to link genotype and phenotype
Magali Michaut
07 July 2011, 14h00 - 07 July 2011, 15h30 Salle/Bat : 455/PCRI-N
Contact : magali.michaut@iscb.org
Activités de recherche :
Résumé :
A better understanding of the link between genotype and phenotype is essential to answer numerous questions related to species diversity, organism development and disease. This link can be clarified by the functions of the various components of an organism, or a cell in the case of unicellular organisms such as bacteria or yeast. We use large-scale networks to elucidate the organization of those biological components and how they interact together at various levels. Notably we develop new methods and systematic approaches to analyze and combine large-scale data sets.
In this talk I will present two approaches: i) understand the phenotypic function with genetic interactions; ii) understand the biochemical function with protein disorder.
If perturbing two genes together has a stronger effect than expected they are said to genetically interact. Genetic interactions are important because they help map gene function - functionally related genes have similar patterns of genetic interactions. Clustering large-scale genetic interaction data clearly shows groupings of genes with similar positive or negative interactions, termed monochromatic groups. In the first story we systematically study the monochromatic groups in genetic interaction networks relating them to known biological processes.
Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes, and have been associated with a plethora of different cellular functions. In the second story we attempt to better understand the different roles of disorder using a novel analysis and uncover that protein disorder can be split into three biologically and biophysically distinct phenomena in yeast. We further analyze the role of conserved disorder in higher eukaryotes including its importance for alternative splicing and cancer mutations.