Most genes are not required for viability. This robustness to mutation is a fundamental property of complex biological systems but the mechanism(s) underlying it and how it evolves is unclear. It has been proposed that robustness...
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Descripción del proyecto
Most genes are not required for viability. This robustness to mutation is a fundamental property of complex biological systems but the mechanism(s) underlying it and how it evolves is unclear. It has been proposed that robustness can result from genetic redundancy through gene duplication and the distributed nature of biological networks. The robustness of both yeast and C. elegans has been dissected using genetic interaction screens, where combinations of mutations are screened for a synthetic effect (i.e. a stronger phenotype than the phenotype of each individual mutation). Since many human diseases are likely to be caused by combinatorial effects between mutations it is important to understand the underlying mechanisms of genetic interactions and also whether genetic interactions in model organisms can be used to predict interactions in humans. In the current work, using phylogenetic analysis we aim to systematically investigate whether genetic redundancy between gene duplicates is maintained across extensive evolutionary periods. As a second aspect of this work, we will use experiments in C. elegans to test whether the connectivity of a gene within a genetic interaction network is conserved between species, despite the fact that individual connections are under high turnover. To gain deeper understanding of the mechanisms underlying genetic interactions we will also investigate the contribution of transcription regulatory interactions to genetic interaction networks. In brief, this proposal aims to investigate the mechanisms and evolution of genetic robustness to mutation. The results will provide a framework for understanding and predicting how mutations combine to cause disease in humans. Moreover the Fellowship will also provide the applicant with advanced training in Systems Biology and in the experimental and computational analysis of genetic networks, and will establish her as an independent scientific investigator.