Computational paradigms for analyzing genetic interaction networks

The advent of sequencing technologies has revolutionized our understanding and approach to studying biological systems. Indeed, whole-genome sequencing projects have already targeted many different species, enabling the identification of most genes in those organisms. However, observed phenotypes cannot be explained by genes alone, but rather by the interactions that their products establish under some environmental conditions (Waddington 1957). Thus, it is through the analysis of these interaction net-works (e.g. regulatory, metabolic, molecular, or genetic) that we can better understand the genotype-to-phenotype relationship, the complexity and evolution of organisms, or the differences among individuals of the same species. The topology and dynamics of these biological networks can be unveiled by systematic perturbation of their nodes (i.e. genes). For instance, upon single-gene deletions in Saccharomyces cerevisiae under standard laboratory conditions, most genes (∼80%) were not found to be essential for cell viability (Giaever et al. 2002). Though many of these genes may be required for growth in other environments (Hillenmeyer et al. 2008), this result suggests extensive functional redundancy among genes. Such functional buffering confers robustness to biological networks and shields the cellular machinery from genetic perturbations (Hartman et al. 2001). Additionally, the small effect on phenotype that many gene deletions exhibit (see Figure 2.1) evidences that single perturbations alone cannot capture the complexity of the genotype-to-phenotype relationship. Therefore, a combinatorial approach to gene perturbations is best suited to elucidate biological systems and can enable a better characterization of genes and cellular functioning. Definition of genetic interaction Genetic interactions reveal functional relations between genes that contribute to a pheno-typic trait. William Bateson first introduced the term, formerly known as epistasis (see Phillips [1998] for a description on the origin and evolution of the definition), to refer to an allele at one locus preventing a variant at another from manifesting its effect (Bateson 1909).