A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

SUMMARY An extracellular matrix composed of a layered meshwork of β-glucans, chitin, and mannoproteins encapsulates cells of the yeast Saccharomyces cerevisiae. This organelle determines cellular morphology and plays a critical role in maintaining cell integrity during cell growth and division, under stress conditions, upon cell fusion in mating, and in the durable ascospore cell wall. Here we assess recent progress in understanding the molecular biology and biochemistry of cell wall synthesis and its remodeling in S. cerevisiae. We then review the regulatory dynamics of cell wall assembly, an area where functional genomics offers new insights into the integration of cell wall growth and morphogenesis with a polarized secretory system that is under cell cycle and cell type program controls.

Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.