Computational analysis of genome-scale growth-interaction data in Saccharomyces cerevisiae

Abstract

In just two decades, advances in the experimental mapping and computational analysis of DNA sequences have resulted in complete reference genomes for thousands of different species. We therefore have a nearly complete "parts list" (that is, genes) for each of these organisms, but the task remains to discover the individual function of each of these genes, as well as characterize the organization and evolution of these individual genes into the many sub-systems at work inside the cell. Perturbation analysis is a crucial tool in identifying gene function and genetic relationships. In perturbation analysis, genes are selectively deleted or mutated, and any change in the resulting phenotype—for example, growth rate—can give an indication of gene function. We can then obtain a more complete functional map by systematically changing or combining genetic perturbations, and/or varying the environment under which we observe the phenotype. The focus of this dissertation is the development of computational methods to enable genome-scale perturbation analyses in yeast.We begin the dissertation with a discussion of the first computational analysis of growth rate data for a comprehensive collection of deletion mutants in a wide variety of truly minimal environments. This analysis revealed how sources of nitrogen and carbon in the environment interact to determine growth rate, both in the context of wild-type strains, and in the context of individual single-mutants. We also discuss comparisons between experimental observation and in silico growth rate predictions which serve as a benchmark for current constraint-based modeling methods. Secondly, we discuss our efforts to map the complete genetic interaction network in yeast through a comprehensive set of double-mutant experiments. We explore the ability of genetic interactions and high-dimensional interaction profiles in to predict gene function, and describe both local and global properties of the genetic interaction network, which may reasonably be expected to be conserved to other organisms, such as humans. Lastly, we describe local properties of the genetic interaction network surrounding genes which have undergone ancient duplication. Using networks derived from both double- and triple-mutant experiments, we explore the consequences of duplication, divergence, and retained common functionality, and speculate about the evolutionary process, and the constraints on that process which govern the fates of duplicate gene pairs.Functional capabilities of genes are conserved across species to a surprising extent. Determining the functions of the remaining uncharacterized genes in yeast, will assist in the functional characterization of the thousands of remaining uncharacterized genes in human. Further, the mapping of the first complete eukaryotic genetic interaction network has direct impact on the study of complex, multi-genic phenotypes, including many human diseases. Meanwhile, the study of genetic interaction network structure, yields fundamental insights into the nature of cellular robustness, redundancy, and the evolutionary processes which give rise to them.In just two decades, advances in the experimental mapping and computational analysis of DNA sequences have resulted in complete reference genomes for thousands of different species. We therefore have a nearly complete "parts list" (that is, genes) for each of these organisms, but the task remains to discover the individual function of each of these genes, as well as characterize the organization and evolution of these individual genes into the many sub-systems at work inside the cell. Perturbation analysis is a crucial tool in identifying gene function and genetic relationships. In perturbation analysis, genes are selectively deleted or mutated, and any change in the resulting phenotype—for example, growth rate—can give an indication of gene function. We can then obtain a more complete functional map by systematically changing or combining genetic perturbations, and/or varying the environment under which we observe the phenotype. The focus of this dissertation is the development of computational methods to enable genome-scale perturbation analyses in yeast.We begin the dissertation with a discussion of the first computational analysis of growth rate data for a comprehensive collection of deletion mutants in a wide variety of truly minimal environments. This analysis revealed how sources of nitrogen and carbon in the environment interact to determine growth rate, both in the context of wild-type strains, and in the context of individual single-mutants. We also discuss comparisons between experimental observation and in silico growth rate predictions which serve as a benchmark for current constraint-based modeling methods. Secondly, we discuss our efforts to map the complete genetic interaction network in yeast through a comprehensive set of double-mutant experiments. We explore the ability of genetic interactions and high-dimensional interaction profiles in to predict gene function, and describe both local and global properties of the genetic interaction network, which may reasonably be expected to be conserved to other organisms, such as humans. Lastly, we describe local properties of the genetic interaction network surrounding genes which have undergone ancient duplication. Using networks derived from both double- and triple-mutant experiments, we explore the consequences of duplication, divergence, and retained common functionality, and speculate about the evolutionary process, and the constraints on that process which govern the fates of duplicate gene pairs.Functional capabilities of genes are conserved across species to a surprising extent. Determining the functions of the remaining uncharacterized genes in yeast, will assist in the functional characterization of the thousands of remaining uncharacterized genes in human. Further, the mapping of the first complete eukaryotic genetic interaction network has direct impact on the study of complex, multi-genic phenotypes, including many human diseases. Meanwhile, the study of genetic interaction network structure, yields fundamental insights into the nature of cellular robustness, redundancy, and the evolutionary processes which give rise to them.