The focus of the project is to understand the effect of sequence variation on the function of molecular networks. Computational algorithms will be developed to integrate genotype, gene expression and phenotype data and to construct models that describe how sequence variation perturbs the regulatory network, alters signal processing and is manifested in complex cellular phenotypes such as growth. Cell growth in a large collection of highly variable yeast strains, for which robust quantitative growth curves under numerous environmental conditions have been generated, will be studied. Yeast is an ideal model system for understanding the genetic complexity of growth as its genome is compact, tangible and well understood, and yeast genetics is available for rapid validation. Contrary to recent works studying the effects of double deletions, a large number of subtle naturally occurring variations will offer insight to forces at play in adaptation and evolution. To analyze this data, sophisticated computational methods are needed to identify which sequence variation contributes to the observed phenotype. The approach used in this project is based on the complementary duality between genetic sequence and functional genomics. A significant influence of genotype on phenotype is induced by fine tuned perturbations to the complex regulatory network that governs a cells activity. Variation in the expression of a single gene is more tractable and can be used as an intermediary to help associate genetic factors to the more complex downstream changes in phenotype in a hierarchical fashion. Conversely, DNA sequence polymorphisms are effective perturb-agens which provide a rich source of variation to help uncover regulatory relations in the molecular network as well as direct their causality. Methods developed here will be used to uncover general principles governing the interplay between regulation, metabolism and growth.
This project will also provide training opportunity for a graduate student.
