Starter Grant: Elucidating Function and Organization of Molecular Networks:From Molecules to a System

该项目的重点是了解序列变异对分子网络功能的影响。将开发计算算法来整合基因、基因表达和表型数据，并构建描述序列变异如何扰乱调控网络、改变信号处理以及在生长等复杂细胞表型中表现的模型。我们将研究一大批高度可变的酵母菌株的细胞生长，这些菌株在许多环境条件下已经产生了稳健的定量生长曲线。酵母是理解生长遗传复杂性的理想模型系统，因为它的基因组紧凑、有形和易于理解，而且酵母遗传学可用于快速验证。与最近研究双重缺失的影响的工作相反，大量微妙的自然发生的变异将为在适应和进化中发挥作用的力量提供洞察。为了分析这些数据，需要复杂的计算方法来确定哪些序列变异对观察到的表型有影响。本项目使用的方法是基于基因序列和功能基因组学之间的互补二元性。通过对控制细胞活动的复杂调控网络的微调扰动，基因对表型的显著影响被诱导。单个基因表达的变异更容易处理，可以作为中介，以分层的方式帮助将遗传因素与更复杂的下游表型变化联系起来。相反，DNA序列多态是有效的扰动抗原，它提供了丰富的变异源，有助于揭示分子网络中的调控关系，并指导它们的因果关系。这里开发的方法将被用来揭示控制调节、新陈代谢和生长之间相互作用的一般原理。该项目还将为研究生提供培训机会。

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.