CAREER: Computational Tools for Fundamental Characterization and Inference of Genetic Interaction Networks

明尼苏达大学双城分校获得了美国国家科学基金会教师早期职业发展（Career）计划的资助，以支持开发用于理解大规模基因相互作用网络的计算方法。了解生物系统的组织及其与功能的关系是现代分子生物学的基本问题之一。表征细胞组织的经典方法是用遗传扰动组合探测细胞并观察所产生的表型。这种方法最近在酵母等模式生物的全基因组范围内得到了应用，实验技术已经使数百万个组合突变体的构建成为可能。初步研究表明，这些数据可能为细胞组织提供了前所未有的视角，但我们产生突变体和测量定量表型的能力很快就超过了我们对它们进行系统解释的能力。该项目将支持解决这一需求的计算基础设施的发展，包括三个具体目标：(1)综合挖掘遗传相互作用网络结构的新算法；(2)从物种内和跨物种的不同基因组数据中获得遗传相互作用的预测模型；(3)用于遗传相互作用网络的综合分析和可视化的软件工具，以促进发现。该项目的研究目标与教育组成部分相结合，这将解决跨学科科学的一个关键需求：尽早发现有才华的学生，使他们能够为跨学科研究建立广泛而坚实的基础。PI将启动一项生物信息学推广计划，邀请地区高中科学和数学教师，特别是来自农村社区的教师参与并开发他们自己的教育计划，重点介绍基因组学和生物信息学的最新成就和未来方向。PI还在本科生和研究生阶段开发新的计算生物学课程，并让本科生和研究生参与高度协作的跨学科项目。更多关于这个项目的信息可以在http://www.cs.umn.edu/~cmyers上找到。

The University of Minnesota-Twin Cities is awarded a grant by the NSF Faculty Early Career Development (CAREER) Program to support the development of computational approaches for understanding large-scale genetic interaction networks. Understanding the organization of biological systems and how it relates to function is one of the fundamental questions of modern molecular biology. A classical approach to characterizing cellular organization is to probe a cell with combinations of genetic perturbations and observe the resulting phenotype. This approach has recently been applied on a genome-wide scale in model organisms like yeast, where experimental technologies have enabled the construction of millions of combinatorial mutants. Preliminary studies hint that these data may provide an unprecedented view of cellular organization, but our ability to generate mutants and measure quantitative phenotypes has quickly surpassed our capacity for systematic interpretation of them. This project will support the development of computational infrastructure for addressing this need, including three specific objectives: (1) novel algorithms for comprehensive mining of genetic interaction network structure, (2) predictive models of genetic interactions from diverse genomic data within and across species, and (3) software tools for integrative analysis and visualization of genetic interaction networks to facilitate discovery.The research goals of the project are integrated with an educational component, which will address a critical need in interdisciplinary science: the issue of identifying talented students early enough such that they are able to establish a broad, but solid, foundation for interdisciplinary research. The PI will start a bioinformatics outreach program in which area high-school science and math teachers, particularly those from rural communities, are invited to participate in and develop their own educational programs highlighting recent successes and future directions in genomics and bioinformatics. The PI is also developing new computational biology courses at both the undergraduate and graduate level and involving undergraduate and graduate students in highly collaborative, interdisciplinary projects. More about the project can be found at http://www.cs.umn.edu/~cmyers.