CAREER: Phenotypic Robustness and Diversity: Integrating Theory and Experiment in Genomics Research and Teaching

职业：表型稳健性和多样性：整合基因组学研究和教学中的理论和实验尽管经历了一系列的环境条件，尽管由突变引起的遗传差异，生物仍能可靠地发育和功能。了解生物体如何实现这种健壮性是现代生物学的一个重要目标。一个关键的相关目标是理解健壮性如何影响进化。PI项目的中心目标是确定能够缓冲环境和遗传变异的基因，从而可能影响新性状的进化方式。该项目建立在PI先前使用计算机模拟的工作基础上，该工作导致了大量此类基因存在的预测。该项目利用了酵母（Saccharomyces cerevisiae）中单基因突变体的全面收集，以及使用荧光显微镜和自动图像分析测量单个酵母细胞物理特性的方法。分析来自4700个突变菌株的数据，将识别出受损导致更大生理变异的基因。据推测，这些基因有助于健壮性。在一项补充实验中，PI将分析实验室酵母和野生酵母杂交后代的物理变化。这将使鉴定调节自然界健壮性的基因成为可能。在生物学研究中，计算方法和实验方法的结合越来越重要。该项目的教育目标是开发两门新课程，利用计算机来加强对困难的定量概念的学习。在第一门课程中，PI将开发密集的动手计算机练习，教高级生物学学生如何分析大型数据集。第二，PI将开发多媒体学习工具，向非科学专业的学生传达定量概念。这些努力的更广泛的影响将是使下一代生物学家准备好解决日益复杂的问题，并使非科学家能够评估对他们的生活越来越重要的技术进步。

Mark L. SiegalIOB-0642999CAREER: Phenotypic Robustness and Diversity: Integrating Theory and Experiment in Genomics Research and TeachingLiving things develop and function reliably, despite experiencing a range of environmental conditions, and despite genetic differences caused by mutations. Understanding how organisms achieve this robustness is an important goal of modern biology. A key related goal is to understand how robustness affects evolution. The central aim of the PI's project is to identify genes that buffer environmental and genetic variation, and that might therefore affect how novel traits evolve. This project builds on the PI's previous work using computer simulations, which led to the prediction that a large number of such genes would exist. The project takes advantage of a comprehensive collection of single-gene mutants in the yeast, Saccharomyces cerevisiae, as well as a method for measuring physical traits of individual yeast cells using fluorescence microscopy and automated image analysis. Analysis of data from 4700 mutant strains will identify genes whose impairment causes greater physical variation. Such genes are inferred to contribute to robustness. In a complementary experiment, the PI will analyze physical variation in progeny from a cross between laboratory and wild yeast. This will permit identification of genes that modulate robustness in nature. The merging of computational and experimental approaches in biological research is increasing in importance. The educational goal of this project is to develop two new courses that use computers to enhance learning of difficult quantitative concepts. For the first course, the PI will develop intensive, hands-on computer exercises to teach advanced biology students how to analyze large data sets. For the second, the PI will develop multimedia learning tools to convey quantitative concepts to non-science majors. The broader impacts of these efforts will be to prepare the next generation of biologists to tackle increasingly complex problems, and to enable non-scientists to evaluate technological advances that will have increasing importance in their lives.