Agroecological Annotation of Gene Function and Computational Analysis of Gene Networks

首席研究员：Cynthia Weinig（怀奥明大学）首席研究员：Sanjoy Das（堪萨斯州立大学），Julin N. Maloof（加州大学戴维斯分校），C. Robertson McClung（达特茅斯学院），Stephen M. Welch（堪萨斯州立大学）Doina Carragea和Gerard Kluitenberg（堪萨斯州立大学）以及Paula X. Kover（英国曼彻斯特大学）的研究将检查植物对植被密度的反应，这通常被称为“避荫”，并且极大地影响作物品种的产量。本研究以油菜和拟南芥两种植物作为模型，开发计算方法，重建调节密度响应的基因网络，并预测复杂基因型的不同性状（如开花时间、高度和坐果）。数据收集将包括1)量化实验遗传物质（如突变体和分离后代）对实际密度处理和指示高密度和低密度的光质量控制操作的发育、形态和转录反应；2)记录田间近厂环境条件，包括土壤和大气变量，以及每隔15 min的入射光谱。实地研究将提供有关基因功能的农业生态学图片。在建模方面，该项目将整合遗传和环境数据，以产生比单独通过突变或实验室研究可能产生的更完整的基因网络。最后，由于产量和其他表型作为模型输出，计算方法将能够预测来自植物基因型的各种性状。该项目将1)开发进化遗传学模块，向教师传授进化理论和基本分子遗传学方法；2)提供种子“工具包”，用于适合年龄的探究式练习。怀俄明州每年将接待大约75名教师，这种教育推广最终将覆盖该州所有生物教师的60-70%。作为进一步扩展的组成部分，所使用的计算技术涉及到计算志愿者网络。通过项目网站，这个网络提供了另一个分发教育材料的场所。最后，长期的、高采样率的入射辐射光谱时间序列非常罕见，但却引起了从植物科学家到太阳能研究人员的兴趣。作为这项研究的一部分，国家可再生能源实验室将通过网络提供宽带光谱测量数据。DNA序列和基因表达信息将存入NCBI (http://www.ncbi.nlm.nih.gov/) SRA和GEO数据库。

PI: Cynthia Weinig (University of Wyoming)CoPIs: Sanjoy Das (Kansas State University), Julin N. Maloof (University of California - Davis), C. Robertson McClung (Dartmouth College), Stephen M. Welch (Kansas State University)Key Collaborators: Doina Carragea and Gerard Kluitenberg (Kansas State University), and Paula X. Kover [University of Manchester (United Kingdom)The research will examine plant responses to vegetation density, which are commonly referred to as "shade-avoidance" and which greatly affect yield in crop species. Two plant species, Brassica rapa and Arabidopsis thaliana, are used in this research as models for developing computational approaches that will enable both reconstruction of gene networks that regulate density responses and prediction of different traits (such as flowering time, height, and fruit set) from complex genotypes. Data collection will include 1) quantifying the developmental, morphological, and transcriptional responses of experimental genetic material (such as mutants and segregating progeny) to realistic density treatments and controlled manipulations of light quality indicative of high- and low-density; and 2) recording near-plant environmental conditions in the field, including soil and atmospheric variables, and incident spectra at 15-min intervals. Field studies will provide an agroecologically relevant picture of gene function. In modeling, the project will integrate genetic and environmental data to generate a more complete gene network than is possible from mutant or laboratory studies alone. Finally, because yield and other phenotypes serve as model outputs, the computational approaches will enable the prediction of diverse traits from plant genotypes. The project will 1) develop evolutionary genetics modules to educate teachers on evolutionary theory and basic molecular genetic approaches and 2) provide seed "kits" to be used in age-appropriate, inquiry-based exercises. Approximately 75 teachers will be hosted annually in Wyoming, and this educational outreach should ultimately reach between 60-70% of all biology teachers in the state. As a further outreach component, the computational technology to be used involves a network of computing volunteers. Via the project website, this network provides another venue to distribute educational materials. Finally, long-term, high-sampling-rate time series of incident irradiance spectra are extremely rare but of interest to communities ranging from plant scientists to solar energy researchers. The National Renewable Energy Laboratory will web-deliver the wide-band spectral measurements made as part of this research. DNA sequence and gene expression information will be deposited in the NCBI (http://www.ncbi.nlm.nih.gov/) SRA and GEO databases.