Agroecological Annotation of Gene Function and Computational Analysis of Gene Networks

PI：Cynthia Weinig（怀俄明州大学）CoPI：Sanjoy Das（堪萨斯州立大学），Julin N. Maloof（加州-戴维斯大学），C.罗伯逊·麦克朗（达特茅斯学院），斯蒂芬·M.韦尔奇（堪萨斯州立大学）主要合作者：多伊娜·卡拉和杰拉德·克鲁滕贝格（堪萨斯州立大学），以及保拉X。Kover [曼彻斯特大学（英国）]这项研究将研究植物对植被密度的反应，这通常被称为“避阴”，极大地影响了作物品种的产量。 两种植物物种，芜菁和拟南芥，在这项研究中被用来作为模型，用于开发计算方法，这将使两个基因网络的重建，调节密度响应和预测不同的性状（如开花时间，高度和坐果）从复杂的基因型。 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）对现实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）记录田间近植物环境条件，包括土壤和大气变量，以及15分钟间隔的入射光谱。 实地研究将提供一个农业生态相关的基因功能的图片。 在建模方面，该项目将整合遗传和环境数据，以生成一个比单独从突变体或实验室研究更完整的基因网络。 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植物基因型的不同性状。 该项目将：1）开发进化遗传学模块，以教育教师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、基于探究的练习。 每年将在怀俄明州接待大约75名教师，这种教育推广活动最终将覆盖该州60-70%的生物学教师。 作为另一个外联部分，将使用的计算技术涉及一个计算志愿者网络。 通过项目网站，该网络提供了另一个分发教育材料的渠道。 最后，长期的，高采样率的时间序列的入射辐照度光谱是非常罕见的，但感兴趣的社区，从植物科学家到太阳能研究人员。 国家可再生能源实验室将通过网络提供作为这项研究一部分的宽带光谱测量结果。 DNA序列和基因表达信息将保存在NCBI（http：//www.ncbi.nlm.nih.gov/）SRA和GEO数据库中。

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.