Arabidopsis 2010: Regulatory Networks Controlling Root Growth and Differentiation

PI：Philip N. BenfeyCo-PI：Uwe OhlerIOS-1021619拟南芥2010：控制根生长和分化的调控网络本项目的长期科学目标是确定控制拟南芥根生长和分化的基因调控网络的动态。根的生长和解剖的简化方面将被利用，以确定参与发展的网络，然后将与环境刺激扰动。通过使用一组新的特异于细胞类型和发育阶段的标记物，并通过使用下一代测序来分析表达，将大大增强目前确定根和沿着发育轴的每种细胞类型中mRNA表达谱的工作。在第二个目标中，表达数据将通过染色质免疫沉淀、测序和酵母单杂交分析进行组合和扩展，以推断控制对农业和生物能源生产至关重要的生物过程的调控网络。在第三个目标中，将确定对于非生物胁迫如温度和干旱以及生物胁迫如细菌的细胞分辨率的响应。为了获得对环境刺激的网络响应的动态建模的参数，将使用RootArray平台。该技术允许同时在体内观察超过60株幼苗的表达反应。第四个目标将是确定如何组织特异性响应非生物胁迫的变化之间的自然分离的拟南芥。所有这些工作都将由理论和建模团队提供信息并进行分析，该团队将领导网络识别，建模动态响应和图像分析。 该项目还产生了一些更广泛的影响。为了实现粮食安全和生物能源生产的植物性状的持续改进，将需要对控制植物生长和分化的网络有深入的了解。这项研究将产生高分辨率的数据集，从中可以识别和表征控制对现实世界农业和生物能源生产力至关重要的生物过程的监管网络。该项目的另一个重要部分将是在系统生物学方面培训下一代植物科学家，该系统生物学将定量和实验方法相结合。所有学员的经验将通过交叉培训的机会之间的计算和实验生物学在杜克中心系统生物学以及参与外展工作，如帮助教课程复杂的遗传性状在北卡罗来纳州中央大学，一个历史上的黑人大学在达勒姆，北卡罗来纳州和参加夏季本科生研究计划的学生从科学代表性不足的群体。

PI: Philip N. BenfeyCo-PI: Uwe OhlerIOS-1021619Arabidopsis 2010: Regulatory Networks Controlling Root Growth and DifferentiationThe long-term scientific goal of this project is to determine the dynamics of the gene regulatory networks that control root growth and differentiation in Arabidopsis. The simplifying aspects of root growth and anatomy will be exploited to identify networks involved in development, which will then be perturbed with environmental stimuli. Current work on determining the mRNA expression profile in every cell type of the root and along the developmental axis will be greatly enhanced through the use of a new set of markers that are specific for both cell type and developmental stage and through use of next-generation sequencing to profile expression. In a second aim, the expression data will be combined and extended by chromatin immunoprecipitation followed by sequencing and by yeast one-hybrid analysis to infer regulatory networks controlling biological processes central to agriculture and bioenergy production. In the third aim the response at cellular resolution will be determined for abiotic stresses such as temperature and drought as well as biotic stresses such as bacteria. To obtain parameters for the modeling of the dynamics of network responses to environmental stimuli, the RootArray platform will be used. This technology allows the simultaneous in vivo observation of expression responses of more than 60 seedlings. The fourth aim will be to identify how the tissue-specific response to an abiotic stress changes among natural isolates of Arabidopsis. All of these efforts will be informed by and analyzed in concert with a theory and modeling team, which will lead the effort on network identification, modeling dynamical responses and image analysis. This project also has several broader impacts. To achieve continued improvement in plant traits for food security and bioenergy production will require a sophisticated understanding of the networks that control plant growth and differentiation. This research will generate high-resolution datasets from which regulatory networks controlling biological processes central to real-world agricultural and bioenergy productivity can be identified and characterized. Another important part of the project will be to train the next generation of plant scientists in systems biology, which integrates quantitative and experimental approaches. The experience of all trainees will be enhanced by cross-training opportunities between computational and experimental biology within the Duke Center for Systems Biology as well as participation in outreach efforts such as helping to teach a course on Complex Genetic Traits at North Carolina Central University, a historically black university in Durham, NC and participating in summer undergraduate research programs for students from groups underrepresented in science.