Modeling the Plant Immune Signaling Network

植物免疫信号网络建模

• 批准号: 1518058
• 负责人: Fumiaki Katagiri
• 金额: $ 70万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518058&HistoricalAwards=false
• 关键词: Modeling; Plant; Immune; Signaling; Network

Plant disease is a major cause of crop loss every year. Plants have developed a complex immune signaling network to counter attacks by disease-causing microbes. This project will develop computer models of the plant immune system based on genes expressed in response to pathogen challenge in the model plant, Arabidopsis thaliana. This information will be used to develop experimental and theoretical approaches suitable for understanding this network. These computer models have the advantage of allowing rapid exploration of the plant's immune system response under various conditions which are not practical to investigate by actual biological experimentation. In this project post-doctoral researchers and graduate students will obtain interdisciplinary training in systems biology and computer modeling. Part of this research will be driven by data generated by teams of undergraduate students in a program that engages biology undergraduate students who typically do not participate in science research. A protocol to extend this club-style, research-driven biology education platform to other institutions will be established in collaboration with a primarily undergraduate institution.The plant immune signaling network is a large, complex network with unusual emergent properties. The network is under constant attack by diverse pathogens that evolve much faster than plants. A plant, unlike an animal, does not have an adaptive immune system, and it must rely on the emergent network properties of its immune signaling network to successfully defend itself. In this project, computer models of the pathogen-triggered immune signaling network will be expanded to facilitate identification of emergent properties of the immune system. Modeling capabilities will be expanded on two fronts. In Aim 1 a fully dynamic differential equation-based model of pathogen triggered immunity will be built and tested. This model will be used for large-scale in silico experiments to identify potential immune engineering targets. In Aim 2 the pathogen triggered immunity model will be extended to explain spatially heterogeneous situations, which commonly occur in nature. To support this modeling effort, data will be collected from leaves with spatial genetic variation generated by laser heat-inducible recombination. The model will be used to predict cell-to-cell signal flows, such as those mediated by the diffusible primary immune hormones salicylate and jasmonate, and the model predictions will be experimentally tested.This award is supported jointly by programs in Systems and Synthetic Biology (Division of Molecular and Cellular Biosciences) and Symbiosis, Defense and Self-Recognition (Division of Integrative Organismal Systems).

植物病害是每年农作物损失的主要原因。 植物已经形成了复杂的免疫信号网络来抵抗致病微生物的攻击。该项目将基于模型植物拟南芥中响应病原体挑战而表达的基因，开发植物免疫系统的计算机模型。这些信息将用于开发适合理解该网络的实验和理论方法。 这些计算机模型的优点是可以快速探索植物在各种条件下的免疫系统反应，而这些条件无法通过实际生物实验进行研究。 在该项目中，博士后研究人员和研究生将获得系统生物学和计算机建模方面的跨学科培训。这项研究的一部分将由本科生团队生成的数据驱动，该项目吸引了通常不参与科学研究的生物学本科生。将与主要本科机构合作建立将这种俱乐部式、研究驱动的生物教育平台扩展到其他机构的协议。植物免疫信号网络是一个大型、复杂的网络，具有不寻常的新兴特性。该网络不断受到各种病原体的攻击，这些病原体的进化速度比植物快得多。与动物不同，植物没有适应性免疫系统，它必须依靠其免疫信号网络的新兴网络特性来成功防御自己。在该项目中，病原体触发的免疫信号网络的计算机模型将得到扩展，以促进免疫系统的新兴特性的识别。 建模能力将在两个方面得到扩展。在目标 1 中，将建立并测试基于完全动态微分方程的病原体触发免疫模型。该模型将用于大规模计算机实验，以识别潜在的免疫工程靶点。在目标 2 中，病原体触发的免疫模型将被扩展以解释自然界中常见的空间异质情况。为了支持这一建模工作，将从具有激光热诱导重组产生的空间遗传变异的叶子中收集数据。 该模型将用于预测细胞间的信号流，例如由可扩散的初级免疫激素水杨酸盐和茉莉酸介导的信号流，模型的预测将接受实验测试。该奖项由系统和合成生物学（分子和细胞生物科学部门）以及共生、防御和自我识别（综合有机系统部门）项目联合支持。