Modeling the Plant Immune Signaling Network

植物病害是每年造成农作物损失的主要原因。 植物已经发展出一种复杂的免疫信号网络来抵抗致病微生物的攻击。该项目将根据模式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于理解该网络的实验和理论方法。 这些计算机模型的优点是允许快速探索植物的免疫系统在各种条件下的反应，这是不切实际的调查，通过实际的生物实验。 在这个项目中，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的跨学科培训。这项研究的一部分将由本科生团队在一个项目中生成的数据驱动，该项目涉及通常不参与科学研究的生物学本科生。一个协议，以扩展这个俱乐部式的，研究驱动的生物学教育平台，以其他机构将建立在合作与主要本科院校。植物免疫信号网络是一个巨大的，复杂的网络与不寻常的涌现属性。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紧急网络特性来成功地保护自己。在这个项目中，病原体触发的免疫信号网络的计算机模型将被扩展，以便于识别免疫系统的紧急属性。 建模能力将在两个方面得到扩展。在目标1中，将建立和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的计算机实验，以识别潜在的免疫工程靶标。在目标2中，病原体引发的免疫模型将被扩展到解释空间异质性的情况下，这通常发生在自然界中。为了支持这一建模工作，数据将收集从叶片与激光热诱导重组产生的空间遗传变异。 该模型将被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和茉莉酸介导的信号流，模型预测将通过实验进行测试。该奖项由系统与合成生物学（分子和细胞生物科学部）和共生，防御和自我识别（综合有机系统部）项目共同支持。

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).