EAGER: Collaborative Research: Novel micromechanical and computational approaches to discover the mechanisms of symmetry breaking and polarized growth in dicot pavement cells

EAGER：协作研究：新的微机械和计算方法，用于发现双子叶植物路面细胞对称性破缺和极化生长的机制

• 批准号: 1249652
• 负责人: Daniel Szymanski
• 金额: $ 17万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1249652&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Novel; micromechanical

In plants, the leaf epidermis is an important architectural control element that defines the growth properties of underlying tissues and the overall form of the organ. The size and shape of leaves are important traits in agricultural production, as is the architecture of the leaf. Therefore, a deep understanding of the genetic and cellular control of leaf development is an important research goal. The tissue-level behavior of the epidermis is driven by the polarized growth of jig-saw-puzzle shaped pavement cells with interdigitating lobes. However, the molecular, cellular, and mechanical control mechanisms for this important cell shape control pathway are not known. This knowledge gap cannot be filled until new experimental and computational approaches are developed. The objective of this project is to create a new set of image analysis techniques, mechanical devices, and mathematical models that will enable a mechanistic understanding of how tissue-level mechanical forces interact with intracellular signaling pathways to control the geometry of morphogenesis. Our approach will include the development of new image analysis and nano-scale mechanical devices that will enable quantitative tests for the mechanisms of pavement cell growth. We will create computational models that will allow us to analyze the plausibility of existing growth control models, and make new predictions about how mechanical forces and intracellular reorganization interact to control cell shape. The research will include interdisciplinary cross-training of the research team, and is expected to generate technical advances in image processing and analysis that will be made publicly available through a collaboration with the iPlant data sharing resource. The project will generate broadly useful computational models that simulate plant cell growth control mechanisms and generate predictions that can be experimentally tested. This project is jointly supported by the Programs in Plant, Fungal and Microbial Development in the Division of Integrative Organismal Systems and by the Networks and Regulation and Cellular Processes Programs in the Division of Molecular and Cellular Biosciences.

在植物中，叶表皮是一个重要的结构控制元件，它决定了下层组织的生长特性和器官的整体形态。叶片的大小和形状是农业生产中的重要特征，叶片的结构也是如此。 因此，深入了解叶发育的遗传和细胞控制是一个重要的研究目标。表皮的组织水平行为是由具有交错叶的拼图形状的铺路细胞的极化生长驱动的。然而，这一重要的细胞形状控制途径的分子、细胞和机械控制机制尚不清楚。这种知识差距无法填补，直到新的实验和计算方法的开发。该项目的目标是创建一套新的图像分析技术，机械设备和数学模型，这将使组织水平的机械力如何与细胞内信号通路相互作用，以控制形态发生的几何形状的机械理解。我们的方法将包括开发新的图像分析和纳米级机械设备，这将使路面细胞生长机制的定量测试。我们将创建计算模型，使我们能够分析现有的生长控制模型的可扩展性，并对机械力和细胞内重组如何相互作用以控制细胞形状进行新的预测。该研究将包括研究团队的跨学科交叉培训，预计将在图像处理和分析方面取得技术进步，并将通过与iPlant数据共享资源的合作公开提供。该项目将产生广泛有用的计算模型，模拟植物细胞生长控制机制，并产生可以通过实验测试的预测。该项目由综合有机系统部的植物，真菌和微生物发展计划以及分子和细胞生物科学部的网络和调节以及细胞过程计划共同支持。