Origins of Cell Geometry

细胞几何的起源

• 批准号: 10565687
• 负责人: Wallace Marshall
• 金额: $ 64.99万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10565687
• 关键词: Abnormal Cell; Address; Biological Models; Cell physiology; Cells; Cellular Structures; Cellular biology; Complex; Development; Diagnosis; Disease; Engineering; Flagella; Genetic; Genomics; Geometry; Human body; Image Analysis; Learning; Length; Life; Link; Methods; Microscope; Microscopy; Modeling; Molecular Biology; Morphogenesis; Natural regeneration; Organelles; Organism; Physics; Physiological; Regulation; Series; Shapes; Structure; Testing; Thinking; Time; Work; cancer type; cell type; interdisciplinary approach; mathematical model; model organism; nanomachine; tool

Abstract Cells are highly complex living nanomachines with beautiful structures of great precision. This is true not only for free living organisms like ciliates or radiolarians, but also for cells inside the human body. These complicated structures are directly linked to the physiological functions of cells, and alterations in cell geometry are a hallmark of many disease states. Yet in most cases we have almost no information about how cells determine their geometry at the level of organelle size and shape. Thus, understanding the origins of cell geometry remains a fundamental unsolved problem in cell biology. Part of the challenge is that cell geometry involves multiple spatial scales ranging from molecules up to the whole cell. Spanning this gap between scales requires us to go beyond traditional molecular biology approaches and bring in methods from physics and engineering. For this reason my proposal is based on an integrated combination of approaches, using several different model organisms and cell types to address the origins of cell geometry at several different size scales. At the level of single organelles, I will continue to probe the mechanism of flagellar length control as a paradigm for organelle size regulation, with a focus on using quantitative methods to test a series of mechanistic models for how a cell might be able to sense the length of its flagellum. At the same time, we will apply the lessons and approaches that we have developed for thinking about flagella to examine size control and geometry of other cellular organelles, singly and in combination. By considering multiple organelles at the same time, we can learn how to view cell geometry at a more integrative level. At a larger scale, we will continue our development of the classic model organism, Stentor coeruleus, as a genomic model system for analyzing global cell morphogenesis and regeneration. Using Stentor, we intend to pursue the two linked questions of how a cell knows that is geometry has been perturbed, and how it directs the re-assembly of a correct cell geometry, both questions that have general significance to all cell types but which are particularly easy to study in Stentor. Our proposed work is unified by the focus on a single question – where does geometry come from inside a cell. We will use different model systems to address different aspects of this question, but in all cases we will take an interdisciplinary approach that combines tools of genetics, genomics, microscopy, image analysis, and mathematical modeling.

抽象的 细胞是高度复杂的活纳米机器，具有精确精度的美丽结构。 这不仅是纤毛或放射性虫等自由的生物体，而且对细胞也是如此 在人体内部。这些复杂的结构直接与 细胞的生理功能以及细胞几何形状的改变是许多人的标志 疾病状态。但是在大多数情况下，我们几乎没有关于细胞如何的信息 确定它们的几何形状在细胞器尺寸和形状的水平上。那，理解 细胞几何形状的起源仍然是细胞生物学中的基本未解决问题。 挑战的一部分是细胞几何形状涉及多个空间尺度。 分子到整个细胞。跨越规模之间的差距需要我们去 除了传统的分子生物学方法，并引入物理学和 工程。因此，我的建议基于 方法，使用几种不同的模型生物和细胞类型来解决 细胞几何形状的起源在几个不同的尺寸尺度上。在单个细胞器的水平上， 我将继续探究鞭毛长度控制的机制，作为范式 细胞器尺寸调节，重点是使用定量方法来测试一系列 细胞如何感知其鞭毛的长度的机械模型。 同时，我们将应用我们为 考虑鞭毛以检查其他细胞器细胞器的尺寸控制和几何形状， 单独而结合。通过同时考虑多个细胞器，我们可以 了解如何以更集成的级别查看细胞几何形状。在更大范围内，我们将 继续我们开发经典模型生物，Stentor Coeruleus，作为一个 用于分析全球细胞形态发生和再生的基因组模型系统。 使用Stentor，我们打算提出两个链接的问题，即一个细胞知道的问题 几何形状受到了干扰，以及如何指导正确的单元重新组装 几何学，这两个问题都对所有细胞类型都具有一般意义，但是 在Stentor中特别容易学习。我们建议的工作是由对 单个问题 - 几何形状来自细胞内部。我们将使用不同的 模型系统以解决此问题的不同方面，但是在所有情况下，我们都将采用 跨学科方法，结合了遗传学，基因组学，显微镜， 图像分析和数学建模。