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Mechanisms of Cytoplasmic Streaming

细胞质流的机制

基本信息

批准号：
1715794
负责人：
Andreas Nebenfuehr
金额：
$ 85.79万
依托单位：
University of Tennessee Knoxville
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715794&HistoricalAwards=false
关键词：
Mechanisms Cytoplasmic Streaming

项目摘要

This project will explain the intricate characteristics of intracellular transport systems by determining the biophysical mechanisms responsible for organelle movements in plant cells through a combination of biological, computational, and statistical approaches. Biological systems are characterized by highly complex traits that result from the interplay of much simpler components. One of the major challenges in modern biology is to define the interactions of these basic components in order to arrive at the properties of the full system. Intracellular transport along cytoskeletal filaments represents such a biological system that is tractable with today's technology. Importantly, this transport plays fundamental functions in establishing cell polarity, in mediating growth, and in responding to the environment or to pathogens. Therefore, better understanding of the mechanisms underlying intracellular movements as provided by this project may impact, for example, agricultural yield or treatment of diseases. This project will also establish a paradigm for the cross-disciplinary training of graduate students at the interface of molecular cell biology, computational biophysics, and statistical machine learning. This training will be extended to undergraduate and high school students who will participate in carefully selected research projects appropriate for their background. Broader dissemination of the research findings will occur via a dedicated website as well as through lectures for the general public.Cytoplasmic streaming in plant cells is characterized by the rapid movement of organelles along the actin cytoskeleton. While it is known that these movements are driven by class XI myosin motor proteins, the precise mechanism for the propulsive mechanism is still debated. One model posits that myosin motors directly associate with individual organelles and pull them actively along actin filaments. Another model proposes that only a few organelles such as the ER directly bind to motors while all other organelles are propelled indirectly by binding to the actively moving organelle(s). A third model predicts that a small number of actively moving organelles generate a hydrodynamic flow in the cytoplasm that transports other organelles passively along this stream. This project will test these motility models by developing a series of rigorous tools to model, measure, and evaluate intracellular dynamics. First, stochastic models will be developed that translate the concepts of the three motility models into explicit biophysical descriptions for computer simulations. Second, novel analytical tools will be developed that are able to capture and describe the complex behavior of organelles in living cells with high spatiotemporal resolution. Third, a machine learning approach based on Bayesian considerations will be designed that can evaluate motility models based on experimental data. Combined with novel tools for experimental interference with specific organelle movements and identification of the cellular cargo of two representative myosin XI motors, this research will deliver a new level of understanding of intracellular transport along the cytoskeleton that will impact cell biological interpretations for all eukaryotic systems.

该项目将通过生物学，计算和统计方法的结合，确定负责植物细胞中细胞器运动的生物物理机制，解释细胞内运输系统的复杂特征。生物系统的特点是高度复杂的特征，这些特征是由简单得多的组成部分相互作用产生的。现代生物学的主要挑战之一是定义这些基本成分的相互作用，以获得整个系统的特性。沿着细胞骨架丝的细胞内运输代表了这样一种生物系统，这种生物系统用今天的技术是容易处理的。重要的是，这种转运在建立细胞极性、介导生长以及对环境或病原体作出反应方面发挥着基本功能。因此，更好地理解该项目提供的细胞内运动的机制可能会影响农业产量或疾病治疗。该项目还将为研究生在分子细胞生物学，计算生物物理学和统计机器学习的界面上的跨学科培训建立一个范例。这项培训将扩大到本科生和高中生谁将参加精心挑选的研究项目适合他们的背景。将通过专门的网站以及通过面向公众的讲座更广泛地传播研究成果。植物细胞内的细胞质流以细胞器沿着肌动蛋白细胞骨架的快速运动为特征。虽然已知这些运动是由XI类肌球蛋白马达蛋白驱动的，但推进机制的精确机制仍有争议。一种模型认为肌球蛋白马达直接与单个细胞器相连，并沿着沿着肌动蛋白丝主动拉动它们。另一个模型提出，只有少数细胞器如ER直接与马达结合，而所有其他细胞器都是通过与主动运动的细胞器结合而间接推动的。第三个模型预测，少量的积极运动的细胞器产生的细胞质中的流体动力学流，运输其他细胞器被动沿着这条流。该项目将通过开发一系列严格的工具来建模，测量和评估细胞内动力学来测试这些运动模型。首先，随机模型将被开发，翻译成明确的生物物理描述的计算机模拟的三个运动模型的概念。其次，将开发新的分析工具，能够捕获和描述活细胞中细胞器的复杂行为，具有高时空分辨率。第三，将设计一种基于贝叶斯考虑的机器学习方法，可以根据实验数据评估运动模型。结合新的工具，实验干扰特定的细胞器运动和识别的细胞货物的两个代表性肌球蛋白XI马达，这项研究将提供一个新的水平的理解细胞内运输沿着细胞骨架，将影响细胞生物学解释所有真核系统。