Mechanisms of Cytoplasmic Streaming

这个项目将通过结合生物学、计算和统计方法来确定负责植物细胞中细胞器运动的生物物理机制，从而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，这些特征是简单得多的成分相互作用的结果。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，以得出整个系统的性质。沿着细胞骨架细丝的细胞内运输代表了这样一种生物系统，这种生物系统可以用今天的技术来处理。重要的是，这种转运在确定细胞极性、调节生长以及对环境或病原体的反应中发挥着基本功能。因此，更好地理解该项目提供的细胞内运动的潜在机制可能会影响农业产量或疾病的治疗。该项目还将建立一个在分子细胞生物学、计算生物物理学和统计机器学习的界面上对研究生进行跨学科培训的范例。这项培训将扩大到本科生和高中生，他们将参加精心挑选的适合自己背景的研究项目。研究结果将通过专门的网站和面向公众的讲座进行更广泛的传播。植物细胞中的细胞质流动的特点是细胞器沿着肌动蛋白细胞骨架快速移动。虽然已知这些运动是由XI类肌球蛋白马达蛋白驱动的，但推进机制的确切机制仍存在争议。一种模型假设，肌球蛋白马达直接与单个细胞器相连，并沿着肌动蛋白细丝主动拉动它们。另一种模型认为，只有少数细胞器，如内质网，直接与发动机结合，而所有其他细胞器通过与主动移动的细胞器结合间接推进(S)。第三个模型预测，少数活跃运动的细胞器在细胞质中产生流体动力流，从而沿着这一流被动地运输其他细胞器。这个项目将通过开发一系列严格的工具来模拟、测量和评估细胞内动力学来测试这些运动性模型。首先，将开发随机模型，将三种运动性模型的概念转换为用于计算机模拟的明确的生物物理描述。其次，将开发新的分析工具，能够以高时空分辨率捕捉和描述活细胞中细胞器的复杂行为。第三，将设计一种基于贝叶斯考虑的机器学习方法，该方法可以基于实验数据来评估运动性模型。结合对特定细胞器运动的实验干扰和识别两个典型的肌球蛋白XI马达的细胞货物的新工具，这项研究将提高对细胞内沿细胞骨架的运输的理解，这将影响所有真核系统的细胞生物学解释。

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.