The design principles of the eukaryotic cell: uncovering the coordination of systems-level organelle dynamics, metabolism and growth

项目摘要 也许真核细胞的定义性特征是其组织成生物化学上不同的区室 称为细胞器。虽然单个细胞器的生化功能通常是众所周知的， 细胞如何调节其不同细胞器的拷贝数、大小和亚细胞位置， 时尚以及细胞器如何相互作用以产生整合的生理输出仍然是人们关注的焦点之一。 细胞生物学的挑战。我的研究计划的目标是发现 控制细胞如何调节系统水平的细胞器动力学，以协调新陈代谢，生长， 增殖为实现这一目标，我的研究策略将沿着沿着两个方向进行。上 方向，我将定量确定细胞如何协调系统水平的细胞器动力学与细胞 增长需求。具体来说，我将量化并建立一个数学模型， 细胞器组成和细胞生长。该模型将根据以下方法获得的数据进行校准： 使用我们的基于机器学习的高光谱同时可视化所有主要的代谢细胞器， 成像平台，对细胞生长和增殖速率施加化学生物学控制， 扰乱关键的细胞器生物发生、组织和相互作用因子。在第二个方向，我将 确定细胞如何协调系统水平的细胞器动力学和基因表达来控制代谢 在生长和增殖过程中。我将根据单个细胞的细胞器内容对它们进行分类， 系统地测量它们的基因表达的时间相关性，这些基因的产物执行 细胞器特异性功能。我将同时测量这些细胞的代谢组学特征， 细胞器含量然后，我将联合收割机这些测量开发一个数学模型，定量 捕捉基因表达和细胞器介导的代谢之间的联系 化妆.随后，我将通过系统地调整细胞器相互作用来测试这个模型的预测 通过调节细胞器生物发生因子和细胞器接触位点的表达来增强细胞的免疫力。成功 沿着这两个方向的研究将产生关于如何解开复杂性的机理性见解 细胞器动力学、代谢和细胞生长与增殖之间的相互依赖性。一个系统- 了解细胞器的组成和相互作用如何协调代谢，以控制细胞 生长和发育将为未来研究细胞如何积极地 塑造其细胞器的组成，以配合生化供应与生理需求，通过，这是如何 可塑性在健康中被多细胞生物利用，以提供发育所需的代谢灵活性。 它的无数细胞类型，而且在疾病中允许多种途径代谢病理学在癌症中， 糖尿病和衰老。

Project Summary Perhaps the defining feature of the eukaryotic cell is its organization into biochemically distinct compartments known as organelles. While the biochemical functions of individual organelles are often well known, how cells regulate the copy numbers, sizes, and subcellular positions of its diverse organelles in a coordinated fashion and how organelles interact to produce integrated physiological outputs remain one of the grand challenges in cell biology. The goal of my research program is to discover the quantitative principles governing how cells regulate systems-level organelle dynamics to coordinate metabolism, growth, and proliferation. To achieve this goal, my research strategy will proceed along two directions. In the first direction, I will quantitatively determine how cells coordinate systems-level organelle dynamics with cellular growth demands. Specifically, I will quantify and build a mathematical model of the relationship between cellular organelle composition and cell growth. The model will be calibrated from data obtained by simultaneously visualizing all major metabolic organelles using our machine learning-based hyperspectral imaging platform, exerting chemical biological control over cell growth and proliferation rates, and genetically perturbing key organelle biogenesis, organization, and interaction factors. In the second direction, I will determine how cells coordinate systems-level organelle dynamics and gene expression to control metabolism during growth and proliferation. I will categorize single cells according to their organelle content and systematically measure the temporal correlations in their expression of genes whose products execute organelle-specific functions. I will concomitantly measure the metabolomic profile of these cells sorted by organelle content. I will then combine these measurements to develop a mathematical model that quantitatively captures the connection between gene expression and metabolism as mediated by the cell's organelle makeup. I will subsequently test predictions of this model by systematically tuning organelle interaction strengths by modulating the expression of organelle biogenesis factors and organelle contact sites. Successful investigations along these two directions will yield mechanistic insight into how to untangle the complex interdependencies between organelle dynamics, metabolism, and cell growth and proliferation. A systems- level understanding of how organelle composition and interactions coordinate metabolism to control cellular growth and development will lay a rigorous foundation into future investigations into how the cell actively shapes its organelle composition to match biochemical supply with physiological demand through, how this plasticity is leveraged in health by multicellular organisms to provide the metabolic flexibility needed to develop its myriad cell types, but also in disease by allowing for multiple routes to metabolic pathologies in cancer, diabetes, and aging.