ILLUMINATING CELLULAR INDIVIDUALITY THROUGH BACTERIOPHAGE INFECTION

项目总结/摘要 生物物理学的一个关键目标是预测生命系统的行为。这一目标受到以下事实的阻碍： 在单细胞水平上进行检查，这种行为似乎在很大程度上是不可预测的：遗传上相同的细胞， 一个统一的环境，在基因表达，信号传导， 命运的抉择。这种细胞个体性在整个生物学中都可以观察到， 细菌中的抗生素耐药性，早期哺乳动物胚胎中的细胞分化，以及许多其他 例子. 过去二十年的研究已经确定了细胞个体性的随机起源， 这表明单分子事件的固有随机性（“噪音”）可以被放大到蛋白质中， 细胞水平的数量波动。从这些研究中得出的结论是，活细胞是“嘈杂的 机器”，无法实现高精度，其命运选择具有很大的随机性。但 “噪音”概念在描述细胞异质性方面的广泛成功也指出了它的弱点： 很容易将单细胞特性描述为“随机”并将其映射到统计分布中，但这样做 并不意味着我们理解了潜在的细胞过程。相反，通过创造一个 理解，随机描述可能会阻碍我们的努力，以揭示决定性因素，推动 单细胞行为近年来，人们越来越认识到这一告诫，并加大了努力， 确定细胞个体性的决定性驱动因素（所谓的“隐藏变量”），但公平地说，我们 即使是在最简单的系统中，也仍然缺乏一个令人满意的画面来驱动单细胞行为，更不用说 更复杂的。 研究目标。感染后，快速细胞死亡（裂解）和病毒休眠（溶原性）之间的选择 E.大肠杆菌的噬菌体λ，作为一个范例的方式遗传网络驱动细胞命运的决定， 以及分子随机性在这一过程中的作用。在我们过去十年工作的基础上， 我们将使用λ感染来识别基因调节和命运选择中细胞个体性的隐藏驱动因素。 通过揭示决策过程的确定性，我们的目标是建立lambda作为一个范式，精确- 而不是“嘈杂的”细胞命运选择。在lambda工作的同时，我们将继续为 操作、成像、分析和建模单个细胞，并将其应用于合作项目 在不同的生物学背景下解决细胞个体性。

PROJECT SUMMARY / ABSTRACT A key goal of biophysics is to predict the behavior of living systems. This goal is hampered by the fact that, when examined at the single-cell level, this behavior appears largely unpredictable: Genetically identical cells, within a uniform environment, exhibit heterogeneous phenotypes in terms of gene expression, signaling, and consequent fate choice. This cellular individuality is observed throughout biology, from the emergence of antibiotic resistance among bacteria, to cell differentiation in the early mammalian embryo, and numerous other examples. Studies over the last two decades have pinpointed the stochastic origins of cellular individuality, by demonstrating that the inherent randomness (“noise”) of single-molecule events can be amplified into protein number fluctuations at the cellular level. The picture that emerged from those studies is of living cells as “noisy machines”, incapable of high precision, whose fate choices are subject to significant randomness. But the widespread success of the “noise” concept in describing cellular heterogeneity also points to its weakness: It is easy to describe single-cell properties as “stochastic” and map them into statistical distributions, but doing so does not mean that we understand the underlying cellular process. On the contrary, by creating a façade of understanding, a stochastic description may impede our efforts to uncover the deterministic factors that drive single-cell behavior. Recent years have seen a growing awareness of this caveat and an increase in efforts to identify the deterministic drivers (so-called “hidden variables”) of cellular individuality, but it is fair to say that we still lack a satisfactory picture for what drives single-cell behavior even in the simplest systems, to say nothing of more complex ones. Research goal. The choice between rapid cell death (lysis) and viral dormancy (lysogeny), following infection of E. coli by bacteriophage lambda, serves as a paradigm for the way genetic networks drive cell fate decisions, and for the purported role of molecular randomness in this process. Building on our work over the last decade, we will use lambda infection to identify hidden drivers of cellular individuality in gene regulation and fate choice. By revealing how deterministic the decision process is, we aim to establish lambda as a paradigm for precise— rather than “noisy”—cell fate choice. In parallel to the work on lambda, we will continue to develop tools for the manipulation, imaging, analysis, and modeling of individual cells, and apply them in collaborative projects addressing cellular individuality across diverse biological contexts.