ILLUMINATING CELLULAR INDIVIDUALITY THROUGH BACTERIOPHAGE INFECTION

项目摘要/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：当 在单细胞水平上检查，这种行为似乎在很大程度上是不可预测的：基因相同的细胞，在 一个统一的环境，在基因表达、信号和 随之而来的命运选择。这种细胞的个性在整个生物学中都被观察到，从 细菌中的抗生素耐药性，对早期哺乳动物胚胎中的细胞分化，以及许多其他 举个例子。 过去二十年的研究已经确定了细胞个性的随机起源，通过 证明单分子事件固有的随机性(“噪声”)可以被放大到蛋白质中 细胞水平上的数字波动。从这些研究中浮现出的画面是活细胞是“嘈杂的” 无法达到高精度的“机器”，它们的命运选择受重大随机性的影响。但是 “噪声”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：它是 很容易将单元格的属性描述为“随机”，并将其映射到统计分布中，但这样做 并不意味着我们了解潜在的细胞过程。相反，通过创造一个 理解，随机描述可能会阻碍我们努力揭示驱动因素的确定性因素 单细胞行为。近年来，人们越来越多地意识到这一警告，并加大了努力 找出细胞个性的决定性驱动因素(所谓的“隐藏变量”)，但公平地说，我们 即使在最简单的系统中，也没有令人满意的图片来说明是什么驱动了单细胞行为，更不用说了 更复杂的问题。 研究目标。在快速细胞死亡(裂解)和病毒休眠(溶原性)之间的选择，感染后 由噬菌体lambda产生的大肠杆菌，为遗传网络决定细胞命运的方式提供了一个范例， 以及分子随机性在这一过程中所扮演的角色。在过去十年工作的基础上， 我们将使用lambda感染来识别基因调控和命运选择中隐藏的细胞个性驱动因素。 通过揭示决策过程的确定性，我们的目标是将Lambda建立为精确的- 而不是“喧闹”--细胞命运的选择。在进行lambda工作的同时，我们将继续为 对单个细胞进行操作、成像、分析和建模，并将其应用于协作项目 解决不同生物背景下的细胞个性问题。

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.