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.