标题： 极端事件支配种群增长率推断 显示英文标题

标题： Extremal events dictate population growth rate inference

摘要： 近年来，已经开发出一些方法，将单细胞谱系统计信息映射到群体生长上。 由于群体生长会选择极稀有的表型，这些方法本质上依赖于从有限数据中采样大偏差，这引入了系统性误差。 在有限数据的背景下，全面理解这些误差仍然难以实现。 为了解决这一差距，我们研究了不同模型中增长率估计的误差。 我们表明，在通常的偏差-方差分解下，偏差可以分解为有限时间偏差和非线性平均偏差。 我们证明了有限时间偏差在短时间内占主导地位，可以通过拟合其单调行为来缓解。 相比之下，在较长时间内，非线性平均偏差成为主要的误差来源，导致相变。 这种相变可以通过随机能量模型（一个无序系统的平均场模型）来理解，在该模型中，少数谱系支配着估计器。 将这些方法应用于实验数据表明，校正基于谱系方法中的偏差可使多种方法对长期增长率得出一致的结果，并能够反向设计动态模型。 这个新框架定量地理解了增长率估计器，澄清了它们在有限数据中有效应用的条件，并引入了无模型的方法来研究生理学与细胞生长之间的联系。 显示英文摘要

摘要： Recent methods have been developed to map single-cell lineage statistics to population growth. Because population growth selects for exponentially rare phenotypes, these methods inherently depend on sampling large deviations from finite data, which introduces systematic errors. A comprehensive understanding of these errors in the context of finite data remains elusive. To address this gap, we study the error in growth rate estimates across different models. We show that under the usual bias-variance decomposition, the bias can be decomposed into a finite-time bias and nonlinear averaging bias. We demonstrate that finite-time bias, which dominates at short times, can be mitigated by fitting its monotonic behavior. In contrast, at longer times, nonlinear averaging bias becomes the predominant source of error, leading to a phase transition. This transition can be understood through the Random Energy Model, a mean-field model of disordered systems, where a few lineages dominate the estimator. Applying these methods to experimental data demonstrates that correcting for biases in lineage-based approaches yields consistent results for the long-term growth rate across multiple methods and enables the reverse-engineering of dynamic models. This new framework provides a quantitative understanding of growth rate estimators, clarifies the conditions under which they can be effectively applied to finite data, and introduces model-free approaches for studying the connections between physiology and cell growth.