发表机构
University of Colorado, Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于WENDy算法的数据驱动框架，从单细胞尺寸轨迹中推断异质性生长规律，并恢复个体参数，进而可扩展至群体水平。
AI 中文摘要
单细胞延时显微镜技术现可提供数千个单细胞的高分辨率尺寸轨迹，然而，同时学习生长规律函数形式及细胞间生理变异分布的规范化方法仍然有限。我们提出了一种基于近期弱形式非线性动力学估计（WENDy）算法的数据驱动框架，用于（i）从一组候选规律中选出最受支持的生长规律，以及（ii）通过随机效应模型中的经验贝叶斯收缩恢复个体水平的生长参数。我们在包含常见生长规律的合成个体尺寸数据以及记录的真实尺寸轨迹上展示了该方法，结果表明，在合理的噪声水平下，该方法能准确恢复生长规律结构和参数异质性。最后，我们讨论了如何将本文方法获得的结果升级到群体水平。
英文摘要
Single-cell time-lapse microscopy now provides high-resolution size trajectories for thousands of individual cells, yet principled methods for simultaneously learning the functional form of growth laws and the distribution of cell-to-cell physiological variability remain limited. We present a data-driven framework based on the recent Weak-form Estimation of Nonlinear Dynamics (WENDy) algorithm to (i) select the best supported growth law from a set of candidate laws, and (ii) recover individual-level growth parameters via empirical Bayes shrinkage within a random-effects model. We demonstrate the approach on both synthetic individual size data incorporating commonly found growth laws and recorded real size trajectories, showing that the method accurately recovers both growth-law structure and parameter heterogeneity at reasonable noise levels. We finally end with a discussion on how the results obtained by the method here can be upscaled to the population level.
