arXiv 2309.005452023-09physics.bio-phphysics.data-anq-bio.CBq-bio.SCq-bio.TO

学习单细胞和集体细胞迁移的动力学模型：一篇综述

Learning dynamical models of single and collective cell migration: a review

David B. Brückner, Chase P. Broedersz

AI总结：

本综述聚焦单细胞与集体细胞迁移动力学模型研究，梳理数据驱动理论方法结合实验数据推断随机迁移模型的进展，涵盖细胞异质性、亚细胞自由度及多细胞集体动力学等应用，阐明其与活性物质模型结合的价值。

摘要：

单细胞和集体细胞迁移是从胚胎发育、免疫反应到伤口愈合和癌症转移等生理现象所必需的基础过程。为从物理学角度理解细胞迁移，研究人员已开发出多种模型，用于阐释调控细胞运动的底层物理机制。这类模型开发的核心挑战在于如何将其与实验观测结果关联，而实验观测往往呈现出复杂的随机行为。本综述探讨了数据驱动理论方法的最新进展，这类方法可直接结合实验数据以推断随机细胞迁移的动力学模型。借助纳米加工、图像分析和追踪技术的进步，当前的实验研究已能提供规模空前的细胞动力学大型数据集。与此同时，理论研究正致力于将这类数据集整合到从单细胞到组织尺度的物理模型中，旨在将细胞的涌现行为概念化。我们首先回顾了在自由迁移细胞和受限细胞中该推断问题的研究进展。接着，我们讨论了这些动力学为何通常表现为欠阻尼随机运动方程的形式，以及如何从数据中推断这类方程。随后，我们综述了数据驱动推断和机器学习方法在细胞行为异质性、亚细胞自由度以及多细胞系统集体动力学中的应用。在这些应用中，我们强调了数据驱动方法如何与迁移细胞的物理活性物质模型相结合，并助力揭示底层分子机制如何调控细胞行为。

英文摘要：

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasize how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour.

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