单个和集体细胞迁移是对从胚胎发育和免疫应答到伤口愈合和癌症转移的生理现象至关重要的基本过程。要从物理角度理解细胞迁移,已经开发了控制细胞运动的潜在物理机制的各种模型。开发此类模型的一个关键挑战是如何将它们与实验观察联系起来,通常表现出复杂的随机行为。在这次审查中,我们讨论了数据驱动的理论方法的最新进展,这些方法直接与实验数据联系起来,以推断随机细胞迁移的动力学模型。利用纳米加工的进步,图像分析,和跟踪技术,现在,实验研究提供了前所未有的大型细胞动力学数据集。并行,理论上的努力已经致力于将这些数据集整合到从单细胞到组织尺度的物理模型中,目的是概念化细胞的出现行为。我们首先回顾了如何在自由迁移和受限细胞中解决此推理问题。接下来,我们讨论了为什么这些动力学通常采用欠阻尼随机运动方程的形式,以及如何从数据中推断出这些方程。然后,我们回顾了数据驱动推理和机器学习方法在细胞行为异质性中的应用,亚细胞自由度,以及多细胞系统的集体动力学。在这些应用程序中,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集成,并有助于揭示潜在的分子机制如何控制细胞行为。一起,这些数据驱动的方法是直接从实验数据建立细胞迁移物理模型的有前途的途径,并提供描述的不同长度尺度之间的概念联系。
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 conceptualising 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 emphasise 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. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.