In this paper, we aim to detect trapping areas (equivalently microdomains or confinement areas) within cells, corresponding to regions where molecules are trapped and thereby undergo subdiffusion. We propose an original computational approach that takes as input a set of molecule trajectories estimated by appropriate tracking methods. The core of the algorithm is based on a combination of clustering algorithms with trajectory classification procedures able to distinguish subdiffusion, superdiffusion and Brownian motion. The idea is to automatically identify trapping areas where we observe a high concentration of subdiffusive particles. We evaluate our proof of concept on artificial sequences obtained with a biophysics-based simulator (Fluosim), and we illustrate its potential on real TIRF microscopy data.

中文翻译：

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一种检测细胞中微区和限制区的计算方法：模拟研究。

在本文中，我们旨在检测细胞内的捕获区域（相当于微区或限制区域），该区域对应于分子被捕获从而发生亚扩散的区域。我们提出了一种原始的计算方法，该方法采用通过适当的跟踪方法估算的一组分子轨迹作为输入。该算法的核心是基于聚类算法和轨迹分类程序的组合，该程序能够区分子扩散，超扩散和布朗运动。这个想法是自动识别我们观察到高浓度的亚扩散粒子的捕获区域。我们评估了使用基于生物物理学的模拟器（Fluosim）获得的人工序列的概念验证，并在真实TIRF显微镜数据上说明了其潜力。