InferenceMAP：基于贝叶斯推断的单分子动力学映射
InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference
摘要：
单粒子跟踪（SPT）在分子尺度上对细胞功能提供了前所未有的洞察力[1]。在整个细胞中，单分子的运动通常是不均匀和复杂的。因此，有必要了解生物系统中单分子动力学的多尺度性质。我们之前已经证明，使用高密度SPT，可以生成指示分子运动的参数的空间图，以复杂地描述细胞环境[2，3，4]。然而，迄今为止，不存在协调轨迹数据以生成上述地图的可编程可用工具。我们解决了这个空白的SPT社区与InferenceMAP：一个交互式软件包，使用强大的贝叶斯方法来映射动态细胞空间所经历的个别生物分子。
英文摘要：
Single-particle tracking (SPT) grants unprecedented insight into cellular function at the molecular scale [1]. Throughout the cell, the movement of single-molecules is generally heterogeneous and complex. Hence, there is an imperative to understand the multi-scale nature of single-molecule dynamics in biological systems. We have previously shown that with high-density SPT, spatial maps of the parameters that dictate molecule motion can be generated to intricately describe cellular environments [2,3,4]. To date, however, there exist no publically available tools that reconcile trajectory data to generate the aforementioned maps. We address this void in the SPT community with InferenceMAP: an interactive software package that uses a powerful Bayesian method to map the dynamic cellular space experienced by individual biomolecules.
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