Holcman D, Hoze N, Schuss Z
Applied Mathematics and Computational Biology, IBENS Ecole Normale Supérieure, Paris, France; Churchill College, Cambridge University, Cambridge, United Kingdom.
ETH Zürich, Institute of Integrative Biology, ETH-Zentrum CHN, Universitätsstrasse 16, Zürich, Switzerland.
Biophys J. 2015 Nov 3;109(9):1761-71. doi: 10.1016/j.bpj.2015.09.003.
A large number (tens of thousands) of single molecular trajectories on a cell membrane can now be collected by superresolution methods. The data contains information about the diffusive motion of molecule, proteins, or receptors and here we review methods for its recovery by statistical analysis of the data. The information includes the forces, organization of the membrane, the diffusion tensor, the long-time behavior of the trajectories, and more. To recover the long-time behavior and statistics of long trajectories, a stochastic model of their nonequilibrium motion is required. Modeling and data analysis serve extracting novel biophysical features at an unprecedented spatiotemporal resolution. The review presents data analysis, modeling, and stochastic simulations applied in particular on surface receptors evolving in neuronal cells.
现在可以通过超分辨率方法在细胞膜上收集大量（数以万计）的单分子轨迹。这些数据包含有关分子、蛋白质或受体扩散运动的信息，在此我们回顾通过对数据进行统计分析来恢复这些信息的方法。这些信息包括作用力、膜的组织、扩散张量、轨迹的长期行为等等。为了恢复长轨迹的长期行为和统计信息，需要一个描述其非平衡运动的随机模型。建模和数据分析有助于以前所未有的时空分辨率提取新的生物物理特征。本文综述了特别应用于神经元细胞中表面受体演化的数据分析、建模和随机模拟。
