Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts; Graduate Program in Biophysics, Harvard University, Cambridge, Massachusetts.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Biophys J. 2012 Aug 8;103(3):616-626. doi: 10.1016/j.bpj.2012.06.029.
Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.
使用活细胞成像进行粒子运动的定量跟踪是理解生物分子、细胞器和细胞运输机制的一种强大方法。然而，由于采样限制和生物异质性带来的噪声，从单个粒子轨迹中客观推断复杂的随机运动模型并非易事。在这里，我们提出了一种基于粒子均方位移的系统贝叶斯方法，用于对一组一般的竞争运动模型进行多重假设检验，该方法能够自动对粒子运动进行分类，正确考虑采样限制和相关噪声，同时根据奥卡姆剃刀适当惩罚模型复杂性，以避免过度拟合。我们使用已知潜在物理过程的模拟轨迹对该程序进行了严格测试，证明它选择了最简单的物理模型来解释观测数据。此外，我们还表明，计算出的模型概率为相关参数值的下游生物学解释提供了可靠性测试。随后，我们通过将其应用于不同的生物系统，包括经历各种复杂运动的染色体、着丝粒和膜受体的实验粒子轨迹，说明了该方法的广泛适用性。这种自动化和客观的贝叶斯框架可以轻松扩展到大量粒子轨迹，使其非常适合从高通量筛选、单细胞、组织和生物体水平的研究中对大量单个分子和细胞的复杂运动进行分类。
