An ongoing rapid development in single particle tracking techniques has opened new possibilities for analysis of particles dynamics inside living cells. Assuming that the motion is governed by a fractional Brownian motion, we have generated a synthetic video resembling a real one from an experimental video of G-proteins and coupled with them receptors inside living cells. Next, we applied Brownian bridge method to study two fundamental analysis tasks on trajectory data: segmentation and classification in context of experimental data. We have shown that, depending on the method of dealing with missing data, the obtained results might vary significantly, obviously influencing the final conclusions.

单粒子跟踪技术的持续快速发展为分析活细胞内部的粒子动力学开辟了新的可能性。假设该运动由分数布朗运动控制，我们从G蛋白的实验视频中生成了一个类似于真实视频的合成视频，并将它们与活细胞内的受体耦合。接下来，我们使用布朗桥方法研究轨迹数据的两个基本分析任务：在实验数据的上下文中进行分割和分类。我们已经表明，根据处理丢失数据的方法，所获得的结果可能会有很大差异，从而明显影响最终结论。