We present a Bayesian framework for inferring spatio-temporal maps of diffusivity and potential fields from recorded trajectories of single molecules inside living cells. The framework naturally lets us regularise the high-dimensional inference problem using prior distributions in order to obtain robust results. To overcome the computational complexity of inferring thousands of map parameters from large single particle tracking datasets, we developed a stochastic optimisation method based on local mini-batches and parsimonious gradient calculation. We quantified the gain in convergence speed on numerical simulations, and we demonstrated for the first time temporal regularisation and aligned values of the inferred potential fields across multiple time segments. As a proof-of-concept, we mapped the dynamics of HIV-1 Gag proteins involved in the formation of virus-like particles (VLPs) on the plasma membrane of live T cells at high spatial and temporal resolutions. We focused on transient aggregation events lasting only on tenth of the time required for full VLP formation. The framework and optimisation methods are implemented in the TRamWAy open-source software platform for analysing single biomolecule dynamics.

中文翻译：

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映射活细胞中单个生物分子的时空动态。

我们提出了一个贝叶斯框架，用于从活细胞内单分子的记录轨迹中推断出扩散率和势场的时空图。该框架自然使我们可以使用先验分布对高维推理问题进行正则化，以获得可靠的结果。为了克服从大型单个粒子跟踪数据集中推断成千上万的地图参数的计算复杂性，我们开发了一种基于局部迷你批次和简约梯度计算的随机优化方法。我们在数值模拟中量化了收敛速度的增益，并首次展示了跨多个时间段的时间正则化和推断势场的对齐值。作为概念证明，我们绘制了在高空间和时间分辨率下活T细胞质膜上涉及病毒样颗粒（VLP）形成的HIV-1 Gag蛋白的动力学图。我们专注于仅持续完整VLP形成所需时间的十分之一的瞬时聚合事件。该框架和优化方法在TRamWAy开源软件平台中实现，用于分析单个生物分子动力学。