论文标题
从多状态扩散过程中学习扩散系数，动力学参数和基础状态的数量：鲁棒性结果和应用于PDK1/PKC $α$，Dynamics
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKC$α$, dynamics
论文作者
论文摘要
由布朗运动驱动的系统无处不在。从数据中推断出一个描述这些随机过程的扩散和动力学参数是从数据中推断出来的。在这项工作中，我们研究了在单个粒子跟踪（SPT）中产生的多状态扩散过程，其中粒子的运动由离散的扩散状态组控制，并且粒子之间切换这些状态的趋势被建模为随机过程。我们考虑了这种行为的两个模型：混合模型和隐藏的马尔可夫模型（HMM）。对于这两者，我们都采用贝叶斯方法来采样基础参数的分布，并实施马尔可夫链蒙特卡洛（MCMC）方案来计算后验分布，如DAS，Cairo，Cairo，Coombs（2009）。这项工作的主要贡献是对这种方法推断三态HMM参数的鲁棒性的研究，并讨论了考虑三个状态而引起的挑战和变性。最后，我们研究了使用模型选择标准确定扩散状态数量的问题。我们从模拟数据中介绍了证明概念证明的结果，并将我们的方法应用于实验测量的单体磷酸肌醇依赖性激酶-1（PDK1）的单分子扩散轨迹（PDK1）在合成靶膜上的结果扩散率。 所有MATLAB软件都可以在此处找到：\ url {https://github.com/mathbiocu/singlemolecule}
Systems driven by Brownian motion are ubiquitous. A prevailing challenge is inferring, from data, the diffusion and kinetic parameters that describe these stochastic processes. In this work, we investigate a multi-state diffusion process that arises in the context of single particle tracking (SPT), wherein the motion of a particle is governed by a discrete set of diffusive states, and the tendency of the particle to switch between these states is modeled as a random process. We consider two models for this behavior: a mixture model and a hidden Markov model (HMM). For both, we adopt a Bayesian approach to sample the distributions of the underlying parameters and implement a Markov Chain Monte Carlo (MCMC) scheme to compute the posterior distributions, as in Das, Cairo, Coombs (2009). The primary contribution of this work is a study of the robustness of this method to infer parameters of a three-state HMM, and a discussion of the challenges and degeneracies that arise from considering three states. Finally, we investigate the problem of determining the number of diffusive states using model selection criteria. We present results from simulated data that demonstrate proof of concept, as well as apply our method to experimentally measured single molecule diffusion trajectories of monomeric phosphoinositide-dependent kinase-1 (PDK1) on a synthetic target membrane where it can associate with its binding partner protein kinase C alpha isoform (PKC$α$) to form a heterodimer detected by its significantly lower diffusivity. All matlab software is available here: \url{https://github.com/MathBioCU/SingleMolecule}
