随机反应网络的单轨迹全局参数辨识
Global parameter identification of stochastic reaction networks from single trajectories
摘要：
我们考虑的问题推断的未知参数的随机生化网络模型从一个单一的测量时间过程中的一些有关物种的浓度。这样的测量是可用的，例如，活细胞荧光显微镜在基于图像的系统生物学。此外，波动时间过程，例如，荧光相关光谱法提供了关于系统动力学的附加信息，与仅考虑平均浓度时相比，其可用于更稳健地推断参数。从一个单一的实验轨迹估计模型参数，使单细胞测量和量化的细胞-细胞的变异性。我们提出了一种新的自适应蒙特卡罗采样器，称为高斯适应，和高效的精确随机模拟算法，允许参数识别从单个随机轨迹的组合。我们基准的线性和非线性反应网络在稳态和瞬态阶段的方法。此外，我们证明，本方法还提供了一个椭球体体积估计参数空间的可行部分，并能够估计所观察到的反应发生的隔室的物理体积。
英文摘要：
We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.
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