Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution. The second virial coefficient is a physical quantity that characterizes the magnitude and sign of pairwise interactions between particles, and hence is related to aggregation propensity, a property of considerable scientific and practical interest. Estimating the second virial coefficient from experimental data is challenging due both to the degree of precision required and the complexity of the error structure involved. In contrast to conventional approaches based on heuristic OLS estimates, Bayesian inference for the second virial coefficient allows explicit modeling of error processes, incorporation of prior information, and the ability to directly test competing physical models. Here, we introduce a fully Bayesian model for static light scattering experiments on small-particle systems, with joint inference for concentration, index of refraction, oligomer size, and the second virial coefficient. We apply our proposed model to study the aggregation behavior of hen egg-white lysozyme and human gammaS-crystallin using in-house experimental data. Based on these observations, we also perform a simulation study on the primary drivers of uncertainty in this family of experiments, showing in particular the potential for improved monitoring and control of concentration to aid inference.
Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering
超分辨率成像技术大大提高了我们在生物系统中可视化纳米结构的能力。它们的应用进一步使人们能够潜在地定量相关参数以确定细胞中的分子组织和化学计量。然而，荧光发射和标记策略的固有随机性迫使使用专用方法来精确测量这些参数。在这里，我们描述了一种贝叶斯方法来精确定量的相对丰度的分子寡聚体从分割的图像。集群中分子数量的代理分布-例如定位数量或荧光强度-通过嵌套采样算法进行拟合，以比较复杂性不断增加的混合模型，并确定混合组分的最佳数量及其权重。我们测试了该算法的性能{ \it in silico}数据作为数据点的数量，阈值和分布形状的函数。我们将这些结果与其他统计方法获得的结果进行比较，显示了我们方法的改进性能。我们的方法提供了一个强大的工具，在拟合从荧光成像中提取的数据的模型选择，从而提高了参数确定的精度。重要的是，这种方法的最大好处发生在小统计量或不完整的数据集上，从而能够在单个图像水平上进行准确的分析。我们进一步提出了其应用的结果，从超分辨率成像的HeLa细胞中的动力蛋白的实验数据，证实了细胞质的单电机和高阶结构的混合人口的存在。
