多聚谷氨酰胺蛋白随机动力学模型的参数推断
Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins
摘要：
细胞中蛋白质聚集体的存在是许多人类年龄相关疾病的已知特征，例如亨廷顿氏病。在细胞中扩展的多聚谷氨酰胺（PolyQ）蛋白的动态演变模型中使用固定参数值的模拟已被用于更好地了解生物系统，如何专注于药物开发以及如何构建未来基于实验室的体外实验的更有效设计。然而，关于控制系统的一些参数的值存在相当大的不确定性。目前，通过特别尝试来选择适当的值以调整参数，使得模型输出与实验数据相匹配。由于数据仅提供了对潜在生物学过程的部分洞察，问题进一步复杂化：数据仅由在几个时间点的细胞死亡和具有包涵体的细胞的比例组成，被测量误差破坏。在这种情况下，开发推理程序来估计模型参数是一项重要的任务。与观察到的比例相对应的模型概率无法准确评估，因此通过重复模拟模型的实现来在推理算法中估计它们。在一般情况下，这样的方法是计算非常昂贵的，因此，我们构建高斯过程模拟器的关键量和重新制定我们的算法，这些快速的随机逼近。最后，我们通过检查我们的模型的拟合，并突出适当的值的模型参数，导致新的见解的基础生物过程，如聚集的动力学。
英文摘要：
The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model of the dynamic evolution of expanded polyglutamine (PolyQ) proteins in cells have been used to gain a better understanding of the biological system, how to focus drug development and how to construct more efficient designs of future laboratory-based in vitro experiments. However, there is considerable uncertainty about the values of some of the parameters governing the system. Currently, appropriate values are chosen by ad hoc attempts to tune the parameters so that the model output matches experimental data. The problem is further complicated by the fact that the data only offer a partial insight into the underlying biological process: the data consist only of the proportions of cell death and of cells with inclusion bodies at a few time points, corrupted by measurement error. Developing inference procedures to estimate the model parameters in this scenario is a significant task. The model probabilities corresponding to the observed proportions cannot be evaluated exactly and so they are estimated within the inference algorithm by repeatedly simulating realisations from the model. In general such an approach is computationally very expensive and we therefore construct Gaussian process emulators for the key quantities and reformulate our algorithm around these fast stochastic approximations. We conclude by examining the fit of our model and highlight appropriate values of the model parameters leading to new insights into the underlying biological processes such as the kinetics of aggregation.
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