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 polyglutaime (PolyQ) proteins in cells have been used to gain a better understanding of the biological system. 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 realizations 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 highlighting appropriate values of the model parameters leading to new insights into the underlying biological processes.

中文翻译：

---

扩展聚谷氨酰胺蛋白随机动力学模型的参数推断

细胞中存在蛋白质聚集体是许多人类年龄相关疾病（例如亨廷顿舞蹈病）的一个已知特征。在细胞中扩展聚谷氨酰胺 (PolyQ) 蛋白的动态进化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。然而，关于管理系统的一些参数的值存在相当大的不确定性。目前，通过临时尝试选择适当的值来调整参数，以便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过程的部分洞察，问题变得更加复杂：数据仅包含几个时间点的细胞死亡比例和包含包涵体的细胞比例，因测量误差而损坏。开发推理程序来估计这种情况下的模型参数是一项重要的任务。无法准确评估与观察到的比例对应的模型概率，因此通过反复模拟模型的实现在推理算法中估计它们。一般来说，这种方法在计算上非常昂贵，因此我们为关键量构建高斯过程仿真器，并围绕这些快速随机近似重新制定我们的算法。最后，我们强调了模型参数的适当值，从而对潜在的生物过程有了新的认识。因此，它们是在推理算法中通过反复模拟模型的实现来估计的。一般来说，这种方法在计算上非常昂贵，因此我们为关键量构建高斯过程仿真器，并围绕这些快速随机近似重新制定我们的算法。最后，我们强调了模型参数的适当值，从而对潜在的生物过程有了新的认识。因此，它们是在推理算法中通过反复模拟模型的实现来估计的。一般来说，这种方法在计算上非常昂贵，因此我们为关键量构建高斯过程仿真器，并围绕这些快速随机近似重新制定我们的算法。最后，我们强调了模型参数的适当值，从而对潜在的生物过程有了新的认识。