IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1180-1192. doi: 10.1109/TCBB.2017.2775219. Epub 2017 Nov 20.
Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here, we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies.
参数校准是反应网络定量建模方法中的一个关键问题。现有的随机模型方法要么依赖于统计抽样，要么只能应用于小系统。在这里，我们提出了一种基于矩匹配方案的平衡态随机模型推断方法，该方法具有最优加权，可与高通量数据（如流式细胞术收集的数据）一起使用。我们的方法不需要对基础平衡概率分布进行近似，如果要学习反应速率常数，则可以通过求解线性方程组来计算最优值。我们讨论了一些重要的实际问题，如矩的选择，并在三个案例研究中评估了所提出方法的有效性。
