Koutroumpas Konstantinos, Ballarini Paolo, Votsi Irene, Cournède Paul-Henry
Lab MICS, CentraleSupélec, University of Paris Saclay, 92295 Chatenay-Malabry, France.
Bioinformatics. 2016 Sep 1;32(17):i781-i789. doi: 10.1093/bioinformatics/btw471.
Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels.
In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data.
Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software
无似然方法，如近似贝叶斯计算（ABC），已广泛应用于具有难以处理的似然函数的基于模型的统计推断中。当与顺序蒙特卡罗（SMC）算法结合使用时，它们构成了一种用于复杂生物系统数学模型参数估计和模型选择的强大方法。ABC-SMC算法中的一个关键步骤，对其性能有重大影响，是使用马尔可夫核通过一系列中间分布传播一组参数向量。
在本文中，我们采用狄利克雷过程混合（DPM）来设计最优转移核，并提出一种具有DPM核的ABC-SMC算法。我们使用经典Wnt信号通路的真实数据说明了所提出方法的使用。开发了该通路的多隔室模型，并将其与现有模型进行比较。结果表明，DPM在探索参数空间方面更有效，并且可以显著提高ABC-SMC的性能。与常用的替代采样方案相比，所提出的方法在估计复杂的多峰分布方面可能带来潜在益处。该方法用于估计Wnt通路两个模型的参数和初始状态，结果表明多隔室模型与实验数据拟合得更好。
狄利克雷过程高斯混合模型和吉布斯采样器的Python脚本可在https://sites.google.com/site/kkoutroumpas/software获取。
