arXiv 1606.082812016-06-27q-bio.QM

大型生化反应模型参数推断的可并行抽样方法

A parallelizable sampling method for parameter inference of large biochemical reaction models

Jan Mikelson, Mustafa Khammash

摘要：

发展生物系统的机械模型是系统生物学的核心部分。在开发这些模型的一个主要任务是正确的模型参数的推断。由于大多数现实模型的大小和它们可能复杂的动力学行为，通常必须依赖于基于样本的方法。在本文中，我们提出了一种新的算法，可靠地估计模型参数的确定性以及随机模型的轨迹数据。我们的算法从似然的水平集中迭代地采样独立的粒子，并从这些水平集中恢复后验。该方法易于并行化，并利用Dirichlet过程高斯混合模型的密度估计，可以处理高维参数空间。我们说明，我们的算法是适用于大型，现实的确定性和随机模型，并成功地推断出正确的后，从一个给定数量的观测轨迹。该算法提出了一种新的，计算上可行的方法来识别基于样本路径数据的大型生化反应模型的参数。

英文摘要：

The development of mechanistic models of biological systems is a central part of Systems Biology. One major task in developing these models is the inference of the correct model parameters. Due to the size of most realistic models and their possibly complex dynamical behaviour one must usually rely on sample based methods. In this paper we present a novel algorithm that reliably estimates model parameters for deterministic as well as stochastic models from trajectory data. Our algorithm samples iteratively independent particles from the level sets of the likelihood and recovers the posterior from these level sets. The presented approach is easily parallelizable and, by utilizing density estimation through Dirichlet Process Gaussian Mixture Models, can deal with high dimensional parameter spaces. We illustrate that our algorithm is applicable to large, realistic deterministic and stochastic models and succeeds in inferring the correct posterior from a given number of observed trajectories. This algorithm presents a novel, computationally feasible approach to identify parameters of large biochemical reaction models based on sample path data.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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