Parameter identifiability-based optimal observation remedy for biological networks.
Parameter identifiability-based optimal observation remedy for biological networks.
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DOI：
10.1186/s12918-017-0432-2
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发表时间：
2017-05-04
影响因子：
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通讯作者：
Miao H
中科院分区：
文献类型：
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作者：
Wang Y;Miao H
为了系统地了解众多生物成分之间的相互作用，建立了不同层次和规模的各种生物网络，并在公共数据库或知识库中提供。结构方程模型等图形模型长期以来一直用于描述生物网络，用于各种定量分析任务，特别是关键生物参数估计。然而，受资源或技术能力的限制，部分观测是生物网络实验观测中的一个普遍问题，如何选择未观测到的节点进行额外的测量，使得所有未知的模型参数都变得可识别，成为一个重要的问题。据我们的作者所知，在这项研究之前，这个问题的解决方案并不存在。基于线性递归结构方程模型，首次建立了生物网络可辨识性观测问题的数学模型，并提出了一种动态规划策略来获得最优观测策略.动态规划算法的效率是通过避免在其他研究中使用的符号计算和矩阵运算。我们还提供了必要的理论依据，所提出的方法。最后，我们使用合成网络结构验证了算法，并说明了所提出的方法在实际中的应用，使用一个真实的生物网络有关的流感病毒感染。所提出的方法是第一个解决方案的结构可识别性为基础的最优观测补救问题。该方法适用于任意有向无环生物网络（递归SEMs），且无双向边，是一种可计算机化的方法。观察补救是生物网络实验设计中的一个重要问题，我们相信这项研究为处理更具挑战性的设计问题（例如，反馈回路、动态或非线性网络）。我们在R中实现了我们的方法，可以在https://github.com/Hongyu-Miao/SIOOR上免费访问。本文的在线版本（doi：10.1186/s12918-017-0432-2）包含补充材料，可供授权用户使用。
To systematically understand the interactions between numerous biological components, a variety of biological networks on different levels and scales have been constructed and made available in public databases or knowledge repositories. Graphical models such as structural equation models have long been used to describe biological networks for various quantitative analysis tasks, especially key biological parameter estimation. However, limited by resources or technical capacities, partial observation is a common problem in experimental observations of biological networks, and it thus becomes an important problem how to select unobserved nodes for additional measurements such that all unknown model parameters become identifiable. To the best knowledge of our authors, a solution to this problem does not exist until this study. The identifiability-based observation problem for biological networks is mathematically formulated for the first time based on linear recursive structural equation models, and then a dynamic programming strategy is developed to obtain the optimal observation strategies. The efficiency of the dynamic programming algorithm is achieved by avoiding both symbolic computation and matrix operations as used in other studies. We also provided necessary theoretical justifications to the proposed method. Finally, we verified the algorithm using synthetic network structures and illustrated the application of the proposed method in practice using a real biological network related to influenza A virus infection. The proposed approach is the first solution to the structural identifiability-based optimal observation remedy problem. It is applicable to an arbitrary directed acyclic biological network (recursive SEMs) without bidirectional edges, and it is a computerizable method. Observation remedy is an important issue in experiment design for biological networks, and we believe that this study provides a solid basis for dealing with more challenging design issues (e.g., feedback loops, dynamic or nonlinear networks) in the future. We implemented our method in R, which is freely accessible at https://github.com/Hongyu-Miao/SIOOR. The online version of this article (doi:10.1186/s12918-017-0432-2) contains supplementary material, which is available to authorized users.