Network reconstruction using nonparametric additive ODE models.
Network reconstruction using nonparametric additive ODE models.
复制标题
使用非参数添加剂模型的网络重建。
DOI：
10.1371/journal.pone.0094003
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Michailidis G
中科院分区：
文献类型：
--
作者：
Henderson J;Michailidis G
生物系统的网络表示广泛存在，从数据中重构未知网络是计算生物学家关注的焦点问题。例如，代谢途径中的一系列生化反应可以表示为网络，其中节点对应于代谢物，边将反应物连接到产物。在另一种情况下，基因之间的调控关系通常被表示为有向网络，其边缘从有影响力的基因指向其目标。从数据中重建这样的网络是一个具有挑战性的问题，在文献中备受关注。特别需要针对时间序列数据而不是依赖直接干预实验的方法，因为前者往往更容易获得。本文介绍了一种基于动态系统模型的有向网络重构方法。我们的方法概括了常用的常微分方程模型的基础上线性或非线性动力学扩展的功能类所涉及的功能从参数到非参数模型。同时，我们限制的复杂性，通过施加一个附加的结构上估计的斜率函数。因此，与每个节点相关联的子模型是单变量函数的总和。这些单变量分量函数形成了一个新的耦合度量的基础，我们定义，以量化拟议的关系的强度，从而排名潜在的边缘。我们显示的效用的方法，通过重建网络使用模拟数据从计算模型的乳酸乳球菌的糖酵解途径和基因网络调节小鼠胚胎干细胞的多能性。为了进行比较，我们还使用来自DREAM挑战的基因网络评估了重建性能。我们比较我们的方法，同样依赖于动态系统模型，并使用结果试图解开的线性，稀疏性和导数估计的不同作用。
Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative estimation.
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