Hierarchical parameter estimation of GRN based on topological analysis
Hierarchical parameter estimation of GRN based on topological analysis
复制标题
基于拓扑分析的GRN层次参数估计
DOI：
10.1049/iet-syb.2018.5015
复制
发表时间：
2018-12-01
影响因子：
2.3
通讯作者：
Wang, Ning
中科院分区：
文献类型：
--
作者：
Zhang, Wei;Zhang, Feng;Wang, Ning
基因调控网络的逆向工程是系统生物学中一项重要而又具有挑战性的研究课题。现有的参数估计方法计算模型参数的重要性相同，通常是计算昂贵的或不可行的，特别是在处理复杂的生物网络，为了提高计算建模的效率，本文提出了一种分层估计方法的计算建模的GRN的拓扑分析的基础上。利用基于图的测度和遗传算法对网络中的节点进行优先级划分。在第一层中的节点，对应于根强连通组件（SCC）的有向图的GRN，被赋予最高优先级的参数估计。在前一个优先级中的顶点的估计参数被用于推断下一个优先级中的节点的参数。所提出的分层估计方法获得了较低的误差指标，同时消耗更少的计算资源相比，单一的估计方法。通过insilico网络和真实网络的实验结果表明，基因网络被分解为不超过4个层次，这与GRN固有的模块性特征相一致.此外，所提出的分层参数估计实现了计算效率和精度之间的平衡。
Reverse engineering of gene regulatory network (GRN) is an important and challenging task in systems biology. Existing parameter estimation approaches that compute model parameters with the same importance are usually computationally expensive or infeasible, especially in dealing with complex biological networks.In order to improve the efficiency of computational modeling, the paper applies a hierarchical estimation methodology in computational modeling of GRN based on topological analysis. This paper divides nodes in a network into various priority levels using the graph-based measure and genetic algorithm. The nodes in the first level, that correspond to root strongly connected components(SCC) in the digraph of GRN, are given top priority in parameter estimation. The estimated parameters of vertices in the previous priority level ARE used to infer the parameters for nodes in the next priority level. The proposed hierarchical estimation methodology obtains lower error indexes while consuming less computational resources compared with single estimation methodology. Experimental outcomes with insilico networks and a realistic network show that gene networks are decomposed into no more than four levels, which is consistent with the properties of inherent modularity for GRN. In addition, the proposed hierarchical parameter estimation achieves a balance between computational efficiency and accuracy.