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.

中文翻译：

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基于拓扑分析的GRN层次参数估计。

基因调控网络（GRN）的逆向工程是系统生物学中一项重要且具有挑战性的任务。现有的计算具有相同重要性的模型参数的参数估计方法通常计算量大或不可行，特别是在处理复杂的生物网络时。为了提高计算建模的效率，本文将分层估计方法应用于基于GRN的计算建模中。关于拓扑分析。本文使用基于图的度量和遗传算法将网络中的节点划分为不同的优先级。第一级节点，对应于GRN有向图中的根强连通分量（SCC），在参数估计中具有最高优先级。前一个优先级节点的估计参数用于推断下一个优先级节点的参数。与单一估计方法相比，所提出的分层估计方法获得了更低的误差指数，同时消耗更少的计算资源。insilico 网络和现实网络的实验结果表明，基因网络被分解为不超过四个级别，这与 GRN 的固有模块化特性一致。此外，所提出的分层参数估计实现了计算效率和准确性之间的平衡。与单一估计方法相比，所提出的分层估计方法获得了更低的误差指数，同时消耗更少的计算资源。insilico 网络和现实网络的实验结果表明，基因网络被分解为不超过四个级别，这与 GRN 的固有模块化特性一致。此外，所提出的分层参数估计实现了计算效率和准确性之间的平衡。与单一估计方法相比，所提出的分层估计方法获得了更低的误差指数，同时消耗更少的计算资源。insilico 网络和现实网络的实验结果表明，基因网络被分解为不超过四个级别，这与 GRN 的固有模块化特性一致。此外，所提出的分层参数估计实现了计算效率和准确性之间的平衡。