Hierarchical parameter estimation of GRN based on topological analysis. Issue 6 (1st December 2018)

• Record Type: Journal Article
• Authors: Zhang, Wei; Zhang, Feng; Zhang, Jianming; Wang, Ning
• Is Part Of: IET systems biology. Volume 12:Issue 6(2018)
• Page Start: 294
• Page End: 303
• Publication Date: 2018-12-01
• Subjects: biology computing -- network theory (graphs) -- reverse engineering -- graph theory -- genetics -- genetic algorithms -- directed graphs -- parameter estimation; hierarchical parameter estimation -- GRN -- topological analysis -- gene regulatory network -- important task -- computational systems biology -- compute model parameters -- complex biological networks -- efficient information -- model quality -- parameter reliability -- computational modelling -- study divides nodes -- priority levels -- graph‐based measure -- previous priority level -- hierarchical estimation methodology obtains -- computational resources -- single time estimation -- insilico network -- realistic network show -- computational efficiency; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5015
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : 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.