Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm.

Linlin Xing,Maozu Guo,Lei Zhang,Chunyu Wang,Xiaoyan Liu

doi:10.3390/genes9070342

Abstract

The explosion of genomic data provides new opportunities to improve the task of gene regulatory network reconstruction. Because of its inherent probability character, the Bayesian network is one of the most promising methods. However, excessive computation time and the requirements of a large number of biological samples reduce its effectiveness and application to gene regulatory network reconstruction. In this paper, Flooding-Pruning Hill-Climbing algorithm (FPHC) is proposed as a novel hybrid method based on Bayesian networks for gene regulatory networks reconstruction. On the basis of our previous work, we propose the concept of DPI Level based on data processing inequality (DPI) to better identify neighbors of each gene on the lack of enough biological samples. Then, we use the search-and-score approach to learn the final network structure in the restricted search space. We first analyze and validate the effectiveness of FPHC in theory. Then, extensive comparison experiments are carried out on known Bayesian networks and biological networks from the DREAM (Dialogue on Reverse Engineering Assessment and Methods) challenge. The results show that the FPHC algorithm, under recommended parameters, outperforms, on average, the original hill climbing and Max-Min Hill-Climbing (MMHC) methods with respect to the network structure and running time. In addition, our results show that FPHC is more suitable for gene regulatory network reconstruction with limited data.

Highlights

The growth and development of organisms and the ability to respond to environmental conditions are controlled by an intrinsic regulation mechanism, which spans multiple molecular levels [1].Gene regulatory networks (GRNs) depict this complex mechanism at the level of genes and provide an intuitive understanding of how these interactions determine the characteristics of organisms [2].The structure of GRN reflects the interactions between the regulatory elements in biological systems, such as genes and proteins [3,4,5]
We analyze the influence of different network and different sample sizes on the state-of-the-art algorithm, Candidate auto selection method (CAS)
FPHCalgorithm algorithm on on simulated simulated networks areare sampled from real biological the middle middleshow showthe theperformance performance sampled from real biologicalnetworks

Summary

Introduction

The growth and development of organisms and the ability to respond to environmental conditions are controlled by an intrinsic regulation mechanism, which spans multiple molecular levels [1].Gene regulatory networks (GRNs) depict this complex mechanism at the level of genes and provide an intuitive understanding of how these interactions determine the characteristics of organisms [2].The structure of GRN reflects the interactions between the regulatory elements in biological systems, such as genes and proteins [3,4,5]. Gene regulatory networks (GRNs) depict this complex mechanism at the level of genes and provide an intuitive understanding of how these interactions determine the characteristics of organisms [2]. The reconstruction of gene regulatory network from gene expression data, known as reverse engineering, is the most fascinating task in system biology and bioinformatics. These predicted networks can generate valuable hypotheses to promote further biological research. This has led to the fast development of computational approaches for the reconstruction of GRNs [6]

Methods

Results

Conclusion